The Elgar Companion to Innovation and Knowledge Creation [Hardcover ed.] 1782548513, 9781782548515

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THE ELGAR COMPANION TO INNOVATION AND KNOWLEDGE CREATION

For Arthur, Becci, Clare, Fiona, Laura, Lucy, Matthieu, Muriel, Noah, Patty, Sarah, and Velida

The Elgar Companion to Innovation and Knowledge Creation

Edited by

Harald Bathelt Professor and Canada Research Chair in Innovation and Governance, Departments of Political Science and Geography and Planning, University of Toronto, Canada

Patrick Cohendet Professor, Department of International Business, HEC Montréal, Canada

Sebastian Henn Professor, Department of Geography, Friedrich Schiller University Jena, Germany

Laurent Simon Professor, Department of Entrepreneurship and Innovation, HEC Montréal, Canada

Cheltenham, UK • Northampton, MA, USA

© Harald Bathelt, Patrick Cohendet, Sebastian Henn and Laurent Simon 2017 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA

A catalogue record for this book is available from the British Library Library of Congress Control Number: 2017939833 This book is available electronically in the Economics subject collection DOI 10.4337/9781782548522

ISBN 978 1 78254 851 5 (cased) ISBN 978 1 78254 852 2 (eBook) Typeset by Servis Filmsetting Ltd, Stockport, Cheshire

Contents

ix xiv

List of contributors Preface 1

Innovation and knowledge creation: challenges to the field Harald Bathelt, Patrick Cohendet, Sebastian Henn and Laurent Simon

PART I

1

INNOVATION AS A CONCEPT

2

A conceptual history of innovation Benoît Godin

25

3

Concepts and models of innovation Patrick Cohendet and Laurent Simon

33

4

Science and innovation Jean-Alain Héraud

56

5

Reverse innovation Thierry Burger-Helmchen and Caroline Hussler

75

6

Broadening the concept of open innovation Wim Vanhaverbeke

87

7

Measurement of innovation Stephane Lhuillery, Julio Raffo and Intan Hamdan-Livramento

99

PART II

INNOVATION AND INSTITUTIONS

8

Institutional context and innovation Johannes Glückler and Harald Bathelt

121

9

Innovation in the practice perspective Deborah Dougherty

138

10

Domesticating innovation—designing revolutions Yellowlees Douglas and Andrew Hargadon

152

11

Innovation and lock-in Uwe Cantner and Simone Vannuccini

165

12

Patents and open innovation Julien Pénin

182

v

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The Elgar companion to innovation and knowledge creation

PART III INNOVATION AND CREATIVITY 13

Managing knowledge, creativity and innovation Patrick Cohendet, Guy Parmentier and Laurent Simon

197

14

Urban diversity and innovation Pierre Desrochers, Samuli Leppälä and Joanna Szurmak

215

15

Innovation and the cultural economy Andy C. Pratt

230

16

Innovation and cultural industries Deborah Leslie and Norma M. Rantisi

244

17

Services and innovation Johannes Glückler

258

18

Design theories, creativity and innovation Pascal Le Masson, Armand Hatchuel and Benoit Weil

275

19

The dark side of creativity David H. Cropley

307

PART IV INNOVATION, NETWORKING AND COMMUNITIES 20

Social networks and innovation Michel Ferrary and Mark Granovetter

327

21

Community, creativity and innovation Joanne Roberts

342

22

Industrial clusters in global networks Elisa Giuliani

360

23

The user innovation phenomenon Cyrielle Vellera, Eric Vernette and Susumu Ogawa

372

24

Horizontal learning Pengfei Li

392

25

Innovation versus technological achievement Dominique Foray

405

PART V INNOVATION IN PERMANENT SPATIAL SETTINGS 26

Geography of innovation, proximity and beyond Alain Rallet and André Torre

421

27

Urban bias in innovation studies Richard Shearmur

440

Contents

vii

28

National and regional innovation systems Harald Bathelt and Sebastian Henn

457

29

National innovation systems and globalization Bengt-Åke Lundvall

472

30

Innovation, regional development and relationality Arnoud Lagendijk

490

PART VI INNOVATION IN TEMPORARY AND VIRTUAL SETTINGS 31

Trade fairs and innovation Harald Bathelt

509

32

Innovation through trade show concertation Francesca Golfetto and Diego Rinallo

523

33

Knowledge collaboration in hybrid virtual communities Gernot Grabher and Oliver Ibert

537

34

Performativity and the innovation–replication dilemma Luciana D’Adderio

556

35

Coworking and innovation Janet Merkel

570

PART VII INNOVATION, ENTREPRENEURSHIP AND MARKET MAKING 36

Markets, marketization and innovation Michel Callon

589

37

Market formation and innovation systems Ulrich Dewald and Bernhard Truffer

610

38

Innovation and entrepreneurship Edward J. Malecki and Ben Spigel

625

39

Transnational entrepreneurs and global knowledge transfer Sebastian Henn and Harald Bathelt

638

40

Institutional entrepreneurship in Alzheimer’s disease treatment Nina Geilinger, Stefan Haefliger, Georg von Krogh and Fotini Pachidou

652

PART VIII GOVERNANCE AND MANAGEMENT OF INNOVATION 41

Relational geographies of knowledge and innovation James R. Faulconbridge

671

42

Innovation, governance and place Maryann Feldman and Nichola Lowe

685

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The Elgar companion to innovation and knowledge creation

43

The dynamics of organizational structures and performances Giovanni Dosi and Luigi Marengo

702

44

Learning through governance Neil Bradford and David A. Wolfe

723

45

Global value chains and innovation Ari Van Assche

739

46

Innovation, development and global destruction networks Andrew Herod, Graham Pickren, Al Rainnie and Susan McGrath-Champ

752

47

Innovation and the global eco-industry Bernard Sinclair-Desgagné

771

Index

787

Contributors

Harald Bathelt, Professor and Canada Research Chair in Innovation and Governance, Departments of Political Science and Geography and Planning, University of Toronto, Toronto, Canada; Zijiang Visiting Professor, Institute of Urban Development, East China Normal University, Shanghai, China (e-mail: [email protected]). Neil Bradford, Professor, Department of Political Science, Huron University College, University of Western Ontario, London, Canada (e-mail: [email protected]). Thierry Burger-Helmchen, Professor, Faculté des sciences économiques et de gestion, Université de Strasbourg, BETA-CNRS, Strasbourg, France (e-mail: burger@unistra. fr). Michel Callon, Professor, Center for the Sociology of Innovation, Ecole des Mines de Paris, Paris, France (e-mail: [email protected]). Uwe Cantner, Professor and Chair of Economics/Microeconomics, Department of Economics, Friedrich Schiller University Jena, Jena, Germany (e-mail: uwe.cantner@ uni-jena.de). Patrick Cohendet, Professor, Department of International Business, HEC Montréal, Montréal, Canada (e-mail: [email protected]). David H. Cropley, Associate Professor, School of Engineering, University of South Australia, Mawson Lakes, Australia (e-mail: [email protected]). Luciana D’Adderio, Reader in Management, Hunter Centre for Entrepreneurship and Department of Strategy and Organisation, Strathclyde Business School, University of Strathclyde, Glasgow, Scotland, United Kingdom (e-mail: luciana.d-adderio@strath. ac.uk). Pierre Desrochers, Associate Professor, Department of Geography and Programs in Environment, University of Toronto Mississauga, Mississauga, Canada (e-mail: pierre. [email protected]). Ulrich Dewald, Scientific Staff, Institute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, Karlsruhe, Germany (e-mail: ulrich. [email protected]). Giovanni Dosi, Professor, Institute of Economics, LEM, Laboratory of Economics and Management, Scuola Superiore Sant’Anna, Pisa, Italy (e-mail: giovanni.dosi@ sssup.it). Deborah Dougherty, Professor Emeritus, Management and Global Business Department, Rutgers Business School, Rutgers University, New Jersey, United States (e-mail: [email protected]).

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Yellowlees Douglas, Associate Professor, Management Communication, Warrington College of Business, University of Florida, Gainesville, United States (e-mail: jane. [email protected]). James R. Faulconbridge, Professor, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom (e-mail: [email protected]). Maryann Feldman, Heninger Distinguished Professor, Department of Public Policy, University of North Carolina, Chapel Hill, United States (e-mail: maryann.feldman@ unc.edu). Michel Ferrary, Professor, Graduate School of Economics and Management, University of Geneva, Geneva, Switzerland; Skema Business School, Lille, France (e-mail: michel. [email protected]). Dominique Foray, Professor and Chair of Economics and Management of Innovation (CEMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland (e-mail: [email protected]). Nina Geilinger, Senior Researcher, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland (e-mail: [email protected]). Elisa Giuliani, Professor, Department of Economics and Management, University of Pisa, Pisa, Italy (e-mail: [email protected]). Johannes Glückler, Professor, Institute of Geography, University of Heidelberg, Heidelberg, Germany (e-mail: [email protected]). Benoît Godin, Professor, Centre Urbanisation Culture Société, Institut National de la Recherche Scientifique, Montréal, Canada (e-mail: [email protected]). Francesca Golfetto, Professor, Marketing Department, Bocconi University; CERMES, Centre for Research on Marketing & Services, Milan, Italy (e-mail: francesca.golfetto@ unibocconi.it). Gernot Grabher, Professor, Urban and Regional Economics, HafenCity University Hamburg, Hamburg, Germany (e-mail: [email protected]). Mark Granovetter, Joan Butler Ford Professor of Sociology, Department of Sociology, Stanford University, Stanford, United States (e-mail: [email protected]). Stefan Haefliger, Professor, Cass Business School, City, University of London, London, United Kingdom (e-mail: [email protected]). Intan Hamdan-Livramento, Economics and Statistics Division, World Intellectual Property Organization, Geneva, Switzerland (e-mail: intan.hamdan-livramento@wipo. int). Andrew Hargadon, Professor of Technology Management and Soderquist Chair in Entrepreneurship, Graduate School of Management, University of California Davis, Davis, United States (e-mail: [email protected]). Armand Hatchuel, Professor and Chair of Design Theory and Methods for Innovation,

Contributors

xi

Center for Management Science, MINES ParisTech PSL Research University, Paris, France (e-mail: [email protected]). Sebastian Henn, Professor, Department of Geography, Friedrich Schiller University Jena, Jena, Germany (e-mail: [email protected]). Jean-Alain Héraud, Professor, Department of Economics and Management, BETA, Université de Strasbourg, Strasbourg, France (e-mail: [email protected]). Andrew Herod, Distinguished Research Professor, Department of Geography, University of Georgia, Athens, United States (e-mail: [email protected]). Caroline Hussler, Professor, IAE Lyon – Centre Magellan, Université Jean Moulin Lyon 3, Lyon, France (e-mail: [email protected]). Oliver Ibert, Professor, Dynamics of Economic Spaces, Leibniz Institute for Research on Society and Space, Erkner, Germany; Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany (e-mail: [email protected]). Arnoud Lagendijk, Professor and Chair of Economic Geography, Nijmegen School of Management, Radboud University Nijmegen, Nijmegen, Netherlands (e-mail: [email protected]). Pascal Le Masson, Professor and Chair of Design Theory and Methods for Innovation, Center for Management Science, MINES ParisTech PSL Research University, Paris, France (e-mail: [email protected]). Samuli Leppälä, Lecturer, Economics Section, Cardiff University, Cardiff, United Kingdom (e-mail: [email protected]). Deborah Leslie, Professor, Department of Geography and Planning, University of Toronto, Toronto, Canada (e-mail: [email protected]). Stephane Lhuillery, Professor and Chair of Bioeconomy, Neoma Business School, Reims, France (e-mail: [email protected]). Pengfei Li, Assistant Professor, Department of International Business, HEC Montréal, Montréal, Canada (e-mail: [email protected]). Nichola Lowe, Associate Professor, Department of City and Regional Planning, University of North Carolina, Chapel Hill, United States (e-mail: [email protected]). Bengt-Åke Lundvall, Professor, Department of Business and Management, Aalborg University, Aalborg, Denmark (e-mail: [email protected]). Edward J. Malecki, Professor, Department of Geography, The Ohio State University, Columbus, United States (e-mail: [email protected]). Luigi Marengo, Professor and Chair, Department of Business and Management, LUISS University, Roma, Italy (e-mail: [email protected]). Susan McGrath-Champ, Associate Professor, Department of Work and Organisational Studies, The University of Sydney, Sydney, Australia (e-mail: susan.mcgrathchamp@ sydney.edu.au).

xii The Elgar companion to innovation and knowledge creation Janet Merkel, Lecturer, Center for Culture and Creative Industries, Department of Sociology, School of Arts and Social Sciences, City University London, London, United Kingdom (e-mail: [email protected]). Susumu Ogawa, Professor, Graduate School of Business Administration, Kobe University, Kobe, Japan (e-mail: [email protected]). Fotini Pachidou, Accenture, Zurich, Switzerland (e-mail: [email protected]). Guy Parmentier, Associate Professor, Department of Innovation, Design, Entrepreneurship and Strategy, Université Grenoble Alpes, IAE, Grenoble, France (e-mail: guy.par [email protected]). Julien Pénin, Professor, BETA (UMR CNRS 7522), Université de Strasbourg, Strasbourg, France (e-mail: [email protected]). Graham Pickren, Assistant Professor, Sustainability Studies, Roosevelt University, Chicago, United States (e-mail: [email protected]). Andy C. Pratt, Professor, Department of Sociology, Centre for Culture and the Creative Industries, City University of London, London, United Kingdom (e-mail: andy.pratt.1@ city.ac.uk). Julio Raffo, Economics and Statistics Division, World Intellectual Property Organization, Geneva, Switzerland (e-mail: [email protected]). Al Rainnie, Creative Industries, Queensland University of New South Wales, Brisbane, Australia (e-mail: [email protected]). Alain Rallet, Professor, RITM, University Paris Sud & Paris Saclay, Sceaux, France (e-mail: [email protected]). Norma M. Rantisi, Professor, Department of Geography, Planning and Environment, Concordia University, Montréal, Canada (e-mail: [email protected]). Diego Rinallo, Associate Professor, Marketing Department, Kedge Business School, Marseille, France; CERMES, Centre for Research on Marketing & Services, Milan, Italy; CERGAM, Centre d’Etudes et de Recherche en Gestion d’Aix-Marseille, France (e-mail: [email protected]). Joanne Roberts, Professor in Arts and Cultural Management and Director, Winchester Luxury Research Group, Winchester School of Art, University of Southampton, United Kingdom (e-mail: [email protected]). Richard Shearmur, Professor, School of Urban Planning, McGill University, Montréal, Canada (e-mail: [email protected]). Laurent Simon, Professor, Department of Entrepreneurship and Innovation, HEC Montréal, Montréal, Canada (e-mail: [email protected]). Bernard Sinclair-Desgagné, Professor and Chair in Environmental Economics and Global Governance, Department of International Business, HEC Montréal, Montréal, Canada (e-mail: [email protected]).

Contributors

xiii

Ben Spigel, Chancellor’s Fellow, Entrepreneurship and Innovation Group, University of Edinburgh Business School, Edinburgh, United Kingdom (e-mail: [email protected]). Joanna Szurmak, Digital Initiatives and Liaison Librarian, University of Toronto Mississauga, Mississauga, Canada (e-mail: [email protected]). André Torre, Professor, UMR SAD-APT, INRA – Agroparistech, University Paris Saclay, Paris, France ([email protected]). Bernhard Truffer, Professor, Environmental Social Sciences, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland; Faculty of Geosciences, Utrecht University, Utrecht, Netherlands (e-mail: [email protected]). Ari Van Assche, Associate Professor and Department Chair, Department of International Business, HEC Montréal, Montréal, Canada (e-mail: [email protected]). Wim Vanhaverbeke, Professor, Department of Business Studies, Hasselt University, Diepenbeek, Belgium; Visiting Professor of ESADE Business School, Spain; Visiting Professor of National University of Singapore, Singapore (e-mail: wim.vanhaverbeke@ uhasselt.be). Simone Vannuccini, Lecturer of Economics/Microeconomics, Department of Economics, Friedrich Schiller University Jena, Jena, Germany (e-mail: simone.vannuccini@uni-jena. de). Cyrielle Vellera, Assistant Professor, IAE Toulouse School of Management, Toulouse University, Toulouse, France (e-mail: [email protected]). Eric Vernette, Professor, IAE Toulouse School of Management, Toulouse University, Toulouse, France (e-mail: [email protected]). Georg von Krogh, Professor and Chair of Strategic Management and Innovation, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland (e-mail: [email protected]). Benoit Weil, Professor and Chair of Design Theory and Methods for Innovation, Center for Management Science, MINES ParisTech PSL Research University, Paris, France (e-mail: [email protected]). David A. Wolfe, Professor, Department of Political Science, UTM and Co-Director, Innovation Policy Lab, Munk School of Global Affairs, University of Toronto, Toronto, Canada (e-mail: [email protected]).

Preface

INNOVATION refers to both the process of producing and diffusing something that is new, useful, and valuable in economic life, and the outcome or product of this process. The word itself, originally having had negative connotations, being regarded almost as a heresy, was transformed into a virtue with the acceleration of modernity. Future historians, looking back at the 20th and early 21st century, may well refer to this turbulent and prolific time period as an era defined and driven by innovation. Yet, at present, we would likely be more cautious with our judgment. Part of this may be caused by the fact that innovation has become a rather fashionable term that is used in many different contexts, ranging from ‘innovative thinking’ to ‘social innovation’ and ‘innovative governance’. It is often assumed that innovation is decisive in strengthening the competiveness of economies, and because of this it is sailing on a wave of support from policymakers, widely publicized by the media. This gives the impression that innovation is everywhere and that governments around the world are unpacking ‘secret weapons’ against unemployment and decline. However, many cities, regions and countries suffer from stagnation and ever growing economic and social disparities. In reality, many firms and organizations are struggling to find innovative solutions to fulfill their missions and keep their workforce employed while facing accelerated environmental changes. In current discourses of managers and policymakers, innovation is too often used as a ‘black box’ that merely serves the purpose of invocation, if not incantation. Considering this inflationary and sometimes presumptuous use of the term for anything that is novel, or expected to be novel, we believe it is time to scrutinize what we know about and how we understand the processes and contexts of innovation and knowledge creation. The Elgar Companion to Innovation and Knowledge Creation provides a comprehensive overview and critical evaluation of existing conceptualizations and discusses new developments in and challenges to innovation research. The companion suggests that innovation, learning, knowledge creation and creativity are processes that are closely linked and need to be analyzed in an integrated fashion. It also points to the risks of oversimplifying and overgeneralizing complex processes that need to be understood within rich contexts, through multiple lenses, and at different scopes and scales. This edited volume explores a wide variety of related topics in eight parts. These cover different concepts and understandings of innovation (Part I), the role and impact of institutions (Part II), linkages with knowledge and creativity (Part III), fundamentals of networks and communities (Part IV), innovation in permanent spatial settings (Part V), innovation in temporary and virtual settings (Part VI), related entrepreneurship and market-making processes (Part VII), and issues of governance and management of innovation (Part VIII). As such, this companion fundamentally draws on inter- and trans-disciplinary views of innovation ranging from economics and geography to management, political science and sociology. It draws together a large unique group of leading innovation researchers from across these disciplines and also echoes new voices in the field. In many ways, this group of researchers has tried to build a cognitive platform for xiv

Preface xv multidisciplinary debate; one that, in turn, can enrich the research process in each of these disciplines. Having put together and orchestrated the contents of this companion, we believe that the final result not only gives a broad and up-to-date overview of the field but also discusses and develops new perspectives that will help to find answers and inspirations for solving some of the pressing economic challenges of our time. Due to its cross-disciplinary perspective, this companion can be utilized as a reference book for researchers, practitioners and policymakers, as well as students across the social sciences who are interested in innovation studies. It can also serve as a sourcebook for lectures and seminars focusing on innovation, innovation economics and innovation policy. This companion could have not been conceptualized and finalized without the support of many colleagues and friends, and myriads of discussions with them over the past years. Together we have experienced an innovative journey: the outcome was hardly predictable and its making involved many ups and downs, with pleasant surprises, unforeseeable events, and sometimes necessary adjustments. We would like to thank all the authors who have supported this edited volume. In preparing and copy-editing the companion manuscript and its individual chapters, we relied heavily on support from David Adams, Michaela Doyle, Daniel Hutton Ferris and Yi-wen Zhu, who did a terrific job. The project was also supported by Alex O’Connell, Alex Pettifer and, particularly, Matt Pitman from Edward Elgar Publishing, who showed tremendous patience with our many delays. We would also like to warmly thank our colleagues, students and collaborators for raising many relevant issues with respect to innovation and always challenging us to develop our arguments further. None of this would have been possible without the support of our loved ones, for which we are most grateful. We believe that the outcome clearly reflects the broad trust and sense of collective endeavor that drove this project from day one. Harald Bathelt, Patrick Cohendet, Sebastian Henn, and Laurent Simon Jena, Montréal and Toronto, November 2016

1.

Innovation and knowledge creation: challenges to the field Harald Bathelt, Patrick Cohendet, Sebastian Henn and Laurent Simon

SETTING THE SCENE Much has been written about innovation since the early works of Schumpeter (1911) and fellow academics, who recognized the fundamental impact of technological innovation on industrial production and economic life at the turn of the 20th century (Godin, Chapter 2, this volume). Technological changes have not only been drivers of growth and development at the level of the firm, but have also shaped entire economic landscapes, providing triggers for the expansion of cities, regions, nations and the global economy. While being characterized by cyclical ups and downs following the long waves of economic development, innovation studies in the last three decades have gradually developed as both an independent field and a perspective of academic enquiry across the social sciences, in disciplines such as economics, geography, history, management science, political science and sociology. At the beginning of the 21st century, despite much progress in academic work on innovation, we are still faced with many unanswered questions and new challenges in economic and social life that need new analytical perspectives as well as new answers and solutions. While having seemingly mastered a global financial crisis, we are facing many instabilities of, and threats to, economic development in the future. In particular, we are at the verge of severe environmental impacts related to ongoing pollution and climate change; we need to overcome new conundrums of resources shortages; and we are faced with huge problems of exponential population growth, urbanization and related economic, social and spatial disparities. The need to find new technologies, processes and products to ensure continuous, sustainable economic development and growth is a top priority, now and in the future, and puts important new demands on innovation research. Within this context, The Elgar Companion to Innovation and Knowledge Creation aims not only to provide an overview about important developments in the field, but also tackles challenges to research on innovation that we see developing within and across different disciplines. In the following, we give an overview of the structure of this volume, highlighting important challenges to and imperfections of innovation research, within what sometimes appears to be a stable, mature field. Each of the subsequent sections corresponds to one of the parts of this edited volume. We begin by discussing the notion of innovation as a concept, before highlighting the interrelationship between innovation and institutions, and the interdependence of innovation and creativity. This is followed by three sections that target innovation as a social process: innovation, networking and communities; innovation in permanent spatial settings; and innovation in temporary and virtual settings. The last sections focus on the relationships between innovation, entrepreneurship and market 1

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making, and on wider issues regarding governance and management of innovation, followed by some final remarks about the unique characteristics of this edited volume.

INNOVATION AS A CONCEPT Since Schumpeter’s (1911) enquiry, much progress has been made in understanding innovation and knowledge creation as the central processes that trigger fundamental changes in economies and societies. From the beginning of the 20th century, based on streams of new findings regarding the production of novelty, different generations of models and understandings of innovation have developed across the social sciences. Some of these became dominant at some point but were eventually replaced by other, new models. At a given moment in time, a dominant model of innovation offers a common understanding, not only on how ideas can be turned into useful products or services, but also about how and where resources should be allocated in order to fuel the innovation process. Such models drive and shape the behaviors and decisions of policymakers, entrepreneurs, business managers and all sorts of economic agents while they are dominant (Cohendet and Simon, Chapter 3, this volume; Clark 2017). The early innovation model, the ‘linear technology-push model’, which revealed the critical role of ‘big science’, emerged in a period marked by the end of World War I (Godin, Chapter 2, this volume; Héraud, Chapter 4, this volume). The linear model, which views technological change as an external condition to economic development, suggests that the key trigger to fuel innovation is the support of basic research. In the 1980s, when evolutionary and institutional perspectives on innovation gained much momentum (Nelson and Winter 1982; Romer 1986; Dosi 1988), a new model became dominant. The ‘interactive model of innovation’ conceptualizes technological change as a process that is internal to the economy (Rosenberg 1982). What drives the emergence and development of innovative ideas according to this model is a high intensity of interactions between multitudes of networked actors in the economy. This perspective has challenged many aspects of the linear model. First, innovation research has for a long time been primarily focused on tangible products and technologies produced within the manufacturing sector. Services and intangibles play only a minor role in this tradition and do not shape the development of concepts and theories. In fact, producer services are often viewed as a support or ‘add-on’ to innovation processes in manufacturing, interacting with the manufacturing sector through networks and innovation systems. As highlighted by Glückler (Chapter 17, this volume), however, there are numerous problems associated with this view. Studies adopting this perspective sometimes fail to regard service providers as innovators. Furthermore, related work overlooks the fact that even manufacturing firms themselves increasingly rely on the provision of services to their clients and often derive most of their profits from such intangible goods. To recognize this increasing importance of services within the economy, a different perspective on innovation needs to be adopted. Second, when thinking about ways to better integrate services into innovation research, new questions also appear with respect to the measurement of innovation (Lhuillery et al., Chapter 7, this volume). This is clearly reflected in the evolution of ‘manuals’ that are widely used as guidelines for the collection and use of data on innovation activities.

Innovation and knowledge creation: challenges to the field

3

While the Frascati Manual (OECD 1963), taking its cue from the linear model of innovation, was based on the measurement of research and development (R&D) data in manufacturing industries, the Oslo Manual (OECD 1992), grounded in the interactive model of innovation, made initial attempts to measure service industries but still focused on manufacturing (Evangelista et al. 1998). Related to this, discussions about the measurement of innovation have been characterized by a bifurcation regarding how to proceed and what activities to include: on the one hand, innovation researchers use data about R&D expenditures or employment and patent data in quantitative studies that compare innovation trends between firms, industries, regions and countries. On the other hand, these approaches are criticized on the grounds that a substantial part of innovation cannot be measured using such indicators, since it has been shown that ‘normal innovation’ often proceeds in incremental steps and is associated with ongoing learning processes (Lundvall 1988; Gertler 1993; Lundvall and Johnson 1994) such as learning by interaction, by observation and by imitation (Malecki 1991; BurgerHelmchen and Hussler, Chapter 5, this volume; Vellera et al., Chapter 23, this volume; Li, Chapter 24, this volume). Much innovation does not follow the linear model and is not related to systematic search processes within the context of dedicated laboratories. Further, for many employees, especially in small and medium-sized firms, incremental innovation occurs as a by-product of their core activities, and sometimes modifications in products or processes are not even perceived as innovations, mainly because the individuals involved are primarily focused on their core activities. That is why qualitative studies of innovation practices are needed that investigate the processes underlying technological change (Dougherty, Chapter 9, this volume). This creates a dilemma because (i) comparative work on innovation across different contexts is difficult to conduct, as it is impossible to draw a perfect representation of innovation processes from the data; (ii) the task of linking quantitative and qualitative findings about innovation is challenging; and (iii) innovation is often viewed as a distinct process within the proprietary context of firms, networks and innovation systems. Since the early 2000s, a number of economic trends and developments have been challenging aspects of the initial interactive model of innovation and its use (Cohendet and Simon, Chapter 3, this volume; Héraud, Chapter 4, this volume). The earlier perspective of the interactive model viewed the innovation process as one that is primarily controlled within the closed boundaries of an organization. Here, innovation does not progress sequentially through definite stages: it can begin in any phase of the process, and oscillates between conception, product development, production and marketing. Such a view was initially challenged by von Hippel’s (1987) work about the importance of lead-users in innovation (Vanhaverbeke, Chapter 6, this volume). Building on von Hippel’s assumptions, Chesbrough (2003) proposed to radically break with closed representations of the innovation model by adopting a conception of ‘open innovation’: a ‘new paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as firms look to advance in their technology’ (Chesbrough 2003: 14). The model of open innovation (which is still in formation) suggests new ways of representing and explaining innovation activities in society (Vanhaverbeke, Chapter 6, this volume). In particular, the model highlights the role of broader knowing communities (Bathelt and Cohendet 2014; Roberts, Chapter 21, this volume) in shaping technology development. Knowledge exchanges and circulation

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underlying these processes are not always driven by economic rationales but are sometimes delivered as free services among dedicated groups of community members. This challenges our understanding of innovation and shifts our attention to what has been described as the ‘underground’ (Arvidsson 2007; Cohendet et al. 2010; Cohendet et al., Chapter 13, this volume) – a term that refers to those individuals that are part of informal communities or movements beyond the industrial or commercial world, yet have capabilities and skills from this world that they also apply in their professional lives in firms and other organizations. The creative moments and collective innovation processes in the ‘underground’ are thereby connected with the ‘upperground’, that is, the firms and organizations in an industry that operate according to formalized economic rationales. The open innovation model further suggests reconsidering the role of property rights. A growing number of scholars question the approach that views patents as instruments dedicated to the exclusion and restoration of appropriation, and suggest a renewed vision of patents as instruments which help to coordinate interactions between actors in an open innovation context (Pénin, Chapter 12, this volume). Since innovation is increasingly a global process involving interactive and iterative knowledge exchanges between countries, a number of scholars are questioning the traditional vision that new products and processes are normally created in highly developed ‘rich countries’ and commercialized there to serve wealthy consumers (BurgerHelmchen and Hussler, Chapter 5, this volume). The concept of reverse innovation (Immelt et al. 2009) describes a counter process of innovation that originates in lessadvanced countries, opening a new avenue for research at the crossroads of international business, economics, geography and political science. Within the perspective of the open innovation model, the concept of reverse innovation takes into account the creative potential of local communities from poor regions and their role in developing new models of doing sustainable business.

INNOVATION AND INSTITUTIONS Another challenge to innovation research is the link between innovation and institutions. Often, the institutional context is not explicitly and systematically discussed when studying innovations. While most obvious in conjunction with situations of institutional hysteresis (Setterfield 1993) and lock-in (Grabher 1993; Cantner and Vannuccini, Chapter 11, this volume), the institutional environment is always of great importance and intrinsically linked to the creation, design and use of innovations (e.g. Pénin, Chapter 12, this volume). For instance, research on national innovation systems (Freeman 1988; Lundvall 1992; Nelson 1993; Edquist 1997), despite being initially established as an institutional approach to investigate the varying nature, focus and outcome of innovation processes in national contexts, has rarely investigated and compared the role of institutions in the innovation process in a systematic way. Different studies on innovation systems apply heterogeneous conceptualizations of institutions. For instance, while Nelson’s (1993) work on innovation systems in different countries focuses on the organization, funding and research system underpinning innovation and how these elements have evolved in a historical perspective, Lundvall and Maskell (2000) emphasize that national variations of innovation systems are related to different structures of producer–user interaction at the

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micro-level (Lundvall, Chapter 29, this volume). In past research, there has been a lack of debate of how to bring these approaches together. To conduct a meaningful analysis of the role of institutions in innovation processes it is necessary to realize that institutions are often viewed and treated in different ways: as organizations (e.g. Nelson 1993), rules (North 1990), practice (Dougherty, Chapter 9, this volume) or routines (D’Adderio, Chapter 34, this volume). Glückler and Bathelt (Chapter 8, this volume) argue that firms and governments are organizations that create rules, regulations and policies to regulate and direct economic action and that neither the organizations themselves nor the rules and regulations they create should be viewed as institutions. Rules and regulations are not institutions because they may not be followed by actors or are interpreted in different ways leading to different outcomes (Bathelt and Glückler 2014). Instead, institutions in a narrow sense are stabilizations of economic interactions or correlated patterns of behavior (Setterfield 1993). Economic action and interaction are guided by systems of rules and regulations that are defined by organizations, but their outcome is not predetermined and can take on different forms. It is the difference between the intended outcomes of rules and regulations and how they unfold in practice and lead to sometimes unintended consequences that is an important, but understudied object of academic enquiry. When studying innovations, attention should therefore be paid to the entire institutional context consisting of organizations, rules and practices – a task that has yet to become standard in innovation research. It is the above distinction between organizations, rules and practices that allows us to explain why specific innovation policies can have unintended effects or lead to heterogeneous results in different countries and regions (Lagendijk, Chapter 30, this volume). As such, a systematic analysis of the components of the institutional context is necessary to find out why similar rules, regulations and policies can lead to different outcomes in terms of producer–user interaction in space and time. Adopting an explicit spatial perspective is therefore an important step to better understand the relationship between innovation and institutions (Glückler and Bathelt, Chapter 8, this volume). Such a perspective enables us to realize that patterns of economic action and innovation do not just differ between national contexts, but also generate regional variations and deviations within a single innovation system under specific localized conditions (Ferrary and Granovetter, Chapter 20, this volume). A broader systematic analysis of the institutional context generates important insights into the successes or failures of innovation processes. For instance, institutional conditions are often not structured in such a way as to support specific technological innovation. Instead, they may be designed in a different way that actually slows down the innovation process; or users may reject an innovation because they are used to different practices and have no incentive to change their behavior (Hargadon and Douglas 2001; Douglas and Hargadon, Chapter 10, this volume). Understanding such tensions and barriers allows us to identify why some innovations are adopted smoothly in a specific territorial context, while being used in different ways or even rejected elsewhere. This is especially important when pro-actively planning and/or designing innovation processes from a corporate perspective (Cantwell and Fai 1999) or when implementing innovation policies in a distinct regional or national setting. When considering the interrelationship between institutions and innovation, it becomes clear that successful innovation is not a linear function of technological and economic

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properties alone. It is the institutional context, among other influences, that makes innovations more or less attractive for users and directly affects market legitimacy. When existing institutional conditions do not suffice to support innovation processes, challenges arise with respect to innovation management and governance. To generate an environment that enables a quick diffusion of new products and technologies, new institutions have to be created through active intervention. This insight has promoted the development of a new research field that focuses on institutional entrepreneurship (Maguire et al. 2004; Geilinger et al., Chapter 40, this volume). It is crucial that these entrepreneurs are able to communicate their interests to users effectively and mobilize other actors to adapt to the new institutional arrangements. This process can be accompanied or guided by political strategies (Mahoney and Thelen 2010) that support the replacement of former institutional settings, generate links to preexisting structures, or reshape existing arrangements to fit the needs of new uses. All of this suggests that innovation research needs to investigate the impact of institutional contexts and the co-development of institutions and innovation more systematically, and compare these processes across different territories. Such research can focus on topics such as the design of new products and technologies, the relationship between firm networks and communities, the learning processes within and beyond technological and territorial innovation systems, and the establishment and implementation of innovation policies.

INNOVATION AND CREATIVITY Many tensions in innovation research revolve around the notion of innovation itself and how it is conceptualized as a process. In a Schumpeterian perspective (1911), innovation is a process in which entrepreneurs leave their normal routines of everyday life, take risks and deviate from existing ‘combinations’ to produce new ones. This understanding views innovation as a risky and uncertain process and posits that not every actor has the capability to engage in this process at any time. Innovation processes are extremely heterogeneous and their structure depends on the context within which innovation occurs (Cohendet and Simon, Chapter 3, this volume). Often, innovation is not an unusual and unpredictable process, but becomes a routine activity itself. This occurs in incremental innovation processes where firms can calculate risks relatively easily based on past experience. Even small firms without R&D laboratories can engage in learning processes that are interactive in nature and lead to a constant stream of modified or improved products and processes (von Hippel 1987; Lundvall 1988; Gertler 1993). Some firms can be classified as ‘permanent innovators’ because they routinely produce new products or services and customized problem solutions. This is especially the case in creative industries, ‘those industries which have their origin in individual creativity, skill and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property’ (DCMS 2001: 4) in areas such as advertising, film production, architecture, music production, engineering and many others. The metaphor of the risk-taking innovator in Schumpeter’s (1911) sense clearly does not fit in these cases, which embody a different logic of producing novelty in society. In the Schumpeterian model, the entrepreneur takes the risk to change the existing structures

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of production in a ‘heroic effort’ by bringing to market new creative ideas which emerge outside of the prevailing economic sphere, driven by what could be labelled the open science world. In the new creative economy, by contrast, the triggers of new ideas are internal to the structures of the economic system. While the Schumpeterian perspective offers a sequential vision of development based on specialized technologies, the creative economy produces novelty in a context of diversity by orchestrating the interface between science, culture, economics, the natural environment and technology. In creative industries, human creativity and innovation at both the individual and group level actively link these domains and become key drivers of development. If we consider creative industries as the ‘labs’ of the creative economy, where new practices to cope with diversity, new forms of open innovation and new ways of managing talents can be observed and tested, the creative economy can be viewed as a new developmental stage of society, in which the Schumpeterian vision of innovation becomes questionable as an analytical tool and in which innovation takes place in many domains, in particular in services, and not just manufacturing (Glückler, Chapter 17, this volume; Rallet and Torre, Chapter 26, this volume). To fully understand the potential for innovation requires more than ever that we investigate the underlying processes of creativity and knowledge production (Cohendet et al., Chapter 13, this volume). Creativity is a crucial process that drives the creative and cultural economy (Pratt, Chapter 15, this volume; Leslie and Rantisi, Chapter 16, this volume) and stimulates diversity and innovation in urban settings (Florida 2002; Desrochers et al., Chapter 14, this volume). As stated by the UNDP (2013: 16) report on the creative economy: Unlocking the potential of the creative economy . . . involves promoting the overall creativity of societies, affirming the distinctive identity of the places where it flourishes and clusters, improving the quality of life where it exists, enhancing local image and prestige and strengthening the resources for imagining diverse new futures. In other words, the creative economy is the fount, metaphorically speaking, of a new ‘economy of creativity’, whose benefits go far beyond the economic realm alone.

However, it can be problematic to classify entire industries or firms as creative per se, or as more creative than others (Meusburger 2009; Le Masson et al., Chapter 18, this volume), if creativity itself is an act or a moment that occurs only occasionally – and there is also a dark side of creativity (Cropley, Chapter 19, this volume). When focusing on creativity and the production of knowledge, we need to understand when and why creativity unfolds and how this is linked to specific institutional settings, as well as specific models of thought taught to design professions such as industrial designers or engineers (Le Masson et al., Chapter 18, this volume). For instance, normal operations in some creative industries may be characterized by routine processes of producing novel solutions within a well-defined set of possible outcomes and a stable institutional framework. This may apply to advertising firms that are specialized in, and known for, generating a certain style of advertising campaign. In such creative work, individuals and firms may never leave their ‘comfort zone’ and are able to keep risks at bay. The respective creative agents or firms may even be identifiable and distinguishable from their outcomes, which have typical features that do not vary much. In contrast, a firm in a mature industrial sector, such as the shoe industry, may be highly creative on a regular basis, as it aims to generate new design elements and technological

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solutions  for  the  next  sports trade show in an attempt to put its products ahead of competition. To escape problems related to the categorization of professions, firms and industries as ‘creative’ or ‘not creative’ and thus generating a static binary, it may be preferable to shift the focus of analysis toward the dynamic interrelationship between creativity and innovation. Instead of viewing creativity (i.e. the process that leads to the emergence and formation of new ideas) and innovation (i.e. the process of bringing these ideas to market) as two separate and sequential processes, Cohendet et al. (Chapter 13, this volume) suggest that we should consider them as parallel processes that are being coupled and decoupled on an ongoing basis, and Le Masson et al. (Chapter 18, this volume) show that the coupling relies on the relationality modelled by design theories. In this perspective, the success of innovative organizations relies on their ability to combine informal activities of creating, which form the basis of idea generation, with the formal logic of innovation. When extending this perspective to the context of the creative city, we need to investigate the emergence of ecologies of creativity and how they crystallize commercially in specific organizational contexts (Cohendet et al. 2014; Leslie and Rantisi, Chapter 16, this volume). Instead of simply assuming that creativity exists, such an approach studies the underlying dynamics that trigger creativity and how these are linked to communities of individuals and to firms and organizations. This opens up opportunities for place-based analyses of innovation processes that take into consideration the institutional context that mobilizes connections between the different levels of creativity and innovation (Cohendet et al., Chapter 13, this volume).

INNOVATION, NETWORKING AND COMMUNITIES The literature has shown that innovation is an interactive social process that goes hand in hand with a specific social and spatial division of labor (Rosenberg 1982; Malecki 1991) as firms develop partnerships or collaborate with other firms and research organizations in their technological field. This process has two components. On the one hand, firms engage with partner firms in innovation and, as a consequence, automatically become embedded over time in structures of social relations (Granovetter 1985). On the other hand, especially in uncertain or highly dynamic technological environments, they aim to reduce risks by actively embedding themselves in such networks (Ferrary and Granovetter, Chapter 20, this volume). The consequence is that innovation processes are fundamentally shaped by social relations and become highly contextual and path-dependent over time (Pavitt 2005; Bathelt and Glückler 2011). The effects of this contextuality on innovation have not yet been fully explored and comparative studies that aim at unravelling common underlying processes are rare. A consequence of this contextuality is that innovation processes, while being contingent in nature, offer in some instances and in some contexts a large variety of possible pathways to proceed but only few options on other occasions and in other contexts (Strambach and Halkier 2013). Embedded action does not always have positive consequences. When power asymmetries between actors are large or institutional conditions inadequate, the contextuality of economic action can lead to suboptimal decisions that generate lock-in (Cantner and Vannuccini, Chapter 11, this volume). The recognition that innovation processes are social in character has led to a rich

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literature about networks, producer–user interaction and the development of innovation systems (von Hippel 1987; Lundvall 1992; Gertler 1993; Lundvall, Chapter 29, this volume). However, this focus has also produced shortcomings in the literature. First, the focus on social learning processes has resulted in an over-emphasis of vertical relationships between suppliers, producers, users and service providers (Vellera et al., Chapter 23, this volume; Giuliani, Chapter 22, this volume; Van Assche, Chapter 45, this volume). As a consequence, research on innovation networks often focuses on relationships between firms that operate at different stages in the value chain and do not directly compete with one another (Malmberg and Maskell 2002). Risks of unintended knowledge transfers in such complementary relationships are relatively low and potential benefits high. However, there are also horizontal learning processes that can play an important role and are beneficial for innovation, both in developed and developing contexts (Li, Chapter 24, this volume). They are based on linkages between firms that operate at the same level of the value chain and are thus directly competing with one another. Firms in such relations try to minimize knowledge spillovers and have little incentive to closely interact with one another. Associated learning processes therefore result from observations and comparisons, rather than direct interaction and communication. Observation processes fulfill an important benchmark function, but they can also directly trigger innovation and need to be investigated more systematically in innovation studies. As the literature on clusters emphasizes, it is the combination of both vertical and horizontal interactions that creates specific dynamic knowledge ecologies conducive for continuous learning (Bathelt and Glückler 2011; Giuliani, Chapter 22, this volume). Second, in terms of the networks relevant for innovation, many studies concentrate on organizational contexts and view technological innovation as a process that involves firms, research facilities and organizational networks (Nelson 1993; Vellera et al., Chapter 23, this volume; Foray, Chapter 25, this volume; Héraud, Chapter 4, this volume). However, as illustrated in studies on project ecologies (Grabher 2002; Grabher and Ibert, Chapter 33, this volume) and open innovation (Chesbrough et al. 2006; Vanhaverbeke, Chapter 6, this volume), crucial parts of the innovation process are not limited to the organizational and inter-organizational domain. Rather, depending on the industry and technology context, different forms of communities play a crucial role in generating a milieu for creative interaction, collective problem solving, brainstorming or recombination of varied skills into new solutions (Amin and Cohendet 2004; Roberts, Chapter 21, this volume). Such communities have been characterized as ‘knowing communities’ (Boland and Tenkasi 1995) that combine communities of practice, epistemic communities and virtual communities and play an important role in setting agendas, creating codebooks and generating structure and dynamics in the innovation process (Bathelt and Cohendet 2014; Cohendet et al. 2014). In order to better understand the role of these communities in industrial innovation and the challenges of commercializing their creative outcomes, which may not involve commercial incentives to begin with, it is necessary to investigate how their collective activities are, or can be, linked to industrial production and marketing. In other words, it becomes key to investigate the connections between the ‘underground’ and the ‘upperground’ and how these can are crystallized within a specific institutional context or ‘middleground’ (Cohendet et al. 2010; Roberts, Chapter 21, this volume; Cohendet et al., Chapter 13, this volume).

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INNOVATION IN PERMANENT SPATIAL SETTINGS Along with the understanding of innovation as an interactive process, various conceptualizations have been developed that emphasize the systemic character of innovation and investigate the linkages between different elements of the system. Aside from conceptualizations about technological and sectoral systems (Carlsson and Stankiewitz 1991; Breschi and Malerba 1997; Dewald and Truffer, Chapter 37, this volume), a rich literature has developed to analyze innovation processes in permanent spatial settings, especially surrounding national innovation systems (Lundvall 1992; Nelson 1993) and regional innovation systems (Asheim and Isaksen 1997; Cooke 2004). This literature focuses on innovation networks that are organized around co-localized or proximate actors and benefit from relational, cognitive and institutional proximity (Rallet and Torre 1999; Gertler 1993; Boschma 2005) or, better, affinity (Bathelt and Glückler 2011; Rallet and Torre, Chapter 26, this volume). The literature about territorial innovation systems has clearly matured since the late 2000s and, while still important in innovation research today, faces a number of problems (Lagendijk, Chapter 30, this volume). First, the increasing significance of open innovation and continued globalization processes raises questions about how such tendencies affect localized learning systems and whether such settings will be able to survive under the new conditions (Lundvall, Chapter 29, this volume). Related to this aspect, the question arises as to how specialized systems can retain their unique character or whether territorial specialties will disappear with the decreasing importance of localized exchange contexts (Hall and Soskice 2001). Second, while national systems are important conceptual tools for understanding national variations in the organization, direction and outcome of innovation processes, few substantial discoveries have been made in this field of research in recent years. This is related to the fact that these systems are quite persistent and change only incrementally over time due to the effects of institutional hysteresis (Setterfield 1993). As a consequence, national systems are relatively stable and studies about the nature of these systems do not need constant updating. In terms of academic enquiry, this leads to elements of stagnation within the otherwise quickly changing field of innovation. This causes loss of interest in studying such systems and a lack of new ideas to revive systems research. It is a dilemma that the excitement about national systems has decreased while they appear more important today in shaping economic action than ever, as for instance the increasing heterogeneity of national views and policies within the European Union demonstrates. As a reaction to the lack of inspiration in the literature, new concepts of innovation ecosystems have recently been discussed (Adner and Kapoor 2009; Autio and Thomas 2014), but it is unclear at this point in which direction this work will lead. Third, the relationship between regional and national innovation systems has never been fully explored and conceptualized (Bathelt and Henn, Chapter 28, this volume). Both strands of literature have tended to acknowledge one another but there is limited conceptual interaction between them. While the concept of national innovation systems focuses on analyzing the nation-specific conditions for innovation and how they are constantly being reproduced (Lundvall, Chapter 29, this volume), the regional innovation systems approach takes a stronger normative orientation to push for (regional) innovation policies that strengthen regional competitiveness and employment. One problem of

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integrating the two approaches is that many regions do not have the preconditions or capacity to develop into drivers of a distinct innovation system (Howells 1999). A closer link between national and regional system approaches, however, would be needed to understand interdependencies between both levels and develop multi-level governance perspectives of how regions can be successfully integrated in the context of the global economy (Bradford and Wolfe, Chapter 44, this volume). Finally, innovation research has mostly focused on industrial agglomeration in large urban areas, which is where the bulk of innovation activities is concentrated. Since these are places with a diversified economy and a high density of leading research organizations, academic studies have stressed their advantages and superiority with respect to innovation (Jacobs 1969; Desrochers et al., Chapter 14, this volume). As emphasized by Shearmur (Chapter 27, this volume), this research tradition has led to an urban bias in innovation research and a neglect of important rural and peripheral contexts. In fact, such areas are characterized by high degrees of innovativeness that benefit from long-established crosssectoral linkages based on embedded personal relationships. Such regions may be characterized by institutional settings that favor the generation of innovation across sectoral boundaries but hinder commercialization, leading to an outflow of resources and profits. This suggests that more research is required to investigate the relationships between urban and rural innovation, especially the underlying institutional conditions and how these can be shaped to support non-urban innovation systems.

INNOVATION IN TEMPORARY AND VIRTUAL SETTINGS The literature that analyzes innovation processes in the context of permanent spatial settings has been challenged in recent years by new forms of innovation that are less predictable in terms of their organization and extend ‘beyond geography’ (Bathelt et al. 2011) to include temporary and virtual settings. Rallet and Torre (Chapter 26, this volume) make a distinction between spatial and organized proximity, suggesting that spatial proximity may only play a role in innovation under certain conditions and/or at certain times. There are other conditions and times, however, when the need for permanent colocation can be overcome and actors successfully develop new products and technologies over large distances through different types of organized proximity. On the one hand, this is supported by relational ties between actors who have developed close linkages based on prior co-localized collaboration or joint membership and participation in communities (Amin and Cohendet 2004; Bathelt and Glückler 2011; Roberts, Chapter 21, this volume). On the other hand, long-distance innovation processes can develop from virtual ties that are associated with systematically using new information and communication technologies, such as video-conferencing and remote process control, in interacting with distant partners, securing close coordination and collectively solving problems (Grabher and Ibert, Chapter 33, this volume; D’Adderio, Chapter 34, this volume). Over time, such computer-based or virtual interaction can lead to the establishment of social relations without the need of co-present interaction (Walther et al. 2005). Successful experience in virtual settings, in turn, improves the conditions for future innovation processes over distance. Virtual interaction may be especially beneficial in contexts that involve occasional

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face-to-face interaction but do not require permanent proximity relations. This includes temporary settings such as coworking spaces (Merkel, Chapter 35, this volume), as well as occasional get-togethers or community meetings such as conferences or trade fairs (Maskell et al. 2006; Bathelt et al. 2014). Although such short-term encounters often do not allow for in-depth debate or interaction, they do provide dense knowledge flows that derive from communication processes between specialized actors and the systematic inspection of the product and process developments that are exhibited or discussed in these settings. Such occasions offer important ideas about the direction of innovation processes (Bathelt, Chapter 31, this volume) and can be the expression of concerted efforts to generate innovation waves, as for instance in the fashion industry (Golfetto and Rinallo, Chapter 32, this volume). Limited research has thus far been undertaken to investigate the connection between innovation and such temporary get-togethers (Vlasov et al. 2017). The study of open, virtual and temporary innovation in the context of globalization raises questions about the ways in which these trends impact existing systems of innovation. This connects to the debate about whether national varieties of production and innovation will converge or diverge over the course of industrialization and globalization (Gerschenkron 1962; Meyer et al. 1975). Studies regarding related effects on the economy and the structure of economic growth indicate that full convergence is an unlikely outcome but that we are experiencing both hybridization and ongoing specialization processes within distinct national systems (Jackson 2003). While trade fairs support the global diffusion of new products and technologies, technological search patterns suggest that firms use them in different ways depending on their production context, thus driving cumulative specialization (Bathelt and Gibson 2015). However, little research exists to date that investigates the evolution of national or regional systems related to temporary and virtual settings for innovation. Innovation processes that are triggered by or conducted through virtual and temporary proximity are linked to existing spatial structures and go hand in hand with intensified forms of mobility (Lassen 2006). While their analysis requires a spatial perspective, it has clearly become necessary to move beyond spatial fixes, such as distinct territorial systems and co-located innovation dynamics. Although innovation processes in localized contexts are still hugely important, even two decades after Audretsch and Feldman’s (1996) widely received study, and despite the fact that they can be very successful as in the case of Silicon Valley (Ferrary and Granovetter, Chapter 20, this volume), today’s innovation processes are less impacted by spatial boundaries through increasingly complex and varied temporary and virtual ecologies of knowledge and creativity. Conceptualizations in innovation research have thus far not convincingly incorporated these dynamics. The increasing importance of temporary and virtual spaces in innovation dynamics raises significant strategic and managerial issues. In the former innovation regime that focuses on permanent settings, multinational firms are assumed to make trade-offs in their locational choices by establishing subsidiaries in selected countries or locations where knowledge flows allow them to strengthen learning processes (Ghoshal and Bartlett 1988; 1990; Cantwell 1989; Bartlett and Ghoshal 1999). In the newly emerging regime, the transient and widespread nature of knowledge spaces and networks requires us to rethink these connections in a more dynamic way by integrating temporary settings and virtual communities of knowledge regularly and permanently. This also impacts human resource strategies concerning where firms can and should hire and train employees in

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order to access the new dynamic knowledge ecologies. This may, for instance, involve delegating parts of the scientific, technological and strategic intelligence to external agents. Questions surrounding these issues open up important tasks for future research in innovation.

INNOVATION, ENTREPRENEURSHIP AND MARKET MAKING Another important set of questions concerns the relation between innovation and entrepreneurship. From a policy perspective it is import to understand how entrepreneurial opportunities are exploited within an economy and which institutional conditions support innovative start-ups. Much of the literature affirms Shane and Venkataraman’s (2000: 225) claim that ‘[t]wo major institutional arrangements for the exploitation of these opportunities exist – the creation of new firms (hierarchies) and the sale of opportunities to existing firms (markets) – but the common assumption is that most entrepreneurial activity occurs through de novo startups.’ Sometimes actors develop creative ideas into innovations within the context of an existing firm – while at other times they establish a new firm to do so. Whereas such alternatives may not always be in place, a question that has not been sufficiently addressed in innovation research is under which conditions a specific organizational form, for instance a start-up firm, is better suited than another to succeed. The choice of start-up entrepreneurship over intra-firm innovation is linked to the institutional conditions and the question of market making. When institutional conditions and organizational structures for intra-firm innovation are unfavorable, individuals will decide to establish new firms. This step is associated with many challenges related to the generation of new markets (MacKenzie et al. 2007), the need to outcompete competitors for scarce resources (Casson 2005) and the task to establish new institutional settings and engage in institutional entrepreneurship (Maguire et al. 2004; Geilinger et al., Chapter 40, this volume). Because of its profound impact on innovation, entrepreneurship plays an important role in the literature. Entrepreneurs have been found to affect processes of knowledge generation and innovation in at least three different ways, all of which generate agendas for research projects at the intersection of innovation and entrepreneurship studies. First, investigations show that entrepreneurs can have a significant impact on the emergence of new markets (Callon, Chapter 36, this volume; Dewald and Truffer, Chapter 37, this volume) and the establishment of institutional conditions that are supportive of innovation (Geilinger et al., Chapter 40, this volume; Foray, Chapter 25, this volume; Glückler and Bathelt, Chapter 8, this volume). We are currently witnessing such processes in an intensive way, for instance in the area of ‘fintech’ with the development of technological solutions to replace traditional banking services; and even more so in environmental or green technologies (Dewald and Truffer, Chapter 37, this volume; Sinclair-Desgagné, Chapter 47, this volume) that need to generate market legitimacy, broad societal support and a clear normative direction. This requires more than narrowly focusing on atomistic innovation processes in isolated corporate or localized contexts (Cropley, Chapter 19, this volume). Spillover effects between firms and industries across spatial entities and between economy, ecology and society are at stake here and demand new visions with respect to the development of sustainable innovation policies. One challenge for innovation research

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may be to rethink entrepreneurship as a connected, collective and collaborative endeavor (Miles et al. 2006). A second way in which entrepreneurs influence innovation is through the creation of new knowledge, especially in the high-technology sector (Malecki and Spigel, Chapter 38, this volume), and by connecting different locations worldwide through personal networks (Henn 2013; Henn and Bathelt, Chapter 39, this volume). Studies indicate that a firm’s embeddedness in diverse social networks requires a profound knowledge of culture and cultural differences. Such work also points to the importance newly founded firms have on regional development and structural change. Transnational entrepreneurs have the opportunity to constantly keep up with market and technological developments, and their presence in a region can generate distinct comparative advantages as the competition between territories increases. Even small and medium-sized enterprises can become part of global value chains (Gereffi and Korzeniewicz 1990; Henderson et al. 2002) and generate competitive advantages over other firms that do not have access to these kinds of global networks (Cantwell 1989). Finally, when existing institutional conditions do not to support the innovation process, challenges arise with respect to innovation management and governance. New entrepreneurs may not find the right conditions to establish successful operations and may thus need to generate new markets for their activities. To be able to grow, firms need to actively shape an institutional environment that may be dominated by former technologies and block off new developments (Glückler and Bathelt, Chapter 8, this volume). It is at this point that institutional entrepreneurship becomes crucial (Maguire et al. 2004), involving ‘change agents who initiate divergent changes, that is, changes that break the institutional status quo in a field of activity and thereby possibly contribute to transforming existing institutions or creating new ones’ (Battilana et al. 2009: 67). The key challenge is how institutional entrepreneurs communicate their interests to users and how they mobilize other actors, particularly in management, to both adapt to and shape the new institutional arrangements. As Geilinger et al. (Chapter 40, this volume) underline, we still have limited knowledge as to whether it is possible for institutional entrepreneurs ‘to change their emphasis from one type of project towards another one, and to acquire the [respective] necessary skills’ (Perkmann and Spicer 2007: 1118) and under what conditions this can happen. This again raises issues for managers in organizations that are being challenged in their ability to detect such institutional movements early, to evaluate potential impacts, and to make a decision about whether to support or resist them. There is much scope for future research on how the emerging, more open and volatile balance of power between organizations, entrepreneurs, communities and institutional stakeholders can be addressed in order to enhance the performance in innovation.

GOVERNANCE AND MANAGEMENT OF INNOVATION The trends and new developments in innovation identified in this volume have a distinct impact on innovation policy and the governance and management of innovation, since conventional perspectives are increasingly challenged and new approaches need to be developed and implemented. We do not yet have answers as to what the best or most appropriate new policy and governance approaches could be. Thus far, innovation policy

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has primarily focused on technology or cost-reducing incentives for innovation and on collaboration within the context of a national state (Bradford and Wolfe, Chapter 44, this volume). It becomes increasingly clear now that policymakers need to go further and take into account the ways in which territorial networks and the global economy generate linkages beyond the local, regional or national context. Ongoing globalization processes have produced extensive global value chains (Gereffi and Korzeniewicz 1990; Van Assche, Chapter 45, this volume), global production networks (Henderson et al. 2002) and even global destruction networks (Herod et al., Chapter 46, this volume) that no longer rely on one distinct set of localized competencies. Instead, they combine diverse research, production and marketing competencies from different parts of the world. This generates great challenges for managing innovation across cultural, social and political boundaries and requires both the development of new and the extension of existing transnational relations (Henn and Bathelt, Chapter 39, this volume; Giuliani, Chapter 22, this volume; Lundvall, Chapter 29, this volume), as well as the establishment of new local relations to be able to integrate these diverse knowledge spaces (Cohendet and Simon, Chapter 3, this volume). On the one hand, it is the mandate and responsibility of policymakers to govern innovation in certain places (Feldman and Lowe, Chapter 42, this volume) and of firms to manage corporate innovation (Dosi and Marengo, Chapter 43, this volume) in order to generate optimal conditions for processes of creativity and knowledge generation. On the other hand, this requires extending policy tools beyond localized corporate contexts and developing what could be referred to as a multilevel relational policy which extends to the transnational level (Amin 2004; Bathelt and Glückler 2011; Faulconbridge, Chapter 41, this volume; Lagendijk, Chapter 30, this volume) and requires new policy approaches at the industry-community level to tap into creative processes that were not integrated before (Cohendet and Simon, Chapter 3, this volume). Again, it is not a routine step to extend policy frames in such ways beyond legitimized action spaces and this may generate competition between governance regimes beyond their assigned territories. The new trends in creativity and innovation discussed in this volume require policy and governance approaches that go beyond the provision of monetary stimuli and the generation of cost-efficient corporate environments. They may require at least partial removal of policy from direct economic intervention, control and hierarchical top-down relations. Things like more democratic and sustainable governance and ‘governance from below’ could be introduced to create alternative policy frames – but much research is necessary to explore such uncharted policy territory. While we are far away from having a clear idea of how such policy environments should be structured and organized, important elements will include the active forging of networks between actors, continuous support of existing networks, and active mediation between various actor groups at a different scale and scope and across jurisdictions in order to solve coordination problems and generate sustainable innovation settings. Research endeavors need to go beyond national, regional and corporate policy domains. They should also remain open to ways in which industries can position and structure themselves through intermediary bodies, such as associations and public–private partnerships, in such a form that the latter can act as collective knowledge aggregators and processors which enhance the innovation potential of the industry and absorptive capacity of their members.

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FINAL REMARKS The arguments developed in this chapter – and indeed throughout the entire edited volume – suggest that understanding innovation requires interdisciplinary explanations and the application of different methods at various levels. Because of the complex nature of the subject, we cannot expect that a single discipline is in the position to provide all the answers to the questions we have raised. In fact, our analysis clearly shows that innovation research is a field of enquiry beyond and across disciplinary boundaries and will require even more integrated endeavors in the future. While this volume aims to provide answers to many of the problems and challenges outlined above, our knowledge regarding these processes is incomplete due to the very nature of and uncertainty associated with innovation. We can offer only a limited glimpse at this complex phenomenon at a given point in time. Many of the questions raised have to remain unanswered for now, but will provide enough scope for new research projects on innovation, knowledge creation and governance in the future. We wish to emphasize that this edited volume presents a unique collection of chapters that systematically analyze and address the challenges, problems and gaps in innovation research. Not only do the chapters summarize the state of the art in innovation research, but they also present original research and develop new agendas and approaches which may facilitate a resolution to some of the challenges, thus opening and describing pathways for future research. As such, this edited volume goes far beyond simply presenting an overview of innovation research; it aims to help students, scholars, managers and policymakers in the field of innovation to develop new understandings of and insights into the characteristics, processes and consequences of knowledge creation and innovation and to drive new lines of enquiry. Acknowledgement We wish to thank Daniel Hutton Ferris for excellent suggestions and remarks on an earlier version of this chapter.

REFERENCES Adner, R. and Kapoor, R. (2009) ‘Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations’, Strategic Management Journal, 31: 306–333. Amin, A. (2004) ‘Regions unbound: Towards a new politics of place’, Geografiska Annaler, 86 B: 33–44. Amin, A. and Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities, and Communities, Oxford, New York: Oxford University Press. Arvidsson, A. (2007) ‘Creative class or administrative class? On advertising and the “underground” ’, Ephemera: Theory and Politics in Organizations, 7: 8–23. Asheim, B. T. and Isaksen, A. (1997) ‘Location, agglomeration and innovation: Towards regional innovation systems in Norway?’, European Planning Studies, 5: 299–330. Audretsch, D. B. and Feldman, M. P. (1996) ‘R&D spillovers and the geography of innovation and production’, American Economic Review, 86: 630–640. Autio, E. and Thomas, L. (2014) ‘Innovation ecosystems: Implications for innovation management’, in M.  Dodgson, M. Gann and N. Phillips (eds), The Oxford Handbook of Innovation Management, Oxford: Oxford University Press, 204–228.

Innovation and knowledge creation: challenges to the field

17

Bartlett, C. A. and Ghoshal, S. (1999) Managing Across Borders: The Transnational Solution, Vol. 2, Boston, MA: Harvard Business School Press. Bathelt, H. (2017) ‘Trade fairs and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 509–522. Bathelt, H. and Cohendet, P. (2014) ‘The creation of knowledge: Local building, global accessing and economic development – toward an agenda’, Journal of Economic Geography, 14: 869–882. Bathelt, H., Feldman, M. P. and Kogler, D. F. (eds) (2011) Beyond Territory: Dynamic Geographies of Knowledge Creation, Diffusion, and Innovation, London, New York: Routledge. Bathelt, H. and Gibson, R. (2015) ‘Learning in “organized anarchies”: Technological search processes and knowledge flows at international trade fairs’, Regional Studies, 49: 985–1002. Bathelt, H. and Glückler, J. (2011) The Relational Economy: Geographies of Knowing and Learning, Oxford: Oxford University Press. Bathelt, H. and Glückler, J. (2014) ‘Institutional change in economic geography’, Progress in Human Geography, 38: 340–363. Bathelt, H., Golfetto, F. and Rinallo, D. (2014) Trade Shows in the Globalizing Knowledge Economy, Oxford: Oxford University Press. Bathelt, H. and Henn, S. (2017) ‘National and regional innovation systems’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 457–471. Battilana, J., Leca, B. and Boxenbaum, E. (2009) ‘How actors change institutions: Towards a theory of institutional entrepreneurship’, Academy of Management Annals, 3: 65–107. Boland, R. J. and Tenkasi, R. V. (1995) ‘Perspective making and perspective taking in communities of knowing’, Organization Science, 6: 350–372. Boschma, R. (2005) ‘Proximity and innovation: A critical assessment’, Regional Studies, 39: 61–74. Bradford, N. and Wolfe, D. A. (2017) ‘Learning through governance’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 723–751. Breschi, S. and Malerba, F. (1997) ‘Sectoral innovation systems: Technological regimes, Schumpeterian dynamics, and spatial boundaries’, in C. Edquist (ed.), Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter, 130–156. Burger-Helmchen, T. and Hussler, C. (2017) ‘Reverse innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 75–86. Callon, M. (2017) ‘Markets, marketization and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 589–609. Cantner, U. and Vannuccini, S. (2017) ‘Innovation and lock-in’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 165–181. Cantwell, J. (1989) Technological Innovation and Multinational Corporations, Oxford: Basil Blackwell. Cantwell, J. and Fai, F. (1999) ‘Firms as the source of innovation and growth: The evolution of technological competence’, Journal of Evolutionary Economics, 9: 331–366. Carlsson, B. and Stankiewitz, R. (1991) ‘On the nature, function and composition of technological systems’, Journal of Evolutionary Economics, 1: 93–118. Casson, M. (2005) ‘Entrepreneurship and the theory of the firm’, Journal of Economic Behavior and Organization, 58: 327–348. Chesbrough, H. W. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Boston, MA: Harvard Business School Press. Chesbrough, H. W., Vanhaverbeke, W. and West, J. (2006) Open Innovation: Researching a New Paradigm, Oxford: Oxford University Press. Clark, G. L. (2017) ‘Behaviour in context’, in G. L. Clark, M. P. Feldman, M. S. Gertler and D. Wójcik (eds), New Oxford Handbook of Economic Geography, Oxford: Oxford University Press, forthcoming. Cohendet, P., Grandadam, D. and Simon, L. (2010) ‘The anatomy of the creative city’, Industry and Innovation, 17: 91–111. Cohendet, P., Grandadam, D., Simon, L. and Capdevila, I. (2014), ‘Epistemic communities, localization and the dynamics of knowledge creation’, Journal of Economic Geography, 14: 929–954. Cohendet, P., Parmentier, G. and Simon, L. (2017) ‘Managing knowledge, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 197–214. Cohendet, P. and Simon, L. (2017) ‘Concepts and models of innovation’, in H. Bathelt, P. Cohendet, S. Henn

18

The Elgar companion to innovation and knowledge creation

and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 33–55. Cooke, P. (2004) ‘Introduction: Origins of the concept’, in H.-J. Braczyk, P. Cooke and M. Heidenreich (eds), Regional Innovation Systems: The Role of Governances in a Globalized World, 2nd edn, London: UCL Press, 2–25. Cropley, D. H. (2017) ‘The dark side of creativity’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 307–323. D’Adderio, L. (2017) ‘Performativity and the innovation–replication dilemma’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 556–569. DCMS (Department for Culture, Media and Sport) (2001) Creative Industries Mapping Document, London: Department for Culture, Media and Sport. Desrochers, P., Leppälä, S. and Szurmak, J. (2017) ‘Urban diversity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 215–229. Dewald, U. and Truffer, B. (2017) ‘Market formation and innovation systems’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 610–624. Dosi, G. (1988) ‘The nature of the innovative process’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds), Technical Change and Economic Theory, London, New York: Pinter, 221–238. Dosi, G. and Marengo, L. (2017) ‘The dynamics of organizational structures and performances’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 702–722. Dougherty, D. (2017) ‘Innovation in the practice perspective’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 138–151. Douglas, Y. and Hargadon, A. (2017) ‘Domesticating innovation—designing revolutions’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 152–164. Edquist, C. (1997) Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter. Evangelista, R., Sandven, T., Sirilli, G. and Smith, K. (1998) ‘Measuring innovation in European industry’, International Journal of the Economics of Business, 5: 311–333. Faulconbridge, J. R. (2017) ‘Relational geographies of knowledge and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 671–684. Feldman, M. and Lowe, N. (2017) ‘Innovation, governance and place’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 685–701. Ferrary, M. and Granovetter, M. (2017) ‘Social networks and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 327–341. Florida, R. (2002) The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life, New York: Basic Books. Foray, D. (2017) ‘Innovation versus technological achievement’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 405–418. Freeman, C. (1988) ‘Japan: A new national system of innovation?’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds) Technical Change and Economic Theory, London, New York: Pinter, 330–348. Geilinger, N., Haefliger, S., von Krogh, G. and Pachidou, F. (2017) ‘Institutional entrepreneurship in Alzheimer’s disease treatment’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 652–667. Gereffi, G. and Korzeniewicz, M. (1990) ‘Commodity chains and footwear exports in the semiperiphery’, in W. G. Martin (ed.), Semiperipheral States in the World-Economy, Westport, CT: Greenwood Press, 45–68. Gerschenkron, A. (1962) ‘Economic backwardness in historical perspective’, in A. Gerschenkron (ed.), Economic Backwardness in Historical Perspective, Cambridge, MA: Harvard University Press, 5–30. Gertler, M. S. (1993) ‘Implementing advanced manufacturing technologies in mature industrial regions: Towards a social model of technology production’, Regional Studies, 27: 665–680. Ghoshal, S. and Bartlett, C. A. (1988) ‘Creation, adoption and diffusion of innovations by subsidiaries of multinational corporations’, Journal of International Business Studies, 19: 365–388.

Innovation and knowledge creation: challenges to the field

19

Ghoshal, S. and Bartlett, C. A. (1990) ‘The multinational corporation as an interorganizational network’, Academy of Management Review, 15: 603–626. Giuliani, E. (2017) ‘Industrial clusters in global networks’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 360–371. Glückler, J. (2017) ‘Services and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 258–274. Glückler, J. and Bathelt, H. (2017) ‘Institutional context and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 121–137. Godin, B. (2017) ‘A conceptual history of innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 25–32. Golfetto, F. and Rinallo, D. (2017) ‘Innovation through trade show concertation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 523–536. Grabher, G. (1993) ‘The weakness of strong ties: The lock-in of regional development in the Ruhr area’, in G. Grabher (ed.), The Embedded Firm: On the Socioeconomics of Industrial Networks, London: Routledge, 255–277. Grabher, G. (2002) ‘The project ecology of advertising: Talents, tasks, and teams’, Regional Studies, 36: 245–262. Grabher, G. and Ibert, O. (2017) ‘Knowledge collaboration in virtual communities’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 537–555. Granovetter, M. (1985) ‘Economic action and economic structure: The problem of embeddedness’, American Journal of Sociology, 91: 481–510. Hall, P. A. and Soskice, D. W. (eds) (2001) Varieties of Capitalism: The Institutional Foundations of Comparative Advantage, Oxford, New York: Oxford University Press. Hargadon, A. B. and Douglas, J. Y. (2001) ‘When innovations meet institutions: Edison and the design of the electric light’, Administrative Science Quarterly, 46: 476–501. Henderson, J., Dicken, P., Hess, M., Coe, N. and Yeung, H. W.-C. (2002) ‘Global production networks and the analysis of economic development’, Review of International Political Economy, 9: 436–464. Henn, S. (2013) ‘Transnational entrepreneurs and the emergence of clusters in peripheral regions: The case of the diamond cutting cluster in Gujarat (India)’, European Planning Studies, 21: 1779–1795. Henn, S. and Bathelt, H. (2017) ‘Transnational entrepreneurs and global knowledge transfer’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 638–651. Héraud, J.-A. (2017) ‘Science and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 56–74. Herod, A., Pickren, G., Rainnie, A. and McGrath-Champ, S. (2017) ‘Innovation, development and global destruction networks’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 752–770. Howells, J. (1999) ‘Regional systems of innovation?’, in D. Archibugi, J. Howells and J. Michie (eds), Innovation Policy in a Global Economy, Cambridge: Cambridge University Press, 67–93. Immelt, J., Govindarajan, V. and Trimble, C. (2009) ‘How GE is disrupting itself’, Harvard Business Review, 87 (10): 56–65. Jackson, G. (2003) ‘Corporate governance in Germany and Japan: Liberalization pressures and responses during the 1990s’, in K. Yamamura and W. Streeck (eds), The End of Diversity? Prospects for German and Japanese Capitalism, Ithaca, NY: Cornell University Press, 261–305. Jacobs, J. (1969) The Economy of Cities, New York: Random House. Lagendijk, A. (2017) ‘Innovation, regional development and relationality’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 490–505. Lassen, C. (2006) ‘Aeromobility and work’, Environment and Planning A, 38: 301–312. Le Masson, P., Hatchuel, A. and Weil, B. (2017) ‘Design theories, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 275–306. Leslie, D. and Rantisi, N. M. (2017) ‘Innovation and cultural industries’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 244–257.

20

The Elgar companion to innovation and knowledge creation

Lhuillery, S., Raffo, J. and Hamdan-Livramento, I. (2017) ‘Measurement of innovation’, in H. Bathelt, P.  Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 99–118. Li, P. (2017) ‘Horizontal learning’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 392–404. Lundvall, B.-Å. (1988) ‘Innovation as an interactive process: From producer–user interaction to the national system of innovation’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg, and L. L. G. Soete (eds), Technical Change and Economic Theory, London, New York: Pinter, 349–369. Lundvall, B.-Å. (ed.) (1992) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 472–489. Lundvall, B.-Å. and Johnson, B. (1994) ‘The learning economy’, Journal of Industry Studies, 1: 23–42. Lundvall, B.-Å. and Maskell, P. (2000), ‘Nation states and economic development: From national systems of production to national systems of knowledge creation and learning’, in G. L. Clark, M. P. Feldman and M. S. Gertler (eds), The Oxford Handbook of Economic Geography, Oxford: Oxford University Press, 353–372. MacKenzie, D., Muniesa, F. and Siu, L. (eds) (2007) Do Economists Make Markets? On the Performativity of Economics, Princeton, Oxford: Princeton University Press. Maguire, S., Hardy, C. and Lawrence, T. B. (2004) ‘Institutional entrepreneurship in emerging fields: HIV/AIDS treatment advocacy in Canada’, Academy of Management Journal, 47: 657–679. Mahoney, J. and Thelen, K. (2010) ‘A theory of gradual institutional change’, in J. Mahoney and K. Thelen (eds), Explaining Institutional Change: Ambiguity, Agency, and Power, Cambridge: Cambridge University Press, 1–37. Malecki, E. J. (1991) Technology and Economic Development: The Dynamics of Local, Regional, and National Change, New York: John Wiley & Sons. Malecki, E. J. and Spigel, B. (2017) ‘Innovation and entrepreneurship’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 625–637. Malmberg, A. and Maskell, P. (2002) ‘The elusive concept of localization economies: Towards a knowledgebased theory of spatial clustering’, Environment and Planning A, 34: 429–449. Maskell, P., Bathelt, H. and Malmberg, A. (2006) ‘Building global knowledge pipelines: The role of temporary clusters’, European Planning Studies, 14: 997–1013. Merkel, J. (2017) ‘Coworking and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 570–586. Meusburger, P. (2009) ‘Milieus of creativity: The role of places, environments, and spatial contexts’, in P. Meusburger, J. Funke and E. Wunder (eds), Milieus of Creativity: An Interdisciplinary Approach to Spatiality of Creativity, Klaus Tschira Symposia: Knowledge and Space 2, Berlin, Heidelberg: Springer, 97–153. Meyer, J. W., Boli-Bennett, J. and Chase-Dunn, C. (1975) ‘Convergence and divergence in development’, Annual Review of Sociology, 1: 223–246. Miles, R. E., Miles, G. and Snow, C. C. (2006) ‘Collaborative entrepreneurship: A business model for continuous innovation’, Organizational Dynamics, 35: 1–11. Nelson, R. R. (ed.) (1993) National Innovation Systems: A Comparative Analysis, Oxford: Oxford University Press. Nelson, R. R. and Winter, S. G. (1982) An Evolutionary Theory of Economic Change, Cambridge, MA: Harvard University Press. North, D. C. (1990) Institutions, Institutional Change and Economic Performance, Cambridge: Cambridge University Press. OECD (Organisation for Economic Co-operation and Development) (1963) The Measurement of Scientific and Technical Activities: Proposed Standard Practice for Surveys of Research and Development. Frascati Manual, 3rd revision, Paris: Organisation for Economic Co-operation and Development. OECD (1992) OECD Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. Oslo Manual, Paris: Organisation for Economic Co-operation and Development. Pavitt, K. (2005) ‘Innovation process’, in J. Fagerberg, D. C. Mowery and R. R. Nelson (eds), The Oxford Handbook of Innovation, Oxford: Oxford University Press, 86–114. Pénin, J. (2017) ‘Patents and open innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 182–194. Perkmann, M. and Spicer, A. (2007) ‘“Healing the scars of history”: Projects, skills and field strategies in institutional entrepreneurship’, Organization Studies, 28: 1101–1122.

Innovation and knowledge creation: challenges to the field

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Pratt, A. C. (2017) ‘Innovation and the cultural economy’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 230–243. Rallet, A. and Torre, A. (1999) ‘Is geographical proximity necessary in the innovation networks in the era of the global economy?’, GeoJournal, 49: 373–380. Rallet, A. and Torre, A. (2017) ‘Geography of innovation, proximity and beyond’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 421–439. Roberts, J. (2017) ‘Community, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 342–359. Romer, P. (1986) ‘Increasing returns and long-run growth’, Journal of Political Economy, 94: 1002–1037. Rosenberg, N. (1982) Inside the Black Box: Technology and Economics, Cambridge, New York: Cambridge University Press. Schumpeter, J. A. (1911) Theorie der wirtschaftlichen Entwicklung (Theory of Economic Development), Berlin: Duncker und Humblot. Setterfield, M. (1993) ‘A model of institutional hysteresis’, Journal of Economic Issues, 27: 755–774. Shane, S. and Venkataraman, S. (2000) ‘The promise of entrepreneurship as a field of research’, Academy of Management Review, 25: 217–226. Shearmur, R. (2017) ‘Urban bias in innovation studies’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 440–456. Sinclair-Desgagné, B. (2017) ‘Innovation and the global eco-industry’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 771–786. Strambach, S. and Halkier, H. (2013) ‘Reconceptualizing change: Path dependency, path plasticity and knowledge combination’, Zeitschrift für Wirtschaftsgeographie, 57: 1–14. UNDP (United Nations Development Programme) (2013) Creative Economy Report 2013. Special Edition. Widening Local Development Pathways, New York: United Nations Development Programme. Van Assche, A. (2017) ‘Global value chains and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 739–751. Vanhaverbeke, W. (2017) ‘Broadening the concept of open innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 87–98. Vellera, C., Vernette, E. and Ogawa, S. (2017) ‘The user innovation phenomenon’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 372–391. Vlasov, S. A., Bahlmann, M. D. and Knoben, J. (2017) ‘A study of how diversity in conference participation relates to SMEs’ innovative performance’, Journal of Economic Geography, 17: 191–216. von Hippel, E. (1987) ‘Has a customer already developed your next product?’, in E. B. Roberts (ed.), Generating Technological Innovation, New York, Oxford: Oxford University Press, 105–116. Walther, J. B., Loh, T. and Granka, L. (2005) ‘Let me count the ways: The interchange of verbal and nonverbal cues in computer-mediated and face-to-face affinity’, Journal of Language and Social Psychology, 24: 36–65.

PART I INNOVATION AS A CONCEPT

2.

A conceptual history of innovation Benoît Godin

INTRODUCTION There are words and concepts – many words and concepts – that we use with no knowledge of their past. Such concepts are taken for granted and their meaning is rarely questioned. Innovation is such an anonymous concept. Today, the concept of innovation is wedded to an economic ideology, so much so that we forget that it has mainly been a political – and contested – concept for most of history. Before the twentieth century, innovation (and novation) was a vice, something explicitly forbidden by law and used as a linguistic weapon by the opponents of change. Innovation had nothing to do with creativity, not yet. The concept has a “negative history”: a history of contestations, refutations, denigrations and denials. Innovation is a term that the opponent of change or the conservative uses in a derogatory sense. In contrast, today innovation is a word of honor. Everyone likes to be called an innovator; every firm innovates (or does it?); governments legislate to make whole nations innovative (Godin 2015). But how could people of the previous centuries constantly innovate but at the same time deny they innovate? In what follows, the paradox is best explained linguistically. Innovation is a bad word and people prefer to cast their innovative behavior using other words. “Il fallait que l’innovation”, claimed the French historian and intellectual Edgar Quinet, “s’accomplît sans que le génie du passé eût le moindre soupçon qu’il entrât quelque chose de nouveau dans le monde” [Innovation had to be carried out without the geniuses from previous times having the least suspicion that something new was being brought into the world] (Quinet 1865: 208). The concept of innovation changed meaning gradually over the last 200 years. Innovation acquired a positive connotation because of its instrumental function to the political, social and material progress of societies. From the early nineteenth century, a whole vocabulary developed that tells a story that “creates, even sanctifies” a progressive future, rehabilitating dirty words until then – revolution – and adding new ones – creativity  – to talk of and about innovation. From that time on, innovation became a catchword that everyone understood spontaneously, or thought they understood; that every theorist talked about; that every government espoused.

INNOVATION AND ORDER From its very emergence in Ancient Greece, the concept of innovation (kainotomia) had a political connotation. As “introducing change into the established order”, innovation was subversive, or revolutionary, as we say today. This political and contested connotation survived, or rather was revived during the Reformation (see below). In the meantime, the concept made its entry into Latin vocabulary, with a positive meaning. From circa 25

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the fourth century, Latin writers, first of all Christian writers and poets, coined in-novo, which means renewing (return to the original or pure soul), in line with other Christian terms of the time – rebirth, regeneration, reformation – and according to the message of the New Testament (God sent his son Jesus to save man from sin). Innovo has no future connotation as such, although it brings a “new order”. Innovo refers to the past: going back to purity or the original soul. The Vulgate was influential here. In 382, Pope Damasus I commissioned Saint Jerome to produce a “standard” version of the Vetus Latina, which he did using original Greek and Hebrew texts. Four books in the Vulgate make use of innovo in a spiritual context (Job, Lamentations, Psalms, Wisdom). Innovation thus began with both a positive and negative meaning, but subsequently lost this valence when it moved to the politico-religious sphere of the Reformation. From the very beginning of the Reformation, royal and ecclesiastical authorities started using innovation in discourse. In 1548, Edward VI, King of England and successor to Henry VIII, issued a Proclamation Against Those That Doeth Innouate. The proclamation places innovation in context, constitutes an admonition not to innovate and imposes punishments on offenders: Considering nothing so muche, to tende to the disquieting of his realme, as diversitie of opinions, and varietie of Rites and Ceremonies, concerning Religion and worshippyng of almightie God . . .; [considering] certain private Curates, Preachers, and other laye men, contrary to their bounden duties of obedience, both rashely attempte of their owne and singulet witte and mynde, in some Parishe Churches not onely to persuade the people, from the olde and customed Rites and Ceremonies, but also bryngeth in newe and strange orders . . . according to their fantasies . . . is an evident token of pride and arrogance, so it tendeth bothe to confusion and disorder . . .: Wherefore his Majestie straightly chargeth and commandeth, that no maner persone, of what estate, order, or degree soever he be, of his private mynde, will or phantasie, do omitte, leave doune, change, alter or innovate any order, Rite or Ceremonie, commonly used and frequented in the Church of Englande . . . Whosoever shall offende, contrary to this Proclamation, shall incure his highness indignation, and suffer imprisonment, and other grievous punishementes.

The proclamation was followed by the Book of Common Prayer, whose preface enjoins people not to meddle with the “folly” and “innovations and new-fangledness” of some men. A hundred years later, King Charles prohibited innovation again, and the Church produced lists of forbidden innovations, required bishops to visit parishes to enforce the ban, instructed bishops and archbishops as well as doctors (universities) and school-masters to take an oath against innovations and ordered trials to prosecute the “innovators”. Advice books and treatises for princes and courtiers supported this understanding, and included instructions not to innovate. Books of manners urged people not to meddle with innovation. Speeches and sermons spoke against innovation, religious and political. Every opponent to innovation – puritans, ecclesiasts, royalists and pamphleteers – regularly repeated the admonitions of monarchs in support of their own case against innovators – until the late nineteenth century in the case of religion. The Reformation was a key moment in the history of the concept of innovation. At a time when the Reformation was incomplete and still in the making, the Catholics accused the reformers of innovating. The Puritans served the same argument to the Protestant Church, accused of bringing the Church back to Catholicism. The word served both sides of the debate: reformers and counter-reformers. It was precisely in the context of the Reformation that the concept entered everyday discourse.

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This was only the beginning. Soon the meaning of innovation was to be enlarged. First, to the political; the monarchists of the seventeenth and eighteenth centuries accused the Republicans of being “innovators”. Innovation is revolutionary . . . and violent. No Republican – no citizen in fact, even the most famous Protestant reformers or the French revolutionaries – thought of applying the concept to his or her own project. Innovation is too bad a word for this. In contrast, and precisely because the word is morally connoted, the monarchists used and abused the word and labeled the Republican as an innovator. This linguistic practice continued until the French Revolution (and later), and casted a general disrepute on the idea of innovation: “Un préjugé général, produit par la haine de la révolution, a établi, avec des apparences assez favorables, que tout ce qui l’a immédiatement précédé, est excellent: c’est comme innovation qu’on la dénigre principalement; et par là même un discrédit général a dû s’attacher à toutes sortes d’innovations” [A general bias, arising from the hatred toward the revolution, established, with apparently considerable support, that everything immediately preceding it was excellent: it is as an innovation that is denigrated; and as a result every innovation has come to be discredited] (Montlosier 1814, Volume 3: 137). Second, innovation widened its meaning to the social. The social reformer or socialist of the nineteenth century is called a “social innovator”, as William Sargant puts it in Social Innovators and Their Scheme (1858). His aim is to overthrow the social order, namely private property. Innovation is a scheme or design in a pejorative sense – as it is a conspiracy in political literature (words used are project or plan or plot or machination). This connotation remained in the vocabulary until late in the nineteenth century – although some writers discuss social innovation using the positive idea of (social) reform. For example, in 1888, a popular edition of the Encyclopedia Britannica included a long article on communism which begins as follows: “Communism is the name given to the schemes of social innovation which have for their starting point the attempted overthrow of the institution of private property”. Everyone shares this representation of innovation. Natural philosophers, from Francis Bacon onward, never refer to innovation as what is certainly the most innovative project in science: the experimental method. Equally, very few artisans and inventors talk of their invention in terms of innovation. Innovation is political.

INNOVATION AND PROGRESS The concept of revolution and the concept of innovation changed meaning and started to be used in a positive sense at about the same time. The “spirit of innovation”, a pejorative phrase of the previous centuries, became one of praise. This occurred gradually over the nineteenth century, particularly in France (“le centre de l’esprit philosophique et novateur” [the centre of philosophical and innovative spirit], Littré 1873: 208), and got full hearing in the twentieth century (Figure 2.1). Two rehabilitations of the concept serve the purpose. One, a semantic re-description: People start producing reflexive thoughts on what innovation is and conclude that the concept admits of different interpretations. Innovation is neutral. There are good and bad innovations. Yet innovation is in fact a word of accusation, the “war cry of the fools”, as Jean d’Alembert puts it in his eulogy of L’Abbé François Régnier Desmarais (1786). Yet, innovation may be a good thing, namely useful.

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Figure 2.1

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Here lies a second rehabilitation, an instrumental one. Innovation is a means to political, social and material progress. Writers narrate or rather rewrite the story of the past in terms of innovation, including the Reformation and the Revolution, and talk of innovators in superlative terms. Innovation is a source of national pride too: L’Américain pris au hasard doit donc être un homme ardent dans ses désirs, entreprenant, aventureux, surtout novateur. Cet esprit se retrouve, en effet, dans toutes ses œuvres; il l’introduit dans ses lois politiques, dans ses doctrines religieuses, dans ses théories d’économie sociale, dans son industrie privée; il le porte partout avec lui, au fond des bois comme au sein des villes [The American must be fervent in his desires, enterprising, adventurous, and above all, innovative. This spirit can be found in everything he does: he introduces it into his political laws, his religious doctrines, his theories of social economy, and his private industry; it remains with him wherever he goes; be it in the middle of the woods or in the heart of cities]. (Tocqueville 1835: 201)

Yet the transition from the negative to the positive was not sudden. One had to wait until the twentieth century for a complete reversal in the representation of innovation. This occurred after World War II. Those who contested innovation in the past – governments – started de-contesting innovation and produce reflexive thoughts on innovation as a policy tool. One after the other, international organizations and governments embrace innovation as a solution to economic problems and international competitiveness, and then launch innovation policies. At that precise moment, the dominant representation of innovation shifts to that of the economy: technological innovation – a phrase that emerged after World War II – as commercialized invention. Technological innovation serves economic growth. A whole new set of arguments develops: research and development (R&D) leads to innovation and innovation to prosperity. Statistics are developed to support the idea: innovation surveys are administered to firms and the numbers collected into “innovation scoreboards” that serve as so-called evidence-based information to policy-makers. Innovation becomes a basic concept of economic policy. In a matter of decades, science policy shifts to technology policy to innovation policy, and indicators on science and technology are relabeled indicators of innovation. In all these efforts, the governments are supported by the academics as consultants, who imagine models of innovation by the dozens, as a way to frame and guide policies. Model itself becomes an integral concept in the literature on innovation (Godin 2017). Ironically, these developments led to the transformation of the concept from a means to an end to an end in itself. Over the twentieth century, innovation has become quite a valuable buzzword, a magic word. Innovation is the panacea to every socioeconomic problem. One need not inquire into the society’s problems. Innovation is the a priori solution.

THEORIZING INNOVATION Beginning in the 1940s, theoretical thoughts on innovation appeared and theories of innovation multiplied afterwards. Psychological, sociological and economically oriented theories followed one after the other. Two theoretical perspectives particularly – economics (technology) and policy – serve a new ideology, and the theorists rapidly got a government hearing.

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Innovation is no longer an individual affair but a collective process. To be sure, the twentieth century has its individual heroes: the entrepreneurs. Yet, entrepreneurs are only one part of the process of innovation: a total process as some call it, or a socioeconomic process. As Jack Morton, Engineer and Research Director at Bell Laboratories, who brought the transistor from invention to market, and who is the author of numerous articles and a book on innovation, suggests (Morton 1968: 57): Innovation is not a single action but a total [my italics] process of interrelated parts. It is not just the discovery of new knowledge, not just the development of a new product, manufacturing technique, or service, nor the creation of a new market. Rather, it is all [my italics] these things: a process in which all of these creative acts, from research to service, are present, acting together in an integrated way toward a common goal.

Defining innovation as a process is a twentieth century “innovation”. Herein lies a semantic “innovation”, an “innovation” that has had a major impact on the modern representation of innovation. Until then, innovation as a concept was either a substantive (something new) or a verb (introducing, adopting something new), an end or a means. Sometimes it is also discussed in terms of a faculty (combination, creativity), an attitude (radicalism) or aptitude (skill) or quality (originality, departure, difference): Substantive: novelties (new ideas, behaviors, objects) Action: introducing (or bringing in) something new Process: a sequence of activities from generating ideas to their use in practice. Since the mid-twentieth century, innovation has been studied as a “process”, a sequential process in time. Innovation is not a thing or a single act but a series of events or activities (called stages) with a purpose. The theorists have made themselves “innovative ideologists” here, to use historian Quentin Skinner’s phrase. They brought in a new definition of innovation, in reaction to a new context. Innovation as a process has contributed to giving the concept of innovation a very large function: innovation encompasses every dimension of an invention, from generation (initiation) to diffusion. To the sociologists, the process is one from (individual) adoption to (social) diffusion; to the economists, from invention to commercialization; to management schools from (product) development to manufacturing. Everywhere, this process is framed in terms of a sequence (with stages) called a model.

CONCLUSION By the end of the nineteenth century, the word innovation had accumulated four characteristics that made it a powerful (and pejorative) term. From the Greeks, the representation of innovation had retained its subversive (revolutionary) character. The Reformation added a heretic dimension (individual liberty), and the Renaissance a violent overtone. Together, these characteristics led to a fourth one: innovation is conspiracy (designs, schemes, plots). Yet in spite of these connotations that have made a word (innovation) part of the vocabulary and discourses, innovation seems to have escaped the attention of intellectual or conceptual historians. Many concepts of change (crisis, revolution, progress, modernity)

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have been studied in the literature, but innovation has not. Is innovation only a word – a mere word – in the vocabulary of adherents to the status quo – Churches, Kings and their supporters – and devoid of sociological meaning? In a certain sense, it is. Before the twentieth century, no theory of innovation existed. Innovation was a concept of limited theoretical content, a linguistic weapon used against one’s enemy. In another sense, innovation is not devoid of sociological meaning. The opponents of innovation in the seventeenth and eighteenth centuries provided the first image of innovation and innovators, one that lasted for centuries. What constitutes innovation and who is an innovator were defined by the enemies of innovation and innovators. It is against this pejorative image or representation that innovators had to struggle in the nineteenth century when they started making use of the concept in a positive sense. Before the twentieth century, the idea of innovation belonged to experience, but very rarely to thoughts and dreams. Innovators themselves made no use of the word. To paraphrase Reinhart Koselleck on deeds, for centuries it was not innovation itself that shocked humanity but the word describing it (Koselleck 1972). The novelty (the “innovation”) of the twentieth century is to enrich the idea of innovation with thought, dreams and imagination. Innovation takes on a positive meaning that had been missing until then, and becomes an obsession. The changing fortune of innovation over the centuries sheds light on the values of a time. In the seventeenth and eighteenth centuries, the uses of the concept were essentially polemical. It served as a linguistic weapon, attaching a pejorative label to the reformers. In contrast, from the nineteenth century onward, innovation started to refer to a central value of modern times: progress and utility. As a consequence, many people started appropriating the concept for their own ends. Yet, there is danger here that a word, as a “rallying-cry”, may become “semantically null”. “Terms of abuse cease to be language” (Lewis 1960: 328). As Pocock puts it on the word revolution: “the term [innovation] may soon cease to be current, emptied of all meaning by constant overuse” (Pocock 1971: 3).

REFERENCES Alembert, Jean le Rond d’ (1786), Histoire des membres de l’Académie française, morts depuis 1700 jusqu’en 1771, pour servir de suite aux éloges imprimés & lus dans les Séances publiques de cette Compagnie, Volume 3, Amsterdam: Moutard. Godin, Benoît (2015), Innovation Contested: The Idea of Innovation over the Centuries, London: Routledge. Godin, Benoît (2017), Models of Innovation: The History of an Idea, Boston: MIT Press. Koselleck, Reinhart (1972), “Begriffsgeschichte and Social History”, in R. Koselleck (ed.), Futures Past: On the Semantics of Historical Time, New York: Columbia University Press, 2004: 75–92. Lewis, Clive Staples (1960), Studies in Words, Cambridge: Cambridge University Press, 1967. Littré, Émile (1873), La science au point de vue philosophique, Paris: Didier et cie. Montlosier, François Dominique de Reynaud de (1814), De la monarchie française, depuis son établissement jusqu’à nos jours; ou recherches sur les anciennes institutions françaises, leur progrès, leur décadence, et sur les causes qui ont amené la révolution et ses diverses phases jusqu’à la déclaration d’empire; avec un supplément sur le gouvernement de Buonaparte, depuis ses comencemens jusqu’à sa chute; et sur le retour de la maison de Bourbon, Three volumes, Paris: H. Nicolle/A. Édron/Gide fils. Morton, Jack A. (1968), “The Innovation of Innovation”, IEEE Transactions on Engineering Management, EM-15 (2): 57–65. Pocock, John G. A. (1971), “Languages and Their Implications: The Transformation of the Study of Political

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Thought”, in Politics, Languages and Time: Essays on Political Thoughts and History, Chicago: University of Chicago Press, 1989: 3–41. Quinet, Edgar (1865), La Révolution, Paris: Félix Alcan, 1891. Sargant, William (1858) Social Innovators and Their Scheme, London: Smith, Elder & Co. Tocqueville, Alexis de (1835), De la démocratie en Amérique I, Paris: Gallimard, 1992.

3.

Concepts and models of innovation Patrick Cohendet and Laurent Simon

INTRODUCTION A model of innovation is a conceptual framework developed for understanding the process of translating an idea into a good or a service that creates value. Beyond this simple and technical definition, a model of innovation can be seen as a core multidisciplinary concept that expresses how change is produced in society. At a given time, a dominant model of innovation results from the efforts of diverse disciplines, including economics, management science, sociology, geography and political science, that come together to explain the production of novelty in society. In line with the perspective of Kuhn (1962), a dominant model of innovation could thus be considered as a paradigm, viewed as “the source of the methods, problem-field, and standards of solution accepted by any mature scientific community at any given time” (Kuhn, 1962: 103). As a general paradigm for society, a dominant model of innovation drives and shapes the behaviours and decisions of policy-makers, economists, entrepreneurs, business managers and all sorts of economic agents. It offers a common understanding not only on how ideas could be turned into useful products or services, but also on where resources should be allocated to fuel innovative processes. A well-known example is the case of the linear technology-pushed model (attributed to Schumpeter), which postulates that innovation starts with basic research, then adds applied research and development (R&D), and ends with production and diffusion: This model clearly suggests to policy-makers that the key measure to fuel innovation is to finance basic research. A dominant model of innovation thus has a strong performative influence: its principles, concepts and theoretical framework “when carried out into the world by professionals and popularizers, reformat, and reorganize the phenomena they purport to describe, in ways that bring the world into line with theory” (Healy, 2015: 175). The multidisciplinary aspect of a model of innovation implies that when considering this common platform of understanding between different disciplines, one should be able to assess the contribution of each of the disciplines to the collective building of the model. Between the lines of the theoretical constructs, one should see the coherence between the dominant model and the bodies of knowledge that form the basis of each of the disciplines. For example, in a series of papers, Godin (2006; Chapter 2, this volume) shows that the linear technology-push model should not be attributed to the sole brilliant genius of Schumpeter, but has been co-constructed by a long process of multidisciplinary inputs and exchanges of all sorts. The model emerged at a period marked by the end of World War I, which revealed the critical role of big science. It was stimulated by sociologists at the beginning of the century, who were lobbying strongly for the reinforcement of open science; by the active involvement of influential industrialists such as John Carty, Vice President of the American Telephone and Telegraph Company (AT&T), who was convinced of the importance of science and advocated for the development of applied 33

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research departments in large companies; by the contribution of mainstream economists such as Mansfield, who brought evidence from econometric models to support the dominant model (Mansfield, 1968); and by the members of the Organisation for Economic Co-operation and Development (OECD), who built adequate statistics to validate and reinforce the dominant position of the model. The multidisciplinary aspect of a model of innovation also explains why when a dominant model in place is challenged, questioned and criticised, and may be replaced by a new model, this sparks violent debates and conflicts in and among each of the constitutive disciplines. To quote Kuhn (1962: 103): As a result, the reception of a new paradigm often necessitates a redefinition of the corresponding science. Some old problems may be relegated to another science or declared entirely “unscientific.” Others that were previously non-existent or trivial may, with a new paradigm, become the very archetypes of significant scientific achievement.

All the constitutive disciplines that contribute to building the previous paradigm may be deeply affected by the creative destruction that accompanies the formation of a new paradigm. Changing a dominant model of innovation also greatly affects policy-makers, business managers, and in general all those who make decisions in society. Chesbrough (2003) insists that the transition from previous closed models of innovation to the new model of open innovation implies a drastic revision of the ways managers organise the principles of production and a profound change in the state of mind of their employees, from the “not invented here” syndrome that corresponded to the closed models of innovation, to the “proudly found elsewhere” philosophy that expresses the value of opening the boundaries of the organisation. Despite these strong multidisciplinary aspects of the models of innovation that suggest that they are socially constructed, the literature (in particular economic) has tended to assign paternity of each of the models to some key influent individual thinkers: Schumpeter (technology-push linear model), Schmookler (demand-pull linear model), Nelson and Winter (evolutionary model), Kline and Rosenberg (interactive model), Chesbrough (open model) and so on. Without denying the importance and role of these scholars, our aim in this chapter is to highlight the composite and hybrid nature of the building of each of the dominant models. We aim to understand the sequence of the generations of the dominant models of innovation, within some of the main disciplines (economics, management, geography and sociology) that contribute to the building of these models (see also Bathelt et al., Chapter 1, this volume; Glückler, Chapter 17, this volume). There are many attempts in the literature to propose a typology of the different generations of dominant models of innovation. Among the main authors, one of the most quoted contributions in the typology is that of (Rothwell, 1994), who suggested five successive generations of models: 1) linear technology-push, 2) linear demand (or need) pull, 3) coupling model with feedback loops, 4) integrated models with simultaneous links between R&D, prototyping and manufacturing, and 5) systems integration/networking model. If he would have rewritten his article recently, he would probably have added at least a 6th generation, the open innovation model. In the present contribution, to focus on the relationships between dominant models and constitutive disciplines, we have purposefully reduced the sequence of generations of dominant models to three main generations: 1)

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the linear and closed model of innovation (from World War I to the mid-1980s); 2) the interactive and closed model of innovation (from the mid-1980s to the first decade of the 21st century); and 3) the interactive and open model of innovation (starting from the first decade of the 21st century, and which in our view has not yet reached its mature stage). For each generation of dominant model, we will summarise the main characteristics of the dominant model, to assess the contribution of each of the constitutive disciplines to the model, and to understand the replacement of a model by a new one.

THE LINEAR AND CLOSED MODEL OF INNOVATION (FROM WORLD WAR I TO THE MID-1980s) The Characteristics of the Model Until the mid-1980s, the model of innovation, which constituted the major paradigm to explain the emergence of products and services, was supposed to be a linear and closed model. The linear property of the model expresses that the process of innovation results from linear sequences of phases (fundamental research, applied research, development, marketing, etc.) going from the emergence of an idea up to its launch on the market. The closed property of the model expresses that most of the activities undertaken in a process of innovation are supposed to be accomplished within the (closed) boundaries of a given organisation. This linear and closed model of innovation could follow two main alternative perspectives that divided the academic community: On the one hand, those who referred to the so-called “technology-push” perspective pinpointed the key role that science and technology play in developing technological innovations and adapting to the changing characteristics of the industry structure. On the other hand, scholars embracing a “demand-pull” approach identified a broader set of market features, including characteristics of the end market (particularly, the users) and the economy as a whole, that affects the performance of innovation. (Di Stefano et al., 2012: 1283)

The “technology-push” perspective was inspired by the “science-push” model of science policy advocated by Vannevar Bush, who headed the US Office of Scientific Research and Development during World War II (and who initiated the Manhattan Project), and by the Schumpeterian vision (1911, 1936, 1942). Schumpeter (1942) describes innovation in the following terms: the fundamental impulse that sets and keeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of production or transportation, the new markets, the new forms of industrial organisation that capitalist enterprise creates. . . . The opening up of new markets, foreign or domestic, and the organisational development from the craft shop and factory to such concerns as U.S. Steel illustrate the same process of industrial mutation . . . that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one. (emphasis added)

This process of Creative Destruction is the essential fact about capitalism. It is what capitalism consists in and what every capitalist concern has got to live in.

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Questioning the conventional wisdom in economics inspired by Schumpeter according to which technological innovations were supply-side driven, Schmookler (1966), through an extensive analysis of time series and cross-sectional patent data and historical case studies, demonstrated that demand-pull influences were also important. He suggested that the more intense the demand, the more creative groups and individuals were drawn to work on an unsolved problem and the more patentable inventions they generated in response to market needs. The juxtaposition of the technology-push and demand-pull approaches, viewed more as substitutes than as complements, led to intense and passionate debates in the literature in the 1970s. However, beyond these controversies on the source of innovation, between the technology-push and the demand-pull approaches, which dominated the academic scene until the mid-1980s, these two alternative perspectives shared some strong common features, in particular the linear dimension, but also the closure of the innovative process within the boundaries of the organisation. This assumption of closure dictates the necessity of protecting the borders of the organisation through diverse appropriate mechanisms (industrial secrets, strong property rights, contracts with reliable partners, etc.). The Multidisciplinary Building of the Model As underlined above, the model has been initiated by the sociological theories of science of the 20th century that emphasised the importance of scientific autonomy and independence as essential for the flourishing of science (Merton, 1973). As Shavinina (2003) underlines, the end of World War I, and the threats of a new world war, coupled with significant scientific and technological successes, led to a strong belief in the power of science to produce radical technologies. “While this idea of ‘big science’ arose in the government funding of science, it translated easily into the management of innovation in large corporations, with the establishment and growth of corporate laboratories” (Shavinina, 2003: 45). Following the initial impulse from sociologists, some influential industrialists pursued the theoretical construction of the linear model (Godin, 2006). Among the main industrialists, Jack Morton, Engineer and Research Director at Bell Laboratories, Kenneth Mees, director of the research laboratory at Eastman Kodak, and John Carty, Vice President of AT&T, promoted the establishment of strategic departments of R&D within large organisations. Their visions were aligned with the principles of organising the firm through the functional structure, which at this time was the dominant form of organisational structure based on a division of the firm into separate departments, viewed as “silos” of specialised knowledge. In the perspective of the linear model of innovation, the role of the R&D department was, grounded in the results of fundamental science, to undertake the necessary practical R&D of new products or processes and then transfer the work to the manufacturing department to prepare for commercialisation. In these efforts to classify the sequential steps constituting the innovation process, the distinction suggested by Schumpeter between invention (“an act of intellectual creativity, without importance to economic analysis”, 1939: 85) and innovation (bringing the invention to an economic use) has made a major contribution to the understanding of the innovation process. The Schumpeterian distinction helped classify the phases of fundamental research, applied R&D as invention, and highlighted the key role of the

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entrepreneur as the hero who takes the risk of bringing the idea from the stage of invention to the market. In particular, once the impulse at the origin of innovation is perceived, each phase of the innovation process obeys a strict division of work in a linear sequence of activities: the fundamental research phase focuses on the activities of conception and production of new knowledge, the applied research phase (the phase of invention) concentrates on turning the scientific ideas from the fundamental research phase into potential industrial applications (patents, prototypes, etc.), the phase of development finalises the new processes to implement, and the phase of production of new products materialises the innovative process. The development phase is followed by the phase of marketisation to place and diffuse the new product on a market. The literature commonly associates a “representative hero” with each phase: the genius at the origin of a discovery, the inventor translating the scientific ideas into patents, the entrepreneur taking the risk of launching the new ideas on the market, and the manager optimising his or her informative chain and rare resources to adapt to the novelty. In this collective process of the social building of the dominant linear model of innovation, economists came late. In the 1960s macroeconomists such as Solow or Mansfield were the first to test the linear model of innovation and its consequences in their representation of societal growth. However, economists’ main contribution to the model came from the seminal work of Arrow (1962) on knowledge creation in the firm. Arrow argued that the process of invention can be interpreted as the production of new knowledge, which he considers comparable to information. Arrow stressed that in such a context, the production of new knowledge faces the key problem of appropriability. He argued that it is difficult or even impossible to create a market for knowledge once it is produced, so it is hard for the producer of knowledge to appropriate the benefits that flow from it. Arrow argues that if the producers of knowledge cannot appropriate the benefits of new knowledge, they have no incentive to produce it. Thus, without external intervention, the level of research in society will be below optimal. The consequences of this vision have been considerable, a key one being the justification for government subsidisation of science, and technological and engineering research. This vision shaped the conception of public intervention in R&D for decades. It justified the creation and role of public laboratories and research centres, public R&D programmes, public institutions (for example, patent offices) and public infrastructures for technology transfer. It explained why public efforts in R&D were generally disconnected from applications and why arguments concerning the existence of spillovers from public research programmes were so important to justify money spent on R&D. It suggested that scientific production was indeed exogenous to the economic sphere, and governed by rules and behavioural norms (reputation effects, peer reviews, etc.) that were drastically different from the norms and behaviours of industry (quest for profits and technical efficiency). In particular, in this perspective, the choice of research themes by academics remained independent of the objectives of industry. From a management perspective, we can extend Burns and Stalker’s (1961) analysis identifying two main approaches of organisation related to innovation, a mechanistic mode (Part I) and an organic mode (Part II), to which we would add an ecosystemic mode (Part III). It can be argued that the linear and closed model requires a rather mechanistic approach in which the structuration of the sequential process of knowledge transformation is central. The organisation of innovation would then rely on 1) the investment in and

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incentives for (mostly specialised) fundamental knowledge production, or the search for markets’ needs, and 2) the formatting of the conversion of knowledge into a functionality (invention) and then into a product (diffusion). The science-and-technology based linear model raises questions of efficiency: should each new knowledge base be systematically explored in order to extract its innovation potential? How to select the lineage of knowledge (in a context where knowledge is merely addressed as information) to exploit? The same question arises in the market-led perspective: which segment to target, with which rationale? Here the literature mostly attributes a decisive, if not demiurgic, power to the manager as entrepreneur, faithful to the Schumpeterian tradition, to orient the search process and to identify and select which opportunity to pursue. Also, managing innovation means organising, coordinating and controlling a strict pipeline where knowledge is sequentially converted. In this process, management has to articulate the competencies of different departments in a sequential fashion. Connections and debates between scientists, engineers and marketers remain limited and channelled by management. This model appears almost fictional in light of more recent empirical works on innovation processes and management, and raises significant questions about how innovation actually occurs in organisations. Questioning the Linear and Closed Model of Innovation Building on the legitimacy of the works from the most influential neoclassical economists, the linear and closed model of innovation was dominant throughout much of the 20th century, inspiring policy-makers and practitioners. After having played a key role in guiding the perspectives and visions of most of the US public agencies in World War II, it probably reached a peak of influence when the OECD after the war decided to build adequate statistics to validate and reinforce the dominant position of the model, in particular through the Frascati Manual of innovation (OECD, 1963). The Manual is a document based on the linear model definitions, intended to serve as a common language for discussions of science and technology policy and economic development policy. It set forth the methodology for collecting statistics about R&D. These principles have been adopted by many governments and by various organisations associated with the United Nations and the European Union. Despite these successes in the diffusion of the linear model principles, opponents to the model started to ask questions and express doubts in the mid-1960s. Thus, “in 1967, the Charpie report, an influential study by the US Department of Commerce on measuring the costs of innovation, estimated that research amounts to 10% of the costs of innovation only” (Godin, 2005: 33). Opponents also started questioning the linearity of the model: they highlighted that from a linear model perspective, innovation is seen as a static process, not influenced by the dynamics and quality of the different interactions between the actors at play. Geographers contended that the linear model cannot understand innovation properly because it does not appreciate the central role of spatial proximity and concentration in this process. Indeed, these geographical dimensions have been neglected by the developments of the dominant linear model. The only reference to geographical proximity was the vague acceptance of the Marshallian industrial district argument (1890) that the local concentration of production in a given district can create positive externalities of spillovers that may affect the regionally residing firms’ ability to innovate.

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Our view is that the main source of opposition to the linear and closed model of innovation came from the scholars working on the role of knowledge in society. Building on the fundamental distinction brought forward by Polanyi (1958, 1967) between tacit and codified knowledge, a series of influential scholars (Machlup, Boulding, Freeman, etc.) highlighted the poor representation of knowledge in the dominant linear model. While knowledge indeed appears as a unique asset that is both an output (innovation) of the production process and an input (competences) of this process, this property is hidden in the representation of mainstream economics, which tends to conflate knowledge with information. While all the theoretical advances in the 1960s suggested that knowledge should essentially be understood as tacit and collective, the mainstream approaches in economics were still supporting the linear model that considered knowledge to be explicit and held by individuals. While the firm as an innovative context should be seen as a repository of competences, creativity and learning, and as a locus of construction, selection, usage and development of knowledge, the neoclassical representation of the firm in the linear model of innovation considers the firm to be a pure processor of information. The focus is on the sole process of allocation of resources needed to cope with such an adaptation, and on the mechanical reactions of the firm to external signs and factors (market prices) detected. As Nonaka and Takeuchi underlined (1995: 56): when organisations innovate, they do not simply process information from the outside in, in order to solve problems and to adapt to a changing environment. They actually create new knowledge and information, from the inside out, in order to redefine both problems and solutions and in the process to re-create their environment.

The growing consensus that the spark of innovation is the interplay of different types of knowledge, and that knowledge emerges out of a dialogue between people’s tacit and explicit knowledge, definitely undermined the dominant linear and closed model of innovation. This explains why Rosenberg could claim: “Everyone knows that the linear model of innovation is dead” (1994: 139).

THE INTERACTIVE AND CLOSED MODEL OF INNOVATION (FROM THE MID-1980s TO THE FIRST DECADE OF THE 21st CENTURY) The Characteristics of the Model In the mid-1980s, the new evolutionary vision of the economic process brought forward by Nelson and Winter (1982) led to a radical questioning of the linear vision by highlighting the interactive nature of the innovative process. This new perspective entailed a major reconsideration of one of the key characteristics of the model of innovation: the dominant model became an interactive and closed model. Accordingly, many authors (such as Kline and Rosenberg, 1986) underlined that the spark of production of new ideas could emerge at any phase of the innovation process, and what matters is the internal capacity of organisation to facilitate the interactions between different actors within it to reinforce innovative ideas. The efficiency of such a decentralisation of the responsibility in engaging the innovative process cannot succeed if it is not accompanied

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by an organisational structure and climate that favour exchanges between all the actors of the innovation process: researchers, and specialists in marketing, production and development. These authors advocated a shift from linear models to interactive models, and insisted on the importance of technology (Dosi, 1982; Malerba, 2002; Arora and Gambardella, 1994; etc.). They highlighted that the technology-push and demand-pull approaches are more complements than substitutes, and considered that the core of the innovative process lies in the interactions between science and technology as key sources of innovation, and demand (and more broadly market and social forces) as the best variable that can be leveraged to commercialise successful innovation. Concomitantly, von Hippel (1986, 1988), through the concept of lead-users innovation, brought new and important insights into the role of demand as a source of innovation. He suggested that users (as opposed to manufacturers) are often the first to develop new products that are commercially successful. He also emphasised that innovation by users tended to be concentrated among the “lead users” of those products and processes (von Hippel, 1986; Urban and von Hippel, 1988; Morrison et al., 2000). “Lead users” are individuals (or organisations) who experience needs for a given innovation earlier than the majority of the target market (von Hippel, 1986). “Von Hippel’s approach largely paved the way for the next dominant model of innovation, where the ‘closed’ characteristic is replaced by the notion of openness (open model) of innovation”, according to Chesbrough (2003). In these efforts to develop an interactive model of innovation linking the two main potential sources of innovation (technology and demand), the importance of a new key source of innovation, namely firm competences, progressively grew. In the perspective of the resource-based and knowledge-based views of the firm, the literature focused on the internal competences of a given organisation as the crucial device to innovate from the interactions between technology and demand (Prahalad and Hamel, 1990; Sanchez and Heene, 1997; Nordhaug, 1998; Dosi and Marengo, 2000; etc.). As this field of research expands, how innovative firms can cope with turbulent environments has become a key issue. The founding article in this direction is Teece et al.’s dynamic capability paper (1997), which defines dynamic capabilities as “the firm’s ability to integrate, build, and reconfigure firm internal and external competences to address rapidly changing environments” (Teece et al., 1997: 516). The dynamic capabilities approach copes with transformations arising well upstream of the innovation process, such as the change of the knowledge base of the organisation. Such a change is considered as the key source for the firm to react to turbulence in the environment. The result of this transformation is mostly materialised by a given innovation, at the level of the product, the processes of the organisation or even the business model of the firm. Teece thus highlighted the role of internally generated competences as a source of innovation resulting from the interactions of technological knowledge and market knowledge. The Multidisciplinary Building of the Model Although they were silent during the building of the linear and closed model of innovation, economic geographers took a leading role in the building of the interactive model of innovation. Notably, economic geographers originated the development of the theoretical approach in terms of “national systems of innovation” (Lundvall, 1992; Freeman, 1995). A

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national system of innovation is defined as a set of institutions that jointly and individually contributes to the development and diffusion of new technologies and provides a framework for the implementation of government policies influencing the innovative process (Metcalfe, 1995: 446). The innovation system theory thus severely questions the linearity of the innovation model. It emphasises that innovation and technology development result from a complex set of relationships and interactions among actors in the system, which includes enterprises, universities and government research institutes. A particular case is the regional system (Dodgson, 1993), in which geographical location has prime importance. In addition to physical proximity, regions share a similar culture and industrial mix, and have economic and administrative homogeneity, which can promote distinctive styles and modes of innovation within regions (Cooke et al., 1998). Regional systems of innovation are also closely linked to the concept of innovative milieu, or to the concept of creative cluster. An innovative milieu is usually defined as a grouping of economic, social, political, cultural and institutional elements (Maillat, 1992) or as a group of relationships having particular characteristics related to innovation processes and potential, and occurring within a specific geographic continuum. Camagni and Capello (2000) emphasised that the interactions creating the innovative milieu are not necessarily based on market mechanisms, but include movement and exchange of goods, services, information and people. Creative clusters are generally viewed as small geographic locations centred on a particular industry, which facilitates close face-to-face communications between the participants of the clusters (Saxenian, 1994; Andersen and Teubal, 1999; Bathelt et al., 2004; Bresnahan et al., 2002; Rullani, 2001; etc.). A creative cluster can thus be interpreted as a localised network that uses the territory for the dissemination of creative ideas (Rullani, 2001). The literature on creative clusters has extensively examined the conditions of success of clusters; among the main determinants of success, the existence of large pillar firms (Bathelt et al., 2004), key agents (Saxenian, 1994), regional specialisation (Bathelt et al., 2004), and local “buzz” (Storper and Venables, 2004; Bathelt et al., 2004) have received specific attention. The theoretical advances from economic geographers in questioning the fundamental basis of the linear model were supported by the development of the concept of networks, viewed as the analytical tool to capture the formation and dynamics of systems of innovation. The theoretical approaches in terms of networks came from two different disciplines: first, from the school of sociology of networks, following Granovetter’s seminal works (1973). As Granovetter and Swedberg (2001: 11) wrote: economic action, in short, is embedded in on-going networks of personal relationships rather than being carried out by atomized actors. By “networks” we mean a regular set of contacts or social connections among individuals or groups. And action by a network member is embedded, since it is expressed in interactions with other people. The network approach helps avoid not only the conceptual trap of atomized actors, but also theories that point to technology, the structure of ownership, or culture as the one and only one explanation of economic events . . . Actors do not behave or decide as atoms outside a social context. Nor do they adhere slavishly to a script written for them by the particular intersection of social categories that they happen to occupy. Their attempts at purposive action are instead embedded in concrete on-going systems of social relations.

Granovetter argued that a social theory of markets should begin with a view of actors as embedded in an evolving structure of concrete social relations (Healy, 2015: 3). From

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this perspective, neoclassical economics is fundamentally misconceived, either because “the fact that actors may have social relations with one another has been treated, if at all, as a frictional drag that impedes competitive markets”, or because, when they are examined, models “invariably abstract away from the history of relations and their position with respect to other relations” (Granovetter, 1985: 485–486). A different school in the domain of networks was that of complexity theory, where Thomas Schelling (1978), followed by Alan Kirman (1983), initiated the development of the economics of interactions: These works place the notion of network, conceived as a structure of particular interaction between economic agents, at the heart of the economic analysis. They express the basic idea that the existence of macro-phenomena in society results from the way economic agents mutually interact at a microeconomic level. From this perspective, the well-being of agents and their decisions depend on the individuals with whom they have direct relations: those with whom they exchange or communicate, or those whose actions they observe to imitate them, or those from whom they wait for information to make decisions. This vision is at the origin of a profound renewal of the economics, in particular regarding how to handle relations between microeconomic and macroeconomic phenomena. It offers a much richer frame of analysis than that of the standard economic theory in which the interactions between economic agents are only mediated by the centralised system of prices. In the early 1990s the two schools on networks, the economy of interactions and the sociology of networks, merged, notably through the multidisciplinary initiatives of the Santa Fe Institute. From this convergence of perspectives, many important and rich results were added to the interactive model of innovation: the “small worlds” effects (Watts and Strogatz, 1998; Uzzi and Spiro, 2005) that clarify the characteristics of a creative local network, the functioning of social networks, and the underlying architecture of the Internet, wikis such as Wikipedia, and gene networks; the “herd behaviour” or “epidemic behaviour” models, which suggest (Barnejee, 1992; Kirman, 1993; Orléan, 1998) that there is a form of externalities generated by choices made by members of a population that conditions individuals’ choices. These externalities explain mimetic behaviours in the diffusion of innovation – the “lock-in” effects (David, 1985; Arthur, 1989; Woerdman, 2004), which explain that a particular technology or product may be dominant, not because its inherent cost is low or performance is good, but because it benefits from increasing returns to scale. In economics, the central role of knowledge led to the reconsidering of fundamental approaches in both macroeconomics and microeconomics. In macroeconomics, the dominant models of the theory of growth became those of the endogenous growth theory (Romer, 1994; Aghion et al., 1998), which holds that investment in human capital, innovation and knowledge are significant contributors to economic growth. The theory also focuses on positive externalities and the spillover effects of a knowledge-based economy, which will lead to economic development. In microeconomics, in the domain of the theory of the firm, influential contributions were made in the knowledge-based approaches of the firm, which views the firm as “a processor of knowledge”, that is, as a locus of construction, selection, usage and development of knowledge. This vision strongly differs from the classical “information-based” theories of the firm (described above in the section on the linear model). Considering the firm as a processor of knowledge leads to the recognition that cognitive and related processes are essential, and that routines play a

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major role in keeping the internal coherence of the organisation. In this perspective, the focus of the governance of the firm shifts from resolution of informational asymmetries to co-ordination of distributed pieces of knowledge and distributed learning processes. The focus of theory is now the process of creation of knowledge resources, as evidenced through the work of, among others, Cyert and March (1963), Cohen et al. (1972), Cohen (1991), Loasby (1976, 1983), Eliasson (1990) and Marengo (1996). In this closed interactive model, management issues are shifting towards a more organic approach, rebalancing activities from the organisation and maintenance of a sequential process to the opening and animation of the process, at each step. In this perspective, the scope of innovation management is widening to the organisation of interactions to produce and validate knowledge in a more communal manner, where the manager is not the only one to identify opportunities and suggest new orientations. Thus, it means searching for and organising dynamic interactions with external partners, beyond the academic realm: members of the industry, consortia, suppliers or event competitors, and between internal members of the firm. The focus then shifts from the implementation of the innovation process and portfolio to the development of an innovation capability fuelled by absorption, interactive exploration and collective learning. In this regard, the innovation sequential process is only one dimension of the innovation dynamics. It is no longer considered as a “reduction” process, only converging towards an “output”, but rather as a dual process that also generates by-products, “outcomes” in terms of formal and informal knowledge, new and generative social connections, learning through interactions and experimentations – or even failures – that feed the repository of knowledge of the organisation. In line with Nonaka and Takeuchi’s contribution (1995), knowledge management then appears as an essential dimension of innovation management, preceding and paralleling the innovation process itself. Nonetheless, this acknowledgement of the centrality of knowledge for innovation raises questions when considering the partial and limited openness of this approach. Questioning the Interactive and Closed Models of Innovation As the new dominant model of innovation, the interactive and closed model was fully based on the idea that the generation, exploitation and diffusion of knowledge are fundamental to economic growth, development and the well-being of nations. In the 1990s this perspective gained major support and recognition from international organisations such as the OECD. The expression “knowledge-based economy” was officially coined by OECD members in 1996, as a fuller recognition of the role of knowledge and technology in economic growth, which was driving and inspiring the economic development of society. Along with this recognition of the central role of knowledge in society came the need for better measures of innovation. Over time, the nature and landscape of innovation have changed, as has the need for indicators to capture those changes and provide policymakers with appropriate tools of analysis. A considerable body of work was undertaken during the 1980s and 1990s to develop models and analytical frameworks for the study of innovation. Experimentation with early surveys and their results, along with the need for a coherent set of concepts and tools, led to the first edition of the Oslo Manual in 1992 (OECD, 1992), which pays significant attention to the interactive dimension of the innovation. This became the reference for various large-scale surveys examining the nature

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and impacts of innovation in the business sector, such as the European “Community Innovation Survey” (CIS), currently in its fourth round. As the recognition of the key role of knowledge in society by international organisations, governments, industrialists and decision-makers of all kinds was growing, and as the diffusion of statistical tools under the principles of the Oslo Manual was supporting the validation of the interactive model as the dominant model, some influential voices in the last decade of the past century started questioning the model. In short, when considering the dominant interactive and closed model, the first characteristic, the interactive nature of the model, was never questioned, and all scholars and professionals agreed that this dimension undoubtedly adds to the understanding of what innovation is. Conversely, growing criticism started reconsidering the closed nature of the model. As we have seen, the closed property of the model expresses that most of the activities undertaken in an innovation process (from the applied research phase to the launch on the market) are supposed to be accomplished within the closed boundaries of a given organisation, which controls within its boundaries the innovation process: the motto is “the higher the control, the more efficient the innovation process”. Only the phase of fundamental research is external to the boundaries of closed organisations, and may be considered as a public and open domain (the “open science”). This closed perspective was reinforced by the quasi-commonly accepted practice according to which all activities performed within  the boundaries of the organisation should be governed by strong property rights (while the world of fundamental science could remain “open”). The first breach in this widely accepted practice of closure of the organisation boundaries was opened by von Hippel (1986, 1988) through the concept of lead-users innovation. As we have seen, von Hippel brought new and important insights into the role of demand as a source of innovation by observing that many products and services are actually developed, or at least refined, by users (individual end-users or user communities) rather than by suppliers. These ideas are then moved back into the supply network. Often, user innovators will share their ideas with manufacturers in the hopes of having them produce the product, a process called free revealing. These examples clearly highlight that important processes of innovation are not controlled and produced by closed entities. A second breach in the belief of having closed boundaries to innovate was epitomised by the development of Linux in the early 1990s (the open source operating system was first released on 5 October 1991 by Linus Torvalds) as one of the most prominent examples of free and open-source software collaboration, development and distribution. In the Linux system, the underlying source code may be used, modified and distributed – commercially or non-commercially – by anyone under the terms of its respective licences, such as the GNU General Public License. The spectacular development of Linux challenged economists’ paradigm in several respects. As an example, Lerner and Tirole (2001), in an essay to interpret this new type of economic development within the economic mainstream framework, asked the following questions about Linux: Why do top-notch programmers choose to write code that is released for free? Is this “gift economy” (Raymond, 1999) consistent with the self-interested-economic-agent paradigm? Why do commercial companies allocate some of their talented staff to open source programmes? Why do software vendors initiate open source projects? Is the apparently anarchistic process of open source production, in which no one tells anybody else what to do, a new model of business organisation? How does the new process fit with the conception of the innovative

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process driven by intellectual property rights (patents, copyrights and trade secrets) that we have inherited from Arrow (1962) and Schumpeter (1942)? We believe that if some of these issues could be well accounted for by the mainstream economic paradigm, the Linux model (and all the new types of cooperative models that followed such as Wikipedia) brings to the academic sphere two radically new principles: the first one is that a complete innovative project could be carried out by an open informal community (the community of Linux contributors) and not by a formal organisation with closed boundaries; the second one is that this innovative endeavour could be achieved without the protection of strong property rights, but through an open system of intellectual property. These two radically new principles are central in the formation of the new paradigm of innovation: the open (and interactive) model.

THE OPEN AND INTERACTIVE MODEL OF INNOVATION The Characteristics of the Model As we have seen above, since von Hippel’s works on the role of lead-users, the “closed” character of the process of innovation was progressively questioned (Tushman, 1977). Chesbrough (2003) proposed to radically break with this closed representation, through the model of “open innovation”: open innovation “is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as firms look to advance in their technology” (Chesbrough, 2003: 14; see also Vanhaverbeke, Chapter 6, this volume). The “open” model of innovation suggests that from now on, thanks in particular to the evolution of digital technologies, firms can take forward their processes of innovation by using and integrating both internal and external knowledge in a systematic way. By confiding a part of its activities of search and development to external partners (customers, suppliers, universities, research centre, etc.), the firm can improve the performance of its processes of learning and knowledge management. As underlined by Chesbrough (2003: 40), a major “erosion has rearranged the landscape of knowledge. Companies can find vital knowledge in customers, suppliers, universities, national labs, consortia, consultants, and even start-up firms. Companies must structure themselves to leverage these distributed pools, instead of ignoring them in the pursuit of their internal R&D agendas”. Such an opening allows a given company to mutualise a wider set of resources than can be achieved through three different main types of processes: 1) an “outside-in” process aiming at enriching the company’s own knowledge base through the integration of suppliers, customers and external knowledge sourcing; 2) an “inside-out” process allowing the company to earn profits by bringing ideas to market, selling intellectual property and multiplying technology by transferring ideas to the outside environment; and 3) a “coupled” process coupling the outside-in and inside-out processes by working in alliances with complementary partners in which give and take is crucial for success (Gassmann and Enkel, 2006). The dominant idea is that innovation in such a model is managed through porous boundaries, exploiting (notably through information and communication technologies) the informative wealth of the environment. This vision puts forward organisations’ need to accumulate and develop, in house, a very specific and very strategic form of organisational

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capacity, namely, absorptive capacities (Cohen and Levinthal, 1990), without which it cannot benefit from its exchanges with the outside. According to West and Bogerts (2014), an important point emphasised by Chesbrough and van Haverbaeke is that “successful commercialisation efforts by a firm – from internal or external sources – need to be aligned to the firm’s business model: open innovation combines internal and external ideas into architectures and systems whose requirements are defined by a business model”. This new approach in terms of open innovation implies that creative ideas can emerge from both inside and outside the organisation. It also supposes that these creative ideas can be turned into innovations, either within the organisation at the origin of the idea, or in a different organisation, including a customer or community of users. While the paradigm of closed innovation holds that successful innovation requires control by an organisation of the generation of its own ideas (as well as control of the phases of production, marketing, distribution, servicing, financing and supporting), the paradigm of open innovation, in contrast, asserts that what matters is not controlling the ideas, but having access to the new knowledge available in the environment and having the capacity to produce (alone or with others) useful products or services from this acquired knowledge. The Multidisciplinary Building of the Model Starting from the initial impulse given in the domain of economics of innovation by von Hippel (1986), a series of major inputs from various disciplines contributed to build the new model of open innovation. An important contribution came first from sociologists who paved the way to take into account the functioning of informal communities as key active units in innovative processes. A community can be broadly defined as a “gathering of individuals who accept to exchange voluntarily and on a regular basis about a common interest or objective in a given field of knowledge” (Amin and Cohendet, 2004). Members of a given community share knowledge on an informal basis, and respect the social norms of their community that drive their behaviour and beliefs. The recognition of the importance for firms of the cognitive work of communities has been growing since the early 1990s. The literature has identified many variants of cognitive communities such as communities of practice (Lave and Wenger, 1990), epistemic communities (Cowan et al., 2000), communities of creation (Sawhney and Prandelli, 2000), communities of innovation (Lynn et al., 1997), open source communities (von Hippel and von Krogh, 2003), and diverse virtual cognitive communities (Bogenrieder and Nooteboom, 2004). The forms of communities differ regarding the type of specialised knowledge activities on which they focus. Most often, the accumulation of knowledge by a given community is shaped by the dominant mode of learning (such as “circulation of best practices”) that it adopts. For instance, epistemic communities are more concerned with the production of new knowledge (exploration), while communities of practice are centred on the circulation of best practices in a given domain of knowledge (exploitation). All cognitive communities share similar characteristics: communities are repositories of useful knowledge, part of which is embedded in their daily practices and habits. The local daily interactions constitute an infrastructure that supports an organisationally instituted learning process that drives the generation and accumulation of knowledge by the community. As Wenger (2000) asserts, a community that draws on interaction

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and participation to act, interpret and innovate, acts “as a locally negotiated regime of competence”. Communities also play a key role in the genuine processes and contexts of creation and diffusion of knowledge (how such knowledge is used, how it acquires meaning and how to interpret its role, etc.). In this perspective, the generic value of communities includes their ability to absorb a significant proportion of the unavoidable sunk costs associated with building and exchanging knowledge (Amin and Cohendet, 2004). These sunk costs (and, more generally, fixed costs) correspond, for instance, to the progressive construction of languages and models of action and interpretation that are required for the implementation of new knowledge that cannot be covered through the classical efforts of hierarchies (or markets). As an example of a successful interaction between communities, institutions and individuals, we can reinterpret Saxenian’s vision of Silicon Valley (1995). Here, interacting communities (of engineers, software designers, venture capitalists, lawyers, specialist firms, etc.) are genuinely self-organising, in such a way that organisation emerges from the interactions rather than the reverse. The management of the cluster is largely autopoetic, and dependent upon the structure of interaction and communication; thus, for example, if a firm goes bankrupt, the collective interaction of communities takes charge to ensure that new organisations are formed and that the competences and experience of individuals are redistributed. Redundancy is maintained and sunk costs are not lost as a result of a systemic vibrancy that emanates from strong local ties. However, the role of specific individuals through their unique ability to bridge together heterogeneous communities must also be emphasised. In Silicon Valley, the key role was played by Frederick Terman (Cariou, 2006). Although talented as a scientist, his contribution to Silicon Valley was not through the emission of any invention. “As a social and institutional innovator, Frederick Terman helped to shape the relationships among individuals, firms and institutions in Silicon Valley, creating a community that has encouraged continuous experimentation and technological advance for more than half a century” (Saxenian, 1995). Without explicitly referring to the notion of ecosystem (a term she currently uses), Saxenian (1995) highlighted the essence of what makes Silicon Valley a self-generating ecosystem, compared to the routinised and rigid procedures of the system of Route 128. She emphasised that the power of regeneration, attraction and resilience of Silicon Valley cannot be understood by observing the interactions between the formal entities of the region (firms, labs, government agencies, etc.); one should focus instead on the vibrant interplay between the informal local active units such as communities and other collectives. The dynamics of the region are based on the continuous interaction between the formal organisations and the informal active units from which emerge ever-evolving networks of workers leaping from start-up to start-up, of companies that fail and then combine with other failures to form big successful firms, and of myriad projects that are continually recombined. By contrast, the evolution of Route 128 is guided by traditional driving mechanisms of innovation, which are mostly based on the activation of a linear process (or value-chain) connecting the formal institutions of science with the industry. Such a system may eventually come to a decline with the obsolescence of the technologies and know-how trapped within the vertically integrated companies of the region. Another discipline that brought a significant contribution to the open innovation model is the community of lawyers. Following the Copyleft movement, which accompanied the emergence of the open source phenomenon in the 1990s, the Creative Common

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movement was initiated by Lawrence Lessig to counterbalance what he considers a dominant and increasingly restrictive permission culture (Lessig, 2004). Lessig aimed at supporting the building of a richer public domain by providing an alternative to the automatic “all rights reserved” copyright or “a culture in which creators get to create only with the permission of the powerful, or of creators from the past” (Lessig, 2004). Lessig maintains that modern culture is dominated by traditional content distributors in order to maintain and strengthen their monopolies on cultural products such as popular music and popular cinema, and that Creative Commons can provide alternatives to these restrictions. It can be argued that the Creative Common principles only applied to cultural and creative products, and that they complement and challenge only the domain of copyrights. However, as Foray (2004: 131) underlined, if in the domain of intellectual property rights a clear distinction was initially made between copyrights covering artistic activities, and patents covering industrial activities, nowadays the boundary tends to be blurred. As a result, the recent developments in the domain of property rights from lawyers and policy scientists have to mitigate the role of strong property rights and facilitate the model of open innovation. These results from the legal and political science disciplines have been echoed in economics and sociology: in the phase of emergence of invention, most of the empirical works and historical analyses have shown that what matters is the progressive building of collective knowledge and understanding between actors. At this stage, there is neither a common language nor a common representation between the different players. The group of agents who succeed in expressing and formalising an innovative idea face a main difficulty: not the risk of being copied (at no cost), but the risk of being ignored or misunderstood by others (including agents belonging to the same institution). It is therefore the risk that their procedures and experience will not be reproduced by others. Without a collective effort to reach a critical mass of common understanding between the different actors committed in this emerging phase, the innovation process cannot be viable. The group of agents at the origin of an innovation must undertake considerable efforts to alert other actors or communities in order to convince them of the usefulness and potential of their discovery. These features of the emerging phase of the process of development of innovation are not captured by Arrow’s vision. In Arrow’s perspective, the producer of knowledge acts in isolation: nothing is said about the complementary forms of knowledge necessary for the producer of knowledge to invent, and nothing is said about the community of agents who supported the producer in the process that led to the invention. More precisely, as Callon in an Actor-Network theory perspective (1999) emphasised, in a network, in the phase of emergence of innovation, the production of knowledge tends to exhibit exactly the reverse properties from the one postulated by the traditional approach: knowledge is essentially rival (it is extremely difficult to reproduce the new knowledge in a place that is not the place where the invention has been first realised) and exclusive (the novelty relies heavily on the tacit knowledge of inventors). In this context of emergence, knowledge is also essentially specific (it can be absorbed and used by a few other agents only), which is the opposite of the traditional vision that postulates that knowledge has a high degree of generality (knowledge with a high degree of generality can be potentially used in various contexts by a large variety of agents: all the agents of the economy have the full capability to absorb the innovative idea emitted by the producer of knowledge.

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The logical conclusion therefore is that in the phase of emergence, there are important reasons to support a hypothesis of strong appropriability. It is not the issue of appropriability that matters the most during this phase, but the issue of the building of a quasi-public good: the critical mass of understanding between inventors or more precisely communities of inventors, from which codes and a grammar of usage of the novelty will be gradually developed, in order to reproduce, extend and make the initial creative ideas viable. Further, in this emerging phase of production of knowledge, what the observations and empirical works show is that the active units of building of a cognitive platform are generally not the individuals, nor the institutions, but the knowing communities of agents that are committed to the creation and accumulation of the new forms of knowledge. Individuals and institutions also play an important role in the microeconomics of collective creation, but the fundamental cognitive building of the codes and grammar that will equip the novelty require the active functioning and interaction of knowing communities. This open and interactive model of innovation raises new challenges for management. Beyond managing processes and portfolios, and fostering the absorptive capability of the firm, we suggest that management responsibilities be expanded to an ecosystemic perspective on innovation. Basically, it would mean 1) opening up further to knowledge sources beyond the expert groups that usually contribute to innovation orientation and development (internal experts, suppliers, etc.) and initiating active and dynamic conversation with users, to identify opportunities more quickly and generate new, more relevant, and better adapted ideas; 2) cultivating different knowledge bases supported by specific and interacting knowing communities inside and outside of the firm, in order to either identify needs and/or craft rich collaborative answers to complex issues through collaborative creation. A new innovation regime arguably occurred around the turn of the century, where information and communication technology gave users, specifically lead-users, more knowledge compared to organisations. This rebalancing of power should incite organisations to invest further in not only capturing knowledge from users, but to involve users in the opportunity identification, idea generation, valuation and selection processes. This has become possible through the implementation of live conversations with users, supported for instance by social media and platforms. The more advanced organisations are also staging open events and idea competitions for users around specific issues to gain more insights into their specific needs and to generate genuine ideas. Innovation is thus further shifting towards the timely identification of legitimate needs, the crafting of design spaces (Baldwin et al., 2006; Baldwin and von Hippel, 2010), and the involvement and support of diverse knowing communities for co-creation approaches. Another key dimension can be summed up as the extension of the absorptive capabilities of the firm to local and global knowing communities as shared, industrial and ecosystemic capabilities. Challenges for management lie in the genuine participation of and contribution to these communities, in listening genuinely to the needs and insights of their members, taking part in conversations, putting forth relevant issues, and sharing tools and methods that have been developed in-house. These interactions are mostly made possible by the recognition that firm employees have to be considered on a par with eventual members of diverse communities, and that members of communities can regularly contribute to the firm’s activities. This raises obvious questions of incentives, property rights and coordination that are at the heart of innovation research today.

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Questioning the Open and Interactive Innovation Model The open and interactive model is still being formed. It is thus too early to draw conclusions about the strength and scope of diffusion of the model. As for any dominant model, the first phase was full of scepticism and doubt. However, even if several works recently began to question aspects of this model, in particular by criticising the nature and degree of the optimal opening of organisations (Laursen and Salter, 2006; Dahlander and Gann, 2010; Zirpoli and Becker, 2011; Birkinshaw et al., 2011), few authors oppose this model today. On the contrary, scholars converge to propose new suggestions and contribution to the cognitive building. For example, West and Bogerts (2014) underlined that the original Chesbrough model of open innovation is a sequential, linear model. However, Enkel et al.’s (2009) concept of a “coupled” practice – two-way interaction between firms and innovative actors outside the firm – is one example that goes beyond this linear model to include reverse flows of knowledge beyond what is predicted by the linear model. Communities and value networks have long been identified as an important source of innovations for firms that source external innovations (Chesbrough et al., 2006; Jeppesen and Frederiksen, 2006; West and Lakhani, 2008). However, such research has emphasised the value created by the inbound flows of innovations, and not the direct or indirect costs of the outbound portion of the coupled process. Future research on open innovation communities and their associated firms should both assess the collective costs and benefits to firms of participating in such a community, as well as the innovation flows between firms through the community.

As for the previous dominant models that receive validation from international organisations such as the OECD (in particular through the Frascati Manual for the linear and closed model, and through the Oslo Manual for the interactive and closed model), the open innovation model has received, at least indirectly, official recognition, through the works of the United Nations and their Creative Economy reports (United Nations/ UNCTAD, 2008, 2010 and 2013) that promote the value behind the open innovation model. However, the strongest support for the open innovation model recently came from Edmund Phelps (2013). In his recent essay, the Nobel Prize winner (2006) advocates for the emergence of a new phase of growth of society, based on the democratisation of ideas. As he wrote, understanding the modern economies must start with a modern notion: original ideas born of creativity and grounded on the uniqueness of each person’s private knowledge information, and imagination. The modern economy was driven by the new ideas of the whole roster of business people, mostly unsung: idea men, entrepreneurs, financiers, marketers, and pioneering end-users.

Phelps’ emphasis on the importance of ideas in the modern economy points to the central concept at the basis of the new dominant model of open innovation for understanding the roles and strategies in the modern economy. While the linear and closed model of innovation was based on firms conceived as processors of information, and the interactive and closed model was based on firms conceived as processors of knowledge, the open innovation model is based on idea-led firms and organisations. The essence of the generative power of the innovation ecosystem of the organisation relies on the continuous interplay between, on the one hand, the organisation as a formal structure, and, on the other, the myriad informal local active units such as communities

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and other collectives. The formal organisation continuously taps into the informal (the communities) to regenerate ideas, access new trends and new modes of usage, validate concepts and co-create products. In turn, the formal also nurtures the informal with events, projects and challenges. Moreover, in line with the biological metaphor of ecosystem, these interactions between the formal and the informal are not neutral. Parts of the informal may become formal and vice versa. Organisations and other forms of institutions will change as the innovative ecosystem evolves, while new ideas and talents emerge continuously from informal activities. Ideas that have been sensed in the informal may become projects that will reconfigure the formal structure of the organisation; talent, attracted and detected at events, festivals and challenges, may become employees of the organisation; and entrepreneurial informal collectives may be transformed into small business units of the organisation. Further, the organisation does not hesitate to decentralise and delegate its competences and capabilities to diverse and distributed communities of specialists that are partly engaged in their own informal activities, and partly contributing to the firm’s project under specific incentives and property rights conditions, and in exchange of specific returns. This dynamic complementarity seems to strongly define the orientation of the evolution of the present model of innovation. Assessing the evolution of the models of innovation thus reveals the parallel evolution of innovation systems as ecosystems, questioning the very nature of the firm itself by reconsidering its processes, capabilities bases and boundaries. This points to the need for research to reconsider and renew the theory of the firm to account more faithfully for the organisation and functioning of this emerging innovation model.

CONCLUDING NOTE Table 3.1 summarises the sequence of generations of dominant models that have been exposed in this chapter. For each generation of dominant model, the table highlights the coherence between the dominant model and the related main characteristics in economics (theory of the firm), geography (role of territory) and management science (managerial orientation). Table 3.1 The evolution of economic contexts Dominant model of innovation

Nature of the firm Role of territory Managerial orientation

Linear and closed

Interactive and closed

Interactive and open

Processor of information Clusters Managing product and process

Processor of knowledge Systems of innovation Human resource management

Processor of ideas? Ecosystems? Managing communities?

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REFERENCES Aghion, P., Howitt, P., Brant-Collett, M. and García-Peñalosa, C. (1998) Endogenous Growth Theory, Cambridge, MA: MIT Press. Amin, A. and Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities, and Communities, Oxford: Oxford University Press. Andersen, E. and Teubal, M. (1999) ‘The transformation of innovation systems: Towards a policy perspective’, paper prepared for the DRUID conference on National Innovation Systems, Industrial Dynamics, and Innovation Policy, Rebild, Denmark, 9–12 June. Arora, A. and Gambardella, A. (1994) ‘The changing technology of technological change: General and abstract knowledge and the division of innovative labour’, Research Policy, 23 (5): 523–532. Arrow, K. J. (1962) ‘Economic welfare and the allocation of resources for invention’, in National Bureau of Economic Research, The Rate and Direction of Inventive Activity: Economic and Social Factors, Princeton, NJ: Princeton University Press, pp. 609–625. Arthur, W. B. (1989) ‘Competing technologies, increasing returns, and lock-in by historical events’, Economic Journal, 99: 116–131. Baldwin, C., Hienerth, C. and von Hippel, E. (2006) ‘How user innovations become commercial products: A theoretical investigation and case study’, Research Policy, 35 (9): 1291–1313. Baldwin, C. and von Hippel, E. (2010) ‘Modeling a paradigm shift: From producer innovation to user and open collaborative innovation’, MIT Sloan Research Paper 4764-09. Barnejee, A. (1992) ‘A simple model of herd behaviour’, Quarterly Journal of Economics, 107 (3): 797–817. Bathelt, H., Cohendet, P., Henn, S. and Simon, L. (2017) ‘Innovation and knowledge creation: challenges to the field’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 1–21. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28: 31–56. Birkinshaw, J., Bouquet, C. and Barsoux, J.-L. (2011) ‘The 5 myths of innovation’, MIT Sloan Management Review, Winter 2011: 1–9. Bogenrieder, I. and Nooteboom, B. (2004) ‘Learning groups: What types are they? A Theoretical analysis and an empirical study in a consultancy firm’, Organization Studies, 25 (2): 287–313. Bresnahan, T., Gambardella, A. and Saxenian, A. (2002) ‘“Old economy” inputs for “new economy” outcomes: Cluster formation in the new Silicon Valley’, working paper, Stanford University. Burns, T. and Stalker, G. M. (1961), The Management of Innovation, London: Tavistock Publications. Callon, M. (1999) ‘Le Réseau comme Forme Émergente et comme Modalité de Coordination: le Cas des Interactions Stratégiques entre Firmes Industrielles et Laboratoires Académiques’, in Callon et al. (eds), Réseau et Coordination, Paris: Économica, pp. 13–64. Camagni, R. and Capello, R. (2000) ‘Beyond optimal city size: An evaluation of alternative urban growth patterns’, Urban Studies, 37 (9): 1479–1496. Cariou, C. (2006) Encastrement socio-cognitif et création de connaissances: essais théoriques et empiriques en économie de la créativité, Thèse, Faculté des sciences Économiques, Université de Rennes. Chesbrough, H. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Cambridge, MA: Harvard Business School Press. Chesbrough, H. (2006) Open Business Models: How to Thrive in the New Innovation Landscape, Cambridge, MA: Harvard Business School Press. Cohen, M. (1991) ‘Individual learning and organizational routine: Emerging connections’, Organization Science, 2 (1): 135–139. Cohen W. M. and Levinthal, D. A. (1990) ‘Absorptive capacity: A new perspective on learning and innovation’, Administrative Science Quarterly, 35: 128–152. Cohen, M., March, J. and Olsen, J. (1972) ‘A garbage can model of organizational choice’, Administrative Science Quarterly, 17 (1): 1–25. Cooke, P., Uranga, M. and Etxebarria, G. (1998) ‘Regional systems of innovation: An evolutionary perspective’, Environment and Planning A, 30: 1563–1584. Cowan, R., David, P. A. and Foray, D. (2000) ‘The explicit economics of knowledge codification and tacitness’, Industrial and Corporate Change, 9: 211–253. Cyert, R. and March, J. (1963) A Behaviourial Theory of the Firm, Englewood Cliffs, NJ: Prentice-Hall. Dahlander, L. and Gann, D. M. (2010) ‘How open is innovation?’, Research Policy, 39 (6): 699–709. David, P. (1985) ‘Clio and the economics of QWERTY’, American Economic Review, 75: 332–337. Di Stefano, G., Gambardella, A. and Verona, G. (2012) ‘Technology push and demand pull perspectives  in  innovation studies: Current findings and future research directions’, Research Policy, 41 (8): 1283–1295.

Concepts and models of innovation

53

Dodgson, M. (1993) Technological Collaboration in Industry: Strategy, Policy, and Internationalization in Innovation, London: Routledge. Dosi, G. (1982) ‘Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change’, Research Policy, 11 (3): 147–162. Dosi, G., and Marengo, L. (2000) ‘Some elements of an evolutionary theory of organizational competences’, in N. Foss (ed.), The Theory of the Firm: Critical Perspectives on Business and Management, Vol. 4, London: Routledge, pp. 50–73. Eliasson, G. (1990) ‘The firm as a competent team’, Journal of Economic Behavior and Organization, 13 (3): 275–298. Enkel, E., Gassmann, O. and Chesbrough, H. (2009) ‘Open R&D and open innovation: Exploring the phenomenon’, R&D Management, 39 (4): 311–316. Foray, D. (2004) The Economics of Knowledge, Cambridge, MA: MIT Press. Freeman, C. (1995) ‘The “National System of Innovation” in historical perspective’, Cambridge Journal of Economics, 19: 5–24. Gassmann, O. and Enkel, E. (2006) ‘Open innovation’, Zeitschrift Führung + Organisation, 75 (3): 132–138. Glückler, J. (2017) ‘Services and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 258–274. Godin, B. (2005) ‘The linear model of innovation: The historical construction of an analytical framework’, Project on the History and Sociology of S&T Statistics. Working Paper number 30, Montreal, Canada: INRS. 36 pages. Godin, B. (2006) ‘The linear model of innovation: The historical construction of an analytical framework’, Science, Technology and Human Values, 31: 639–667. Godin, B. (2017) ‘A conceptual history of innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 25–32. Granovetter, M. S. (1973) ‘The strength of weak ties’, American Journal of Sociology, 78 (10): 1360–1380. Granovetter, M. S. (1985) ‘Economic action and social structure: The problem of embeddedness’, American Journal of Sociology, 91 (3): 481–510. Granovetter, M. S. and Swedberg, R. (eds). (2001) The Sociology of Economic Life, Vol. 3, Boulder, CO: Westview Press. Healy, K. (2015) ‘The performativity of networks’, European Journal of Sociology, 56 (2): 175–205. Jeppesen, L. B. and Frederiksen, L. (2006) ‘Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments’, Organization Science, 17 (1), 45–63. Kirman, A. P. (1983) ‘On mistaken beliefs and resultant equilibria’, in R. Frydman and E. Phelps (eds), Individual Forecasts and Aggregate Outcomes, Cambridge: Cambridge University Press. pp. 147–165. Kirman, A. P. (1993) ‘Ants, rationality and recruitment’, Quarterly Journal of Economics, 108: 137–156. Kline, S. and Rosenberg, N. (1986) ‘An overview of innovation’, in R. Landau and N. Rosenberg (eds), The Positive Sum Strategy: Harnessing Technology for Economic Growth, Washington, DC: National Academy Press, pp. 275–305. Kuhn, T. (1962) The Structure of Scientific Revolutions, Chicago: University of Chicago Press. Laursen, K. and Salter, A. (2006) ‘Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms’, Strategic Management Journal, 27 (2): 131–150. Lave, J. and Wenger, E. (1990) ‘Situated learning: Legitimate peripheral participation’, working paper, Palo Alto, CA: Institute for Research Learning. Lerner, J. and Tirole, J. (2001) ‘The open source movement: Key research questions’, European Economic Review, 45 (4–6): 819–826. Lessig, L. (2004) Free Culture: The Nature and Future of Creativity, New York: The Penguin Group. Loasby, B. (1976) Choice, Complexity and Ignorance, Cambridge: Cambridge University Press. Loasby, B. (1983) ‘Knowledge, learning and enterprise’, in Jack Wiseman (ed.), Beyond Positive Economics?, British Association for the Advancement of Science, London: Macmillan, pp. 104–121. Lundvall, B. A. (1992) National Systems of Innovation, London: Anthem Press. Lynn, L. H., Aram, J. D. and Reddy, N. M. (1997) ‘Technology communities and innovation communities’, Journal of Engineering and Technology Management, 14 (2): 129–145. Maillat, D. (1992) ‘La relation des entreprises avec leur milieu’, in D. Maillat and J-.C. Perrin (eds), Entreprises innovatrices et développement territorial, Neuchâtel: GREMI and EDES, pp. 3–22. Malerba, F. (2002) ‘Sectoral systems of innovation and production’, Research Policy, 31 (2): 247–264. Mansfield, Edwin (1968) Industrial Research and Technological Innovation: An Econometric Analysis, New York: W.W. Norton. Marengo, L. (1996) ‘Structure, competence and learning in an adaptive model of the firm’, in G. Dosi and F. Malerba (eds), Organization and Strategy in the Evolution of the Enterprise, Basingstoke, UK: Palgrave Macmillan, pp. 124–154.

54

The Elgar companion to innovation and knowledge creation

Merton, R. (1973) The Sociology of Science, Chicago: University of Chicago Press. Metcalfe, S. (1995) ‘The economic foundations of technology policy: Equilibrium and evolutionary perspectives’, in P. Stoneman (ed.), Handbook of the Economics of Innovation and Technological Change, Oxford: Blackwell, pp. 409–512. Morrison, P., Roberts, J. and von Hippel, E. (2000) ‘Determinants of user innovation and innovation sharing in a local market’, Management Science, 46 (12): 1513–1527. Nelson, R. and Winter, S. (1982) An Evolutionary Theory of Economic Change, London: Belknap Press. Nonaka, I. and Takeuchi, H. (1995) ‘Theory of organizational knowledge creation’, in The Knowledge Creating Company, Oxford: Oxford University Press, pp. 56–94. Nordhaug, O. (1998) ‘Competence specificities in organizations’, International Studies of Management and Organization, 28 (1): 8–29. OECD (1963) The Measurement of Scientific and Technical Activities: Proposed Standard Practice for Surveys of Research and Development. Frascati Manual, 3rd revision, Paris: Organisation for Economic Co-operation and Development. OECD (1992) OECD Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. Oslo Manual, Paris: Organisation for Economic Co-operation and Development. Orléan, A. (1998) ‘The evolution of imitation’, in P. Cohendet, P. Llerena, H. Stahn and G. Umbhauer (eds), The Economics of Networks: Interaction and Behaviours, Berlin: Springer-Verlag, pp. 325–339. Phelps, E. (2013) Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge, and Change, Princeton, NJ: Princeton University Press. Polanyi, M. (1958) Personal Knowledge, London: Routledge. Polanyi, M. (1967) The Tacit Dimension, London: Routledge. Prahalad, C. and Hamel, G. (1990) ‘The core competence of the corporation’, Harvard Business Review, 68: 79–91. Raymond, E. (1999) The Cathedral and the Bazaar, Sebastopol, CA: O’Reilly. Romer, P. M. (1994) ‘The origins of endogenous growth’, Journal of Economic Perspectives, 8 (1): 3–22. Rosenberg, N. (1994) Exploring the Black Box: Technology, Economics, and History, New York: Cambridge University Press. Rothwell, R. (1994) ‘Towards the fifth-generation innovation process’, International Marketing Review, 11 (1): 7–31. Rullani, E. (2001) ‘The industrial cluster as a complex adaptive system’, in A. Q. Curzio and M. Fortis (eds), Complexity and Industrial Clusters: Dynamics and Models in Theory and Practice, Heidelberg: Physica-Verlag, pp. 35–62. Sanchez, R. and Heene, A. (1997) ‘Reinventing strategic management: New theory and practice for competence-based competition’, European Management Journal, 15 (3): 303–317. Sawhney, M. and Prandelli, E. (2000) ‘Communities of creation: Managing distributed innovation in turbulent markets’, California Management Review, 42 (4): 24–54. Saxenian, A. L. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128, Cambridge, MA: Harvard University Press. Saxenian, A. L. (1995) ‘Creating a twentieth century technical community: Frederick Terman’s Silicon Valley’, The Inaugural Symposium on The Inventor and the Innovative Society, The Lemelson Center for Invention and Innovation, National Museum of American History, Smithsonian Institute, 11 November. Schelling, T. (1978) Micromotives and Macrobehavior, New York: W.W. Norton & Company. Schmookler, J. (1966) Invention and Economic Growth, Cambridge, MA: Harvard University Press. Schumpeter, J. (1911) The Theory of Economic Development, Piscataway, NJ: Transaction Publishers. Schumpeter, J. (1936) ‘Review: The General Theory of Employment, Interest and Money by John Maynard Keynes’, Journal of the American Statistical Association, 31 (196): 791–795. Schumpeter, J. (1939) Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process, Vol. 1, New York: McGraw-Hill. Schumpeter, J. (1942) Capitalism, Socialism and Democracy. Floyd, VA: Impact Books. Shavinina, L. (2003) The International Handbook on Innovation, Oxford: Elsevier. Storper, M. and Venables, A. J. (2004) ‘Buzz: Face-to-face contact and the urban economy’, Journal of Economic Geography, 4 (4): 351–370. Teece, D., Pisano, G. and Shuen, A. (1997) ‘Dynamic capabilities and strategic management’, Strategic Management Journal, 18 (7): 509–533. Tushman, M. L. (1977) ‘Special boundary roles in the innovation process’, Administrative Science Quarterly, 22: 587–605. United Nations/UNCTAD (2008) Creative Economy Report. The Challenge of Assessing the Creative Economy: Towards Informed Policy-Making, Geneva: UNCTAD. (http://www.unctad.org/creative-economy). 357 pages. United Nations/UNCTAD (2010) Creative Economy Report. A Feasible Development Option, Geneva: UNCTAD. (http://www.unctad.org/creative-economy). 423 pages.

Concepts and models of innovation

55

United Nations/UNCTAD (2013) Creative Economy Report. Widening Local Development Pathways, Geneva: UNCTAD. (http://www.unctad.org/creative-economy). 190 pages. Urban, G. and von Hippel, E. (1988) ‘Lead user analyses for the development of new industrial products’, Management Science, 34 (5): 569–582. Uzzi, B. and Spiro, J. (2005) ‘Collaboration and creativity: The small world problem’, American Journal of Sociology, 111: 447–504. Vanhaverbeke, W. (2017) ‘Broadening the concept of open innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 87–98. von Hippel, E. (1986) ‘Lead users: A source of novel product concepts’, Management Science, 32 (7), 791–805. von Hippel, E. (1988) The Sources of Innovation, New York: Oxford University Press. von Hippel, E. and von Krogh, G. (2003) ‘Open source software and the “private-collective” innovation model: Issues for organization science’, Organization Science, 14 (2): 209–223. Watts, D. and Strogatz, S. (1998) ‘Collective dynamics of “small-world” networks’, Nature, 393: 440–442. Wenger, E. (2000) ‘Communities of practice and social learning systems’, Organization, 7 (2): 225–246. West, J. and Bogerts, M. (2014) ‘Leveraging external sources of innovation: A review of research on open innovation’, Journal of Product Innovation Management, 31 (4): 814–831. West, J. and Lakhani, K. R. (2008) ‘Getting clear about communities in open innovation’, Industry and Innovation, 15 (2): 223–231. Woerdman, E. (2004) Politiek en politicologie, Groningen: Wolters-Noordhoff. Zirpoli, F. and Becker, M. (2011) ‘The limits of design and engineering outsourcing: performance integration and the unfulfilled promises of modularity’, R&D Management, 41 (1): 21–43.

4.

Science and innovation Jean-Alain Héraud

INTRODUCTION Science and innovation are distinct but related domains of human activity (see also Godin, Chapter 2, this volume). Technology is one of the fields that connect them. Technical change has radically altered economic development in the industrialized world and it has become ever more important to understand the sources, nature and consequences of innovation (Martin and Nightingale 2000). Quoting Simon Kuznets, Geuna et al. (2003) state that what distinguishes the modern economic epoch is the extended application of science to problems of economic production. After World War II, generating new knowledge and harnessing it for invention has been at the core of public policies in advanced industrial countries. If the economy is increasingly “science-based” (a model of exploitation of science by the socio-economic system), it is logical that the same system also spends money for the development of science: this exploration process has the nature of an investment. Vannevar Bush coined the expression “the endless frontier” to characterize this role of science. Nevertheless, the scientific activity cannot be completely reduced to such a role: researcher’s motivations are to a large part intrinsic, and investment in scientific knowledge is different from ordinary capital accumulation because is it basically a creative activity. We clearly need to position all these concepts (scientific or technological knowledge, research and innovation, creativity, etc.) before analyzing the relationships between science and innovation. In this chapter, we understand Innovation in an economic sense – and here the reference to Schumpeter is unavoidable. It is a creative activity that has an impact in the fields of economy and society. As a very coarse definition (to be reviewed later in this chapter), let us first consider that Science is the accumulation of research operated by professional actors with the intention to understand, explain or modelize specific aspects of the reality. Such basic research (pure science) is in some cases relatively close to applied (technological) research. Feldmann et al. (2002) propose a set of contributions to the “economics of science and technology” with the sub-title “an overview of initiatives to foster innovation, entrepreneurship, and economic growth”, and it is obvious that many studies devoted to science are focusing on its potential or effective applications in terms of technology and markets. Nevertheless, basic research, applied research and innovation are three logically separated creative activities, with their respective rules, types of actors, motivations and outcomes. Scientific discoveries, technological inventions or innovative outcomes are all commonly described in terms of knowledge creation, but it is not the same sort of knowledge and not with the same purposes. As underlined by Paula Stephan, in many cases “basic research provides answers to unposed questions” (Stephan 1996 p. 1205), which is definitely not the case for the engineer’s search for a workable technology. The relationship of innovation to knowledge is even more complicated as we will see in this 56

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chapter: is it mainly a question of knowledge? Isn’t entrepreneurship of a completely different nature? One of our main aims in this chapter is to position the fields of science and innovation in relationship with the actors of both scenes. Are there individuals, organizations and institutions specialized in the respective fields, with a division of labor leading to professional monopolies? Must we introduce other elements in the creative ecosystems (communities, intermediaries, policy settings, etc.)? The first section below presents and discusses the concept of innovation because our topic is not science in itself, but the forms of knowledge (including “scientific” knowledge) involved in the process. Then we focus on the actors supposed to produce this knowledge. The following sections come to central scientific issues: the nature of scientific research and the evolution of the science system. As an intermediary step, we will then define and compare the three arenas of creativity: science, technology and economy/society. The last three sections address the contribution of scientific research to technical progress and innovation, the public investment in these fields and the critique of the received idea that organizations and institutions could have the monopoly of knowledge creation.

INNOVATION AND KNOWLEDGE Science in itself is a subject of increasing interest for economists, since Nelson (1959) and a series of contributions not necessarily linked to innovation. Many aspects of the economics of science could be mentioned in a text devoted to the scientific institution or to research activities in general. For instance, Dasgupta and David (1994) propose a broader view than “the economics literature addressed specifically to science and its interdependences with technological progress”. These authors associate the classic approach of economists like Kenneth Arrow and Richard Nelson (about allocative efficiency in research activity) with insights from the sociology of science in the tradition of Robert K. Merton. But in the short space of this chapter we will focus on the relationship between science and innovation. Innovation can be considered nowadays as the main basis of economic growth and, for that reason, deserves great attention, but the early macroeconomic approach, using aggregated neoclassical production functions, considered technical progress only as a “residual” in the calculation of total factor productivity: the main topic of this traditional economic literature was the accumulation of capital and the productivity of labor. Of course, such an approach is very far from the analysis of innovation and its relationship with scientific progress. In the after-war neoclassical modelling, typically represented by the work of Solow (1957), growth is explained by a “technical progress” influencing the other production factors, but the causes and modalities of such a fundamental change are not explained within the economic model. Progress is “falling from heaven”: knowledge production is deliberately considered as an exogenous mechanism. The neoclassical approach was considerably improved – from this chapter’s viewpoint, dealing with the causes of long-run economic evolution – by the development of the endogenous growth theory (Paul Romer, Robert Lucas). In such models, growth is primarily the result of investments in human capital and knowledge, and not external mechanisms (Romer 1990). This trend in the literature gave interesting insights on specific

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mechanisms like knowledge externalities and spillover effects. Nevertheless, growth models are not yet a real approach of economic evolution. The first economic analysis of long-run changes in mainstream economics was not focused on the process of innovation. The literature concentrated to a large extent on the economics of technology before the 1980s. It was only during the last two decades of the century that the development of innovation studies, in the context of the new evolutionary economics (Nelson and Winter 1982), contributed to the clear perception by theorists that innovation is not just the result of research in science and technology. In theoretical terms, it is a return to Schumpeterian basics: innovation must be considered as a complex process, embodied in the economy and the society, rather than falling from heaven (i.e. coming from a distinct socio-cultural system called “science”). All the literature on national systems of innovation (Lundvall 1992; Nelson 1993) or regional innovation systems (Braczyk et al. 1998; Cooke 2001) underlines the embeddedness of innovation in economic, institutional and cultural frameworks (Lundvall, Chapter 29, this volume; Bathelt and Henn, Chapter 28, this volume). As a consequence, innovation policies must be distinguished from research policies, despite their obvious interrelations (Larédo and Mustar 2001), and the propensity of many policymakers to focus the innovation policy on science and technology development is, from our point of view, potentially a mistake (Héraud 2016). On the relationship between innovation and science, Josef A. Schumpeter himself changed progressively his perception: in his first works, like the Theory of Economic Development published in 1911, he tended to define the entrepreneur/innovator as a person able to translate new scientific or technological ideas into economic ventures (historians of economic thought sometimes call this period Schumpeter.1), but in his later works (in particular Capitalism, Socialism and Democracy 1942), he proposed a more endogenous explanation of technological change. The author increasingly took into consideration the fact that large companies could also behave as actors in the field of science. Furthermore, the perception of Schumpeter.2 gets closer to other contributions in the field, like that of Schmookler (1966), who was the first to advocate for a demand-pulled mechanism that triggers innovation, in addition to the science-pushed mechanism. Indeed, the historians of innovation have found more examples of innovations mainly driven by the market (or the innovator’s perception of a potential market) than cases of pure science-pushed innovations. Nevertheless, Schumpeter’s views have not completely changed across the periods, as underlined by Antonelli (2015), and his last article (Schumpeter 1947) called “The creative response in economic history” describes a systemic mechanism across the individual level and the macro level, where brand-new ideas are introduced by gifted individuals in response to global incentives. To put it briefly: the evolution of markets disturbs micro-agents in their routinized activities and force them to find new ideas; afterwards, organizations and macro-institutions help inventors and innovators to develop and implement the new ideas. Knowledge creation appears here as an emergent characteristic of a complex system (Cohendet and Simon, Chapter 3, this volume). What can we conclude at this stage in terms of relationships between science and innovation? The first representation (Schumpeter.1) of the father of the economics of innovation is a model in which science has a sort of monopoly of knowledge creation and works relatively independently of the rest of society. Of course, science needs economic means and the main issue in this case is the societal incentive to fund institutions and

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individuals specialized in the production of a pure public good: formal knowledge to be called “science”. Then, innovation can source knowledge from science through the action of visionary entrepreneurs. The second representation (Schumpeter.2) gives more importance to the economic organizations (firms) for producing the necessary basic knowledge for their intended innovative projects. In such a world, the public good is produced by private entities and the appropriation issue is raised – hence Schumpeter’s interest in analyzing the role of intellectual property rights such as patents, and his theory of the public tradeoff between appropriation and diffusion of the knowledge. In the third and most complete representation (Schumpeter’s late synthesis), the limits of the concepts and institutions are relatively blurred because the ideation process is shared between individuals, organizations, and even the whole system. In the literature of the last few decades, the theoretical representations based on a linear model of knowledge transformation (Schumpeter.1’s science-pushed model or Schmookler’s demand-pulled model) have been replaced by more systemic representations since Kline and Rosenberg (1986), who introduced the chain-linked model of innovation, with many feedback loops. Innovation can be driven by scientific progresses, and scientific discoveries are stimulated by questions raised during the design of the innovative product, along the process of industrial development, or even in the commercialization phase. Science is potentially involved in every part of a chain of problem-solving steps, not specifically at the top of a linear mechanism running from front-end basic research to downstream commercial application (Cohendet and Simon, Chapter 3, this volume). To describe the role of science in the innovation process, we need to analyze the relationship between knowledge in general and innovation, and to understand which actors contribute to the most relevant knowledge creation. A central issue already raised (above) is the collective nature of innovation, because in this case knowledge is a priori distributed among several actors.

WHO PRODUCES KNOWLEDGE FOR INNOVATION? Nathan Rosenberg was one of the first economists to underline the role of collective learning effects in the innovation process. Looking “inside the black box” of innovation he found many examples of knowledge co-construction between users and producers (Rosenberg 1982). In terms of technical progress, he was opposed to the neoclassical approach focused on learning by doing, his vision of innovation being driven by processes of learning by using. The idea that users play an important role was developed to a considerable extent by Eric A. von Hippel: there are innovation opportunities for users as well as for producers of a given technology, and their respective contribution and motivation are specific and complementary (von Hippel 1976). Previous literature in economics and management science studied innovative projects (products or processes) mainly in terms of expected profitability for the producers, innovator’s rent, and so on, but many examples of innovation (e.g. the mountain bike or medical equipment) owe their existence to the willingness of particularly entrepreneurial users to improve the goods or to find a better way to fulfill a given function. In terms of knowledge creation it means that the producers are no longer systematically considered as the main actors of technological evolution and economic

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change. User-driven innovation is an increasingly important paradigm, while the system is entering the third Industrial Revolution. Such “innovation democratization” is attacking the knowledge monopoly of firms – and institutions like public research labs. Using an example given by von Hippel with the wordings of another trend of literature (Wenger 1998): if doctors contribute to improve medical technologies, it is not as private or public “researchers”, but as members of a knowing community, the community of practice of specialized professionals (caregivers). David J. Teece also contributed to develop a vision of innovation as a collaborative process and added an interesting dimension: the boundaries of the firm. He first observes that the profits from innovation accruing to the innovator are only a part of the overall economic returns: important shares are captured by customers and suppliers as well as “imitators and other followers”. Being first to market is often not so much a strategic advantage – and this is particularly true in high-tech and science-based sectors. Imitators can outperform innovators because they are “better positioned with respect to critical complementary assets” (Teece 1986 p. 304). The suggested strategic solution for innovators is therefore to develop joint ventures, coproduction agreements, cross-distribution arrangement, technology licensing and so on. Teece’s contribution on the issue of collective knowledge creation and appropriation leads to a major theoretical observation: market structures are not the most important aspects in economics of innovation, but rather the structure of firms (the scope of their boundaries). Going further in this direction, at the crossroads of economics and management of innovation, Henry Chesbrough stimulated the successful research domain of open innovation. Observing that many large firms (the first observation was the case of Xerox) are not organized in a way that allows them to fully profit from the discoveries of their research and development (R&D) department, and that many good ideas can also come from outside, he proposes a different business model (Chesbrough 2003). In an open system of management, companies better profit from all their ideas (inside-out process) even if the ideas do not initially fit very well with the actual positioning of the firm: by selling or licensing patents, creating start-ups, and so on. The complementary outside-in process is based on a firm’s acquisitions, patents acquisitions, and various forms of agreements with external actors. In this new organizational paradigm (in the implementation of this couple process), the role of smart users or suppliers is of course important, as well as contacts with public research institutions, but intermediaries (consultancy firms, ideas platforms like InnoCentive) are key actors in the environment of the innovative firm. The source of knowledge in this new management context is closer to a dynamic market for ideas than to a hierarchical procedure (pure internal R&D) or a classical market for existing ideas (patent market). It includes radically new procedures like organizing contests of ideas with the distribution of prizes. A prize is not a price, and sourcing knowledge is different from producing it, but all these elements can in fact be combined. The open model of innovation does not abolish the other forms of knowledge creation but combines them (Vanhaverbeke, Chapter 6, this volume). Edmund Phelps, in his recent, successful book on grassroots innovation (Phelps 2013), popularized the idea that modern economic growth is based on human creativity and therefore (macroeconomic) public policy should focus more on how entrepreneurship and innovation generate endogenous growth than on classical “growth economics” recipes. Phelps clearly presents his book as an attempt to prevent economists from thinking of

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innovation as the commercial application of scientific achievements. The introduction of the notion of creativity enlarges the basic conception of knowledge as the basis of innovation – and introduces at the same time another dimension concerning the role of the economy as a way to improve human satisfaction. In the new context of open innovation, science still plays an important role, but has lost its supposed monopoly for the creation of new ideas that can be useful for innovation. One of the main functions of the scientific institution is its support of absorptive capacities everywhere in society. Scientific production (basic research) and the diffusion of scientific culture favor the articulation of ideas and the “sense-making” leading to economic and societal innovations. On this ground, innovation can be the result of any idea occurring by chance to a person working in a business. We will relate this idea with the notion of serendipity, which is one important aspect of creativity. The grassroots dynamics presented by Phelps lead to mass innovation (mass flourishing in the title of the book).

EMPIRICAL OBSERVATIONS CONCERNING THE NATURE AND THE ROLE OF SCIENTIFIC RESEARCH Scientific research is an activity performed and financed for its own sake (curiosity, contribution to culture, enlightenment, etc.), for its close relation to education and training (and therefore human capital building), and increasingly for the strategic interests of public or private actors (technological spillovers, access to information and knowledge networks, reputation, image, etc.). At an individual level, obviously, researchers cannot fulfill all the roles, but is there a complete division of labor in the knowledge production function? And who does what exactly? Donald Stokes, in his book Pasteur’s Quadrant (Stokes 1997), proposed an interesting distinction between three sorts of scientists: ●

●

Niels Bohr, a physicist of the early 20th century, is associated by Stokes with the notion of pure basic research. In fact one could also typically associate Albert Einstein, whose intrinsic motivation was to solve theoretical contradictions in the theory of physics of his time. There is no contradiction with the fact that some scientists of this type have been involved afterwards in strategic projects (like Einstein). The point is that the motivation for their discoveries was not the use of knowledge but the production of knowledge. In Stokes’ “Bohr quadrant”, researchers have few considerations for use but a strong quest for fundamental understanding. Most university scientists could be considered as working in this quadrant, although the evolution of the academic institution – or of our representation of it (see Gibbons et al. 1994, or Etzkowitz and Leydesdorff 1997) – has considerably blurred such a simple image of the division of labor. Pure applied research is associated with Thomas Edison, the typical “inventor”. Firm or governmental engineers belong to Edison’s quadrant: strong consideration for the use of knowledge and no real quest for fundamental understanding (unless strictly necessary). The motivation here is to find good answers to relatively welldefined questions – and certainly not to raise interesting new questions while trying to solve initially badly formulated problems as pure scientists often do.

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The Elgar companion to innovation and knowledge creation Louis Pasteur’s quadrant characterizes basic research inspired by the use of knowledge. For Pasteur, the fields of inspiration and application were not restricted to medicine, since in many cases business issues motivated his researches. Individuals like Pasteur are exceptional: he was a scientist as well as an entrepreneur, a theorist as well as an experimentalist, an intellectual and a lobbyist, and so on. It is no easier nowadays than in the 19th century to find people of this sort, but research organizations (firms’ departments, public labs, universities) try to build teams able to fulfill this complex role.

The respective roles of this variety of scientific activities vary across sectors. Some businesses depend more specifically on basic research. Historically, this was the case for the chemical industry (it is not by chance that the name of the industrial sector is akin to that of a scientific discipline, chemistry). The development of the modern chemical industry, which started at the end of the 19th century and reached its peak in the 1910s in Germany, is typical of the second Industrial Revolution. The second Industrial Revolution was quite a bit more science-based than the first that occurred at the end of the 18th century. BASF was the first industrial group to implement a full-fledged R&D program, monitoring basic science as well as industrial development for the catalytic synthesis of ammonia. Without that risky industrial project, modern organic chemistry wouldn’t exist, nor the Nobel Prize of Fritz Haber and all the following developments in catalysis. Nowadays R&D is part of firms’ core business in many sectors. Globally, national R&D expenses amount to 2 or 3 per cent of gross domestic product (GDP) in most developed countries, with a major percentage of private contribution – in the performance of the research activity, if not in funding. Of course, the situation varies across firms and sectors. Pavitt (1984) proposed a typology that has been often used (Daniele Archibugi carried out a review and critical evaluation in 2001), distinguishing the following four situations: ●

● ● ●

supplier-dominated: traditional manufacturing firms relying on external sources of innovation; scale intensive: large firms with internal and external sources of innovation; specialized suppliers: producing technology for other firms; science-based: high-tech firms relying on in-house R&D and university research.

The last type includes industries such as pharmaceuticals and electronics. One must observe at this point that Pavitt’s paper presents some weaknesses: it is relatively unclear if the typology applies mainly to firms or to sectors, as underlined by Archibugi (2001). In the case of supposedly science-based sectors, there are firms that do not exploit the very last discoveries of the related scientific field, but specialize in other market strategies; and conversely some firms in low-tech sectors can be very creative in applying brand-new ideas inspired by the scientific advances of any discipline. This case is fully consistent with the Kline–Rosenberg chain-linked innovation model, since the impact of science can take place anywhere along the process. In the above-mentioned open innovation model, such sectoral division is also less relevant, and the strategy of the firm (the perimeter of the quasi-firm formed with partners) appears more important for understanding the precise meaning of “science-based”.

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A COMPLEX ARCHITECTURE OF KNOWLEDGE IN EVOLUTION We can go a little further in the analysis of scientific activities as a global phenomenon, not restricted to specific organizations, sectors or institutions. The significant change experienced by the world in recent decades has been the professionalization of scientific discovery, technological invention and commercial innovation. The three levels considered here (science, technology, economy) must be distinguished from a theoretical point of view, as explained in the next section, but they are strongly interlinked. Professionalization occurred in parallel with the emergence of expensively equipped and professionally run laboratories in academic institutions and business firms. It also involved increased competences at the level of human capital and organizational skills. Innovation has now to be considered as a creative mix of knowledge and method. Can we imagine nowadays an important theoretical contribution in physics made by a single gifted individual like Einstein, working alone in his house? A negative answer is suggested by the incredibly long lists of co-authors one generally find at the top of articles in high-energy physics and by the budgets involved. We have entered into the era of techno-science. Within the techno-scientific system, the creative process (discoveries, inventions, innovations) appears increasingly interdisciplinary and the result of multi-actor strategies. The conclusion of Irvine and Martin (1984) is still very relevant 30 years later: “organizations which support and guide research must increase their emphasis on communication particularly among disciplines and between non-mission and missionoriented research.” New products and processes need applied research and technological development that are by definition mission-oriented activities, but the flow of innovations in the long run depends on radically creative new solutions that come from basic scientific research where non-expected results are sometimes even more important than the original aim of the research. In this context, the nature of scientific activity has changed – and/or the perception of the social scientists: according to Gibbons et al. (1994) the mode of organization of scientific production underwent a paradigmatic change. Traditionally, the dominant system in universities and in the public sector laboratories was discipline-based, internally driven, in individually dominated structures (Mode 1). In the most advanced and efficient sectors, nowadays, the mode of organization of science and technology is more practically oriented, transdisciplinary, network dominated and flexible (Mode 2). In a way, there is a paradox in the fact that the results of science cannot be planned (scientific creativity implies a strong dimension of serendipity) and at the same time scientific research is increasingly motivated by mission-oriented projects – not only for funding but also for the intellectual stimulation. Universities and public labs often collaborate with firms, for financial reasons and for scientific interest. Private R&D departments hire researchers for applied or at least finalized research (basic research on topics that look promising for the economy or society), but sometimes give them also the opportunity to work on a project without immediate application if it is valuable for science itself. Indeed, this can be part of a strategy of creative human resource management, and it could be good for the image of the firm if the basic knowledge produced can be published and gets some visibility. The firm is to be seen as a “platform of communities” (Amin and Cohendet 2012),

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including the scientific community. It is interesting to observe that a similar situation is developing in academic institutions with Mode 2 organization. The system of science is complex and the observer gets the impression that borders are constantly violated between fields and institutions. Reality is complex, but we need clear terminology to describe it. This is the aim of the next section, with the introduction of the general notion of creativity.

THREE LEVELS OF CREATIVITY Speaking of science and innovation implies using words like basic research, applied research, technological development, innovative activities and so on. We need clearer definitions of all these forms of creativity (see also Cohendet et al., Chapter 13, this volume; Le Masson et al., Chapter 18, this volume). We also need to understand the rules applying at each of these levels of knowledge creation. Table 4.1 below proposes a simplified description of three arenas respectively dealing with pure knowledge production (called discovery in science), the production of technical artifacts (invention), and the implementation of new products, processes, organizations and so on that successfully find a market and generate profits, jobs and other social impacts (innovation). The three levels of knowledge production can be considered as arenas in the sense that for each of them a specific set of actors and rules is to be found. The complexity of the global system lies in the fact that actors typical of one level can act at the two other levels as well. It is also possible to “cheat” with the rules of the game of one level for the sake of some rule or objective of another level (for an example of actors interacting and competing in diverse social arenas, see Bonneuil et al. 2008). 1.

The scientific arena is characterized by actors supposedly motivated by curiosity. Their aim is to produce models representing reality. A discovery is not supposed to transform the world, but to cast a new light on the world, to change our representation. Motivation for basic research can be pure curiosity (whatever the potential practical

Table 4.1 Creativity in science, technology and economy Level (arena)

Science Technology

Economy/Society

Creative activities Nature of the activity

Results of the activity

Measure of the activity

Basic research (curiositydriven or finalized) Applied research

Discovery

Industrial and commercial development

Innovation

Publications (quantity) Citation index (quality) Patents (if possible) Other intellectual property rights Sometimes secrecy is a better strategy Sales, profits, jobs Innovation is difficult to measure

Invention

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2.

3.

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issues) or, in finalized science, the interest of mankind and other practical stakes. Even in the case of finalized science the nature of research is the understanding of the world and not (yet) the application to the world. The overwhelming majority of discoveries is published and the “market” for publications follows very precise rules defined by the scientific community itself. The number of publications accepted in the system of peer-reviewed evaluation is a good measure of scientific production (for individuals, institutions, countries, etc.). By computing the citations of one paper by the other papers in the same field, it is even possible to give a scientific value to any discovery. The individual objective of the scientist is to be read by other scientists. For the best or for the worst, the number and quality of publications is increasingly considered as the only criterion for the careers of academic researchers and the evaluation of research organizations – and therefore of their funding. In the technological arena, any valuable increment in knowledge should bring us closer to efficient applications (applied research aims at technical artifacts that are new or improved). The achievement in technology (or applied science) is called invention. The proof of invention lies in the efficiency of the artifact. The product or process must be new and be useful to something – but at this stage it is still not possible to know if it has a real economic value. Unlike pure scientific productions, the rule here is not to maximize the diffusion of the new knowledge, but to protect it from imitation, before the economic value can be appropriated in one way or another. Intellectual property rights are guaranteed by the law, through various institutional instruments (depending on the nature of the knowledge): patents, copyrights, specific regimes for new plants and animals and so on. Therefore, statistics like the number of patents could be used as a proxy variable for technical creativity, but such indicators are not perfect since the optimal strategy of the actors could be to keep it secret for as long as possible instead of revealing the information (in cases where legal protection is not considered as very efficient). The socio-economic arena is the space where innovations are designed and diffused. The main actor here is the entrepreneur – as a creative person, not just a manager. Innovation, like discoveries and inventions, is often interpreted as an increment in knowledge, although many other aspects should be taken into consideration. We consider that interpreting the innovation process purely in the framework of an economics of knowledge is misleading. We do not mean that contributions like Cowan et al. (2000) are not interesting. Indeed, the literature on knowledge underlined very important issues about tacitness and codification that helped to understand the distance between science and innovation. Nevertheless, there are components of the innovation phenomenon that cannot really be classified under the regular concept of knowledge. From a Schumpeterian viewpoint, the innovator possesses not only cognitive assets like new knowledge, but also entrepreneurial capabilities. This is true for organizations as well: in order to innovate, the CEO running the firm must have visions, not just scientific/technological/organizational pieces of knowledge. Leaders involved in innovative projects must also exhibit the necessary competencies for playing in a complex arena, with competitors, potential allies, and partners sharing to a certain extent the same interests (clients, suppliers, governments, etc.). Innovation means creative destruction (Schumpeter 1942), and therefore the art of introducing novelties on the market, or creating new markets, is a real strategic game. Innovation

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The Elgar companion to innovation and knowledge creation is not good news for everybody, and therefore it will always meet resistance. For playing in the economic and societal arena, a great variety of cognitive resources is required: information, tacit knowledge, scientific knowledge, a form of wisdom (meta-knowledge), the understanding of human relationships and so on. The success of innovation is also very difficult to assess because of the variety of impacts it has, in the short and long run, and for many actors.

The three arenas are very different but not independent. Actors can play simultaneously on several arenas. Rules and instruments can be diverted from their original function. We will briefly give some examples for the general understanding of the issue: ●

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It is not really possible to protect an innovation with a patent because the latter is designed for inventions; nevertheless, patents can be part of the global strategy of protecting the innovator’s rent for a certain time. Other strategic tools are used in parallel: secrecy, lead time and so on. Patenting can be used as a strategy of communication and not only of protection (firms signalling their competences when they are searching for partners). The emergence of many open innovation situations contradicts the pure principle of protecting scientific/technical information at firm level. Users and suppliers can contribute to knowledge production and increase the value of firms’ new products. Researchers in private R&D departments must be allowed to follow, at least partially, the rules of their own community (if not, the firm will not benefit from the whole creativity potential of the scientific assets they try to develop for their commercial use), but this hybrid organization could lead to conflicting situations between the rules of scientific publication and the rules of invention and patenting. The same situation arises in the case of partnerships between firms and university labs: they can be very fruitful for both partners, but also complicated from an institutional point of view. For instance, PhD projects funded by firms are sometimes subject to contractual constraints on knowledge disclosure that break academic rules concerning the diffusion of scientific results. The professionalization of scientific research and the development of a real market for scientists introduce some biases: is the “publish or perish” rule compatible with the traditional ethics and values in science? Up to a certain point, the introduction of efficient methods of production and evaluation in science leads to a decrease of real creativity.

THE CONTRIBUTION OF SCIENTIFIC RESEARCH TO TECHNICAL PROGRESS AND INNOVATION Edwin Mansfield made a noticeable contribution to our topic with his article “Academic research and industrial innovation” (Mansfield 1991). He investigated to what extent technological innovations in various industries were based on recent academic research. He tried also to measure the time lag between those research projects and the industrialization of the findings, and looked for a measure of the social rate of return from academic research.

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The author himself recognizes some of the limitations of his work. First of all, it ignores long-run effects of academic research. Another limitation is the choice of industry as the only sector impacted by research spillovers. As a matter of fact, innovations in services are now increasingly considered in the literature – for themselves and as transmitters of innovative solutions to industrial firms (see e.g. Miles 2007, and Muller and Zenker 2001). Fundamentally, we consider that econometric calculations like Mansfield’s and those made by many authors after him rely too strongly on the linear model of innovation. Furthermore, they consider academic research as the only source of basic science, even though many science-based firms participate in the creation of knowledge at a fundamental level. Nevertheless, the results of the pioneering work of Mansfield are interesting to consider. On his sample of 76 major American firms he found that 10 per cent of new products and processes commercialized during the period 1975–85 would not have been developed without recent academic research. The average time lag is seven years, the lag being shorter than average in smaller-sized firms. A “very tentative estimate” of the social rate of return from academic research (through this specific channel of direct impact of science) is 28 per cent, which is not too bad after all; bearing in mind all the other possible indirect impacts of science, this figure reveals a significant lower limit for the estimation of the economic value of science for society. Edwin Mansfield concludes: “because the results of academic research are so widely disseminated and their effects are so fundamental, subtle, and widespread, it is difficult to identify and measure the links between academic research and industrial innovation” (Mansfield 1991 p. 11). Nevertheless, many other aspects of the link between science and innovation have been explored in the 1990s and after: ●

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An important issue is the status of basic science: it is definitely a public good (whether publicly or privately produced), but not a free good because acquiring the competence to read and understand it is costly (Pavitt 1991). Doing research is a way for firms to acquire this competence (a case of building absorptive capacities in the sense of Cohen and Levinthal 1990), but what exactly are the incentives for firms to publish their results? Hicks (1994) considers that publishing mediates links with other organizations, for instance by demonstrating a firm’s tacit knowledge, and it helps to build the reputation necessary to engage in the barter-governed exchange of scientific and technical knowledge. The complex systems where formalized basic knowledge (science) is produced, exchanged and translated are embedded in cultural, social and institutional frameworks. Therefore, another important approach to understanding the  link  between science and innovation is the study of such organizational/ institutional settings. On that point, the contributions of researchers who take into consideration national systems of innovation (Lundvall 1992; Nelson 1993) are essential, as well as the efforts of economists and geographers who try to understand the role of geographical space in the process of R&D spillovers (Audretsch and Feldman 1996), the potential of specific territories like regions (Braczyk et al. 1998) or innovative clusters (Porter 1998; Cooke 2001), the difference between proximity and localization (Torre and Rallet 2005), the difference between geographical proximity and organizational proximity (Zimmermann 2008), and so on.

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The Elgar companion to innovation and knowledge creation A specific analysis of localized innovation systems where firms and universities collaborate with the help of local/regional administrations has been developed by Etzkowitz and Leydesdorff (1997). The phrase Triple Helix of development expresses the intertwined actions of firms, researchers and policymakers. In Etzkowitz and Leydesdorff (2000), the authors clearly relate their model to the Mode 2 organization of science proposed by Gibbons et al. (1994). Authors like Keith Pavitt, Paul David and David Mowery contributed by exploring the nature and extent of the contribution of academic research to technical change. Their contributions underlined the fact that technical knowledge is not just “applied science”: it is a capacity to solve complex problems, including many cognitive and organizational aspects that are not of a scientific nature. Pure (academic) science is not often a direct input to invention and innovation through the provision of immediately applicable ideas (David et al. 1993), but acts indirectly through the adoption of skills, techniques, instruments and professional networks created by researchers and academic institutions (Pavitt 1991). Indirect impact is difficult to measure in detail, but the overall reality of the effect is proved on the macroeconomic level by comparing countries: world-class technology requires world-class basic research (Patel and Pavitt 1994). The research agenda on the indirect effects of basic research is still open today and seems to be very important for theoretical as well as practical reasons (policy evaluations and recommendations).

THE IMPACT OF PUBLIC INVESTMENT IN SCIENCE AND TECHNOLOGY The evaluation of the impact of science on the economy in general is an important issue. Since a large part of basic science is financed by the taxpayer, there is a need to know the level of social return as precisely as possible, and arguments must be given for publicly funded research. As a matter of fact, relatively fewer public funds are available for scientific research in most of the advanced countries, and this evolution started at the end of the 20th century. One reason is the reduction in publicly funded military research after the fall of the communist regime in USSR. More fundamentally, a sort of new technological mercantilism seemed to emerge: the governments tended to favor appropriable applications of science (knowledge externalities) instead of contributing to the common scientific asset. In the perception of the population, the myth of science is also progressively vanishing and therefore governments are required to strictly apply the general principle of evidence-based policy in this area like in the others. Furthermore, even publicly funded applied research (typically the role of the large national labs created in the after-war period in many Western countries) has a more modest role nowadays: we can call it the end of the Colbertist State, which is clearly visible in a country like France that had a strong tradition of administrative interventionism in many high-tech sectors (Héraud and Lachmann 2015). Let us briefly comment on these points and give some examples and provide some precision. ●

In the after-war period, one of the major topics was the economic spillovers of military-oriented R&D. Science has always produced many new ideas for the

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development of weapons. On the other hand the public investments in defence technologies gave rise to many civilian innovations: in the fields of electronic, computers, new materials and so on. In the US, for instance, the military sector used to be the leading innovator in advanced technologies through organizations such as the Defense Advanced Research Projects Agency (DARPA). A big issue around the turn of the millennium was the evolution of the strategy of many Departments of Defence in the world: with the reduction of their budgets, they preferred to cut some upstream technological research and favored the purchase of “off the shelf ” technologies and products. For a description of the complex role of defence in national innovation systems, see Bellais and Guichard (2006). In Europe, EU policy in the late 1990s was also strongly pushing for more “applied” research and “technology transfer”. Observing the so-called European paradox (the old continent was lagging behind North America for successful innovation, while it looked increasingly competitive in science), the Research and Development Framework Programs put incentives on funding applied research and university– industry cooperation rather than funding pure academic science. For an illustration of the “myths and realities” of the so-called European paradox, see Dosi et al. (2006). The issue of evaluation is a central topic of debate, for political reasons as mentioned above, but also from a methodological point of view. Evidence-based policies need indicators, but quantification is often a double-edged sword. The most interesting impacts of science are of an evolutionary nature and take time to realize. The administrations need evidence in the short run (because the political game is also short run), and the indicators in use are therefore very partial. Taking the example of the evaluation of academic activities in a given territory, the measurement of proved economic impacts gives an overwhelmingly strong weight to static benefits like territorial income distribution (Gagnol and Héraud 2001), at the expense of more interesting long-run impacts. This is a very important research agenda for the economists interested in science and innovation. The production of science and the process of innovation are systemic phenomena. It is essential to have a clear look at the whole set of actors participating in such organizational changes. Some intermediary actors play a crucial role, even if that is not so visible in the current statistics. Knowledge-based business services (KIBS) belong to such a category of hidden champions of knowledge creation. They strongly contribute to the innovativeness of territories (Muller and Zenker 2001). The mechanism of knowledge creation they implement in the innovation system is related to the concept of translation. KIBS do not just act as knowledge brokers between firms and between research labs and firms: they translate and adapt knowledge, adding significant value to knowledge. Since creativity is novelty plus relevance (Sternberg and Lubart 1999 [2008]), KIBS contribute to building creative firms and territories through their capacity of finding the relevant knowledge to solve problems or finding relevant contexts for the application of new advances in science and technology. The capacity to translate and adapt knowledge between different cognitive worlds is often related to the role of specific gifted individuals working within KIBS (Muller et al. 2015). Such people are not necessarily researchers (although they sometimes have been trained in science and

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The Elgar companion to innovation and knowledge creation know the scientific institution very well) and they are not necessarily entrepreneurs (but definitely have an entrepreneurial spirit). We call them Knowledge Angels.

TRANSLATION AND ENROLMENT AS MEANS OF KNOWLEDGE PRODUCTION AND RADICAL INNOVATION Let us now turn to another possible meaning of “translation”. The engineer and sociologist Michel Callon has developed a crucial theoretical instrument for understanding the complex relationship between science, economy and society. His concept of traduction casts a light on the reality of the process of knowledge production. The latter is not mainly organized on the basis of institutions (research labs, industrial firms, consultants, etc.) with clear-cut borders, but appears to be the product of strings of “actants” that progressively construct and transform – through progressive adaptations – the mental representations we call afterwards “concepts”, “theories” or “scientific facts”. This collective creative process produces tacit knowledge (individual competences), as well as formal knowledge. Among the outputs, embedded knowledge also occurs in the form of technical artifacts like instruments, materials, software and so on. The complex of actors, artifacts and pieces of knowledge is in fact at the same time an input and an output of this sort of production function: the system evolves step by step, with an enrichment of all the elements together. It is therefore difficult to distinguish in a radical way, for instance, basic science and individual competences as input, and invention or innovation as output. Such research systems or knowing communities – Callon (2003) uses the phrase collectif de recherche – do not only produce and translate pieces of formal knowledge, but also try to negotiate the new ideas among their members and external communities. When the knowledge field is particularly new, and the members of the community relatively heterogenous (collectif de recherche distribué), the issue is not exclusively the construction of knowledge, but also the “enrolment” of individuals and organizations, because the issue is more the seduction of individuals and organizations (publicity and conviction) than the protection of knowledge (appropriation of knowledge), at least at the beginning of the process. This process can be called “translation and enrolment”. At this point it is important to distinguish between two situations. Michel Callon considers learning outcomes in (1) stable or routinized knowledge contexts, and (2) emergent or experimental networks dealing with radically new forms of “knowing” (see also Callon, Chapter 36, this volume). In the first context, “innovation objectives and goals are known ex ante and expectations are rationally ordered, within an emphasis placed on combining known and complementary competences with codified or tacit knowledge, in pursuit of programmable action” (Amin and Cohendet 2004 p. 82). We consider this vision of knowledge building and the innovative project management process as typical of the causation approach in the sense of Sarasvathy (2001), for relaying Callon’s representation to the managerial literature on entrepreneurship. Emerging networks, in contrast, implement the traduction scheme where actors with different knowledges meet in a common effort of producing a new cognitive setting for practical (innovation) and/or epistemic (science) reason. Strategies of translation/enrolment are at the core of such creative processes – in which objectives are still ill-specified, and

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the concepts and instruments not yet fully defined. We are here in a situation which is closer to Sarasvathy’s model of effectuation: we start from where we are (knowledge, competences, practical and financial means, individual objectives, etc.) and try to imagine possible futures. Just speaking in terms of commercial objectives, the issue is not to meet the requirement of a given market and to maximize the share of this market we can get, but to create a market. In terms of knowledge creation, the model does not deal with the recombination of existing pieces of knowledge, but with forging completely new visions. Amin and Cohendet (2004) adapt the sociological approach of Michel Callon and Bruno Latour to their analysis of the role of communities in the “architectures of knowledge”: “the innovative diffusion of ideas (for example, from the lab to the market) can be integrated as a process of progressive contagion of communities, where each community makes effort to ‘command the attention’ of other communities to convince them of the relevant interest of the knowledge it has elaborated” (p. 149). Many issues of the economics of innovation and the methods for designing innovation policies must be reconsidered taking into account the type of context: projects using routinized knowledge or cases of radical innovation. The role of patents for instance is completely different. The emerging literature on open innovation (Chesbrough et al. 2006) is also typical of the attention now given to situations where creativity is distributed among different actors and not exclusively the output of R&D departments. Crossing these issues, Pénin and Wack (2008) show that in very creative fields like biotechnology, patents play different roles and can promote open innovation by ensuring the freedom of some pieces of knowledge.

CONCLUSION We want to focus the conclusion on the progressive aspects of the relationship between science and innovation. One striking evolution in the long run is the professionalization of research, along with the increasing size of equipment in certain sectors. The logical conclusion could be that science is now extremely specialized and characterized by an extensive division of labor. The paradox is that, in parallel, we observe a growing number and variety of partners contributing to applied knowledge creation in the model of open innovation, and large interdisciplinary teams that are necessary to achieve breakthroughs in basic science. Scientist are trained and selected like high-level athletes, exchanged on academic markets, and evaluated according to criteria of “excellence” in the respective discipline, but they can no longer be considered as having the monopoly of the discovery. We observe a democratization of the ideas, as Edmund Phelps says. Innovation studies as well as science studies are increasingly aware of the complex translation chains occurring in the process of ideation prior to the stage of discovery, invention or innovation. Gifted individuals are still recognized as key elements in the process but their “creation” is generally prepared by the subtle alchemy at work in communities of practice (particularly in technological domains) or epistemic communities (specific to science). Since creativity appears increasingly distributed, the role of intermediaries is growing. Both the system of science and the system of innovation need gate keepers, knowledge brokers, translators and facilitators. There is not only a large number but also a large variety of such catalytic agents: individuals (Knowledge Angels) and firms (knowledge-intensive

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business services), as well as places and spaces (fairs, meetings, conferences, clusters, local milieus, interactive websites, social networks, etc.). In addition to knowledge accumulation and knowledge crossing, scientific breakthroughs and radical innovations need entrepreneurial spirit. The innovator is not only a learned person, he or she is also a visionary. Science needs the same sort of capability: the figure of the entrepreneurial scientist. An interesting research question could be the following: will the increasing complexity of distributed creativity lead to a higher demand for “principal investigators” of the Louis Pasteur type? We consider that the issues raised in this chapter are not only relevant for people interested in science or innovation studies, but also stimulating for today’s managers and policymakers.

REFERENCES Amin, A. and Cohendet, P. (2004), Architectures of Knowledge: Firms, Capabilities, and Communities, Oxford University Press, Oxford, New York. Amin, A. and Cohendet, P. (2012), “The firm as a ‘Platform of Communities’: A contribution to the Knowledge-Based Approach of the Firm”, in R. Arena, A. Festré, N. Lazaric (eds), Handbook of Knowledge and Economics, Edward Elgar Publishing, Cheltenham, UK and Northampton, MA, (403–434). Antonelli, C. (2015), “Innovation as a creative response: A reappraisal of the Schumpeterian legacy”, History of Economic Ideas, 23(2), (99–116). Archibugi, D. (2001), “Pavitt’s taxonomy sixteen years on: A review article”, Economics of Innovation and New Technology, 10(5), (415–425). Audretsch, D. and Feldman M. P. (1996), “R&D spillovers and the geography of innovation and production”, American Economic Review, 86, (631–640). Bathelt, H. and Henn, S. (2017) “National and regional innovation systems”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA (457–471). Bellais, R. and Guichard, R. (2006), “Defence innovation, technology transfers and public policy”, Defence and Peace Economics, 17(3), June, (273–286). Bonneuil, C., Joly, P.-B. and Marris, C. (2008), “Disentrenching experiment: The construction of GM-crop field trials as a social problem”, Science, Technology, and Human Values, 33(2), (201–229). Braczyk, H., Cooke, P. and Heidenreich, M. (1998), Regional Innovation Systems, UCL Press, London. Callon, M. (2003), “Laboratoires, réseaux et collectifs de recherche”, in P. Mustar and H. Penan (eds), Encyclopédie de l’innovation, Economica, Paris, (693–722). Callon, M. (2017) “Markets, marketization and innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (589–609). Chesbrough, H. (2003), Open Innovation: The New Imperative for Creating and Profiting from Technology, Harvard Business School Press, Cambridge, MA. Chesbrough, H., Vanhaverbeke, W. and West, J. (2006), Open Innovation: Researching a New Paradigm, Oxford University Press, Oxford. Cohen,W. M. and Levinthal, D. A. (1990), “Absorptive capacity: A new perspective on learning and innovation”, Administrative Sciences Quarterly, 35, (569–596). Cohendet, P., Parmentier, G. and Simon, L. (2017) “Managing knowledge, creativity and innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (197–214). Cohendet, P. and Simon, L. (2017) “Concepts and models of innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (33–55). Cooke, P. (2001), “Regional innovation systems, clusters, and the knowledge economy”, Industrial and Corporate Change, 10(4), (945–974). Cowan, R., David, P.-A. and Foray, D (2000), “The explicit economics of knowledge codification and tacitness”, Industrial and Corporate Change, 9, (211–253). Dasgupta, P. and David, P. (1994), “Towards a new economics of science”, Research Policy, 23(5), (487–521).

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David, P. A., Mowery, D. C. and Steinmuller, W. E. (1993), “Analyzing the economic payoffs from basic research”, Economics of Innovation and New Technology, 2(4), (73–90). Dosi, G., Llerena, P. and Sylos-Labini, M. (2006), “The relationships between science, technologies and their industrial exploitation: An illustration through the myths and realities of the so-called ‘European Paradox’”, Research Policy, 35(10), (1450–1464). Etzkowitz, H. and Leydesdorff, L. (1997), Universities in the Global Economy: A Triple Helix of University– Industry–Government Relations, Cassell, London. Etzkowitz, H. and Leydesdorff, L. (2000), “The dynamics of innovation: From national systems and ‘Mode 2’ to a triple helix of university–industry–government relations”, Research Policy, 29, (109–123). Feldman, M. P., Link A. N. and Siegel, D. (eds) (2002), The Economics of Science and Technology. An Overview of Initiatives to Foster Innovation, Entrepreneurship, and Economic Growth, Springer, Berlin, Heidelberg, New York. Gagnol, L. and Héraud, J. A. (2001), “Impact économique régional d’un pôle universitaire: application au cas strasbourgeois”, Revue d’Economie Régionale et Urbaine, 4, (581–604). Geuna, A., Salter, A. J. and Steinmueller, W. E. (eds) (2003), Science and Innovation: Rethinking the Rationales for Funding and Governance, Edward Elgar Publishing, Cheltenham and Northampton, MA. Gibbons, M., Limoges, C., Novotny, H., Schwartzmann, S., Scott, P. and Trow, M. (1994), The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies, Sage, London. Godin, B. (2017) “A conceptual history of innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (25–32). Héraud, J. A. (2016), “A new approach of innovation: From the knowledge economy to the theory of creativity applied to territorial development”, Journal of the Knowledge Economy. doi:10.1007/s13132-016-0393-5. Héraud, J. A. and Lachmann, J. (2015), “L’évolution du système de recherche et d’innovation: ce que révèle la problématique du financement dans le cas français’, Innovations, 46, (9–32). Hicks, D. (1994), “Published papers, tacit competencies and corporate management of the public/private character of knowledge”, Industrial and Corporate Change, 4(2), (401–424). Irvine, J. and Martin, B. R. (1984), Foresight in Science: Picking the Winners, Pinter, London. Kline, S. J. and Rosenberg, N. (1986), “An overview of innovation”, in N. Rosenberg and R. Landau (eds), The Positive Sum Strategy: Harnessing Technology for Economic Growth, National Academy Press, Washington, (275–305). Larédo, P. and Mustar, P. (eds) (2001), Research and Innovation Policies in the New Global Economy: An International Comparative Analysis, Edward Elgar Publishing, Cheltenham and Northampton, MA. Le Masson, P., Hatchuel, A. and Weil, B. (2017) “Design theories, creativity and innovation”, in H. Bathelt, P.  Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (275–306). Lundvall, B.-Å. (1992), National Systems of Innovation: An Analytic Framework, Pinter, London. Lundvall, B.-Å. (2017) “National innovation systems and globalization”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (472–489). Mansfield, E. (1991), “Academic research and industrial innovation”, Research Policy, 20, (1–12). Martin, B. R. and Nightingale, P. (eds) (2000), The Political Economy of Science, Technology and Innovation, Edward Elgar Publishing, Cheltenham and Northampton, MA. Miles, I. (2007), “Research and development (R&D) beyond manufacturing: The strange case of services R&D”, R&D Management, 37, (249–268). Muller, E., Héraud J. A. and Zenker, A. (2015), “Knowledge Angels: Creative individuals fostering innovation in KIBS – observations from Canada, China, France, Germany and Spain”, Management International, 19, (201–218). Muller, E. and Zenker, A. (2001), Business services as actors of knowledge transformation: The role of KIBS in regional and national innovation systems”, Research Policy, 30, (1501–1516). Nelson, R. R. (1959), “The simple economics of basic scientific research”, Journal of Political Economy, 67, (297–306). Nelson, R. R. (1993), National Innovations Systems: A Comparative Analysis, Oxford University Press, New York. Nelson, R. R. and Winter, S. G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA. Patel, P. and Pavitt, K. (1994), “Uneven (and divergent) technological accumulation among advanced countries: Evidence and a framework of explanation”, Industrial and Corporate Change, 3(3), (759–787). Pavitt, K. (1984), “Sectoral patterns of technical change: Towards a taxonomy and a theory”, Research Policy, 13, (343–373). Pavitt, K. (1991), “What makes basic research economically useful?”, Research Policy, 20, (109–119).

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Pénin, J. and Wack, J.-P. (2008), “Research tools patents and free-libre biotechnology: A suggested unified framework”, Research Policy, 37(10), (1909–1921). Phelps, E. (2013) Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge and Change, Princeton University Press, Princeton, NJ. Porter, M. (1998), “Clusters and the new economics of competition”, Harvard Business Review, Nov–Dec, (77–90). Romer, P. M. (1990), “Endogenous technological change”, Journal of Political Economy, 98(5), (71–102). Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, Cambridge University Press, Cambridge. Sarasvathy, S. (2001), “Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial contingency”, Academy of Management Review, 26(2), (243–263). Schmookler, J. (1966), Invention and Economic Growth, Harvard University Press, Cambridge, MA. Schumpeter, J. A. (1911 [1934]), The Theory of Economic Development, Harvard University, Cambridge, MA (English version of the original work in German: Die Theorie der wirtschaftlichen Entwicklung, 1911). Schumpeter, J. A. (1942), Capitalism, Socialism and Democracy, Allen & Unwin, London. Schumpeter, J. A. (1947), “The creative response in economic history”, Journal of Economic History, 7(2), (149–159). Solow, R. M. (1957), “Technical change and the aggregate production function”, Review of Economics and Statistics, 39, (312–320). Stephan, P. E. (1996), “The economics of science”, Journal of Economic Literature, 34, (1199–1235). Sternberg, R. J. and Lubart, T. I. (1999 [2008]), “The concept of creativity: Prospects and paradigms”, in R. J. Sternberg (ed.), Handbook of Creativity, Cambridge University Press, Cambridge, (3–15). Stokes, D. E. (1997), Pasteur’s Quadrant: Basic Science and Technological Innovation, Brookings Institution Press, Washington, DC. Teece, D. J. (1986), “Profiting from technological innovation: Implications for integration, collaboration, licensing, and public policy”, Research Policy, 15(6), (285–305). Torre, A. and Rallet, A. (2005), “Proximity and localization”, Regional Studies, 39(1), (47–60). Vanhaverbeke, W. (2017) “Broadening the concept of open innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham and Northampton, MA, (87–98). von Hippel, E. A. (1976), “The dominant role of users in the scientific instrument innovation process”, Research Policy, 5(3), (212–239). Wenger, E. (1998), Communities of Practice: Learning, Meaning and Identity, Cambridge University Press, Cambridge. Zimmermann, J. B. (2008), “Le territoire dans l’analyse économique. Proximité géographique et proximité organisée”, Revue Française de Gestion, 4(184), (105–118).

5.

Reverse innovation Thierry Burger-Helmchen and Caroline Hussler

INTRODUCTION The idea that innovation originates in other than advanced countries is not new. Neither is the idea that subsidiaries of multinational corporations (MNCs) can play a significant role in the globalization of innovation. Kenney et al. (2009) already forecast subsidiaries located in emerging countries as giving “rise to born-global innovations that could never have taken place at home” (p. 894). Almost 20 years before, Bartlett and Ghoshal (1988) also shed light on new products and services originally developed by subsidiaries primarily targeting local needs and subsequently sold at a global scale. More recently, Nokia chose to develop phones in its Beijing research and development (R&D) lab, first serving the Chinese market before eventually introducing and marketing them in Europe (von Zedtwitz et al. 2015). Chinese engineers of Siemens also gave birth to an inexpensive and easy-to-use computer tomography device, which is now sold on the US market, this market remaining the biggest global market for such a device (Radjou and Prabhu 2015). Trying to account for this growing trend in MNC innovative practices, the term “reverse innovation” has progressively become popular in managerial discourses and papers (Bloomberg Businessweek, Harvard Business Review, etc.). It describes innovations emanating from developing countries, first serving developing countries consumers’ needs and second diffusing to markets in advanced countries. Historically, indeed, Vernon (1966), in his well-known International Product Life Cycle Model, explained that innovations were created in rich countries and first commercialized in these countries to serve the wealthiest consumers. When sales on this primary market started decreasing, innovations were sold and diffused in a more basic and less expensive form to consumers in less developed countries. For Govindarajan and Trimble (2012) reverse innovation thus accounts for an opposite international diffusion path: products are originally designed for developing countries and subsequently marketed in advanced countries (Figure 5.1). If reverse innovation sounds like an easy-to-use concept at first glance, its peculiarity and boundaries remain blurred. First, many other concepts seem to overlap partially or even completely with the idea of reverse innovation (von Zedtwitz et al. 2015; Brem and Wolfram 2014). Among them, Jugaad innovation (Radjou et al. 2012) and frugal innovation (Zeschky et al. 2011) are regularly and indifferently used with reverse innovation to report innovative solutions for and in emerging markets. Second, the very notion of reverse innovation itself is also subject to major criticisms and evolutions due to its initial fuzziness (Radojevic 2013, 2015; Govindarajan and Ramamurti 2011). If such a lack of cohesiveness might be expected when a research field is growing, it however hinders reliable theory development and empirical testing of the reverse innovation phenomenon. At the same time, papers on reverse innovation mostly report success stories, at General 75

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Reverse innovation: Innovations trickle up from developing to developed countries

Rich countries

Poor developing countries

Traditional view: Innovations trickle down from developed to developing countries

Source: Govindarajan and Ramamurti (2011).

Figure 5.1

A one-dimensional representation of reverse innovation

Electrics (GE) (Immelt et al. 2009) or Renault (Laperche and Lefebvre 2012) for instance. If this literature anchors the more active role played by subsidiaries in MNC innovation, it fails however to explain how successful reverse innovation occurs. But, beyond theoretical interest, understanding how to undertake successful reverse innovation might be valuable to improve MNCs’ ability to efficiently compete against emerging market MNCs (Govindarajan and Ramamurti 2011) on global markets. The present chapter precisely tackles the reverse innovation conceptual ambiguity as a prerequisite to better identify its managerial stakes and its analytical scope. To do so, the first part consists in clearly delineating the phenomenon, by distinguishing it from close notions and limiting its internal fuzziness. In the second part, we refine the concept of reverse innovation. Linking disruptive innovation and international business literatures, we outline two types of reverse innovation and highlight their respective major bottlenecks. Lastly, we introduce reverse innovation into the more general debate on global innovation dynamics, in order to question its sustainability.

REVERSE INNOVATION: DELINEATING THE PHENOMENON In this first part, we present the concept of reverse innovation and highlight the current debates it raises due to its blurred borders with other concepts (a) coupled with an internal definitional fuzziness (b). (a) Reverse Innovation: Drawing the Boundaries MAC 400 is often presented as an archetypal example of reverse innovation. This portable electrocardiogram created by GE’s engineers in India emerged as a response to the very specific needs of local physicians working in rural regions and suffering from power shortage and difficult access to hospitals. Engineers from the Indian subsidiary thus brought to market a light, portable and easy-to-use device with long battery life and at low cost. Today this device is regularly used by Indian physicians but also by numerous emergency units in the US (Govindarajan and Trimble 2012). The ingenuity and creativity of the

Reverse innovation 77 Indian subsidiary first targeted Indian needs but also succeeded in seducing users in more advanced countries. If the overall idea of reverse innovation sounds rather clear, this notion remains, however, conceptually vague. Indeed, over the last decade, innovation management literature has produced a lot of studies (Hart and Christensen 2002; Prahalad 2004; Immelt et al. 2009; Hang et al. 2010), largely based on empirical evidence, trying to depict new ways of undertaking and implementing innovation in emerging economies. The notion of reverse innovation itself flourishes, despite apparent partial or complete overlap with other concepts. Unfortunately, there seems to be no common understanding either of the very definition of each of those concepts, or regarding their potential interactions/ links (see Table 5.1). Some press articles even use those terms concomitantly in their titles. “Fathers” of the different terms also exacerbate the confusion by recalling similar case studies to illustrate their respective concepts. For instance, illustrations of frugal innovation provided in Tiwari and Herstatt (2012) are the same cases presented by Immelt et al. (2009) when configuring the reverse innovation phenomenon. In a first attempt to differentiate among existing concepts, Brem and Wolfram (2014) show that reverse innovation, Jugaad innovation and frugal innovation are the three terms which are the most frequently used and confused in the literature. However, if Jugaad innovation refers to an innovation developed in a poor-resources environment (Pina e Cunha et al. 2014) thanks to local actors’ ingenuity and their ability for bricolage, this notion does not integrate any explicit reference to the spatial diffusion of the new products/ services. Regarding frugal innovation, it accounts for the idiosyncratic and resourcessaving way of designing products or processes implemented in emerging countries. It challenges the innovation logics at stake in most advanced countries, where R&D teams are often looking for improvement and sophistication, instead of minimizing inessential costs during the creative, production and marketing process (Radjou and Prabhu 2015; Burger-Helmchen 2015; Cohendet et al., Chapter 13, this volume). But again this notion does not include any reference to the geographical diffusion of novelty. Going into more detail, Brem and Wolfram (2014) assume that reverse innovation, frugal innovation and Jugaad innovation differ along three criteria: sophistication of products, emerging market orientation and sustainability allowed. To sum up, even if recurrently confused with other concepts, reverse innovation sounds peculiar and deserves to be investigated in more depth. (b) Reverse Innovation: Reducing Internal Fuzziness The most authoritative definition of the reverse innovation notion suffers from several criticisms due to its fuzziness. Hence, according to Govindarajan and Ramamurti (2011), reverse innovation refers to cases “where an innovation is adopted first in poor (emerging) economies before ‘trickling up’ [i.e. diffusing] to rich countries” (p. 191). One can first notice a supposition that poor and emerging countries constitute the primary market of the innovative product/service. In that context, the difference between reverse innovation and the bottom of the pyramid strategy (Prahalad 2004) sounds really thin. Moreover, nothing is said regarding the origin of the innovation itself: who gives birth to the new product/service? Where? Hence, the nationality and size of the innovative firm is not explicit in the definition. Illustrative cases of reverse innovation account

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Table 5.1 Concepts referring to innovation for and from developing economies Type of innovation for/from developing countries

Definition

References

Disruptive innovation

Affordable, “good enough” products that meet consumers’ basic needs at a relatively low cost Innovation developed in and targeting the large unserved segments of poor people inhabiting emerging economies Innovations developed for the bottom of the pyramid that subsequently trickle up to the developed world A process of making use of technologies transferred from the advanced economies to develop superior technologies at home Innovative solutions developed and adopted first in emerging markets Leveraging developing economies’ cost advantage to develop innovation at dramatically lower costs Innovations adopted first in poor (developing) countries before being adopted in developed economies

Christensen (1997), Hang et al. (2010), Hart and Christensen (2002) London and Hart (2004), Prahalad (2004)

Innovation at the bottom of the pyramid Trickle-up innovation

Indigenous innovation

Blowback innovation Cost innovation

Reverse innovation

Shanzhai innovation Jugaad/Gandhian innovation Frugal innovation

Resource-constrained innovation

Chinese low-quality, low-price imitations of foreign branded products Innovations developed for the Indian market that respond to two Gandhian tenets: affordability and sustainability Innovation that has a large cost advantage, and in some cases inferior performance, compared to existing solutions, and developed in a resource-constrained context Innovation developed in emerging economies in a context characterized by lower purchasing power, lower understanding of technology and lower investment resource

Prahalad (2004)

Lazonick (2004), Lu (2000)

Brown and Hagel (2005) Zeng and Williamson (2007)

Govindarajan and Ramamurti (2011), Govindarajan and Trimble (2012), Immelt et al. (2009) Peng et al. (2009) Prahalad and Mashelkar (2010) Zeschky et al. (2011)

Ray and Ray (2011)

Source: von Zedtwitz et al. (2015).

for strategies of MNCs from advanced countries, but can we also talk about reverse innovation when Tata Motors puts its Nano onto the European market, or if an emerging Vietnamese small and medium-sized enterprise (SME) creates an original product locally but then succeeds in selling it on US markets? Up to now, authors do not converge on that point (Radojevic 2015).

Reverse innovation 79 Lastly, no information is provided on the actual location where the innovation was imagined and developed. Nevertheless, a product targeted at emerging markets might have been created and produced in an advanced country, and von Zedtwitz et al. (2015) precisely build on this weakness to outline various forms of reverse innovations. They decompose the flow of innovation into four steps: ideation, development, commercialization in the primary market and commercialization in a secondary market. According to them, each of those steps can take place in a specific geographic area (an advanced country or a developing country, A and D, respectively, in Figure 5.2). As soon as at least one of those four steps is run in a different geographical area than the preceding one, the authors consider that a reversion occurs. As a consequence they conclude that ten different cases of reverse innovation can be distinguished (see Figure 5.2), We are far away from Corsi and Di Minin’s (2014) view, where reverse innovation “configures a process of innovation that no longer sees developed economies as the locus where new products are conceived, designed and commercialized but instead take on the role of the last recipient of innovations developed in and for emerging economies”, that is, where reverse innovation exclusively refers to what von Zedtwitz et al. (2015) labeled the reversed PLC (Product Life Cycle). In the next part, we go one step further in understanding reverse innovation. We propose to disentangle two dimensions of reverse innovation that are most of the time taken for granted: the reversal of the flow of innovation (shifting locus of innovation), and the reversal of the target of the innovation (shifting focus of innovation).

FROM REVERSE INNOVATION TO REVERSE INNOVATIONS: FINE-TUNING THE CONCEPT In this second part, we propose to refine the concept by distinguishing two versions of reverse innovation (a) and stressing the respective managerial challenges they are associated with (b). (a) Two Types of Reverse Innovation Looking for original characteristics of reverse innovation, we chose to build on Corsi and Di Minin’s (2014) argument, according to which combining the disruptive innovation (Christensen 1997) and reverse innovation (Immelt et al. 2009) paradigms might be valuable to enlighten the reverse innovation phenomenon. For them, reverse innovation is “a form of disruptive innovation that originates not from the same geographical market that incumbent companies dominate, but rather from the markets of emerging economies” (Corsi and Di Minin 2014). They view reverse innovation as a specific type of disruptive innovation: an international one. Using the disruptive literature in a finer grained way, we propose to refine the reverse innovation concept itself. Following Christensen and Raynor (2003) and Govindarajan and Kopalle (2006), we distinguish between low-end disruptions and (new-market) high-end disruptions. “The former are those offering lower performance at a cheaper price but no other performance improvements, while the latter are described as products and services that offer better performance on attributes that differ from those valued by mainstream customers.” Put

80

D

Figure 5.2

D

A

D

A

Development

D

A

D

A

D

A

D

A

Primary Market

D

A

D

A

D

A

D

A

D

A

D

A

D

A

D

A

Secondary Market

Reversed PLC

Advanced Country-Targeted Innovation

Developing Country Innovation

Double Reverse Innovation

Developing Country-Inspired PLC

Front-End Reverse Innovation

Developing Country Spillover

Reverse Spillover

Cost/Capacity Innovation

Spill-Back Innovation

Type of Reverse Innovation

A map of global innovation flows with reverse innovations in the strong and weak sense

Source: von Zedtwitz et al. (2015).

Strong Reverse Inno.

Weak Reverse Inno.

GLOBAL INNOVATION

A

Concept

Reverse innovation 81 differently, in low-end disruption the main source of value creation lies in cost reduction: the innovation introduces a new set of features and performances, but existing customers are not convinced by this novelty since this new set offers inferior attributes, except regarding the price. In high-end disruption, value lies in satisfying new users, thanks to the provision of completely new attributes (sometimes coupled with the degrading of features desired by mainstream customers). Thinking in terms of reverse innovation, up to now, and in most minds, the innovating process occurring in emerging countries looks like a “cost innovation”, resulting in “products or services that initially look inferior to existing ones in the eyes of established players” (Zeng and Williamson 2007; p. 55). Hence, innovations for and from emerging markets are mostly examples of low-end disruptive innovations, where the same (or degraded) functionalities of a given product and service are provided at a dramatically lower price. The primary targets being populations from emerging countries, that is, less wealthy ones, new goods/services are reduced to their basics by eliminating unessential functions to lower costs while maintaining quality. For instance, if the French car maker Renault has succeeded in launching the Logan at a significantly low price, it is mainly thanks to the reuse and recombination of former car model mechanical parts (Laperche and Lefebvre 2012). By offering essential functions at lower cost, those new products/ services thus seduce a large range of customers in advanced countries, whose purchasing power has shrunk recently. On the contrary, one can pinpoint cases of products imagined and produced in and for the emerging market and then sold in developed economies but for different uses than the ones valued by mainstream customers in advanced countries (traditional) markets. We are here faced with high-end disruptive innovations: new products and services are developed in emerging markets and some attributes upgraded (even if degrading others at the same time). In that case, reverse innovation is not necessarily synonymous with a simplifying innovative process but might also give birth to more value-added products and services. Rather than only defeaturing or selling over-simplified technology, MNCs can recombine the most novel technologies and offer 50 percent of performance at 15 percent of the price (Govindarajan and Trimble 2012). This leads us to delineate two types of reverse innovation: a simple reverse one versus a double reverse one. Reverse innovation might be either unique or multiple, depending on whether or not MNCs invert the locus of innovation, and/or the focus of innovation, which allows us to build the following typology of reverse innovations (Table 5.2). Table 5.2

Two types of reverse innovation Locus of primary use

Focus on secondary market

Developing country

Advanced country

Existing customers

Simple reverse innovation

New customers

Double reverse innovation

Low-end disruptive innovation High-end disruptive innovation

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(b) Two Types of Managerial Challenges In both cases new (local) consumers are the targets of innovation on the primary market (i.e. the developing country) and the prerequisite lies in an innovative foreign subsidiary. Subsidiaries in the developing countries try to configure new products (less sophisticated, low-cost ones) which are affordable and adapted to local populations and local infrastructures. Here the international business literature concludes that subsidiaries achieve better innovative performances when they are properly embedded in a double network (Dörrenbacher and Gammelgaard 2010; Achcaoucaou et al. 2014; Yamin and Andersson 2011; Birkinshaw and Hood 2001): their internal, intra-MNC network on the one hand, and their external, local network on the other. The main difference lies in the characteristics of the secondary market (advanced country) served by the new product. In the simple reverse innovation case, the innovation is sold on the old, original and mainstream market in the advanced countries. In that case, the reversal of innovation denotes the diffusion of the original concept created in a developing country to a subsidiary located in an advanced country, the latter adding this new product to its portfolio and commercializing it (or a slight adaptation of it to be compatible with regulations in advanced countries) for its “old” customers. For instance, when Renault brought the Logan back to France, the vehicle originally designed and sold in and for Eastern European countries had been distributed through the French MNC’s traditional channels and to its traditional customers. In other words, the Logan became a direct competitor of other vehicles traditionally available on the French private car market. This may lead to resistance from the advanced country subsidiary who distrusts defeatured, anonymous products which challenge high standards of technical sophistication, traditionally provided in its own advanced home country (Gallis and Rall 2012). Finally, a shift from internal collaboration between subsidiaries to internal competition (Reilly et al. 2012) among them might occur, the new product putting on trial the advanced subsidiaries’ offer. The secondary market target in case of a double reverse innovation is completely different: the MNC has to try and identify new potential users in advanced countries who might be interested by the functionalities, the performance and the attributes of the innovation initially created for customers located in emerging countries. Hence, after the success of its new electrocardiogram in India, GE explored additional markets for the portable MAC 400 but in advanced countries this time. “It soon found new applications where portability was critical or space was constrained, such as at accident sites where the portable machines could be used to diagnose [cardiac] problems . . . in emergency rooms” (Hang et al. 2010). In this case, the competition with other products of the advanced subsidiary is less tough and frontal: big, expensive, highly precise and reliable electrocardiograms are still required to equip hospitals and provide refined diagnosis, whereas MAC 400 proves useful for emergency units or physicians visiting their patients. We forecast less resistance from the advanced country subsidiary in adopting the innovation created in emerging countries than in simple reverse innovation cases. However, in order for double reverse innovation to take place, the MNC has to identify those new targets in the advanced home country market. This opens up the question of the identity of the unit which plays this market discovery role: is it the developing country subsidiary, the advanced country subsidiary or the headquarters? Each answer raises specific managerial challenges.

Reverse innovation 83 All in all, many international business studies already present subsidiaries as the main actors of globalized innovation (Harzing and Noorderhaven 2006). Others analyze the drivers of subsidiaries’ innovation (Reilly and Sharkey-Scott 2014), whereas a third group investigates the conditions for reverse knowledge transfers (Michailova and Mustaffa 2012; Mudambi et al. 2014). But as far as we know the interplay between those literatures (required in order to achieve reverse innovation) are understudied and deserve additional investigation before being able to understand how to implement reverse innovation with success. In the concluding part we put reverse innovation into (macro) context and question the persistency of the phenomenon.

REVERSE INNOVATION AND BEYOND: CHALLENGING THE PROCESS In this chapter we aimed at clarifying the reverse innovation phenomenon. Building on innovation management literature, we disentangled two dimensions of reverse innovation that were, up to now, taken for granted, and exhibited two ideal-typical cases of reverse innovation. If such a typology provides a more comprehensive understanding of the underlying logics at stake, at the same time the future of reverse innovation practices and their persistency remain an open question. Indeed, global innovation is a relatively new phenomenon yet a rapidly evolving one. Innovation did not used to be global; it was intensely local for many centuries. What has changed in recent years is not so much the speed or intensity of innovation, but where and how MNCs spend their innovation budget. The challenges of innovation for MNCs relate to the global organization of the innovation process: reshaping, relocating and resizing the functional roles within the MNC sound determinant for the future (Doz and Wilson 2012). Within half a century, the global model of innovation shifted (see Figure 5.3) from Vernon’s (traditional) model, to the transnational innovation model developed by Bartlett and Ghoshal (1988), which is essentially an extension of the traditional model (Doz et al. 2015), and to reverse innovation at the beginning of the 21st century. But what are the next steps of reverse innovation and, in a broader perspective, the next steps of the globalization of the innovation process? Global innovation calls today for an interactive and iterative knowledge exchange, and a knowledge integration process, between home bases (in advanced countries) and creative local units in emerging markets. For instance, GE’s well-known cost reduction breakthroughs in medical monitoring devices in India not only required GE’s R&D efforts in India, but also involved competences and technologies from Norway, Germany, the US and Japan. In other words (see “reverse [enriched] innovation” in Figure 5.3), the innovation process becomes host-centric, driven by low cost and rugged functionality needs, but at the same time draws on technological contributions from the most advanced R&D centers around the world. Hence, many authors claim that the next step will be a rebalancing of activities between advanced and developing countries (Doz et al. 2015; Radjou and Prabhu 2015). In the coming years, as the global economy becomes more tightly integrated and interconnected, resourceful innovators (whatever their geographical locations) should

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Traditional

H

“Home”-centric

“Project” innovations into the world (export, invest, license)

Reverse

Reverse (enriched)

H

H

“Host”-centric

Host-centric

Challenge to innovate • Performance • Cost

Market insights and technological contributions from multiple sources

Reversing the knowledge flow

Knowledge attractor in host country

Global

Poly-centric networked Markets and competencies from the world over

Source: Adapted from Doz et al. (2015).

Figure 5.3

The global innovation process

be able to combine their ingenuity and expertise with specialized R&D competences (whatever their geographical locations) to co-create breakthrough frugal solutions that no single region could have entirely conceived on its own. One might designate this synergistic form of collaboration as globally networked innovation (on the extreme right of Figure 5.3): markets, money, competences and customers are everywhere on the planet and are multi-connected. In such a context, the challenge would not be about reversing the flow anymore, but rather about having constant flows in every direction, reverse innovation being thus labeled as a transient phenomenon.

REFERENCES Achcaoucaou, F., Miravitlles, P. and Leon-Darder, F. (2014) “Knowledge sharing and subsidiary R&D mandate development: a matter of dual embeddedness”, International Business Review, 23, pp.76–90. Bartlett, C. A. and Ghoshal, S. (1988) “Organizing for worldwide effectiveness: the transnational solution”, California Management Review, 31 (1), pp. 54–74. Birkinshaw, J. and Hood, N. (2001) “Unleash innovation in foreign subsidiaries”, Harvard Business Review, 79 (3), pp. 131–138. Brem, A. and Wolfram, P. (2014) “Research and development from the bottom-up: introduction of terminologies for new product development in emerging markets”, Journal of Innovation and Entrepreneurship, 3, article 9. Brown, S. J. and Hagel, J. (2005) “Innovation blowback: disruptive management practices from Asia”, McKinsey Quarterly, no. 1, pp. 34–45.

Reverse innovation 85 Burger-Helmchen, T. (2015) The Economics of Creativity: Ideas, Firms and Markets, Abingdon, UK: Routledge. Christensen, C. M. (1997) The Innovator’s Dilemma, Boston, MA: Harvard Business School Press. Christensen, C. M. and Raynor, M. E. (2003) The Innovator’s Solution: Creating and Sustaining Successful Growth, Boston, MA: Harvard Business School Press. Cohendet, P., Parmentier, G. and Simon, L. (2017) “Managing knowledge, creativity and innovation”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, UK, Northampton, MA: Edward Elgar Publishing, 197–214. Corsi, S. and Di Minin, A. (2014) “Disruptive innovation in reverse: adding a geographical dimension to disruptive innovation theory”, Creativity and Innovation Management, 23 (1), pp. 76–90. Dörrenbacher, C. and Gammelgaard, J. (2010) “Multinational corporations, inter-organizational networks and subsidiary charter removals”, Journal of World Business, 45 (3), pp. 206–216. Doz, Y., Ben Mahmoud-Jouini, S., Charue-Duboc, F. and Burger-Helmchen, T. (2015) “Global organization of innovation processes”, Management International, 19, pp. 112–120. Doz, Y. and Wilson, K. (2012) Managing Global Innovation: Framework for Integrating Capabilities around the World, Boston, MA: Harvard Business Review Press. Gallis, M. and Rall, E. L. (2012) “Global development cycles: redefining technological innovation cycles and their impacts within a global perspective”, International Journal of Innovation and Technology Management, 9, pp. 1–21. Govindarajan, V. and Kopalle, P. K. (2006) “The usefulness of measuring disruptiveness of innovations ex post in making ex ante predictions”, Journal of Product Innovation Management, 23, pp. 12–18. Govindarajan, V. and Ramamurti, R. (2011) “Reverse innovation, emerging markets, and global strategy”, Global Strategy Journal, 1 (3–4), pp. 91–205. Govindarajan, V. and Trimble, C. (2012) Reverse Innovation: Create Far from Home, Win Everywhere, Boston, MA: Harvard Business Review Press. Hang, C.-C., Chen, J. and Subramanian, A. M. (2010) “Developing disruptive products for emerging economies: lessons from Asian cases”, Research-Technology Management, 53 (4), pp. 21–26. Hart, S. L. and Christensen, C. (2002) “The great leap: driving innovation from the base of the pyramid”, Sloan Management Review, 44 (1), fall, pp. 51–56. Harzing, A. W. and Noorderhaven, N. G. (2006) “Knowledge flows in MNCs: an empirical test and extension of Gupta and Govindarajan’s typology of subsidiary roles”, International Business Review, 15 (3), pp. 195–214. Immelt, J. R., Govindarajan, V. and Trimble, C. (2009) “How GE is disrupting itself ”, Harvard Business Review, 87 (10), pp. 56–65. Kenney, M., Massini, S. and Murtha, T. P. (2009) “Offshoring administrative and technical work: new fields for understanding the global enterprise”, Journal of International Business Studies, 40, pp. 887–900. Laperche, B. and Lefebvre, G. (2012) “The globalization of research and development in industrial corporations: towards ‘reverse innovation?’”, Journal of Innovation Economics and Management, 2, pp. 53–79. Lazonick, W. (2004) “Indigenous innovation and economic development: lessons from China’s leap into the information age”, Industry and Innovation, 11, pp. 273–297. London, T. and Hart, S. L. (2004) “Reinventing strategies for emerging markets: beyond the transnational model”, Journal of International Business Studies, 35, pp. 350–370. Lu, Q. (2000) China’s Leap into the Information Age: Innovation and Organization in the Computer Industry, Oxford: Oxford University Press. Michailova, S. and Mustaffa, Z. (2012) “Subsidiary knowledge flows in multinational corporations: research accomplishments, gaps and opportunities”, Journal of World Business, 47, pp. 383–396. Mudambi, R., Piscitello, L. and Rabbiosi, L. (2014) “Reverse knowledge transfer in MNEs: subsidiary innovativeness and entry modes”, Long Range Planning, 47, pp. 49–67. Peng, S. Z., Xu, Y. F. and Lin, Q. X. (2009) “The great revolution of Shanzhai economy: the innovation comes from imitation”, Taipei: Showwe Information Co., Ltd. Pina e Cunha, M., Rego, A., Oliveira, P., Rosado, P. and Habib, N. (2014) “Product innovation in resource-poor environments: three research streams”, Journal of Product Innovation Management, 31, pp. 202–210. Prahalad, C. K. (2004) The Fortune at the Bottom of the Pyramid: Eradicating Poverty through Profits, Upper Saddle River, NJ: Pearson Education. Prahalad, C. K. and Mashelkar, R. A. (2010) “Innovation’s Holy Grail”, Harvard Business Review, July–August, pp. 2–10. Radjou, N. and Prabhu, J. (2015) Frugal Innovation: How to do More with Less, London: The Economist. Radjou, N., Prabhu, J., Ahuja, S. and Roberts, K. (2012) Jugaad Innovation: Think Frugal, Be Flexible, Generate Breakthrough Growth, San Francisco, CA: Jossey-Bass. Radojevic, N. (2013) “Reverse innovation and the bottom-of-the-pyramid proposition: new clothes for old fallacies?”, available at SSRN: http://ssrn.com/abstract52461092. Radojevic, N. (2015) “Much geo-economic ado about primary market shift: reverse innovation reconceptualised”, Management International, 19, pp. 70–82.

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Ray, S. and Ray, P. K. (2011) “Product innovation for the people’s car in an emerging economy”, Technovation, 31, pp. 216–227. Reilly, M., Scott, P. and Mangematin,V. (2012) “Alignment or independence? Multinational subsidiaries and parent relations”, Journal of Business Strategy, 33, pp. 4–11. Reilly, M. and Sharkey-Scott, P. (2014), “Subsidiary innovation: a phenomenon under threat?”, Technovation, 34 (3), pp. 190–202. Tiwari, R. and Herstatt, C. (2012) “India – a lead market for frugal innovations? Extending the lead market theory to emerging economies”, Hamburg University of Technology, Technology and Innovation Management, Working Paper, no. 67. Vernon, R. (1966) “International investment and international trade in the product life cycle”, Quarterly Journal of Economics, 80 (2), pp. 190–207. von Zedtwitz M., Corsi, S., Søberg, P. and Frega, R. (2015) “A typology of reverse innovation”, Journal of Product Innovation Management, 32, pp. 1–12. Yamin, M. and Andersson, U. (2011) “Subsidiary importance in the MNC: what role does internal embeddedness play?”, International Business Review, 20 (2), pp. 151–162. Zeng, M. and Williamson, P. J. (2007) Dragons at Your Door: How Chinese Cost Innovation Is Disrupting the Rules of Global Competition, Boston, MA: Harvard Business School Press. Zeschky, M., Widenmayer, B. and Gassmann, O. (2011) “Frugal innovation in emerging markets”, ResearchTechnology Management, 54 (4), pp. 38–45.

6.

Broadening the concept of open innovation Wim Vanhaverbeke

INTRODUCTION What is open innovation? It is useful to start from the definition (to compare with other understandings, see Cohendet and Simon, Chapter 3, this volume; Héraud, Chapter 4, this volume). Open innovation has been described as a process, a set of inter-firm relationships, and a cognitive paradigm. Henry Chesbrough originally explained it as follows: Open Innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology. Open Innovation combines internal and external ideas into architectures and systems whose requirements are defined by a business model. (Chesbrough 2003: xxiv)

He published a slightly adapted version in 2006: Open innovation is the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. [This paradigm] assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology. (Chesbrough 2006b: 1)

Recently, the definition of open innovation has been adapted again to accommodate a number of criticisms on the previous definitions: “we define open innovation as a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with the organization’s business model” (Chesbrough and Bogers 2014: 7). Although these definitions provide a solid insight into what open innovation is all about, they also reveal its limitations. First, these definitions focus on a particular (innovating) firm and its business model; in this way open innovation focuses on the firm level of analysis, neglecting for instance open innovation approaches at the ecosystem level, the project level and the individual level. Second, they focus on knowledge flows across organizations, leaving out the opportunity to consider the combination of other assets with partnering organizations. The focus on interorganizational ties also conceals the intra-organizational open innovation in which different departments, functions and management levels share knowledge with each other. Third, the definitions assume that the firm is a large company professionally involved in new product development: this is not necessarily the only approach for service companies and is not the right approach for small and medium-sized enterprises (SMEs) that engage in open innovation activities (Vanhaverbeke, 2017). Finally, the focus on how to connect with innovation partners also masks the need to reorganize the company internally in order to team up with innovation partners in an effective way. Of course, parts of these critiques have been countered by research on, for instance, open innovation in services (Chesbrough 2011), open innovation 87

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in SMEs (van de Vrande et al. 2009), open innovation at the project level (Du et al. 2014), and changes in a firm’s organizational structures and management systems that are required to shift from closed to open innovation (Chiaroni et al. 2010, 2011). Although our understanding of the open innovation paradigm has significantly improved over the last few years through a rapid increase of research on the topic, a number of important questions are still unanswered. Given these limitations, I intend to stretch our understanding of open innovation in this chapter. I will focus on three research topics that, in my view, need more attention to make open innovation more relevant for both scholars and practitioners. In the next section I discuss “the different faces of open innovation”: managing open innovation to strengthen competitiveness in current businesses is quite different from managing relationships with partners to develop new businesses in the long term. This distinction has received scant attention in prior work on open innovation management although it has important implications for the organization and management of open innovation activities. In the third section, I illustrate how open innovation can be applied in many different strategic settings which we can hardly compare with the showcases described in different publications during the last decade. In the fourth section, I make a comparison between open innovation and some insights developed by Rita McGrath in her recently published book entitled The End of Competitive Advantage. The book offers several handles to understand open innovation in a broader, strategic context. I wrap up the most important conclusions in the last section.

UNDERSTANDING THE DIFFERENT “FACES” OF OPEN INNOVATION One topic that has received insufficient attention from innovation researchers is what we could label the different “faces” of open innovation. Open innovation activities in large companies cannot be understood independently from strategic objectives. To simplify the picture, I make a distinction between open innovation activities to strengthen competitiveness in a firm’s current businesses and open innovation with external partners to develop new businesses in the long run to guarantee corporate growth in the future. Examples of (usually successful) open innovation projects have been around for years (Chesbrough et al. 2014). You can find them in books on open innovation, numerous websites focusing on open innovation management, corporate websites and press releases. Yet, it has always struck me that the vast majority of the examples can be categorized as new products/services that are offered through existing business units, sold through existing distribution channels and, in some cases, are just an add-on to existing products. Take for instance the Pringles Prints of Procter & Gamble (P&G): the fast-moving consumer goods (FMCG) giant was using a technology where words and images can be printed on the crisp. It adapted an ink jet technology that a bakery in Bologna used to print messages on cakes and cookies. Other open innovation examples represent products that provide a better technology for an existing application. Take for instance the Swiffer Duster of P&G. P&G got the technology from Japan’s Unicharm. P&G signed a deal with Unicharm to distribute the duster under the P&G name everywhere in the world except Japan.

Broadening the concept of open innovation

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These two products are typical exemplars of open innovation success stories that we have witnessed in the last decade. Yet, they represent only “one face” of open innovation and they may blindside us from the full potential of open innovation. Open innovation has the potential to be deployed as a viable innovation approach for a broad range of innovation objectives. Open innovation activities can be connected to different corporate strategy objectives. Discussing open innovation and corporate strategy, one has, for instance, to focus on the different growth objectives: there are usually several growth horizons – as explained by the three growth horizons of McKinsey (http://www.mckinsey. com/insights/strategy/enduring_ideas_the_three_horizons_of_growth). The basic idea is that in order to achieve consistent levels of growth, firms must not only be innovative to continue growth or attenuate growth slowdown in existing businesses, but must also explore areas they can turn into high-growth business opportunities in the future. This challenge to balance the typical tension between short-term priorities and the longer-term vision is called ambidexterity. The concept is central in innovation management and an increasing number of innovative companies understand what is at stake. Yet, many others are imbalanced as they are very good at handling the demands of today’s businesses, but not so successful at being creative and renewing themselves over the long term. Ambidexterity has been discussed for almost two decades in the literature, but it has not yet been introduced as a central notion in the open innovation literature. The ambidexterity notion is nevertheless crucial to understand the full potential of open innovation. The examples of the Print Pringles and the Swiffer Duster only show the relevance of open innovation for the short-term growth objectives of a company. Yet, open innovation can have a stronger impact on the development of long-term growth objectives of companies. There is no consensus on how exactly ambidextrous organizations should handle their “long-term” innovation streams, but it is obvious that open innovation initiatives fostering long-term growth will be quite different compared to open innovation that is intended to support the short-term actions of the existing/mainstream businesses. Take the example of DSM’s Emerging Business Areas (EBAs) (http://www.dsm.com/ corporate/about/innovation-at-dsm/long-term-innovation.html).  DSM starts with the incubation of ideas (in several cases these ideas were generated outside DSM). Ideas that have the potential to grow into a significant business are turned into growth platforms and are organized as EBAs: they are structured as separate units, with the agility and flexibility of a start-up, yet can still benefit from the services and resources that a large company can offer. Although EBAs only represent one type of long-term growth organizational vehicle, they are interesting because of the role of open innovation in realizing long-term growth objectives for the chemical company. Compare now the role and management of open innovation in EBAs to open innovation initiatives deployed in existing businesses. In EBAs the business model is not yet crystallized, technology and business uncertainty is much larger, and new partners have to be found not only to help out the company with technology, but also with new business models, new routes to market and so on. Organizing open innovation for long-term growth is clearly different from the more popular form of open innovation, where companies rely on external partners for sustaining growth in the short term. It is important that managers understand that open innovation is a multiplex concept when it comes to using it for different corporate growth targets. Open innovation has different “faces” and each of them needs a different way of

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organization. Compared to open innovation supporting short-term growth objectives, initiatives that enable long-term innovation streams will be taken away from established business units and management will give them some sort of formal authority. They tend to be organized centrally and they report directly to top management. External sourcing modes are different (long-term strategic research projects, corporate venture capital (CVC), minority positions in high-tech ventures, etc.), and so will be the type of partners who have to work under high levels of technological and market uncertainty. More important, open innovation is required for the development of new capabilities, as a company may not have the required technological and business capabilities to compete successfully in emerging business areas: capability development is less of a priority in the case of existing businesses since capabilities have been developed over the years. In sum, open innovation targeting long-term growth is hardly comparable with open innovation projects that sustain short-term growth. Achieving ambidexterity has several implications for the organization and management of open innovation in large companies. Yet, it is exceptional that professionals develop a management framework or tool that (re)balances different open innovation approaches catering for the divergent needs of short-term and long-term growth objectives leading to a consistent level of corporate growth over time.

MAKING OPEN INNOVATION RELEVANT TO MORE ECONOMIC PLAYERS I argue in this section that open innovation can be applied in various strategic settings compared to the showcases described in several prominent publications of the last decade. Open innovation has always been centered on new product development. Firms source external knowledge from technology and market partners to speed up product launches and to get access to complementary technologies. The open innovation funnel has been used to explain open innovation, implicitly assuming that open innovation is always related to new product development. Accordingly, open innovation has been defined in terms of inside-out or outside-in innovation: external knowledge is acquired to strengthen internal research and development (R&D) related competencies and to speed up the innovation process within the company, and unused, internal knowledge is monetized through external paths to market. In both cases, new product development determines the value of knowledge: external knowledge only creates value when a firm’s new product development benefits from it, and internal knowledge which is no longer useful for a firm’s new product development is a candidate to be licensed or sold to other firms in other industries. However, open innovation is also valuable in other business activities that are not related to new product development. New product development is only one of many activities where open innovation is applicable and valuable. New product development based on new technologies is not an option in many industries such as services where firms typically focus on creating solutions for customers rather than producing and selling products based on new technologies. Moreover, in many manufacturing industries, companies produce and sell commodities: new product development is hardly an option. Therefore, new product development should be considered as a special application field of open innovation.

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Instead of focusing on the technological needs for a firm’s new product development, one should examine which strategic drivers of a (focal) firm’s business can be leveraged to gain competitive advantage. To lever these drivers the firm doesn’t have to be involved into new product development itself, but it should identify how technological innovations of other companies may lever these strategic drivers. Therefore, the focal firm should spur them to develop these innovations in line with its needs. To reach that objective, it has to establish a network (or an innovation ecosystem) of external partners who have deep expertise in the required technologies: when partners develop these technological innovations the focal firm should gain a competitive advantage. In sum, management scholars have been connecting open innovation to new product development activities within the firm but, in this way, they have been limiting drastically the scope of open (innovation) strategies. Instead, management should look for specific strategic drivers and for a network of partners whose technological expertise can leverage these strategic drivers. Let’s illustrate this with an example. Assume you are the general manager of the crude oil business within a large oil company (this example has been developed in Vanhaverbeke and Roijakkers 2013). The product sold by the business unit is a commodity and therefore new product development is not an option (at least at the business-unit level). Competitive advantage in the crude oil industry is determined by a number of strategic drivers. Two of them are early detection of large oil wells and effective drilling of these wells. Therefore competitiveness in the crude oil business depends on various technologies that boost the productivity of exploration and extraction. Oil companies have to find the richest oil wells before their competitors do and drill them more effectively through new technologies that extract oil more productively at greater depths. Oil companies rely on specialized oil services companies such as Schlumberger and others to develop new technologies for oil exploration and extraction. An oil company gains a competitive advantage if it partners with Schlumberger (in combination with other specialized services companies) by setting up a research consortium with these partners and (co-)finance the R&D of new exploration and drilling technology. The oil company will typically require exclusive use of the technology for several years before oil service companies can sell the newly developed technology to other oil companies. This is just one example of how companies that were not typically considered as open innovators can still thrive through orchestrating innovation ecosystems. There are many other possibilities. Elsewhere, we provided examples illustrating how firms may thrive by setting up innovation networks in which other firms’ innovation results are used to strengthen some of a focal firm’s strategic drivers. Examples are service companies that develop superior business models deploying new technologies from their innovation partners. Similarly, small firms may not have the required technology in-house to develop new value propositions for their customers: interesting examples are Curana (www.curana. be) and Quilts of Denmark (www.qod.dk). Also governmental institutions such as the European Space Agency (ESA) or NASA derive their success mainly from new technologies developed by their technology partners. In all these examples, the focal actors orchestrate a network of partners whose technological developments aid them in improving particular strategic drivers, helping them in this way to become competitively stronger. To sum up, open innovation – once sundered from the open innovation funnel and new product development – offers business opportunities for a broad range of companies that

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were not previously considered as beneficiaries of open innovation strategies. Within this broadened view on open innovation, new product development should be considered as a strategic driver that applies to some situations but not to others. Therefore, one should start from the strategy of a firm, identify the key strategic drivers for creating value/ enhancing the firm’s competitive position, spot and select potential innovation partners, and set up a joint project to develop technologies or strengthen the firm’s strategic drivers. Even in the absence of internal new product or service development, companies can still nurture their innovation network to boost their competitiveness.

RELATING OPEN INNOVATION TO RECENT STRATEGIC INSIGHTS OF RITA MCGRATH One way to “push the boundaries” of open innovation research is to relate it to developments in other research fields. Research in the field of strategic management is changing rapidly, and one of the authors driving that change is Rita McGrath. She argues in her book The End of Competitive Advantage that the worlds of strategy and innovation have gotten much closer to one another than a few decades ago. Historically, strategy and innovation were two separate disciplines: strategy was dealing with finding an interesting position in a well-established business. Innovation, in contrast, was about building new business and about future opportunities. Therefore, it was not related to the core business of companies (McGrath 2013: xii). As the two disciplines covered different fields of research it was not necessary to combine them. This gradually changed, and in the late 1990s the connection between strategy and innovation was becoming mainstream. McGrath contends that competitive advantages today are eroding quickly; there are only temporary competitive advantages – she calls them “transient advantages”. Conventional thinking about long-lived advantages and industry-oriented thinking has to be changed. Product features, new technologies and market power and other sources of competitive advantages are proving nowadays to be less durable than some decades ago. Firms need to create a pipeline of advantages to replace those that have been competed away. Within-industry competition – the major focus of strategy research – is becoming less relevant as most important competitive threats nowadays come from the outside the industry. The mobile phone industry of the last ten years is an excellent illustration of this phenomenon, as Apple and Google had no foothold in this industry before 2005. Industries have been a central concept to explain competition in the past, but they are becoming less relevant as a competition-framing concept. Industries are increasingly competing with other industries, or business models are competing with other business models even in the same industry. McGrath prefers the concept “arena” to define current strategic battles between companies. If competitive advantages are not sustainable but temporary, we should think of the evolution of a particular competitive advantage in several phases of the product life cycle. Most strategic models are designed for the exploitation (phase) of a competitive advantage because the exploitation of an advantage can go on for decades according to the traditional, sustainable competitive strategies approach. In contrast, when advantages are temporary, exploitation is just one of the five phases in the evolution of a competitive advantage. A firm starts a new business by identifying and developing an opportunity.

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That is where innovation comes in. Second, when the opportunity starts to be successful in the market a firm has to ramp up the business: experimentation and speed are crucial in this stage. After the ramp up a company can enjoy the exploitation of a business. This phase should last as long as possible but companies should also be mindful that the advantage will eventually erode. When erosion starts, there is a need for continuous reconfiguration to remain relevant in the rapidly changing economic environment. Reconfiguration is a process through which assets, people and internal capabilities make the transition from one advantage to another one (McGrath 2013: 27). Finally, when an advantage is exhausted, firms have to dispose of the assets and capabilities that are no longer relevant to their future in a process of disengagement. As economic opportunities change continuously, firms have to be agile in phasing out mature businesses while seizing new business opportunities. This, in turn, implies that innovation needs to be a systematic, ongoing process in which firms achieve innovative outcomes reliably or abandon them inexpensively. As such, this novel way of thinking about strategy has no direct connection with open innovation. However, there are several interesting similarities between the new insights of Rita McGrath and the assumptions underlying the open innovation literature. Connecting open innovation to the “transient advantage” strategy framework offers us the possibility to (re)consider open innovation within a broader strategic framework. What are the similarities of McGrath’s The End of Competitive Advantage and the assumptions behind the open innovation paradigm as advocated by Henry Chesbrough in Open Innovation / Open Business Models (2003, 2006a). In my view, there are several similarities and they are worth exploring, as it is no coincidence that both best-selling books have converging ideas even though they look at the same phenomena from a different perspective. Business Models and Strategy Are Central Both books – The End of Competitive Advantage and Open Innovation – emphasize the need to bring together the disparate fields of competitive strategy, innovation and organizational change. Rita McGrath is explicit in making this claim. If continuous change and building new businesses become important strategic imperatives, management has to add new frameworks on top of the traditional methods and tools used in traditional strategy analyses: examples are options reasoning applicable to the nurturing and selection of future business opportunities, changing procedures for resource allocation, a different way to discover market needs (current customers may be irrelevant), and business model innovation being as important as research and innovation efforts in the company. McGrath is describing different strategies over the product life cycle. Firms have to develop simultaneously a variety of strategies: they need to launch and ramp up new businesses, they can use exploitation strategies for established businesses, and have to get involved in reconfiguration and healthy disengagement for mature and declining businesses. This dynamic view on strategy pays attention to the creation and ramp up of a business, as well as to the exploitation and the exit phases of a business. The attention for both entry strategies as well as exit strategies is something we retrieve in the open innovation literature. Through this dynamic view on strategy, McGrath brings the fields of strategy and innovation together. These worlds cannot be examined separately.

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Strategy and business models are also central concepts in open innovation: “The value of an idea or a technology depends on its business model. There is no inherent value in the technology per se. The value is determined instead by the business model used to bring it to the market” (Chesbrough 2003: xxx). Chesbrough (2003, 2006a) also uses the (open) innovation funnel as a central concept to develop several key insights about open innovation. The funnel is an interesting visualization to connect strategy. The labels “new market” and “current market” at the right side of the innovation funnel refer to the business model of a company. Therefore, business model thinking is at the heart of open innovation: the business model of a company determines which ideas and technologies have to be sourced from external partners and which new business projects will be outlicensed or spun out. Open innovation can thus only be correctly understood when it is integrated into firms’ strategy. Yet, few publications have examined how strategy and open innovation interconnect with each other (a notable exception is Chesbrough and Appleyard 2007). Summarizing, both approaches bring strategic objectives and innovation management together as inextricable parts of firms’ long-term growth ambitions. There is no way to think about strategy without innovation, and innovation management should be an integral part of a firm’s strategy. The specific and novel approach of McGrath – transient advantages – offers an interesting strategy framework to reconsider the intertwinement of strategy and innovation management. A Dynamic View on Competitive Advantages Rita McGrath argues that sustainable competitive advantage is no longer achievable and firms have to attempt to achieve transient advantage. In her view, rather than focusing on a distinct set of capabilities, companies must continuously be moving on to greener pastures. This uninterrupted search for new business opportunities is a logical consequence of the shift from sustainable to “transient” advantages. As mentioned above, the emphasis is no longer on the exploitation of a competitive advantage for an extended period of time, but on the continuous search for new business opportunities. This shift also entails a change in how to approach strategy. When companies have to look for new businesses, strategy becomes intimately blended with innovation management and entrepreneurship. This dynamic view is also prevailing in the open innovation literature. Firms have to continuously evaluate new ideas and technologies on their value for the company. The open innovation funnel concept reflects this dynamic approach: “Internal IP [intellectual property] that is not supporting the BM [business model] becomes a candidate for external licensing or outright sale. External IP that complements the BM becomes an attractive candidate for acquisition from the outside” (Chesbrough 2006a: 131). McGrath emphasizes that business strategy requires a much higher pace of business development activities to continuously feed a company with new growth business (assuming that many other business development projects will never make it to the market). Similarly, Chesbrough argues that firms have to speed up their innovation activities keeping in mind that most innovative ideas do not survive one of the subsequent stages in the innovation funnel. In open innovation, firms have to accelerate new product development activities by tapping more effectively into external sources of technology, optimizing internal development and commercialization processes, discontinuing investments early

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enough when they are no longer commercially viable, and monetizing on ideas that do not fit the firm’s business model. The transient advantage and open innovation have a dynamic view on competitive advantage in common. Although they focus on different topics, they share the underlying view that the world is in flux and that companies can only survive if businesses and innovation projects are built, scaled and abandoned in a swift way. Increasing speed is only possible when firms open these processes. This is the topic of the following two sections. Access to Assets, not Ownership of Assets One of the most striking similarities between The End of Competitive Advantage and open innovation is the switch from ownership to access to assets. McGrath shows that our world is increasingly one where companies can and should opt for access to assets they need rather than developing these assets internally. The abundance of technology and the rapid growth of venture capital backed start-ups has led to growing opportunities for companies to license, buy or co-develop external knowledge. Start-ups have turned industries upside-down in cases where incumbents continue to stick to traditional business models and when they are slow in adapting to new technologies. Examples from the music industry, printing, technology services, manufacturing and others show that firms can start asset-light organizations. The reason why access to assets rather than ownership “is increasingly attractive is that it allows firms to adjust their structures and assets quickly as competitive dynamics unfold” (McGrath 2013: 95). When product cycles become shorter, companies have to rely more on access to assets. It makes them more agile and allows them to be less vulnerable to financial risks. Open innovation strongly advocates the sourcing of external knowledge – the most important asset in contemporary economies. Companies can no longer rely on internal R&D only as technologies become more complex and expensive while product life cycles have shortened systematically during the last few decades. Outside-in open innovation describes in detail how innovative firms can benefit from external technology sourcing. Recently, Vanhaverbeke and Chesbrough (2014) have developed a framework explaining how companies that are not involved in R&D activities can still benefit from external technological developments in other organizations. McGrath goes one step further and considers access to all types of external assets – and not only knowledge or technology – as a major driver for improving competitiveness. Therefore, it is interesting to explore how McGrath’s and Chesbrough’s view on access to external assets can be integrated into a broader strategic framework where access to knowledge is considered a specific category of assets to generate a competitive advantage. Exit Decisions: Healthy Disengagement Another similarity between the two approaches is the emphasis they put on exit decisions. As businesses mature and decline management has to take exit decisions: businesses have to stop at a particular point in time when they don’t demonstrate a growth potential anymore. In a world of transient advantages, stopping businesses is as important as starting new ones. What are the early warnings of decline? When to exit a business? How to end businesses in a systematic way? These are all crucial questions in an era where

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advantages are temporary. McGrath develops a set of strategies for disengagement based on two dimensions: the time pressure and the value of the existing capabilities for the company’s other or future businesses. The inside-out dimension of open innovation describes how companies monetize knowledge they cannot commercialize internally. Although Chesbrough is focusing on ideas and technologies under development – and not businesses at the end of the product life cycle – the similarities between the two approaches are amazing: they both emphasize the need for a formal and systematic approach to license, sell or divest knowledge or assets to external parties. The need to do so stems from the need to stay competitive through an increase in the number of new business ideas. In the case of open innovation, firms have to develop and nurture many business ideas to end up with a few new businesses with the expected business potential. Most ideas do not make it to the market, but are still useful for other firms that use this knowledge/these ideas in a different business setting. Similarly, transient advantages imply that firms have to exit businesses once they no longer have a promising growth potential. When a firm exits a business, assets may be still valuable for other firms as they work with different business models or operate in different industries. In sum, as firms have to process many business ideas/technologies to develop new businesses or when they have to continuously develop new businesses to stay competitive, there is need to take a systematic approach to monetize technologies that are no longer valuable for the firm or businesses that no longer have a growth potential. Reconsidering Openness in Terms of a (Real) Option Approach When advantages are temporary there should be a shift in emphasis from “exploiting the core business” to creating options for new business creation. This will allow management to generate continuous renewal and innovation. Option thinking is crucial in an era of transient advantages. Firms have to experiment with new ideas; they have to get access to early stage technology or business ideas as (real) options. This gives them the advantage to explore these opportunities early on, without major financial commitments. New business ideas are laden with technology and market uncertainty: getting access is important but a firm should not own the technology at this stage. Options give companies the possibility to delay the financial commitment till they have learned enough about the technology and they can estimate the market potential in a realistic way. The real options approach enables managers to learn from what is happening around them (e.g. new technologies whose market potential is uncertain) and to modify their subsequent investment decisions based upon that learning. The real option approach is essential to understand the rationale behind “transient advantages” and open innovation. This approach allows managers to consider the value of particular options even though information is currently missing. Buying an option (e.g. a company takes a minority share in a venture capital backed start-up) allows management to delay commitment until they have learned more about the potential investment (e.g. the company can acquire the start-up or license its technology). Options enable companies to learn early on about new technologies and business opportunities, which is crucial if competitive advantages are transient. The real option approach is not new in the innovation management literature, but has become essential in the open

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innovation literature (Vanhaverbeke et al. 2008). Its application to the field of strategy is fairly new and implies major shifts vis-à-vis mainstream strategy approaches. Openness and the Creation of Sustainable Competitive Advantage: A Weak Spot in McGrath’s Approach? Although open innovation and the “transient” advantage approach have many commonalities, there are obviously also several differences. For instance, open innovation focuses on the development and commercialization of new technologies or ideas, but it remains silent about the life cycle of a business and how to manage that cycle effectively. A more important difference is related to the role of the partners and networks of external knowledge sources. “Openness” to external sources is at the core of open innovation. Companies build competitive advantage through networks of partners. One can consider these ties with partners as short-lived connections: switching regularly to new partners and type of partners may explain in this way how access to external knowledge/assets may lead to “transient” advantages. However, relational or network-based advantages (Dyer and Singh 1998) are not necessarily related to short-lived competitive advantages. On the contrary, networks of (innovation) partners can be a source of sustainable competitive advantage. Open business models – but also open innovation based advantages – may lead to long-lasting competitive advantages. Take for example Amazon. This company was not only able to maintain, but also to deepen its competitive advantages over time. Through its open business model, it increased its scope and scale, but it also has improved its core businesses. Amazon is not an exception; Chesbrough (2006a; 2011) provides many similar examples. A strategy, based on the deepening and widening of networks of partners and platforms for collaboration has the potential to generate long-lasting competitive advantages. How networks of (innovation) partners lead to these long-term advantages goes beyond the scope of this chapter, but interesting thoughts have been developed by Gomes-Casseres (1996) and Nambisan and Sawhney (2011).

CONCLUSION The concept of open innovation has attracted considerable attention among practitioners as well as academics since Henry Chesbrough (2003) first coined the term to capture the reliance of companies on external sources of innovation. Open innovation has developed into a bourgeoning area of innovation management research. There is a fast-growing number of scientific publications referring to the concept as well as many special issues in management journals entirely devoted to open innovation. I argue, however, that despite the success of open innovation, open innovation research has a much larger potential as an innovation framework than what has been suggested in the literature in the last decade. It is fairly easy to broaden the scope of open innovation research by relating it to corporate strategy or to refocus the scope from open innovation practices developed in the context of new product development towards its potential role in realizing a broader set of strategic drivers in industries where new product development is less important or not possible at all. Finally, there is an increasing need to connect and integrate open innovation literature to new developments in other segments of the management literature. The comparison with

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the latest book of Rita McGrath is just an example. There is a lack of coherence in the body of research about open innovation and inadequacy in its grounding in theory. Pushing the boundaries of open innovation research is necessary: the concept is powerful and can be applied to several unexplored management contexts, but we also need more coherence in our thinking about open innovation and a better integration into the current theory.

REFERENCES Chesbrough, H.W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Boston, MA: Harvard Business School Press. Chesbrough, H.W. (2006a). Open Business Models: How to Thrive in the New Innovation Landscape. Boston, MA: Harvard Business School Press. Chesbrough, H.W. (2006b). “Open innovation: A new paradigm for understanding industrial innovation.” In H.W. Chesbrough, W. Vanhaverbeke and J. West (eds), Open Innovation: Researching a New Paradigm. Oxford: Oxford University Press, pp. 1–12. Chesbrough, H.W. (2011). Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era. San Francisco, CA: Jossey-Bass. Chesbrough, H.W. and Appleyard, M.M. (2007). “Open innovation and strategy,” California Management Review, 50(1): 57–76. Chesbrough, H.W. and Bogers, M. (2014). “Explicating open innovation: Clarifying an emerging paradigm for understanding innovation.” In: H.W. Chesbrough, W. Vanhaverbeke and J. West (eds), New Frontiers in Open Innovation. Oxford: Oxford University Press, pp. 3–28. Chesbrough, H.W., Vanhaverbeke, W. and West, J. (eds) (2014). New Frontiers in Open Innovation. Oxford: Oxford University Press. Chiaroni, D., Chiesa, V. and Frattini, F. (2010). “Unraveling the process from closed to open innovation: Evidence from mature, asset-intensive industries,” R&D Management, 40 (3): 222–245. Chiaroni, D., Chiesa, V. and Frattini, F. (2011). “The open innovation journey: How firms dynamically implement the emerging innovation management paradigm,” Technovation, 31(1): 34–43. Cohendet, P. and Simon, L. (2017). “Concepts and models of innovation”. In: H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 33–55. Du, J., Leten, B. and Vanhaverbeke, W. (2014). “Managing open innovation projects with science-based and market-based partners,” Research Policy, 43(5): 828–840. Dyer, Jeffrey H. and Singh, H. (1998). “The relational view: Cooperative strategy and sources of interorganizational competitive advantage,” Academy of Management Review, 23(4): 660–679. Gomes-Casseres, B. (1996). The Alliance Revolution: The New Shape of Business Rivalry. Boston, MA: Harvard Business School Publishing. Héraud, J.-A. (2017). “Science and innovation”. In: H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 56–74. McGrath, R. (2013). The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business. Boston, MA: Harvard Business School Publishing. Nambisan, S. and Sawhney, M. (2011). “Orchestration process in network-centric innovation: Evidence from the field,” Academy of Management Perspectives, 25(3): 40–57. van de Vrande, V., de Jong, J.P.J., Vanhaverbeke, W. and de Rochemont, M. (2009). “Open innovation in SMEs: Trends, motives and management challenges,” Technovation, 29(6–7): 423–437. Vanhaverbeke, W. (2017). Managing Open Innovation in SMEs. Cambridge: Cambridge University Press. Vanhaverbeke, W. and Chesbrough, H.W. (2014). “A classification of open innovation and open business models.” In H.W. Chesbrough, W. Vanhaverbeke and J. West (eds), New Frontiers in Open Innovation. Oxford: Oxford University Press, pp. 50–68. Vanhaverbeke, W. and Roijakkers, N. (2013). “Enriching open innovation theory and practice by strengthening the relationship with strategic thinking.” In N. Pfeffermann, T. Minshall and L. Mortara (eds), Strategy and Communication for Innovation. Berlin: Springer-Verlag, pp. 15–25. Vanhaverbeke, W., Van de Vrande, V. and Chesbrough, H. (2008). Understanding the advantages of open innovation practices in corporate venturing in terms of real options, Creativity and Innovation Management, 17, 17(4), 251–258.

7.

Measurement of innovation Stephane Lhuillery, Julio Raffo and Intan Hamdan-Livramento

INTRODUCTION Since the contributions of Francis Bacon, observation and measurement have been central to scientific research. Research on knowledge production is no exception, and efforts were made early on to collect data measuring scientific and technological capacities at the country level (Godin 2012). For decision makers, collecting data on scientific and technological capacities at the country level has become a priority because innovation creates national competitive advantages. Over the past 50 years, important efforts have been undertaken to capture, categorize and standardize measures related to innovative activities. These ongoing efforts include the collection of research and development (R&D) and non-R&D activities, as well as the collection of technological and nontechnological factors that may affect economic activity (see OECD 2010; OECD 2013 for an overview). There is a growing interest in broadening the measurement scope of innovation to consider “creative” activities, suggesting that the usual indicators of innovation satisfy neither scholars nor policy makers. Conceptually, there is little difference between innovative and creative activity. According to Schumpeter, innovative activities usually involve an inventive step in which new knowledge or ideas are processed, whereas an innovation step addresses the use of or commercialization of an invention. Additionally, environments that stimulate creativity are likely to motivate innovation (see, for example, Amabile et al. 1996). Creativity based on imagination and originality can thus be considered as overlapping strongly with or even included as part of inventive activities (Cohendet and Simon, Chapter 3, this volume). The first issue is to know to what extent current measures that capture innovation are relevant for creativity. However, some aspects of creativity may not be fully or even partially captured by innovation measurements, such as the “irrational” elements that are often associated with creativity. Similar to technological innovation activities, scientific and technological intelligence is not contingent on creative activities in which sensibility or faith can be central. Furthermore, for some scholars, creativity can exist per se with aesthetic value, without any relation to a new process or product (Runco 2014). The usual innovation measures may thus be inappropriate and new data must be collected. A second issue then becomes whether the new measures for creativity can benefit from the experience accumulated through R&D and innovation. In this chapter, we provide insights and lessons learned from using measures of innovative activities for scholars who are interested in capturing creative activities. We underscore the difficulties faced when measuring innovation and draw some parallels between these difficulties and the efforts undertaken to measure creativity. 99

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Reviewing the enormous body of literature on the topic is not easy. Direct measures of innovation are being proposed by different surveys administered by academic bodies, government, international organizations, consulting firms and think tanks, and some indirect measures are available through financial statements, tax credit files and intellectual property rights (IPR) registration data. For the sake of brevity, we focus on the measurements taken at the firm level and on the large-scale and standardized national surveys defined in the Frascati Manual (OECD 1962) or Oslo Manual (OECD 1992). We also consider artistic creativity as a role model for creative activity in challenging the usual measures. The lessons learned and the problems highlighted in this chapter should be relevant for measurements performed by public research organizations at the employee level and for many types of creative activities. This chapter is structured as follows: first, we identify the factors that are considered inputs into innovation production and differentiate between R&D and non-R&D activities that a firm can undertake. We then delve into the various outputs of the innovation process and distinguish between direct measures used in innovation surveys and indirect measures proposed in alternative databases.

INNOVATION INPUTS One of the oldest and most common methods of measuring innovative activities is through capturing R&D data (OECD 1962; UNESCO 1968; Godin 2009). The popularity and prevalence of R&D indicators stem from their ability to quantitatively capture efforts related to innovation directly. However, these data neither provide a complete picture of innovation nor are they the most reliable or easiest indicators to interpret. This section discusses the different input measures of innovative activities, highlights their limitations, and shows how they can be relevant for measuring creative activities. R&D Inputs Research and experimental development (usually just named Research and Development or R&D) refers to “creative work undertaken on a systematic basis to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications” (OECD 2002, p. 30). R&D should thus capture a large share of creativity inputs. The scope of R&D activities is limited by definition problems and by the use of multiple categorizations. Additional lessons for creativity measurement can be derived from the efforts made to address R&D accumulation and organization. (a) R&D definition and categorization First, R&D efforts must be intentional. Unintentional processes will not be considered R&D, and some intended heuristics are required in creative tasks (Amabile 1983). Even if it is successful, a random process cannot be considered R&D. “Systematic” activities were historically interpreted as planned, organized and continuous cognitive activities (Uhlmann 1977; Godin 2004). However, evidence has shown that industrial R&D is often not necessarily planned, organized or even continuous because firms often lack a

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dedicated R&D department (Kleinknecht 1989; Kleinknecht and Reijnen 1991; Santarelli and Sterlacchini 1990) or clear R&D budget (Gault and von Hippel 2009). Furthermore, firms may strategically declare that they do not conduct R&D (Hunter et al. 2012) or refuse to disclose their R&D activities (Koh and Reeb 2015; Chen et al. 2015), which leads to the artificial observation of non-continuous R&D activity. Scholars have addressed this issue by focusing on R&D-performing firms employing at least one full-time equivalent (FTE) researcher, even if doing so causes scholars to overlook up to one-third of firms (Kleinknecht and Reijnen 1991; Bönte and Keilbach 2005). The most recent Frascati manuals finally acknowledged the existence of “informal” and “occasional” R&D activities (OECD 2002, p. 17). However, the “systematic” trait remains and suggests that the measured R&D activities data are still biased toward organized, formal and continuous activities (Godin 2004). Creative activities, such as ideation activities or artistic work performed by individuals who prefer independence, are likely to be underestimated. Second, R&D activity must possess an uncertain element. The Frascati Manual states that “[t]he basic criterion for distinguishing R&D from related activities is the presence in R&D of an appreciable element of novelty and the resolution of . . . uncertainty” (OECD 2002, p. 34). Such a distinction in cognitive activities is necessary to differentiate experimental development from other types of development activities, such as marketingrelated activities. However, the (auto-)evaluation of uncertainty is difficult, particularly when R&D measurement methods fail to follow up on the outcome of R&D projects, such as their failures (data on R&D failures are only collected by sponsoring bodies, see Link and Wright 2015). The measurement of uncertainty in creative activities remains a challenge. Third, the degree of novelty will depend on a benchmark: “someone familiar” (OECD 2002) with the state-of-the-art knowledge or an “appropriate observer” (Amabile 1983). This benchmark can be achieved through standard or novel heuristics. However, knowledge is either assumed to be common to all of the knowledge producers or dispersed, leading to different conclusions regarding R&D. The former Mertonian view implies that the R&D definition is universal and applicable in every country despite their different contexts (as in OECD 2015). The latter view implies that the novelty related to the declared R&D actually depends on a local benchmark. Once the benchmark is identified, the inventive step or degree of novelty must also meet the “non-obviousness” criterion. A particular case emerges for artistic activities based on aesthetic values and originality criteria, in which a claimed inventive step may be subjective and not consensual. Despite the problems cited, R&D remains the main measure of innovation inputs because the definition of R&D can be fine-tuned by users to their advantage (Bosworth et al. 1993; OECD 2010). For example, in general, R&D in the social sciences, arts or humanities is ineligible for R&D tax credits in the UK (HMRC 2014). Managers and accountants can consider some expenses as R&D expenditures, such as downstream activities that include pilot plants or marketing activities (see Hunter et al. 2012), to reinforce positive signaling for shareholders (Chen et al. 2015). In a case in which there is a non-incremental R&D tax credit, relabeling activities can be particularly rewarding. However, in purely incremental R&D tax credit schemes, firms may underestimate their initial R&D budgets to boost their marginal effort and obtain a higher tax credit (Hall and Van Reenen 2000). Similar practices may emerge from the creativity tax credits that were recently implemented in the UK, for example.

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The usual definition of R&D sounds flexible enough to be compatible with different views of creativity, including artistic activity. However, the OECD manual restricted the scope of measured creative activities in two drastic ways: first, in R&D activities, uncertainty must be a “scientific and/or technological” uncertainty (OECD 2002, p. 34), thus excluding artistic and non-scientific techniques, such as “traditional knowledge” (OECD 2010). Second, R&D activity is considered to occur when there is a utility. Three different categorizations linking R&D activities to improved industrial products or processes can be found: development activities (as well as applied research) must “be directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed related products or processes” (OECD 2002, p. 30). R&D surveys must classify R&D budgets according to the different lines of businesses targeted (see for the US, NSF 2014), whereas a distinction between process R&D and product R&D can also be found (see Bogers and Lhuillery 2011 on Swiss data). However, the delineation by firms between fundamental research activities, applied research and experimental development activities is unstable and unexplainable, which renders it difficult to link R&D to improved firm performance (see Czarnitzki and Thorwarth 2012). The attribution of R&D budgets to different lines of business increases the size of R&D questionnaires (cf. the US questionnaire, NSF 2014) and induces a severe downward bias of the declared variety of R&D activities. Furthermore, the business line categorization is irrelevant for new key technology fields and industries, such as software, biotech, nanotech, environmental protection, new materials, and social sciences and humanities and other classifications based on scientific and technology fields in which socio-economic objectives were implemented (see OECD 2002, pp. 85–88; NSF 2014). These measures are maintained in surveys despite their limited quality and their limited use by scholars or policy makers. (b) R&D accumulation and organization Computing the volume and accumulation of R&D is important for approximating firms’ real R&D efforts and capabilities (Griliches 1979). However, these measures depend heavily on R&D price indices and depreciation rates. Inflation can indeed be specific to R&D inputs, for example, a shortage in the local supply of skilled researchers. One method for overcoming this lingering problem is neglecting individual effects and considering that different firms in an industry face the same inflation rates. Thus, the R&D price index used is the standard gross domestic product (GDP) or a set of more detailed price indices applied to different R&D components, such as wages, materials or capital (NSF 1972; Dougherty et al. 2007; OECD 2002, annex 9). R&D depreciation rates are also notoriously difficult to calculate, particularly because the rate is oftentimes endogenously determined by the firm, its competitors or universities (Griliches 1979). Even when the shelf life of an invention can be observed – either through records kept on the maintenance period for patents (Pakes and Shankerman 1984) or through the existence of a market for technological knowledge (Arora et al. 2004) – the actual rate of knowledge depreciation remains largely unknown. A recent UK R&D survey conducted in 2011 included a question on R&D service lives and showed that R&D depreciation rates are smaller for high-tech industries and for fundamental research activities (Ker 2013).

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The amortization data in financial statements are easier to observe. In the new EU accounting frameworks, R&D expenses can be considered an investment for the D-portion when the “technical and commercial feasibility of the asset for sale or use have been established” (in the IAS38 standard on intangible assets). The declared values of capitalized R&D provide an interesting measure of industrial R&D capabilities. The disadvantage of using this source of information is that the declared values are biased because firms can strategically increase capitalization to raise their financial performance (Prencipe et al. 2008). A strategy can aim, for example, to inflate R&D transactions because R, and not only D, can then be capitalized. A second strategy is to play with the frequent changes in accountancy norms (Clem et al. 2004). The capitalization of creative activities is thus a critical task that may be even more complex for artistic activities. What is the depreciation rate of artistic capital paid by creative firms? At odds with R&D activities, accountants consider that the depreciation rate for artistic goods is null, partially reflecting copyright protection terms, which span for several decades. Few systematic efforts have been made to measure how R&D is accumulated in organizations. The first attempts to measure R&D addressed allocation problems, with R&D measured at the plant level (Klette 1996), project level (Henderson and Cockburn 1996), divisional level (Argyres and Silverman 2004) or business group level (Arora et al. 2014a). The identification of R&D allocation is complex for multinational enterprises (MNEs) – which account for approximately 80 percent of industrial R&D activity worldwide – because standard R&D surveys usually adopt a national point of view (OECD 2002). The location of R&D facilities is usually badly approximated by the addresses of inventors or applicants, as shown by Arora et al. (2014b). Some national R&D surveys attempted to measure R&D activities conducted by worldwide affiliates (e.g., NSF 2014; OFS 2014), whereas some international organizations launched specific surveys (UNCTAD 2005; JRC-IPTS 2014) to fill the gap. The direct measure of cross-country R&D is a laudable solution but can be problematic; the aggregation of international R&D values depends on the scope of consolidation and currency rates (NSF 2014; OECD 2002, annex 9). R&D activities conducted by a national subsidiary of an MNE may be consolidated only if the MNE owns at least 50 percent of equities and the exchange rates are applied at the end of the accounting period (in EU IAS or US GAAP accounting standards). The disclosed R&D levels also rely on accountancy optimization. The R&D levels declared by MNEs are often highly dependent on the different national tax systems (Heckemeyer et al. 2014) and related intra-group transfer pricing strategies (Barry 2005). The external organization of R&D activities has been studied in greater depth. The measurement of R&D activities conducted by firms in collaboration with other firms or universities was first captured by the financial flows resulting from R&D links or R&D public funding (OECD 2002). These links can be used to approximate the level of R&D transactions performed in the markets for knowledge (Arora et al. 2001). However, the resulting information has rarely been used by scholars. First, external R&D expenditures are not reported for non-R&D-performing firms in R&D surveys, despite their importance (Cassiman and Veugelers 2006; Rammer et al. 2009). Second, many R&D collaborations between firms do not induce financial flows. Finally, the external R&D expenditures

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measured do not identify the types of goods and services bought (licenses, R&D services, partnerships, etc.) or the types of industrial partners (e.g., suppliers). For example, Community Innovation Surveys (CISs) addressed this last deficiency by introducing qualitative questions on the type of innovation partners chosen, covering formal versus informal links, whereas the complementary questions on the sources of innovation broadened the measure to incoming spillovers, including scientific and technological knowledge and possibly other influential types of knowledge, such as artistic knowledge (Belderbos et al. 2004). The CIS’s qualitative measures of external innovation cooperation and knowledge sourcing were so successful that they supplanted the historical and public data on R&D partners and partnerships (see Hagedoorn et al. 2000 for an overview of these databases). Still, the standard innovation surveys are not perfect with respect to external arrangements because they restrict the means deployed for knowledge sourcing to the role of fairs and scientific and patent publications (Eurostat 2012), despite the fact that more interesting and comprehensive measures can be employed (see Arora et al. 2014b). Non-R&D Inputs (a) Non-R&D costs and links Since the first innovation studies were conducted, efforts have been made to include non-R&D inputs that contribute to technological innovation (Rothwell et al. 1974; Mansfield and Rapoport 1975). Recent innovation surveys confirmed the importance of non-R&D inputs, with R&D representing only one-third of innovation costs (Brouwer and Kleinknecht 1997; Sterlacchini 1998) and providing measures of different non-R&D costs (Santamaría et al. 2009), such as machinery and equipment (Pellegrino et al. 2011); licenses, software or external know-how (Czarnitzki and Kraft 2005); specific training (Evangelista and Savona 2003); design (Marsili and Salter 2006); and marketing costs (Lhuillery 2014). Innovation costs are cumbersome or strategic for firms, thus rendering firms unlikely to disclose them or to do so only for the R&D component. Consequently, many countries have put an end to quantifying non-R&D-related expenditures, requesting only qualitative information. The tool used to measure non-R&D-related expenditures thus became similar to qualitative questionnaires that use a functional view and measure the importance of marketing, manufacturing and managerial functions at the team (Bunderson and Sutcliffe 2002) or firm level (Bogers and Lhuillery 2011) using a Likert scale. Teece (1986) underlined that distribution channels, services or complementary technologies are critical non-R&D assets enabling firms to exploit innovation. However, in general, it is difficult to know to what extent these assets are actually deployed for innovation purposes. Åstebro and Serrano (2015) used phone calls to verify the role of declared complementary assets and to overcome this issue. Despite its outstanding impact on scholars and policy makers, the systematic measurement of complementary assets has still not been achieved or even proposed. Only scattered econometric results can be found on the level and role of these non-R&D assets (see Cohen 2010 for a survey). The burgeoning literature on servitization and its difficulties regarding measuring and categorizing product-related services is a good introduction to the issue (see Eggert et al. 2011).

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Despite their frequency and importance (Colombo et al. 2006) and their availability in large data sets (Schilling 2009 for an overview), production and marketing alliances have also been overlooked in questionnaires focused on R&D alliances (see the 2002 Swiss innovation survey, KOF 2002). Recent studies of startups or small and medium-sized enterprises (SMEs) with low endowments in some innovation capabilities proposed to measure new non-R&D partners involved in innovation: consultants, law firms, accounting firms, talent search firms, and financial service firms, including venture capitalists (e.g., Zhang and Li 2010). A final interesting strand in the literature measures innovation networks, including non-R&D links (e.g., Powell et al. 2005). However, it is still difficult and costly to collect data on knowledge networks through questionnaires (Broekel and Boschma 2012). An additional problem with declarative measures is that the respondents are usually not aware of the indirect links they have, or they possess a biased representation of their innovation networks (see Lhuillery and Pfister 2011 and references therein). (b) Intangible assets and smart activities Some scholars choose a more general path, considering the measurement of intangible assets (e.g., Corrado et al. 2009; Marroccu et al. 2012). A major avenue of research has been the disentanglement of R&D from non-R&D intangibles in financial statements (Marrocu et al. 2012) and specific surveys (e.g., Montresor et al. 2014). In this literature, the level of non-R&D intangibles is assumed to be a decent approximation of the non-R&D knowledge involved in innovation. However, a substantial part of these non-R&D intangibles may be used for purposes other than knowledge production and can even hamper innovation (e.g., organizational capital, specific human capital or brands). Recent studies of management and economics delineating and measuring “creative” classes, industries or cities have attempted to more broadly measure the non-R&D activities likely to be performed by poets, novelists, artists, entertainers, actors, designers and architects (Florida 2005). At the firm level, the identification and quantification of the workers, firms and industries considered as non-creative is difficult (Rodgers 2015) and should require, similar to R&D activities, a measure of FTE employees working on creative tasks. It should be mentioned that some non-R&D creativity costs are already delineated and measured in the tax credit schemes for culturally creative activities (e.g., video games, film, fashion) that were recently implemented in several OECD countries (e.g., Canada, France, the UK), and applicant data can be potentially matched with R&D and innovation data. A less ambitious but workable solution was proposed by the 2010 CIS questionnaire, which introduced a set of items identifying the use of eight “creative skills” (Eurostat 2010; OECD 2013). An analogous effort to define and measure “talented” individuals and positions in organizations has been proposed (Collings and Mellahi 2009). The measurement of creativity at the individual level remains challenging because creativity can be tacit and difficult to observe. Psychologists tried for decades to measure individuals’ creativity, skills and creative orientation through self-reports and checklists (see Plucker and Makel 2010 for a survey). Perhaps more convincing for scholars in management and economics is the type of questionnaire used at the employee level to identify creative tasks (see Lorenz and Lundvall 2011 regarding the Fourth European Working Conditions Survey).

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(c) Knowledge management practices Knowledge management practices (KMPs) are a final means to identify creative production (see Cohendet and Simon, Chapter 3 this volume). IPR tools, business intelligence practices, concurrent engineering, computer-aided-design (CAD) methods, concept/knowledge (C/K) methods, crowd-sourcing practices, and design thinking are some of the KMPs used by firms that can be identified and quantified through questionnaires focused on organization or innovation. Some KMPs are more oriented toward the management of technological innovation processes, whereas other practices are more broadly dedicated to the early stages of creativity through the identification of different ideation methods (brainstorming, TRIZ or lateral thinking). Efforts were made to enlarge and standardize the measurement of KMPs (see OECD 2003). Some questions were even introduced in European innovation surveys (CIS3), enabling the identification of many innovating firms with no R&D but with KMPs and their positive roles in innovation success (see Kremp and Mairesse 2004; Cantner et al. 2011). However, these KMP questions were focused on knowledge sharing and knowledge integration practices. Some recent contributions have emphasized the importance of other KMPs, such as teaming or incentives (Amabile et al. 1996; Sauermann and Cohen 2010). The 2010 CIS questionnaire thus introduced a set of six KMPs: brainstorming, work teams, job rotation, training, financial incentives and non-financial incentives (Eurostat 2010). After the Yale survey (Levin et al. 1987), appropriation practices are the most surveyed KMPs in standard questionnaires (e.g., Eurostat 2012).

INNOVATION OUTPUTS Direct Measures: Innovation Survey Part of the challenge of measuring creative outputs relates to the difficulty of agreeing on a definition. In general, existing definitions focus on those creative outputs related to new final and intermediary products produced by firms, new production processes employed to produce products, new ways of organizing firm resources and new means of commercializing products. Joseph Schumpeter was the first to tackle all of these elements together in a systematic manner (Schumpeter 1939). The first large-scale attempts to directly measure innovation output can be traced to the 1980s, when a round of at least seven national innovation surveys was conducted (Arundel and Smith 2013; Crespi and Peirano 2007). These national initiatives paved the way for the first edition of the Oslo Manual in 1992 and the international effort to create a standardized innovation survey questionnaire (CIS). The Oslo–CIS template focused on the micro-perspective of the innovation process, capturing innovation activity and outputs mainly at the enterprise statistical unit level. The first two editions of the proposed guidelines for measuring innovation – the Oslo Manual – considered innovations to be new or significantly improved products or processes, which together were referred to as technological innovations (OECD 1997). At that time, the manual mentioned organizational innovations and other creative outputs – such as artistic designs – but recommended not measuring them unless they were related to technological innovations. Many specific surveys were launched on organizational

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innovation (see Greenan and Lorenz 2013). However, several innovation surveys already provide evidence of firms declaring design and marketing innovations. For instance, Lhuillery (2001) documents that 27 percent and 22 percent of firms surveyed in the French CIS2 reported design and marketing innovations, respectively. Beginning with the third edition (OECD 2005), the Oslo Manual broadened the innovation scope to include organizational and marketing innovations. According to this new definition, marketing innovations can be related to the creative output of firms – such as product design or branding – which arguably can involve new artistic traits but not new technical or technological traits (Stoneman 2010). The first rounds of the CIS-based survey excluded service sectors from the sample, but later rounds included them. These surveys were based on the Oslo Manual guidelines and measured innovation output in the service sectors the same way that they measured it in the manufacturing sector. In the discussion that follows, we refer to both the manufacturing and the service sectors. For a discussion on the pertinence of CISs in capturing innovation in the service sectors, see Drejer (2004). (a) Main direct innovation output indicators The main creative output indicator assessed by innovation surveys is qualitative in nature and captures whether the respondent firms have achieved a product, process, or organizational or marketing innovation during a given period, often the past three years. As mentioned, one clear advantage of this indicator is that it attempts to capture firms becoming innovators – or continuing to be – regardless of how large the innovative leap is, how far from the innovative frontier firms are, and their ability to disentangle the different types of innovation (Simonetti et al. 1995). This is an extremely relevant trait in the vast majority of innovation surveys, which has spurred hundreds of articles about different dimensions correlating with innovation at the firm level (Arundel and Smith 2013). However, this strategy has proven to be hard to scale up to national indicators – such as counts or shares of innovative firms by country – due to the limited insights obtained from their comparison (Arundel and Hollanders 2005). We often observe economies being compared using aggregated R&D indicators but rarely using innovation indicators (Hollanders and Janz 2013). A main problem is the critical lack of cardinality of the previous indicator. For instance, two firms innovating their production processes may achieve different productivity gains, but both are considered equally innovative according to such indicators. This situation demonstrates the limitations in capturing the degree of novelty of a given innovation (Duguet 2006). To overcome this limitation, three main alternatives exist: innovation counting, innovation novelty identification and innovation impact. A set of studies – predating CIS surveys – proposed to count the number of innovations achieved by firms (Pavitt et al. 1987; Acs and Audretsch 1988). Such a solution is limited because major and minor innovations carry the same weight in the count measure. Furthermore, innovations were still counted (by experts in Pavitt et al. 1987) beyond a subjective threshold. Alternatively, CISs and other similar innovation surveys cope with this latter limitation by distinguishing firms attaining disruptive innovations – that is, new to the world or market – from those achieving just new-to-the-firm innovations. Implicitly, such variations require respondents to have perfect knowledge of the state of the technology

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and either local or worldwide market structures and to be willing to disclose this knowledge. Unfortunately, the asymmetry of information – both among heterogeneous respondents and between surveyors and respondents – has made such variation of limited value. Furthermore, the obtained leader/laggard distinction does not cover the usual concepts of radical and incremental innovation. Finally, an imitator can consider itself a laggard or a non-innovator. A third and dominant innovation output indicator refers to an innovation’s impact on a firm’s economic performance. In particular, innovation surveys have requested the percentage of turnover related to product innovations, which is more quantitative in nature. Another main interest of this measure is that innovative sales also cover non-R&D investments and all of the complementary assets involved in innovation projects to achieve their success. In theory, this indicator can be easily transformed into a pecuniary form and, given its broad use in CIS-based surveys, also easily scaled-up to a macro level – for example, country, region or even industry – for comparison purposes. However, a problem for aggregation is the difficult distinction between the zero values relating to product innovations that are market failures or still in an early stage compared with those related to non-product innovators. Moreover, this indicator suffers partially from the same limitations discussed above when splitting innovation-related sales into those relating to new-to-the-world, new-to-the-market and new-to-the-firm product innovations. A final issue is the lack of similar inquiry regarding process innovation, given that the Swiss survey has requested that the percentage of costs be lowered by process innovation for the past 15 years (KOF 2013). (b) Indirect measures in innovation surveys Interestingly, innovation surveys have also collected information related to indirect measures of creative outputs, particularly regarding patents, utility models, trademarks, industrial designs and copyrights. However, innovation surveys have been typically confined to those outputs related to the product or process innovations of the firm. As such, innovation surveys as indirect measures of creative outputs are severely hampered. Not surprisingly, scholars have not used these indicators much as measures of creative output; instead, they have been primarily used as controls for the appropriation capabilities of innovation. Some exceptions are the use of trademarks as proxies for innovation activities (e.g., Mendonça et al. 2004). The most notable exception to this trend concerns the use of patent counts issued from the first waves of CIS-based and other innovation surveys. In the past, innovation surveys captured the number of patents that were related to the product or process innovations of a firm. An interesting variation of this indicator has been to ask for the percentage of patent-protected sales (Mairesse and Mohnen 2005). Scholars have used the patent count indicator as an indirect measure of technological innovation, particularly in industrialized economies. This idea is supported by Crepon et al. (1998), who found a near unit elasticity of patent counts with respect to R&D capital intensity. Mairesse and Mohnen (2005) tested the similarities between several technological innovation outputs against three patent indicators from innovation surveys, finding limited differences in terms of R&D intensity and firm-size elasticities. However, the patent count indicator has lost ground lately, disappearing from most recent CIS-based surveys. Several limitations at least partially explain the declining trend

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in the use of patent counts in innovation surveys. First, as will be discussed in further detail later, patents have limits as a measure of innovation. Second, patenting is an extremely skewed phenomenon to measure, even more so than performing R&D or innovating. This measurement difficulty is worse in developing economies, in which the patent system is rarely used and much of the innovation by firms concerns the acquisition and use of preexisting technologies, which by definition are not patentable. Finally, the quantitative advantage of patent counts is in many cases deceptive because patent unit record data has shown that patents are often misrepresented in innovation surveys (Raffo and Lhuillery 2008). This misrepresentation can likely be explained, at least partially, by the following: (i) the fact that patenting activities are often centralized at the firm’s headquarters, rendering respondents at remote units unaware of the precise amount of patenting activity; (ii) patents have many dates – priority filing, subsequent filing, grant, expiration and so on – which make it confusing to non-expert respondents to state how many patents were filed (or are active) in a certain period; and (iii) the same patent can be filed in many different jurisdictions, and innovation surveys have done little to account for patent applications corresponding to the same technologies, that is, patent families. Martínez (2011) showed that approximately two-thirds of patent applications filed in the US, France and Germany are also filed elsewhere. Moreover, Martínez found that approximately one-quarter of patent families have complex structures that can lead to bias in patent counting. Indirect Measures of Innovation Outputs (a) Intellectual property (IP) unit record data Basberg (1987), Pavitt (1985) and Griliches (1990) shared the conclusion that patent statistics are a relatively good proxy for measuring innovation (see particularly Pénin, Chapter 12, this volume), but are not without limitations. Intellectual property (IP) unit record data documents contain broader and more useful information on creative activities and output than patent statistics. Arguably, patents, trademarks, industrial designs, copyrights and any other form of IP reflect, to some extent, the inventive, innovative, artistic and other creative activity occurring within a firm. Contrary to data issued from innovation surveys, IP data were not originally conceived to be used in innovation or other statistics. Each form of IP is simply a governmentsanctioned exclusive right, granted for a set amount of time, which typically leaves a paper trail. For instance, to obtain patent protection, an applicant must disclose information about the invention to the public, and the invention must meet the patentability criteria of novelty, non-obviousness and industrial application. The requirement of disclosure and the examination of patentability led to the creation of patent documents databases, which eventually allowed scholars to compute patent statistics. IP statistics are thus a by-product of a legal system and are therefore subject to legal and institutional idiosyncrasies across countries and, many times, among sectors. By all accounts, patent bibliographical information is the most sourced IP unit record data. Patent counts, in particular, have been found to approximate technological innovative outputs fairly well at the national (e.g., Basberg 1987; Kortum and Lerner 1998), regional (e.g., Acs et al. 2002) and micro levels (e.g., Griliches and Lichtenberg 1984). In principle, patent counts can quantify both process and product innovation, but in practice, making such a distinction can be difficult. Moreover, at least in some jurisdictions, such as the US,

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patents can cover non-technological innovations, such as business or financial methods and software (Allison and Tiller 2003; Lerner 2008). However, service sector firms are less prone to using patents (Edler et al. 2003). Many jurisdictions – but not the US – allow for utility model protection, which is an IP instrument similar to patents but typically with a lower inventive threshold and shorter exclusive right protection. Scholars have found – particularly in the case of Asian economies – that utility models better reflect innovative activity than patents in the early stages of industrial development (Kim et al. 2012). In following patents, it can be observed that trademark unit record data are likely the second most frequently sourced IP unit record data. Trademark counts approximate marketing innovation as they are closely related to brand and marketing strategies (Millot 2009). Additionally, trademark counts can approximate product innovations (Mendonça et al. 2004). In this respect, some argue that trademarks are a better indicator of product launches than patents because of their lower level of selectivity and because they are closer to market entry (Hipp and Grupp 2005). In some cases, trademarks can also point to other creative and more artistic outputs, such as sounds (jingles), text (slogans) or shapes (packaging) (Stoneman 2010). Historically, scholars and policy makers have made less use of unit record data than other forms of IP despite their valuable information. Industrial designs can, for example, indicate product and marketing innovations (Walsh 1996). However, most industrialized economies observe more patent and trademark applications than industrial design applications, which may explain the lower amount of interest in using such an indicator (WIPO 2014). In turn, copyrights can approximate several different creative outputs within a firm. For instance, some firms have sought copyright protection for their designs or package inserts. Nevertheless, the fact that copyright unit record data are reported on a voluntary basis has made them of limited value, particularly to monitoring creative outputs within a firm. (b) Advantages and limitations of IPR sources The richness of IP unit record data allows us to go beyond the simple IP counts provided in innovation surveys. First, there is the possibility of constructing IP stock measures for firms over time, which can provide metrics for the accumulation and path dependence of knowledge and creative capabilities, offering a more accurate indicator of technological and artistic capabilities (Park and Park 2006). Firms holding IP rights must actively maintain them during their limited (e.g., patents) or unlimited (e.g., trademarks) time span. For instance, the decay of the number of patents in a patent portfolio can be an interesting indication of the depreciation of R&D assets (e.g., Bessen 2008). Firms may well seek protection for the same IP in different countries, reflecting the geographic distribution of their market of interest and existing competition. In addition, firms may hold the same IP right in different countries but not for the same duration, indicating when the marginal benefit from holding the IP no longer covers the marginal cost of holding it in each country. In spite of this, we have limited information regarding IP families beyond those that include patents and utility models. Second, IP examination – particularly in the case of patents – imposes a threshold on innovation novelty. On the one hand, thresholds avoid the comparability problems of innovations being only new to the firm. On the other hand, thresholds prevent the analysis of the subset of laggard innovators if they do not file for IP. In this respect, patent examination

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makes counts of granted patents a more reliable source than counts based on patent applications (Guellec and van Pottelsberghe de la Potterie 2000), even if national differences in the required inventive step exist (Ordover 1991). However, many IP collections do not trace refusals and withdrawals, which, in addition to the important and growing examination backlog in many countries, advocates instead for the use of IP filing information. In the case of patents, examination is not the only approach for handling the unobserved value of inventions. One typical way is to make use of forward citation information to measure the value of the invention (Harhoff et al. 2003). Another approach is to consider the information about the patent family, such as international size (Harhoff et al. 2003) or simultaneous filing at the US Patent and Trademark Office (USPTO), European Patent Office (EPO) and Japan Patent Office (JPO) (Dernis and Khan 2004). Renewal, fast-track search requests, accelerated examination requests, filing routes, oppositions, litigations or the number of claims can also be considered signals correlated with patent values (van Zeebroeck and van Pottelsberghe de la Potterie 2011; Lanjouw and Schankerman 2004). Third, the use of IP unit record data allows empirical studies to dissociate the categorization of innovations from that of innovators, which is particularly useful when comparing the innovator’s industry with the innovation’s technological field or type. For instance, a firm in a given industry may hold in its portfolio many patents or utility models that were classified in several different technological fields according to national or international classifications of technologies – such as the International Patent Classification (IPC) or the Cooperative Patent Classification (CPC). Similarly, one firm can hold industrial designs classified as different products – using the Locarno classification – or trademarks from different industries – using the Nice classification. The Nice classification even allows for the broad distinction between product and services trademarks, which is a valuable trait of the trademark data (Hipp and Grupp 2005). Fourth, in the case of patents, citation data can be used to track knowledge flows or spillovers. Such data have been used to localize in space knowledge flows and, particularly, the spillovers of public research work (Jaffe et al. 1993). There is evidence that not all patent citations are appropriate indicators of knowledge flows because many patent citations are not introduced by the inventors (Alcácer et al. 2009) and reflect duplicative effort (Baruffaldi and Raffo 2013). Patents better capture invention than innovation (Griliches et al. 1988). Inventions tend to be the result of R&D activities, but not all inventions are patented, either because the inventions do not meet the criteria of patentability or because the inventor prefers other legal means of protecting his or her IP or other appropriation tools that can be less costly and more efficient (Giuri et al. 2007; Cohen et al. 2000). The same remarks hold for other types of new knowledge, including artistic creations. Firms often do not seek protection through trademarks, industrial designs or copyrights for many of their artistic creations. However, the work of many artists and firms who do seek protection for their creations is never used commercially.

CONCLUSIONS The present chapter addressed how measurement is conducted by scholars and statisticians. We observed that either inputs or outputs have been expanded over time to escape from

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restricted scientific and technological considerations. By highlighting the use and the problems and limitations, we provided some insights into the future development of indicators of creativity that are, despite some efforts, still far from being diffused and standardized in international surveys. Multiple-level questionnaires, big data and complementarity analysis should consolidate the current improvements. The present overview paid limited attention to the measurement of critical social conventions and institutional environments. Tools can measure the declared role of public research organizations or the use of IPR. However, it remains difficult to measure these elements completely. A solution used by innovation surveys is to measure the obstacles likely to identify the different boundaries that surround firms. The obstacles are usually biased because they are identified merely when innovation and creativity are experienced (D’Este et al. 2012). A further problem is that the creative environment must be considered not only at the firm level but also at the personnel level. A promising solution for capturing the context of where individual creativity takes place would be to issue questionnaires to both employers and employees (Greenan and Lorenz 2013). Technology has changed and will further change the type and the way data are collected. Online surveys, e-administrative records, internet data and social media data provide new opportunities (Sauermann and Roach 2013; Geuna et al. 2015), even if confidentiality remains a serious problem. We have discussed how the digital collection of IP unit record data has increased the scope of possible creative outputs analysis. This has been the case for patent data in the past two decades, and we are now observing a new and rising trend toward recently available bulk trademark unit record data (Graham et al. 2013). It is not hard to foresee that other equivalent unit record data in digital form – for example, industrial designs or copyrights – will follow a similar a trend. The measurement of artistic activities should thus be eased. Technology also changes creativity and innovation processes. The scanty use of the distinction between labor costs, material costs and capital costs available from standard R&D surveys reminds us of the lack of awareness regarding the role of instruments and materials in R&D and creative activities (see Stephan 2012; Lane et al. 2015). This is an overlooked avenue for R&D activities as well as for artistic activities in firms that are, to a certain extent, computerized. The measurement of innovation inputs and outputs has made important progress over the past 20 years. A last challenge will be to articulate these various measures and to measure the complementarity or substitutability among innovation inputs for the production of new knowledge (innovativeness) or the complementarity or substitutability of innovation outputs for firm performance (productivity). The multiplicity of the inputs and outputs now available is a critical problem. One solution is to use multiple equation models to examine the decisions regarding innovation inputs or innovation outputs in which the positive correlation among residuals is a test for complementarity (Arora and Gambardella 1990; Belderbos et al. 2004). An alternative solution is to test for complementarity among innovation inputs, comparing their sole and joint impacts on innovation outputs (e.g., Cassiman and Veugelers 2006) or the synergies between technological and non-technological innovation outputs on performance (Doran 2012; Ballot et al. 2015). A difficulty with the last supermodularity tests is that the number of explanatory sets that can be introduced into econometric equations rises exponentially (Carree et al. 2011). Thus, other methods should be kept in mind (see Ichniowski et al. 1997; Battisti and Stoneman 2010).

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Acknowledgements We thank Yves Habran, Christian Le Bas, Gunter Schumacher and Stephanie ThierryDubuisson for their comments. The usual disclaimers apply.

REFERENCES Acs, Z. J., Anselin, L. and Varga, A. (2002) Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7), 1069–1085. Acs, Z. J. and Audretsch, D. B. (1988) Innovation in large and small firms: An empirical analysis. American Economic Review, 78(4), 678–690. Alcácer, J., Gittelman, M. and Sampat, B. (2009) Applicant and examiner citations in U.S. patents: An overview and analysis. Research Policy, 38(2), 415–427. Allison, J. R. and Tiller, E. H. (2003) The business method patent myth. Berkeley Technology Law Journal, 18, 987–1084. Amabile, T. M. (1983) The Social Psychology of Creativity. New York: Springer-Verlag. Amabile, T. M., Conti, R., Coon, H., Lazenby, J. and Herron, M. (1996) Assessing the work environment for creativity. Academy of Management Journal, 39(5), 1154–1184. Argyres, N. S. and Silverman, B. S. (2004) R&D, organization structure, and the development of corporate technological knowledge. Strategic Management Journal, 25(8-9), 929–958. Arora, A., Belenzon, S. and Rios, L. A. (2014b) Make, buy, organize: The interplay between research, external knowledge, and firm structure. Strategic Management Journal, 35(3), 317–337. Arora, A., Cohen, W. M. and Walsh, J. P. (2014a) The acquisition and commercialization of invention in American manufacturing: Incidence and impact. National Bureau of Economic Research, Working Paper No. 20264. Arora, A., Fosfuri, A. and Gambardella, A. (2001) Markets for Technology: The Economics of Innovation and Corporate Strategy. Cambridge, MA: MIT Press. Arora, A., Fosfuri, A. and Gambardella, A. (2004) Markets for Technology: The Economics of Innovation and Corporate Strategy. Cambridge, MA: MIT Press. Arora, A. and Gambardella, A. (1990) Complementarity and external linkages: The strategies of the large firms in biotechnology. Journal of Industrial Economics, 38, 361–379. Arundel, A. and Hollanders, H. (2005) EXIS: An exploratory approach to innovation scoreboards. European Trend Chart on Innovation. Brussels: European Commission. Arundel, A. and Smith, K. (2013) History of the Community Innovation Survey. In F. Gault (ed.), Handbook of Innovation Indicators and Measurement. Cheltenham, UK and Northampton, MA, USA: Edward Elgar, pp. 60–87. Åstebro, T. and Serrano, C. J. (2015) Business partners: Complementary assets, financing, and invention commercialization. Journal of Economics and Management Strategy, 24(2), 228–252. Ballot, G., Fakhfakh, F., Galia, F. and Salter, A. (2015) The fateful triangle: Complementarities in performance between product, process and organizational innovation in France and the UK. Research Policy, 44(1), 217–232. Baruffaldi, S. H. and Raffo, J. (2013) Knowledge spillovers and the geography of duplicated inventions: An analysis from patent citations. Academy of Management Proceedings, 2013(1), 12227. Barry, F. (2005) FDI, transfer pricing and the measurement of R&D intensity. Research Policy, 34(5), 673–681. Basberg, B. L. (1987) Patents and the measurement of technological change: A survey of the literature. Research Policy, 16(2–4), 131–141. Battisti, G. and Stoneman, P. (2010) How innovative are UK firms? Evidence from the Fourth UK Community Innovation Survey on Synergies between Technological and Organizational Innovations. British Journal of Management, 21(1), 187–206. Belderbos, R., Carree, M. and Lokshin, B. (2004) Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492. Bessen, J. (2008) The value of U.S. patents by owner and patent characteristics. Research Policy, 37(5), 932–945. Bogers, M. and Lhuillery, S. (2011) A functional perspective on learning and innovation: Investigating the organization of absorptive capacity. Industry and Innovation, 18(6), 581–610. Bönte, W. and Keilbach, M. (2005) Concubinage or marriage? Informal and formal cooperations for innovation. International Journal of Industrial Organization, 23(3), 279–302. Bosworth, D.L., Wilson, R.A. and Young, A.J. (1993) Research and development statistics. In Research and Development. Dordrecht: Springer, pp. 1–217.

114

The Elgar companion to innovation and knowledge creation

Broekel, T. and Boschma, R. (2012) Knowledge networks in the Dutch aviation industry: The proximity paradox. Journal of Economic Geography, 12(2), 409–433. Brouwer, E. and Kleinknecht, A. (1997) Measuring the unmeasurable: A country’s non-R&D expenditure on product and service innovation. Research Policy, 25(8), 1235–1242. Bunderson, J. S. and Sutcliffe, K. M. (2002) Comparing alternative conceptualizations of functional diversity in management teams: Process and performance effects. Academy of Management Journal, 45(5), 875–893. Cantner, U., Joel, K. and Schmidt, T. (2011) The effects of knowledge management on innovative success – an empirical analysis of German firms. Research Policy, 40(10), 1453–1462. Carree, M., Lokshin, B. and Belderbos, R. (2011) A note on testing for complementarity and substitutability in the case of multiple practices. Journal of Productivity Analysis, 35(3), 263–269. Cassiman, B. and Veugelers, R. (2006) In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52(1), 68–82. Chen, E., Gavious, I. and Lev, B. (2015) The positive externalities of IFRS R&D rule: Enhanced voluntary disclosure. Working Paper, March, 61 p. Clem, A., Cowan, A. R. and Jeffrey, C. (2004) Market reaction to proposed changes in accounting for purchased research and development in R&D-intensive industries. Journal of Accounting, Auditing and Finance, 19(4): 405–428. Cohen, W. M. (2010) Fifty years of empirical studies of innovative activity and performance. In B. H. Hall and N. Rosenberg (eds), Handbook of the Economics of Innovation. Vol. 1. Economics of Innovation. Amsterdam: North-Holland, pp. 129–213. Cohen, W. M., Nelson, R. R. and Walsh, J. P. (2000) Protecting their intellectual assets: Appropriability conditions and why US manufacturing firms patent (or not). National Bureau of Economic Research, Working Paper No. 7552. Cohendet, P. and Simon, L. (2017) Concepts and models of innovation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 33–55. Collings, D.G. and Mellahi, K. (2009) Strategic talent management: A review and research agenda. Human Resource Management Review, 19(4), 304–313. Colombo, M. G., Grilli, L. and Piva, E. (2006) In search of complementary assets: The determinants of alliance formation of high-tech start-ups. Research Policy, 35(8), 1166–1199. Corrado, C., Hulten, C. and Sichel, D. (2009) Intangible capital and US economic growth. Review of Income and Wealth, 55(3), 661–685. Crepon, B., Duguet, E. and Mairesse, J. (1998) Research, innovation and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology, 7(2), 115–158. Crespi, G. and Peirano, F. (2007) Measuring innovation in Latin America: What we did, where we are and what we want to do. In United Nations University and Maastricht Social and Economic Research and Training Institute on Innovation and Technology (UNU-MERIT) Conference on Micro Evidence on Innovation in Developing Countries. Vol. 31, May. Czarnitzki, D. and Kraft, K. (2005) License expenditures of incumbents and potential entrants: An empirical analysis of firm behavior. ZEW – Centre for European Economic Research. Discussion Paper No. 05-035. Czarnitzki, D. and Thorwarth, S. (2012) Productivity effects of basic research in low-tech and high-tech industries. Research Policy, 41(9), 1555–1564. D’Este, P., Iammarino, S., Savona, M. and von Tunzelmann, N. (2012) What hampers innovation? Revealed barriers versus deterring barriers. Research Policy, 41(2), 482–488. Dernis, H. and Khan, M. (2004) Triadic patent families methodology. OECD Science, Technology and Industry Working Papers, 2004/02. Doran, J. (2012) Are differing forms of innovation complements or substitutes? European Journal of Innovation Management, 15(3), 351–371. Dougherty, S. M., Inklaar, R., McGuckin, R. H. and Van Ark, B. (2007) International comparisons of R&D expenditure: Does an R&D PPP make a difference? In E. R. Berndt and C. R. Hulten (eds), Studies in Income and Wealth. Vol. 67. Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches. Chicago: University of Chicago Press, pp. 291–322. Drejer, I. (2004) Identifying innovation in surveys of services: A Schumpeterian perspective. Research Policy, 33(3), 551–562. Duguet, E. (2006) Innovation height, spillovers and TFP growth at the firm level: Evidence from French manufacturing. Economics of Innovation and New Technology, 15(4–5), 415–442. Edler, J., Schmoch, U., Andersen, B., Howells, J., Miles, I., Roberts, J. and Evangelista, R. (2003) Patents in the Service Industries. Final Report, EC Contract No. ERBHPV2-CT-1999-06). Luxembourg: ISI Fraunhofer; PREST, CRIC; Hamburg-Harburg, TU. Eggert, A., Hogreve, J., Ulaga, W. and Muenkhoff, E. (2011) Industrial services, product innovations, and firm profitability: A multiple-group latent growth curve analysis. Industrial Marketing Management, 40(2), 661–670.

Measurement of innovation

115

Eurostat (2010) 2010 Community Innovation Survey, Questionnaire. Eurostat, Luxembourg. http://ec.europa. eu/eurostat/web/microdata/community-innovation-survey. Accessed May 30, 2017. Eurostat (2012) 2012 Community Innovation Survey, Questionnaire, Eurostat, Luxembourg. http://ec.europa. eu/eurostat/web/microdata/community-innovation-survey. Accessed May 30, 2017. Evangelista, R. and Savona, M. (2003) Innovation, employment and skills in services: Firm and sectoral evidence. Structural Change and Economic Dynamics, 14(4), 449–474. Florida, R. (2005) Cities and the Creative Class. New York: Routledge. Gault, F., and von Hippel, E. A. (2009) The prevalence of user innovation and free innovation transfers: Implications for statistical indicators and innovation policy. MIT Sloan School, Working Paper 4722-09. Geuna, A., Kataishi, R., Toselli, M., Guzmán, E., Lawson, C., Fernandez-Zubieta, A. and Barros, B. (2015) SiSOB data extraction and codification: A tool to analyze scientific careers. Research Policy, 44(9), 1645–1658. Giuri, P., Mariani, M., Brusoni, S., Crespi, G., Francoz, D., Gambardella, A. and Verspagen, B. (2007) Inventors and invention processes in Europe: Results from the PatVal-EU survey. Research Policy, 36(8), 1107–1127. Godin, B. (2004) Measurement and Statistics on Science and Technology: 1920 to the Present. London: Routledge. Godin, B. (2009) The Making of Science, Technology and Innovation Policy: Conceptual Frameworks as Narratives, 1945–2005. Montreal: Centre – Urbanisation Culture Société de l’Institut national de la recherche scientifique. Godin, B. (2012) “Innovation studies”: The invention of a specialty. Minerva, 50(4), 397–421. Graham, S. J., Hancock, G., Marco, A. C. and Myers, A. F. (2013) The USPTO trademark case files dataset: Descriptions, lessons, and insights. Journal of Economics and Management Strategy, 22(4), 669–705. Greenan, N. and Lorenz, E. (2013) Developing harmonized measures of the dynamics of organizations and work. In F. Gault (ed.), Handbook of Innovation Indicators and Measurement. Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 247–278. Griliches, Z. (1979) Issues in assessing the contribution of research and development to productivity growth. Bell Journal of Economics, 10(2), 92–116. Griliches, Z. (1990) Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707. Griliches, Z. and Lichtenberg, F. (1984) R&D and productivity growth at the industry level: Is there still a relationship? In Z. Griliches (ed.), R & D, Patents, and Productivity. Chicago: University of Chicago Press, pp. 465–502. Griliches, Z., Pakes, A. and Hall, B. H. (1988) The value of patents as indicators of inventive activity. National Bureau of Economic Research, Working Paper No. 2083. Guellec, D. and van Pottelsberghe de la Potterie, B. (2000) Applications, grants and the value of patent. Economics Letters, 69(1), 109–114. Hagedoorn, J., Link, A. N. and Vonortas, N. S. (2000) Research partnerships. Research Policy, 29(4), 567–586. Hall, B. and Van Reenen, J. (2000) How effective are fiscal incentives for R&D? A review of the evidence. Research Policy, 29(4–5), 449–469. Harhoff, D., Scherer, F. M. and Vopel, K. (2003) Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343–1363. Heckemeyer, J., Richter, K. and Spengel, C. (2014) Tax planning of R&D intensive multinationals. ZEW-Centre for European Economic Research Discussion Paper, No. 14-114. Henderson, R. and Cockburn, I. (1996) Scale, scope, and spillovers: The determinants of research productivity in drug discovery. Rand Journal of Economics, 27(1), 32–59. Hipp, C. and Grupp, H. (2005) Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies. Research Policy, 34(4), 517–535. HMRC (2014) CIRD81900 – R&D tax relief: conditions to be satisfied: BIS Guidelines (formerly DTI Guidelines) (2004), https://www.gov.uk/hmrc-internal-manuals/corporate-intangibles-research-and-develop ment-manual/cird81900. Accessed May 30, 2017. Hollanders, H. and Janz, N. (2013) Scoreboards and indicators reports. In F. Gault (ed.), Handbook of Innovation Indicators and Measurement. Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 279–297. Hunter, L., Webster, E. and Wyatt, A. (2012) Accounting for expenditure on intangibles. Abacus, 48(1), 104–145. Ichniowski, C., Shaw, K. and Prennushi, G. (1997), The effects of human resource management practices on productivity: A study of steel finishing lines. American Economic Review, 87(3), 291–313. Jaffe, A. B., Trajtenberg, M. and Henderson, R. (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598. JRC-IPTS (2014) The 2014 EU Survey on Industrial R&D Investment Trends. Sevilla: European Commission, Joint Research Centre Institute for Prospective Technological Studies Sevilla. Ker, D. (2013) Service Lives of R&D Assets: Questionnaire Approach. London: Office for National Statistics, 22 March 2013. Kim, Y. K., Lee, K., Park, W. G. and Choo, K. (2012) Appropriate intellectual property protection and economic growth in countries at different levels of development. Research Policy, 41(2), 358–375.

116

The Elgar companion to innovation and knowledge creation

Klette, T. J. (1996) R&D, scope economies, and plant performance. RAND Journal of Economics, 27(3), 502–522. Kleinknecht, A. (1989) Firm size and innovation. Small Business Economics, 1(3), 215–222. Kleinknecht, A. and Reijnen, J. O. (1991) More evidence on the undercounting of small firm R&D. Research Policy, 20(6), 579–587. KOF (2002) 2002 Innovation Survey Questionnaire, https://www.kof.ethz.ch/en/surveys/structural-surveys/kofinnovation-survey/innovation-survey-2002.html. Accessed May 30, 2017. KOF (2013) 2013 Innovation Survey Questionnaire, http://www.kof.ethz.ch/de/umfragen/strukturumfragen/ innovationsumfrage/inno-2013/. Accessed May 30, 2017. Koh, P. S. and Reeb, D. M. (2015) Missing R&D. Journal of Accounting and Economics, 60(1), 73–94. Kortum, S. and Lerner, J. (1998) Stronger protection or technological revolution: What is behind the recent surge in patenting? Carnegie-Rochester Conference Series on Public Policy, 48, 247–304. Kremp, E. and Mairesse, J. (2004) Knowledge management, innovation, and productivity: A firm level exploration based on French manufacturing CIS3 data. National Bureau of Economic Research, Working Paper No. 10237. Lane, J. I., Owen-Smith, J., Rosen, R. F. and Weinberg, B. A. (2015) New linked data on research investments: Scientific workforce, productivity, and public value. Research Policy, 44(9), 1659–1671. Lanjouw, J. O. and Schankerman, M. (2004) Patent quality and research productivity: Measuring innovation with multiple indicators. Economic Journal, 114, 441–465. Lerner, J. (2008) The litigation of financial innovations. National Bureau of Economic Research, Working Paper No. 14324. Levin, R. C., Klevorick, A. K., Nelson, R. R., Winter, S. G., Gilbert, R. and Griliches, Z. (1987) Appropriating the returns from industrial research and development. Brookings Papers on Economic Activity, 1987(3), 783–831. Lhuillery, S. (2001) Managing surveys on knowledge: The French practices in the nineties. In J. de la Mothe and D. Foray (eds), Economics of Science, Technology and Innovation. Vol. 24. Knowledge Management in the Innovation Process. Boston, MA: Kluwer Academic Publishers, pp. 123–150. Lhuillery, S. (2014) Marketing and persistent innovation success. Economics of Innovation and New Technology, 23(5–6), 517–543. Lhuillery, S. and Pfister, E. (2011) Do firms know the scope of their R&D network? An empirical investigation of the determinants of network awareness on French survey data. Industry and Innovation, 18(1), 105–130. Link, A. N. and Wright, M. (2015) On the failure of R&D projects. IEEE Transactions on Engineering Management, 62(4), 442–448. Lorenz, E. and Lundvall, B. Å. (2011) Accounting for creativity in the European Union: A multi-level analysis of individual competence, labour market structure, and systems of education and training. Cambridge Journal of Economics, 35(2), 269–294. Mairesse, J. and Mohnen, P. (2005) The importance of R&D for innovation: A reassessment using French survey data. In A. N. Link and F. M. Scherer (eds), Essays in Honor of Edwin Mansfield. New York: Springer, pp. 129–143. Mansfield, E. and Rapoport, R. (1975) The costs of industrial product innovations. Management Science, 21(12), 1380–1386. Marrocu, E., Paci, R. and Pontis, M. (2012) Intangible capital and firms’ productivity. Industrial and Corporate Change, 21(2), 377–402. Marsili, O. and Salter, A. (2006) The dark matter of innovation: Design and innovative performance in Dutch manufacturing. Technology Analysis and Strategic Management, 18(5), 515–534. Martínez, C. (2011) Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63. Mendonça, S., Pereira, T. S. and Godinho, M. M. (2004) Trademarks as an indicator of innovation and industrial change. Research Policy, 33(9), 1385–1404. Millot, V. (2009) Trademarks as an indicator of product and marketing innovations. OECD Science, Technology and Industry Working Papers, 2009/06, http://www.oecd-ilibrary.org/science-and-technology/trademarks-asan-indicator-of-product-and-marketing-innovations_224428874418. Accessed May 30, 2017. Montresor, S., Perani, G. and Vezzani, A. (2014) How Do Companies “Perceive” Their Intangibles? New Statistical Evidence from the INNOBAROMETER 2013. European Commission. JRC Report, 26561. NSF (1972) A Price Index for Deflation of Academic R&D Expenditures, NSF 72-310. Washington, DC: National Science Foundation. NSF (2014) 2014 Business R&D and Innovation Survey, US Department of Commerce, https://www.nsf.gov/ statistics/srvyindustry/about/brdis/surveys/srvybrdis-2014-BRDI-1.pdf. Accessed May 30, 2017. OECD (1962) The Measurement of Scientific and Technical Activities: Proposed Standard Practice for Surveys of Research and Development, DAS/PD/62.47. Paris: OECD Publishing. OECD (1992) Proposed Guidelines for Collecting and Interpreting Technological Innovation Data: Oslo Manual. Paris: OECD Publishing. OECD (1997) Proposed Guidelines for Collecting and Interpreting Technological Innovation Data: Oslo Manual. Paris: OECD Publishing.

Measurement of innovation

117

OECD (2002) The Measurement of Scientific and Technical Activities: Proposed Standard Practice for Surveys of Research and Development. Paris: OECD Publishing. OECD (2003) Measuring Knowledge Management in the Business Sector: First Steps. Paris: OECD Publishing. OECD (2005) Proposed Guidelines for Collecting and Interpreting Innovation Data: Oslo Manual. Paris: OECD Publishing. OECD (2010) Measuring Innovation: A New Perspective. Paris: OECD Publishing. OECD (2013) Science, Technology and Industry Scoreboard 2013: Innovation for Growth. Paris: OECD Publishing. OECD (2015) Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. Paris: OECD Publishing. OFS (2014) La recherche et le développement dans l’économie privée en Suisse 2012. Neuchatel, Switzerland: Office for Federal Statistics. Ordover, J. A. (1991) A patent system for both diffusion and exclusion. Journal of Economic Perspectives, 5(1), 43–60. Pakes, A. and Schankerman, M. (1984) The rate of obsolescence of patents, research gestation lags, and the private rate of return to research resources. In Z. Griliches (ed.), R&D, Patents, and Productivity. Chicago: University of Chicago Press. pp. 73–88. Park, G. and Park, Y. (2006) On the measurement of patent stock as knowledge indicators. Technological Forecasting and Social Change, 73(7), 793–812. Pavitt, K. (1985) Patent statistics as indicators of innovative activities: Possibilities and problems. Scientometrics, 7(1), 77–99. Pavitt, K., Robson, M. and Townsend, J. (1987) The size distribution of innovating firms in the UK: 1945–1983. Journal of Industrial Economics, 35(3), 297–316. Pellegrino, G., Piva, M. C. and Vivarelli, M. (2011) How do young innovative companies innovate? In D. B. Audretsch, O. Falck, S. Heblich and A. Lederer (eds), Handbook of Research on Innovation and Entrepreneurship. Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 403–420. Pénin, J. (2017) Patents and open innovation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 182–194. Plucker, J. A. and Makel, M. C. (2010) Assessment of creativity. In J. C. Kaufman and R. J. Sternberg (eds), The Cambridge Handbook of Creativity. Cambridge: Cambridge University Press, pp. 48–73. Powell, W. W., White, D. R., Koput, K. W. and Owen-Smith, J. (2005) Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology, 110(4), 1132–1205. Prencipe, A., Markarian, G. and Pozza, L. (2008) Earnings management in family firms: Evidence from R&D cost capitalization in Italy. Family Business Review, 21(1), 71–88. Raffo, J. and Lhuillery, S. (2008) Matching procedures and harmonization methods. In Workshop on Harmonisation of Applicants’ names in Patent Data, OECD, 13 March, Paris La Defense. Rammer, C., Czarnitzki, D. and Spielkamp, A. (2009) Innovation success of non-R&D-performers: Substituting technology by management in SMEs. Small Business Economics, 33(1), 35–58. Rodgers, J. (2015) Jobs for creatives outside the creative industries: A study of creatives working in the Australian manufacturing industry. Creative Industries Journal, 8(1), 3–23. Rothwell, R., Freeman, C., Horlsey, A., Jervis, V. T. P., Robertson, A. B. and Townsend, J. (1974) SAPPHO updated – project SAPPHO phase II. Research Policy, 3(3), 258–291. Runco, M. A. (2014) Creativity: Theories and Themes: Research, Development, and Practice. London: Academic Press. Santamaría, L., Nieto, M. J. and Barge-Gil, A. (2009) Beyond formal R&D: Taking advantage of other sources of innovation in low-and medium-technology industries. Research Policy, 38(3), 507–517. Santarelli, E. and Sterlacchini, A. (1990) Innovation, formal vs. informal R&D, and firm size: Some evidence from Italian manufacturing firms. Small Business Economics, 2(3), 223–228. Sauermann, H. and Cohen, W. M. (2010) What makes them tick? Employee motives and firm innovation. Management Science, 56(12), 2134–2153. Sauermann, H. and Roach, M. (2013) Increasing web survey response rates in innovation research: An experimental study of static and dynamic contact design features. Research Policy, 42(1), 273–286. Schilling, M. A. (2009) Understanding the alliance data. Strategic Management Journal, 30(3), 233–260. Schumpeter, J. A. (1939) Business Cycles. Vol. 1. Cambridge: Cambridge University Press. Simonetti, R., Archibugi, D. and Evangelista, R. (1995) Product and process innovations: How are they defined? How are they quantified? Scientometrics, 32(1), 77–89. Stephan, P. E. (2012) How Economics Shapes Science. Vol. 1. Cambridge, MA: Harvard University Press. Sterlacchini, A. (1998) Inputs and outputs of innovative activities in Italian manufacturing. Economics of Innovation and New Technology, 7(4), 323–344.

118

The Elgar companion to innovation and knowledge creation

Stoneman, P. (2010) Soft Innovation: Economics, Product Aesthetics, and the Creative Industries. Oxford: Oxford University Press. Teece, D. J. (1986) Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305. Uhlmann, L. (1977) The innovation process in industrialized countries: Some empirical results. In K. A. Stroetmann (ed.), Innovation, Economic Change and Technology Policies. Stuttgart: Birkhäuser, pp. 21–38. UNCTAD (2005) Transnational Corporations and the Internationalization of R&D. World Investment Report. New York and Geneva: United Nations. UNESCO (1968) Provisional guide to the collection of science statistics. United Nations Educational Scientific and Cultural Organization, COM/MD/3, Paris, 31 December 1968. van Zeebroeck, N. and van Pottelsberghe de la Potterie, B. (2011) Filing strategies and patent value. Economics of Innovation and New Technology, 20(6), 539–561. Walsh, V. (1996) Design, innovation and the boundaries of the firm. Research Policy, 25(4), 509–529. WIPO (2014) World Intellectual Property Indicators. 2014 edition. Geneva: World Intellectual Property Organization. Zhang, Y. and Li, H. (2010) Innovation search of new ventures in a technology cluster: The role of ties with service intermediaries. Strategic Management Journal, 31(1), 88–109.

PART II INNOVATION AND INSTITUTIONS

8.

Institutional context and innovation Johannes Glückler and Harald Bathelt

INTRODUCTION One of the most interesting questions in the study of the economics and geography of innovation is how new knowledge is generated and how new technological, organizational, design and marketing solutions are introduced into markets. If the economy was an unbiased world, in which superior products, technologies and designs inevitably came to dominate the market, economic progress would be easy to explain: under conditions of perfect knowledge, people and organizations would align resources and creative efforts to invent better solutions than those offered before. These inventions would then immediately be adopted and absorbed by users to replace older and inferior ones. However, we know that this kind of functionalist perspective on technological development is naïve and has been proven to be wrong in many studies. It is wrong because perfect knowledge of the world does not exist and because people do not operate in an atomistic manner. Innovation is a deeply social and uncertain process that depends on interactions between different groups of actors that need to develop reliable expectations about the state of the world and the actions of others they rely on (Dosi 1988; Lundvall 1992). In that respect, their actions and interactions are shaped by ‘institutions’. However, the ways in which actors develop expectations and regular practices of interaction are not constant. Like economic growth, they vary substantially over time and space. A World Bank (2008: 2) study, for instance, concluded that there is no single growth model across the world’s economies but that ‘[e]ach country has specific characteristics and historical experiences that must be reflected in its growth strategy’. The conditions under which actors can develop expectations therefore vary widely. In an uncertain and heterogeneous world, it becomes crucial to understand the role of institutions in economic interaction, and particularly their impact on innovation processes. As will be discussed below, institutional contexts may slow down or resist innovation processes or they may support and accelerate them. This chapter builds on a relational perspective of the economy (Bathelt and Glückler 2011) and proposes a concept of institutions at the micro level of social practices. We realize that the institutional context relies on specific institutional actors, such as governments, and on the rules and regulations introduced by them. But the central advantage of our micro-institutional perspective is that it enables us to identify deviations between the formal rules and regulations that exist at different geographical scales and the specific legitimate patterns of interaction displayed at the localized level of agency. In this chapter, we first define institutions and then discuss the relation between institutions and innovations, before analyzing some of the mechanisms which further or hamper innovations in a spatial perspective. Our analysis demonstrates that a profound understanding of institutional change is necessary if we are to develop support policies for socially accepted and successful innovations. 121

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CONCEPTUALIZATION OF INSTITUTIONS Organizations, Rules and Regularities Throughout the social sciences, institutions have become fundamental reference points in the analysis of differential spatial and temporal dynamics in the economy, as evidenced by research in economics (Acemoglu et al. 2005; Acemoglu and Robinson 2012), organization studies (Powell and DiMaggio 1991), history (North 1990; Greif 2006) and economic geography (Amin 1999; Storper 2004; Rodríguez-Pose and Storper 2006; Farole et al. 2011; Bathelt and Glückler 2014). Perhaps inevitably, the surge of institutional theory has led to a multiplicity of conceptual framings and definitions. This diversity of understandings has not contributed to more clarity in research practice, nor are all contributions crystal-clear or consistent in their conceptualization. In many studies, institutions are viewed as organizations or (prescriptive) rules or behavioral regularities. Although each of these conceptions is important and has an impact on innovation, the analysis of institutions in this chapter takes a different approach. We firmly believe that it is crucial to be precise about the notion of ‘institutions’, because the above conceptualizations are not interchangeable and confusion between them can lead different researchers to arrive at mutually contradictory interpretations of the same empirical reality (Bathelt and Glückler 2014). We therefore start by distinguishing organizations, rules and regularities from our understanding of institutions. First, we do not view individual behavioral regularities as institutions if they are not related to the legitimate expectations of other actors or are not enforced through sanctions by others. A morning routine, for instance, during which a person takes a shower, gets dressed and reads the newspaper over breakfast, is a habit that is not the consequence of some social expectation and has no consequences if stopped or altered; nor does it place expectations on the behavior of others. Second, organizations such as firms, public authorities, associations or universities are often considered institutions, for instance when referring to the last as ‘higher education institutions’. However, we view organizations as collective actors that align resources and interests in pursuit of a common goal. They draw on institutions, rather than being institutions themselves. Following North (1990), we see organizations as the ‘players of games’, and not as the institutions which underpin that game. Third, a substantial part of the literature, especially in economics, defines institutions as rules, laws, directives, legal norms or standards. It is argued that these prescriptive norms set the incentive structures for social and economic development (North 1990; Nelson 1993; Acemoglu et al. 2005). Rules are clearly crucial for action and interaction but they are, from our perspective, ‘not yet institutions’ (Bathelt and Glückler 2014). This is because routines of social interaction in economic life often deviate from codified norms and rules, such as those defined by research and development policies. Norms and rules specify the basic framework for interaction, yet different economic practices can unfold within a given framework. Clearly, formal rules are important to understand patterns of economic interaction, and we may, in fact, only understand these patterns by being aware of these rules; but their impact can vary. In some situations they can be almost deterministic and in others quite meaningless, depending on whether actors comply with the corresponding expectations or whether they respond to the rules with alternative

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legitimate practices. In other words: institutions develop in relation to rules, in response to them, or even against them – but they are conceptually distinct from rules. Institutions as Correlated Interaction The above considerations already illustrate that we understand institutions as patterned interactions which are neither fully determined by organizations nor by rules and regulations. Economic action and interaction have multiple degrees of freedom despite being surrounded by webs of rules, policies and prescriptions. And, yet, interaction patterns or orders are often remarkably consistent and stable. From a relational perspective (Bathelt and Glückler 2003; 2011), individual preferences, norms, values and aspirations neither emanate from ‘undersocialized’ individuals that strive to fulfill their own goals regardless of others, nor from ‘oversocialized’ actors that fulfill internalized normative orders like robots (Granovetter 1985). Instead, individual values and orientations are continuously constituted through processes of socially embedding economic interaction (Faulconbridge, Chapter 41, this volume). This relational understanding of action has three implications for conceptualizing economic processes and their outcomes (Bathelt and Glückler 2011). It suggests that social action is contextual, path dependent and contingent. First, social interaction is informed by the legacy of historically confirmed and legitimized expectations (path dependence). Second, it takes place in, and is shaped by, specific contexts and, third, contributes to the transformation of these contexts in non-deterministic ways, based on the principle of contingency. The interrelationship between instituted expectations and behavioral opportunities in a specific context drives a recursive process of correlated interactions (Setterfield 1993), or what could be referred to as a process of transformative institutionalization. From this perspective, we define institutions as ongoing and relatively stable patterns of repeated social interaction, based on mutual expectations that owe their existence to purposeful constitution or unintentional emergence (Bathelt and Glückler 2014). In contrast to simple behavioral regularities, social institutions are based on the legitimate expectations of others and can potentially be enforced through sanctions if actions deviate from these expectations. For instance, a firm may cumulatively sanction a partner until finally stopping joint problem-solving activities if the long-time collaborator repeatedly misses deadlines. In contrast to organizations, social institutions do not refer to actors or collective entities but to established ways in which actors interact with each other in specific situations. In contrast to codified rules, social institutions refer to the factual interaction practices rather than the normative prescriptions, which may be quite different. This definition has two key implications for investigations of innovation processes. First, the analytical interest is less focused on legal, constitutional or otherwise formally regulated contexts of economic actors per se, but is more concentrated on actual practices of interaction and how they unfold over space and time (Dougherty, Chapter 9, this volume). Second, this perspective generates an opportunity to study differences or inconsistencies between formal rules and actual interaction practices within a particular context (Glückler and Lenz 2016). For instance, it is possible that similar sets of rules generate varying industrial structures of interaction in different settings or that interaction orders change over time even without changes in the regulatory framework.

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This perspective thus enables us to better understand interregional variations between institutions, as well as changes in institutions over time and their place-specific emergence.

THE RELATION BETWEEN INSTITUTIONS AND INNOVATION Having clarified our understanding of institutions, this section proceeds to investigate the influence institutions can have on innovation processes. We illustrate the complex relations between the two, and explore whether and how, in the face of institutional resistance and opportunities, innovations can fail or succeed. Arduous Innovation Innovation can be understood as the act of successfully introducing and disseminating a novel product or process in the market (Akrich et al. 2002; Cohendet and Simon, Chapter 3, this volume). Contrary to the assumption that new and improved products and technologies diffuse smoothly throughout the economy, many examples of seemingly superior novelties can be found that experience difficulties and delays in the innovation process and either have to overcome opposition or eventually fail. In this respect, innovation can become an arduous process for several reasons. A new product may, for instance, face resistance due to immediate competition from other new developments or because actors do not understand the institutional context, underestimate its importance, or behave in ways that are incompatible with certain environmental features (Agócs 1997; Glückler and Panitz 2014). In such cases, new products may take a long time before they find their place in the market and become accepted in it. Sometimes, new products and technologies take years or even decades before they are successfully received by customers. Well-known historical examples include microwave technology, touch screen smartphones, baking powder (Jungbluth 2008), roll-film cameras (Munir and Phillips 2005) or the electric light (Hargadon and Douglas 2001). All these innovations were far from novel at the time they became established in the market. Baking powder, for instance, was already known in the 1600s and its final composition was already clear in the mid-1800s. Yet, it took until 1893 before German pharmacist August Oetker introduced it successfully as an innovation into the consumer market. Oetker had the idea of filling small paper bags with the exact amount of baking powder needed for one pound of flour. This guaranteed that the users (usually housewives) could consistently achieve perfect baking results. A few years after its market introduction, the demand skyrocketed and Oetker’s firm grew into one of the most well-known brands in the German food industry (Jungbluth 2008) as users began to develop their own baking techniques and customization processes were introduced to target regional baking traditions. The key to success here was not simply a new product, but the creation of instruments that helped insert this product into institutionalized practices: the offering of exact dosages for easy home usage and the provision of baking recipes helped to match the new product with established institutional cooking practices in German households at that time. In other situations, arduous innovation results from unintended research outcomes that run the risk of being rejected by corporate management and can take a long time

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to unfold. Well-known examples of such processes are the innovation processes of postit® notes (Brand 1998) and Viagra (Chesbrough 2003). The invention of Viagra by the pharmaceutical firm Pfizer can be characterized as a so-called false negative, that is, a novelty that failed to solve the problem it was targeted at, but had side-effects with the potential to solve a different type of problem. Since the latter is often not anticipated in innovation processes, such novelties are likely to be overlooked or not followed up when approaching them with a narrowly defined search pattern. Viagra was originally developed to cure angina, but failed in clinical trials. It was only discovered by accident that it had different features to treat male erectile dysfunction. Eventually, this led to the development of an entirely different drug later on, albeit not without friction (Chesbrough 2003). The key challenge for Pfizer was to overcome the institutionalized limits of targeted research, first, in order to detect alternative uses and, second, to create legitimacy and attain resources to pursue an alternative research path for the development of an entirely different drug. Reasons for controversy and resistance in innovation can also be related to blockages within organizational hierarchies, in both a bottom-up and a top-down manner. This may be the case when novel standards and procedures run up against established interests and routines, as in the case of new business models (Glückler 2014) or new strategic orientations (Glückler and Panitz 2014). The continuous reinforcement of established ways of thinking and working through members of an organization leads to the formation of mental models and institutional orders. These may be important for certain kinds of innovations; however, when innovations interfere with these institutions, negative sanctions by those that are affected may hinder their diffusion. Institutional Hysteresis and Innovation Failure While our prior discussion has focused on difficulties in innovation resulting from the need to overcome certain institutional patterns, preexisting institutional practices sometimes persist and block off technological development or even cause innovation failure. The defeat of steam and electric engines by combustion engines in the evolution of the automobile industry (Dosi and Nelson 1994) and the inability of the apparently more efficient Dvorak Simplified Keyboard to challenge the dominance of the existing QWERTY standard (David 1985) are historical examples of novel technologies that were blocked off by persistent institutional patterns and were not able to compete against already established standards. These cases illustrate that institutional rigidities or hysteresis (Setterfield 1993; Martin and Sunley 2006) can be critical influences that prevent innovation processes. In the case of the Dvorak keyboard, the organizational field consisting of typewriter manufacturers, customer firms, typists and training schools had already adjusted to the QWERTY standard, and the institutionalized interrelations between these actors led to a lock-in rendering the cost of changing to another standard prohibitive. Typists would have had to unlearn internalized skills from using the QWERTY keyboard; training schools would have had to develop new teaching curricula to teach the new standard; user firms would have had to replace their entire typewriting hardware; and manufacturers would have had to adapt their production process to the new keyboard. Increasing returns to scale from the early advantages of adoption (Arthur 1989), habituation and institutionalized patterns of interaction, as well as the corresponding

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switching costs (David 1985), finally led to institutional hysteresis and rejection of further changes in technology. While such hysteresis may have positive economic impacts in the short run, it serves to lock out economic opportunities in the long run as the institutional context fails to adjust to changing environmental conditions. A case with a negative outcome is the failure of Kodak to recognize the potential of digital photography (Olive 2012). This eventually led to the firm’s demise, even though Kodak had originally invented the digital camera in 1975 and had been a pioneer of modern photography for much longer, as discussed below. Even if they are no longer needed, existing institutions may persist as a default mechanism, for instance to avoid conflicts over the redistribution of resources (Setterfield 1993). Institutions may even persist if the original conditions under which they were formed have vanished. Habituated interaction orders are often sustained over time despite the fact that the initial environmental conditions have changed and would allow for new institutions that are better suited for the altered conditions. Marquis (2003) illustrated this in a study of the geographical composition of corporate boards of directors in large U.S. cities. He showed that board membership continued to be strongly locally composed in cities where local board interlocks had been well established before air travel was introduced. Although air transport was available almost everywhere at the time of the study, the imprinted pattern of geographically bounded board composition was still prevailing in older cities and prevented the recruitment of highly qualified directors from outside (Marquis 2003). In general, institutional hysteresis may be a consequence if a high degree of interrelatedness and interdependence in the existing institutional context prevails that makes it very difficult to change one institutional order without affecting others (Frankel 1955). If such a situation in a certain region becomes dominant, it may threaten the ability of industries in that area to adapt to changing technological contexts, as in the case of the Swiss watchmaking industry (Maillat et al. 1997) or the Route 128 minicomputer industry (Saxenian 1994). The consequence may be regional crisis or decline (Cantner and Vannuccini, Chapter 11, this volume; Lagendijk, Chapter 30, this volume). Robust Design: Adjusting Innovation to the Institutional Context To avoid negative effects of institutional hysteresis, innovation in reality goes hand in hand with institutional changes – and often purposely so. Since innovation processes are always to some degree affected by preexisting institutional settings, it is important to understand the interdependencies between innovations and institutions – and to identify the institutional conditions under which innovators succeed. When a new product or technology challenges an existing one, institutional resistance and hysteresis can be built into the solution by partially adjusting the innovation to prior institutional settings and by anticipating the respective effects. Hargadon and Douglas (2001) suggest that innovations can succeed in such situations when designed in compliance with established interpretative contexts in order to increase an innovation’s initial acceptance. They reconstruct Edison’s introduction of the electric light into the market against the dominant gas light industry of the time, and suggest that he was successful because he presented the new technology in a way that took into account existing institutional conditions, which meant that consumers were able to make sense of and gain trust in the electric light. In other words,

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the introduction of the electric light benefited from a robust design of the new technology that met customer expectations related to the existing institutional context of the gas light and, yet, was capable of developing far beyond those expectations once it had gained momentum in the market (Douglas and Hargadon, Chapter 10, this volume). The design strategy of so-called skeuomorphism emphasizes those design elements that connect new technological options with common understandings and preexisting patterns of behavior and thus helps develop shared conceptions about the purpose of new technologies. In Edison’s design of the electric light, for instance, power lines were initially laid underground and low-voltage light bulbs were installed to make the new light appear in a form that was familiar to long-term gas light users. Similar design elements play a role in many of today’s innovation processes, as in the development of electric cars, which retain multiple design elements of former technologies that could be replaced or omitted. These include charging connections, which are located in the same place as traditional fuel tank caps, or engine noises that are acoustically emulated to familiar sound patterns (Norman 2013). Volkswagen, for instance, used a strategy called conversion design in producing its electric car e-Golf (Rammler and Weider 2011). Apart from replacing the combustion with an electric engine, Volkswagen aimed to replicate the design of regular cars in its strategy in order to offer the ‘latest technology in a familiar guise’ in its electric car (Volkswagen 2015). All these elements point at the importance of robust design strategies in supporting innovations: to gain legitimacy from customers by adjusting new technologies to habituated tastes, customs and aesthetics while invoking a potential for more radical technological changes down the road. Such designs are important in situations where new institutional contexts need to develop while being confronted with rather different preexisting structures. In terms of economic policies, Streeck and Thelen (2005) and Mahoney and Thelen (2010) discuss different institutional strategies that can be used to support the required changes in these contexts. Possible strategies range from layering strategies (that link new institutions to existing ones) to drift avoidance strategies (that actively target the adjustment of existing institutions) and conversion strategies (that redirect existing institutions to new purposes). Peripheral Dominance: Circumventing Institutional Resistance to Innovation A specific set of challenges arises in innovation processes facing opposition or resistance within powerful institutional contexts, such as rigid hierarchical control mechanisms. Corporate hierarchies, for instance, that target certain outcomes of an innovation process may not allow for alternative pathways. While this may be a cost-efficient way to achieve the envisioned research goals, it may lock out substantial innovation opportunities. In such situations, a successful response may be a geographical and/or organizational strategy that circumvents the corporate core by conducting experiments in the periphery. Successes from these experiments can later be used to put pressure on the core to add to and revise the corporate innovation portfolio. The principle of peripheral dominance suggests that, especially in cases of controversy, change is more likely to occur if innovators are located in the periphery rather than in the core of an organization (McGrath and Krackhardt 2003). An innovation may be controversial because its chances of success are highly uncertain. Alternatively, financial risks could be too high or core management may reject

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the changes outright to defend their position of power. Actors at the periphery of an organization may be more difficult to monitor, more prone to deviate, and potentially able to advance controversial innovation processes without the same level of resistance that actors in the core have to face (Thomke and Kuemmerle 2002; McGrath and Krackhardt 2003). This supports Granovetter’s (1985) finding that, due to homogenous perspectives and social control mechanisms in the core, deviating ideas are more likely to originate from peripheral actors. Peripheral actors also benefit from a lower degree of institutional rigidity and a different set of local institutions that are more open to new perspectives and may challenge the current state of affairs. The innovation of a controversial business model at German multinational BASF is an illustrative example for how peripheral innovation can succeed against initial rejection from the core (Glückler 2014). At a time when BASF’s coating business in the automobile industry was focused on selling their products without offering specific services, a group of managers in its Argentinean subsidiary saw great potential by introducing additional painting services at the customer sites. Although such additional service offerings had been an industry trend for some time, this was not viewed as a strategic business area by the firm’s headquarters. While the Argentinean subsidiary saw an opportunity to increase their profits through complementary services, managers of BASF’s core operations found this business model to be inconsistent with the prior focus on bulk production and sales. Despite repeated disapproval by the German headquarters, the Argentinian team introduced this business model in its local market and experienced high growth rates as a consequence (Glückler 2014). Only after the new business model had proven to be unexpectedly successful was it approved by the corporate headquarters and subsequently introduced into global operations. Mechanisms of peripheral innovation also play a role at the level of organizational fields. The evolution of the U.S. radio broadcasting industry, for instance, demonstrates how new players at the fringe of an industry introduced new technologies and practices that were originally not considered by the established firms. They introduced third party advertising as a form of financing – a practice which was only later adopted by  the dominant radio stations and then became common practice. In this case, peripheral innovation caused centripetal dynamics to adapt ideas and practices and thus put  pressure on transforming the institutional context in the core (Leblebici et al. 1991). These examples point to the importance of innovations that are not at the center of attention or are rejected altogether. It also suggests that allowing for decentralization in the innovation process and opening up institutional rigidities may lead to unexpected innovation success. In a study of large multinational enterprises in the U.S., Schoenberger (1999) found that although corporations actively created peripheral plants to pursue novel ways of doing things, they often blocked potential changes that novel practices could incur in the center. This suggests that it is difficult for peripheral innovations to make their way to the core when underlying conventions, habituated practices and institutionalized mental models operate against them. However, trends in foreign direct investment processes suggest that transnational firms increasingly establish research operations in peripheral markets to encourage decentralized innovation that may eventually strengthen core activities (Bathelt and Li 2015).

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Institutional Entrepreneurship: Creating New Institutions to Enforce Innovations Often it is not enough to prove the viability or superior utility of a novelty for it to become a successful innovation. Gaining customer support and market legitimacy may not be possible under the preexisting institutional settings but require the generation of new or redefinition of existing institutions. This requires what is often referred to as institutional entrepreneurship (Geilinger et al., Chapter 40, this volume). Such entrepreneurship is a challenging process that is not only associated with generating innovations, but also requires overcoming the so-called paradox of embedded agency. This paradox refers to situations where actors are required to change those very institutions in which their own intentions, actions and rationalities are fundamentally grounded (Greenwood and Suddaby 2006). To accomplish this, institutional entrepreneurs need to mobilize resources and develop capabilities (Perkmann and Spicer 2007) that allow them to change legitimate institutional practices and initiate new ones. If successful, these new practices become reinforced over time and develop into proto-institutions (Lawrence et al. 2002) that are widely accepted. They may form the institutional pillars of a new organizational field and create a pathway for innovations and further institutional adjustments in the future. Kodak’s innovation of the roll-film camera is an illustrative historical example of this phenomenon (Munir and Phillips 2005). In contrast to Edison’s innovation of the electric light, Kodak did not adapt its camera technology to existing institutions. Instead, Kodak created and promoted new institutional practices that made the roll-film camera almost a necessity in everyday life. By designing a new institutional context, it introduced legitimate expectations of owning and using such a camera. When Kodak introduced the roll-film camera in 1882, the technology did already exist but was rejected by professionals. The advantage of roll-film over the traditional glass plate technology was that it allowed for the separation of two tasks: taking a photo and developing the print image. The production step could now be done in a specialized laboratory and users were no longer required to have the respective technical knowledge and equipment. All of a sudden, the roll-film camera allowed non-professionals to use cameras and at the same time offered a lot more mobility. Through effective advertising, the firm was able to attract the interest of the many people who wanted to take photos and become photographers themselves. The discursive strategies pursued in advertising campaigns (e.g. ‘a holiday without a Kodak is a holiday wasted’) eventually changed the meaning of the technology and photography became a legitimate part of everyday life. With the ‘Kodak album’, new institutional patterns emerged that changed the way people would tell stories about their lives and holidays. Initial concerns of professionals vanished and related innovations eventually changed existing institutional practices of photography in such a way that the roll-film camera became a mass consumer product. Kodak had transformed photography from a specialized professional field to a societal practice in everyday life (Munir and Phillips 2005). As this example demonstrates, institutional entrepreneurship plays a crucial role in social contexts where the meaning of technologies needs to be redefined to enable successful innovation and attract many new users. It requires transforming the very institutions within which these technologies are originally produced and interpreted. There are now many such cases that show how the introduction of new technologies, such as new methods of surrogacy (Kuchař 2016), or new services, such as whale watching

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(Lawrence and Phillips 2004), has been successful because institutional entrepreneurs managed to legitimize new meanings and create new institutional practices that fitted their innovations. While research on institutional entrepreneurship often focuses on the key actors that purposively promote change, recent work on institutions has extended this focus by studying the diverse practices which facilitate innovation and change and that unfold at multiple social levels (Lounsbury and Crumley 2007; Zilber 2013).

GEOGRAPHICAL CONTINGENCY OF INSTITUTIONS AND INNOVATION Our analysis of arduous innovation processes has shown how critical it is for successful innovation to occur in a consistent and fitting institutional context. This consistency can either be attained by adjusting the innovations to the institutions or vice versa – or by circumventing an institutional context by means of peripheral innovation. And this can be done in advance of or during the process of introducing new products and technologies into the market. The potential for different (that is, contingent) outcomes becomes even more complex when applying a geographical perspective, since innovation, economic prosperity and institutions all vary over space – and since these spatial disparities can be large. Institutional economists, such as Rodrik et al. (2004), go as far as suggesting that when controlling for the effects of institutions on economic development, the independent influence of other geographic variables becomes weak or insignificant. Geographic context, however, is crucial to understand the specific form of institutions that emerge in a place, and how and why they differ between locales, regions and nations. National Variations in Innovations and Institutions Different bodies of theoretical work have developed over the past decades trying to explain the effect of spatially bound institutional conditions on innovation and economic growth. These include the regulation school and national systems of production, national innovation systems, varieties of capitalism, and triple helix and coevolution models. Such approaches have informed innumerable studies, which demonstrate how institutional conditions are related to differential levels and qualities of innovation (Bathelt and Henn, Chapter 28, this volume). The approaches are, on the one hand, inspired by the wish to explain the contingent relationship between geography, institutions and innovation processes. On the other hand, they realize that customized policy solutions are necessary for different territories as generic policy blueprints for growth do not work under the conditions of fundamental spatial disparities. With respect to our previous discussion, the national innovation systems approach is particularly relevant (Lundvall 1992; Nelson 1993) because it offers an explanation of how institutional settings co-develop with specialized production and innovation structures, all of which result in innovation processes that systematically differ between countries (Lundvall and Maskell 2000; Lundvall, Chapter 29, this volume). Although these approaches to institutions, innovation and growth acknowledge the institutional specificity of nation states and their economies, they often focus on codified rules of legislation and regulation – such as property rights and patent law – or on the role

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of organizations – such as universities or business associations – rather than on the more varied, yet stable, patterns of social interaction across places. This is related to a tendency to over-emphasize structures at the macro level at the expense of the conditions and intentions of individual actions that exist at the micro level. While Lundvall and Maskell (2000) focus their explanation on the varying practices in producer–user interaction that develop in different national settings and cultural traditions, much of the innovation systems literature compares organizational structures and rules, such as different national policies, research and development support, education and training systems, and the like (e.g. Nelson 1993). As much as these national systems frameworks have been successful in unpacking differences in institutional contexts and innovation performance at the macro level, they often take regulatory regimes and aggregated organizational landscapes as a given. While much of this work characterizes itself as institutional, it is often surprisingly silent about how differential institutions emerge, how exactly different interaction orders develop, or why it is so difficult to transfer specific innovation practices to other contexts. As suggested by Acemoglu et al. (2005: 389), ‘we are far from a useful framework for thinking about how economic institutions are determined and why they vary across countries’. This gap in current research is largely a consequence of research designs that treat institutions as an independent variable and analyze their effects on innovation as the dependent variable. Developing a more profound understanding of the creation and change of institutions in the innovation process requires research designs that trace the generation and development of institutions, which includes viewing them as the dependent variable or, in conjunction with other influences, as an interaction variable. It also requires integrating the level of actors and their actions into the analysis of innovation processes. A Geographical Perspective of Institutions In order to develop a more fine-grained understanding of the role of institutions in innovation processes, it is helpful to employ a relational understanding of institutions. Such an approach recognizes that institutions play an intermediate role between the micro level of social interaction and the macro level of societal structures and normative orders (Jessop 2001; Bathelt 2006; Bathelt and Glückler 2014). Both levels affect the formation and change of institutions while being at the same time affected by them. Institutions are therefore imbricated in simultaneous processes of downward and upward causation. Institutional approaches at the macro level tend to concentrate on downward causation, where codified normative orders define social incentive structures and thus constrain or guide economic interactions in a top-down fashion. Since changes in laws and formal rules shape social practices at the micro level (Hooghe and Marks 2003), policies are conceived of as purposively guiding these social practices. However, this process is not predictable, as actors may interpret these rules in different ways, according to their own preferences or the needs associated with the innovation process. Rules and regulations thus come to be used in ways other than intended or designed originally (Thelen 2004; Streeck and Thelen 2005). The process of upward causation, which connects the micro and macro levels in a bottom-up fashion, is equally important in shaping institutions (Hodgson 2003). As emphasized by Hall and Thelen (2009: 17): ‘[c]hanges in rules often follow the accumulation of “deviant” behavior.’ It is this deviance that, when practiced

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repeatedly by an increasing number of actors, may successively convert into legitimate practice and become an institution. And it is this process through which innovations may be increasingly adopted up to the point when governments enforce them as new norms or standards. Essentially this interplay of upward and downward causation explains why institutions and formal rules are not identical. To acknowledge this difference opens analytical scope for alternative ways in which codified rules and institutions can be related to one other. This understanding has a number of consequences regarding the relation between institutional contexts and innovation. First, one may wonder as to whether codified rules are the cause or consequence of social practice. The institutional literature in economics assumes that formal institutions are antecedents of economic outcomes and should therefore be designed to enhance growth and prosperity. But are institutions really the instruments of economic development? Van Waarden (2001) expected the Netherlands to be more prone to entrepreneurship than the U.S. because its legal system imposes less uncertainty on entrepreneurs in cases of legal dispute. What he found, however, in a comparative analysis of the legal system and the rate of entrepreneurship, is a superior entrepreneurial culture in the U.S. His evidence suggests that regulation in the Netherlands is a consequence of a specific culture and response to risk-aversion rather than causing more risk-seeking economic behavior. In other words, while institutional approaches implicitly view rules and regulations as instruments that induce certain economic behavior, these rules might simply be expressions of the underlying institutionalized beliefs and practices and may not have much of an effect in terms of an intended policy outcome. Second, the codification of rules does not replace institutions of social practice. Despite their undoubted impact on social and economic life, codified and legalized rules do not determine the outcome of social situations, but leave actors with a considerable degree of freedom in terms of their actions and uncertainty in terms of what actions they can expect from others. Any rules come with a sophistication cost (Shapiro 1987), and generic regulation can hardly precisely define all possibilities of actions and states of the world in a particular situation. Uncertainty and deviating behavior are omnipresent, despite rules and regulations. This leads to the paradox that ‘the more we control the institution of trust, the more dissatisfied we will be with its offerings’ (Shapiro 1987: 652). Once institutions become codified and are being imposed upon a population of actors, authorities are necessary to monitor compliance and sanction infringements of the corresponding rules, for instance with respect to safety regulations in the innovation process. As the number of laws, rules and regulations in the innovation process increases, more detailed professional expertise is required to understand these regulations and to act accordingly in pursuit of prior innovation goals. The fact that small and medium-sized enterprises often avoid legal contracts in inter-firm cooperation and that they sometimes even interpret contracts as signals of mistrust (Macaulay 1963) is indicative of the high costs of legal disputes. The more rules that are prescribed and codified to reduce mutual uncertainties in innovation, the more possibilities arise to abuse related ‘coded trust’, requiring another round of more detailed codifications and so on. Formal rules and codes are clearly not the only predictors of economic action and interaction and cannot replace the impact of ‘social practice institutions’ (Glückler 2005). Third, while the importance of state regulation makes it crucial to identify and

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distinguish institutional conditions at the national level, the above findings suggest that we should not assume that a homogenous set of economic structures and practices develops within national borders. In fact, we can regularly identify substantial interregional variation in economic trajectories and institutions even within the same national regulatory regime. Although we can expect notable differences in institutions between countries due to distinct legal settings and orders (Lundvall and Maskell 2000), the arguments developed here suggest that local contexts can also drive specific adjustments in economic practices and therefore variation. Even within a national system, this leads to a degree of institutional variety – rather than homogeneity – over space. Research in economic geography demonstrates that stable patterns of social interaction are often strongly shaped and reproduced within social networks and communities in local as well as non-local contexts (Rodríguez-Pose and Storper 2006; Farole et al. 2011). While national differences in entrepreneurship are often attributed to national variations in the stigma associated with failure, regional analyses illustrate how belief structures and institutional contexts vary between places and regions and account for different rates of entrepreneurship. Rural Catalonia, for example, experiences far more entrepreneurialism than other rural regions in Spain because business founders have less fear of failure (Vaillant and Lafuente 2007), and regions such as Greater Boston and Silicon Valley (Saxenian 1994; Glaeser 2005) are examples of impressive economic prosperity and innovation, each with specific and quite different sets of institutions that have developed within the context of the same national innovation system. Silicon Valley, for instance, has become a global innovation hub based on remarkable localized knowledge spillovers. These spillovers occur, among other reasons, because inventors change jobs more often within the region than beyond it (Almeida and Kogut 1999; Breschi and Lissoni 2009), and because people maintain local institutionalized practices of being committed to knowledge sharing, dense communication and intense deliberations that enforce local externalities and induce innovation dynamics (Ferrary 2003; Ferrary and Granovetter, Chapter 20, this volume).

CONCLUSION We have emphasized in this chapter that innovation does not occur in a social vacuum. The goal of this chapter has been to show that successful innovation rests on the design and redesign of institutional contexts, whether by their transformation, circumvention or active creation. Institutions, defined as stabilized interaction orders, are always present and impact our understanding and use of technology. Institutional contexts can be supportive of and enable innovation processes that would be otherwise unthinkable. However, if the institutional context becomes too rigid and critically restricts the absorptive capacity of actors, efficient technological change may be rendered impossible or have undesirable or counterproductive consequences. Our analysis points at three crucial aspects of this phenomenon. First, our understanding of what constitutes ‘good’ and ‘supportive’ versus ‘bad’ and ‘disruptive’ institutional conditions for technological change and innovation is quite limited at this point and requires substantial research in the future. The evidence presented suggests that an interdisciplinary research agenda is needed to extend our understanding of the relationship between institutions and innovations.

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Second, our understanding of the role of policy needs to be extended beyond viewing government as a generator of rules and regulations that determine innovation success. Economic support and innovation policies can have very significant impacts on innovation and on the conditions under which new products and technologies are produced and adapted. The need for careful policy formulation becomes even greater if we consider the fact that the outcome is never deterministic. Policy designs can support innovation processes by preparing firms to adjust their innovations according to varying and changing institutional contexts – but they can also fail to do so by ignoring existing institutional practices. Finally, our understanding of the spatial variation of institutions and the relationship between innovation success and institutional context is incomplete. It is clear that these relationships vary over time and space and that any analysis of the role of institutional conditions for innovation needs to acknowledge a geographical perspective. It has long been recognized, for example in models of national innovation systems, that there are fundamental variations in institutional conditions at the national level. But it is also clear that there is necessarily regional variation in the way in which institutional contexts develop. It is in this realm that an interdisciplinary research agenda is needed to understand the relationship between micro and macro levels of institutional conditions and how they co-evolve, vary and co-exist in a geographical perspective. Acknowledgements We would like to thank Daniel Hutton Ferris, Sebastian Henn and Regina Lenz for manifold suggestions of how to strengthen our argument.

REFERENCES Acemoglu, D., Johnson, S. and Robinson, J. (2005) ‘Institutions as a fundamental cause of long-run growth’, in P. Aghion and S. Durlauf (eds) Handbook of Economic Growth, Amsterdam: Elsevier, 385–472. Acemoglu, D. and Robinson, J. (2012) Why Nations Fail: The Origins of Power, Prosperity, and Poverty, New York: Crown Publishers. Agócs, C. (1997) ‘Institutionalized resistance to organizational change: Denial, inaction and repression’, Journal of Business Ethics, 16: 917–931. Akrich, M., Callon, M., Latour, B. and Monaghan, A. (2002) ‘The key to success in innovation part I: The art of interessement’, International Journal of Innovation Management, 6: 187–206. Almeida, P. and Kogut, B. (1999) ‘Localization of knowledge and the mobility of engineers in regional networks’, Management Science, 45: 905–917. Amin, A. (1999) ‘An institutionalist perspective on regional economic development’, International Journal of Urban and Regional Research, 23: 365–378. Arthur, W.B. (1989) ‘Competing technologies, increasing returns, and lock-in by historical events’, Economic Journal, 99: 116–131. Bathelt, H. (2006) ‘Geographies of production: Growth regimes in spatial perspective 3 – Toward a relational view of economic action and policy’, Progress in Human Geography, 30: 223–236. Bathelt, H. and Glückler, J. (2003) ‘Toward a relational economic geography’, Journal of Economic Geography, 3: 117–144. Bathelt, H. and Glückler, J. (2011) The Relational Economy: Geographies of Knowing and Learning, Oxford: Oxford University Press. Bathelt, H. and Glückler, J. (2014) ‘Institutional change in economic geography’, Progress in Human Geography, 38: 340–363. Bathelt, H. and Henn, S. (2017) ‘National and regional innovation systems’, in H. Bathelt, P. Cohendet, S. Henn

Institutional context and innovation

135

and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 457–471. Bathelt, H. and Li, P.-F. (2015) Mapping Networks of Innovative Clusters between China and Canada, Policy Research Grants (Innovation) Report. Vancouver: Asia Pacific Foundation of Canada. Online. Available HTTP: (15 December 2015). Brand, A. (1998) ‘Knowledge management and innovation at 3M’, Journal of Knowledge Management, 2: 17–22. Breschi, S. and Lissoni, F. (2009) ‘Mobility of skilled workers and co-invention networks: An anatomy of localized knowledge flows’, Journal of Economic Geography, 9: 439–468. Cantner, U. and Vannuccini, S. (2017) ‘Innovation and lock-in’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 165–181. Chesbrough, H. (2003) ‘Managing your false negatives’, Harvard Management Update, 8: 3–4. Cohendet, P. and Simon, L. (2017) ‘Concepts and models of innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 33–55. David, P.A. (1985) ‘Clio and the economics of QWERTY’, American Economic Review, Papers and Proceedings, 75: 332–337. Dosi, G. (1988) ‘The nature of the innovative process’, in G. Dosi, C. Freeman, R.R. Nelson, G. Silverberg and L.L.G. Soete (eds) Technical Change and Economic Theory, London, New York: Pinter, 221–238. Dosi, G. and Nelson, R.R. (1994) ‘An introduction to evolutionary theories in economics’, Journal of Evolutionary Economics, 4: 153–172. Dougherty, D. (2017) ‘Innovation in the practice perspective’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 138–151. Douglas, Y. and Hargadon, A. (2017) ‘Domesticating innovation—designing revolutions’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 152–164. Farole, T., Rodríguez-Pose, A. and Storper, M. (2011) ‘Human geography and the institutions that underlie economic growth’, Progress in Human Geography, 35: 58–80. Faulconbridge, J. R. (2017) ‘Relational geographies of knowledge and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 671–684. Ferrary, M. (2003) ‘The gift exchange in the social networks of Silicon Valley’, California Management Review, 45: 120–138. Ferrary, M. and Granovetter, M. (2017) ‘Social networks and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 327–341. Frankel, M. (1955) ‘Obsolescence and technological change in a maturing economy’, American Economic Review, 45: 296–319. Geilinger, N., Haefliger, S., von Krogh, G. and Pachidou, F. (2017) ‘Institutional entrepreneurship in Alzheimer’s disease treatment’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 652–667. Glaeser, E.L. (2005) ‘Reinventing Boston: 1630–2003’, Journal of Economic Geography, 5: 119–153. Glückler, J. (2005) ‘Making embeddedness work: Social practice institutions in foreign consulting markets’, Environment and Planning A, 37: 1727–1750. Glückler, J. (2014) ‘How controversial innovation succeeds in the periphery? A network perspective of BASF Argentina’, Journal of Economic Geography, 14: 903–927. Glückler, J. and Lenz, R. (2016) ‘How institutions moderate the effectiveness of regional policy: A framework and research agenda’, Investigaciones Regionales – Journal of Regional Research, 36: 255–277. Glückler, J. and Panitz, R. (2014) ‘Command or conviction? Informal networks and the diffusion of controversial innovations’, in A. Berthoin-Antal, P. Meusburger and L. Suarsana (eds) Learning Organizations: Extending the Field, Klaus Tschira Symposia: Knowledge and Space, 6, Berlin and Heidelberg: Springer, 49–67. Granovetter, M. (1985) ‘Economic action and economic structure: The problem of embeddedness’, American Journal of Sociology, 91: 481–510. Greenwood, R. and Suddaby, R. (2006) ‘Institutional entrepreneurship in mature fields: The big five accounting firms’, Academy of Management Journal, 49: 27–48. Greif, A. (2006) Institutions and the Path to the Modern Economy: Lessons from Medieval Trade, Cambridge: Cambridge University Press. Hall, P.A. and Thelen, K. (2009) ‘Institutional change in varieties of capitalism’, Socio-Economic Review, 7: 7–34.

136

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Hargadon, A.B. and Douglas, J.Y. (2001) ‘When innovations meet institutions: Edison and the design of electric light’, Administrative Science Quarterly, 46: 476–501. Hodgson, G.M. (2003) ‘The hidden persuaders: Institutions and individuals in economic theory’, Cambridge Journal of Economics, 27: 159–175. Hooghe, L. and Marks, G. (2003) ‘Unraveling the central state, but how? Types of multi-level governance’, American Political Science Review, 97: 233–243. Jessop, B. (2001) ‘Institutional re(turns) and the strategic-relational approach’, Environment and Planning A, 33: 1213–1235. Jungbluth, R. (2008) Die Oetkers: Geschäfte und Geheimnisse der bekanntesten Wirtschaftsdynastie (The Oetkers: Businesses and Secrecies of One of the Most Famous Economic Family Dynasties), Frankfurt: Campus. Kuchař, P. (2016) ‘Entrepreneurship and institutional change’, Journal of Evolutionary Economics, 26: 349–379. Lagendijk, A. (2017) ‘Innovation, regional development and relationality’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 490–505. Lawrence, T.B., Hardy, C. and Phillips, N. (2002) ‘Institutional effects of interorganisational collaboration: The emergence of proto-institutions’, Academy of Management Journal, 45: 281–290. Lawrence, T.B. and Phillips, N. (2004) ‘From Moby Dick to Free Willy: Macro-cultural discourse and institutional entrepreneurship in emerging institutional fields’, Organization, 11: 689–711. Leblebici, H., Salancik, G.R., Copay, A. and King, T. (1991) ‘Institutional change and the transformation of interorganizational fields: An organizational history of the U.S. radio broadcasting industry’, Administrative Science Quarterly, 36: 333–363. Lounsbury, M. and Crumley, E.T. (2007) ‘New practice creation: An institutional perspective on innovation’, Organization Studies, 28: 993–1012. Lundvall, B.-Å. (ed.) (1992) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 472–489. Lundvall, B.-Å. and Maskell, P. (2000) ‘Nation states and economic development: From national systems of production to national systems of knowledge creation and learning’, in G.L. Clark, M.P. Feldman and M.S. Gertler (eds) The Oxford Handbook of Economic Geography, Oxford: Oxford University Press, 353–372. Macaulay, S. (1963) ‘Non-contractual relations in business: A preliminary study’, American Sociological Review, 28: 55–67. Mahoney, J. and Thelen, K. (2010) ‘A theory of gradual institutional change’, in J. Mahoney and K. Thelen (eds) Explaining Institutional Change: Ambiguity, Agency, and Power, Cambridge: Cambridge University Press, 1–37. Maillat, D., Léchot, G., Lecoq, B. and Pfister, M. (1997) ‘Comparative analysis of the structural development of milieux: The watch industry in the Swiss and French Jura arc’, in R. Ratti, A. Bramanti and R. Gordon (eds) The Dynamics of Innovative Regions: The GREMI Approach, Aldershot, Brookfield: Ashgate, 109–137. Marquis, C. (2003) ‘The pressure of the past: Network imprinting in intercorporate communities’, Administrative Science Quarterly, 48: 655–689. Martin, R. and Sunley, P. (2006) ‘Path dependence and regional economic evolution’, Journal of Economic Geography, 6: 395–437. McGrath, C. and Krackhardt, D. (2003) ‘Network conditions for organizational change’, Journal of Applied Behavioral Science, 39: 324–336. Munir, K.A. and Phillips, N. (2005) ‘The birth of the “Kodak Moment”: Institutional entrepreneurship and the adoption of new technologies’, Organization Studies, 26: 1665–1687. Nelson, R. (ed.) (1993) National Innovation Systems: A Comparative Analysis, Oxford: Oxford University Press. Norman, D. (2013) The Design of Everyday Things: Revised and Expanded Edition, New York: Basic Books. North, D.C. (1990) Institutions, Institutional Change and Economic Performance, Cambridge: Cambridge University Press. Olive, D. (2012) ‘The last picture show’, Toronto Star, 7 January. Perkmann, M. and Spicer, A. (2007) ‘“Healing the scars of history”: Projects, skills and field strategies in institutional entrepreneurship’, Organization Studies, 28: 1101–1122. Powell, W.W. and DiMaggio, P. (eds) (1991) The New Institutionalism in Organizational Analysis, Chicago, London: University of Chicago Press. Rammler, S. and Weider, M. (2011) Das Elektroauto: Bilder für eine zukünftige Mobilität (The Electric Car: Visions for Future Mobility), Münster: LIT-Verlag. Rodríguez-Pose, A. and Storper, M. (2006) ‘Better rules or stronger communities? On the social foundations of institutional change and its economic effects’, Economic Geography, 82: 1–25. Rodrik, D., Subramanian, A. and Trebbi, F. (2004) ‘Institutions rule: The primacy of institutions over geography and integration in economic development’, Journal of Economic Growth, 9: 131–165.

Institutional context and innovation

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Saxenian, A.L. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128, Cambridge, MA, London: Harvard University Press. Schoenberger, E. (1999) ‘The firm in the region and the region in the firm’, in T. Barnes and M.S. Gertler (eds) The New Industrial Geography: Regions, Regulation and Institutions, London: Routledge, 205–224. Setterfield, M. (1993) ‘A model of institutional hysteresis’, Journal of Economic Issues, 27: 755–774. Shapiro, P.P. (1987) ‘The social control of impersonal trust’, American Journal of Sociology, 93: 623–658. Storper, M. (2004) Institutions, Incentives and Communication in Economic Geography, Hettner-Lectures 2003, Wiesbaden: Franz Steiner. Streeck, W. and Thelen, K. (2005) ‘Introduction’, in W. Streeck and K. Thelen (eds) Beyond Continuity: Institutional Change in Advanced Political Economies, Oxford: Oxford University Press, 1–39. Thelen, K. (2004) How Institutions Evolve: The Political Economy of Skills in Germany, Britain, the United States and Japan, Cambridge: Cambridge University Press. Thomke, S. and Kuemmerle, W. (2002) ‘Asset accumulation, interdependence and technological change: Evidence from pharmaceutical drug discovery’, Strategic Management Journal, 23: 619–635. Vaillant, Y. and Lafuente, E. (2007) ‘Do different institutional frameworks condition the influence of local fear of failure and entrepreneurial examples over entrepreneurial activity?’, Entrepreneurship and Regional Development, 19: 313–337. van Waarden, F. (2001) ‘Institutions and innovation: The legal environment of innovating firms’, Organization Studies, 22: 765–795. Volkswagen (2015) Volkswagen E-Mobility. Think Blue. Online. Available HTTP: http://emobility.volkswagen. de/de/de/private/Autos/eGolf.html (18 October 2015). World Bank (2008) The Growth Report: Strategies for Sustained Growth and Inclusive Development, Commission on Growth and Development. Washington, DC: The World Bank. Zilber, T.B. (2013) ‘Institutional logics and institutional work: Should they be agreed?’, in M. Lounsbury and E. Boxenbaum (eds) Institutional Logics in Action, Part A, Research in Research in the Sociology of Organizations, Vol. 39, Bingley: Emerald, 77–96.

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Innovation in the practice perspective Deborah Dougherty

INTRODUCTION An organization’s ability to generate streams of new products and services is vital to its continued success. Studies demonstrate that innovative organizations generate more profits, capture more market share, hire more people, and grow more effectively (Andrew et al. 2010; Markham and Lee 2013; see literature summaries in Dougherty 2006). The centrality of the capability to innovate continuously is not surprising, since markets shift, user needs evolve, new applications arise unexpectedly, and emerging technologies transform former ones. Most organizations must innovate with respect to their products, services, programs, and processes to not only adapt to these constant changes, but also to proactively take advantage of them. Not-for-profit organizations and associations also require the capability to innovate continuously, because resolving society’s most pressing problems requires new solutions. Health care, drug discovery for unmet medical needs such as post-traumatic stress disorder (PTSD), schizophrenia, cancer, or malaria, new alternate energy systems, climate and ecology management, and economic revitalization are all complex systems of innovation (Dougherty 2016). To address these “grand challenges” (Ferraro et al. 2015), people must engage in ongoing innovation to address concrete problems, learn from these efforts, and continually improve underlying processes and capabilities. Yet many organizations struggle to develop and maintain the organizational capability for continual innovation. Surveys find that innovation is among the top concerns of major business firms year after year (see Andrew et al. 2010 for the Boston Consulting Group’s annual survey). For their part, public organizations are notorious for avoiding the risks that are inherent in innovation (Nembhard et al. 2009). One major reason for this lack of organizational innovativeness is that conventional approaches to managing organizations fail to encompass the actual everyday work of product/service innovation. The practice perspective reflects the actual work of innovation and explains how managers and innovation teams can readily carry out the contextualized, interpersonal, learning-based, and collective nature of the work of innovation (Glückler and Bathelt, Chapter 8, this volume). Using the practice perspective to understand the work of innovation provides the foundation for the organizational capability for continuous innovation, and how to develop and maintain it. The practice perspective used in this chapter builds on Schon’s (1983) ideas about knowledge workers as reflective practitioners, along with insights from Barley (1996) on technicians in the workplace, and from Brown and Duguid’s (1991) applications of Orr’s (1996) study of copy repair technicians. Schon published articles (e.g. Schon 1963, on the product champion) and books (e.g. Schon 1967) on innovation management decades before most others. In the 1970s, he moved to the topic of organizational learning, and in the 1980s he moved to a practice perspective. In this perspective, practice shapes the 138

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kinds of interpersonal relations among professionals, because knowledge workers interact with one another in order to tap into distributed expertise, build on each other’s insights, and work together to figure out complex issues (Orr 1996; Barley 1996). Practice-based knowledge is a collective entity, since no person can know all the heuristics or principles involved, or possess all necessary experience. Knowledge therefore exists in the practice of work. The practice perspective begins with the simple idea that many workers, including technicians, teachers, architects, and innovators, are professional practitioners (Barley 1996; Dougherty 1992a; Bechky 2003). In the practice perspective, knowledge is understood as an ongoing process that is embedded in what people do in their work (Orlikowski 2002; Dougherty 2004; Cohendet et al., Chapter 13, this volume), and people can acquire considerable knowledge by drawing skillfully on work situations (Lave and Wenger 1991). When work is understood as practice, jobs embody the means and the ends of work, the practical wisdom people rely on (Schon 1983), socially embedded know-how that encompasses perceptual skills, transitional understandings across time, and understanding of the particular in relation to the general. The practice perspective emphasizes the tricky interpolations between abstracted accounts and situated demands that constitute successful innovation (Brown and Duguid 1991). This chapter focuses on product and service innovation, and on process and strategy innovations necessary to support continuous product and service innovation across an organization. The next section explains the actual process of product and service innovation based on the work roles in innovation, the learning involved, and the underlying nature of the work. Several subsections that follow focus on complex innovation (since complexity characterizes 21st century science-based innovation; Pisano 2006) and on the grand challenges of pressing societal problems such as sustainable development, climate management, health care, or poverty (Ferraro et al. 2015). The first section closes with a brief summary of research showing that conventional approaches to managing and organizing prohibit or slow down the process of innovation, which is why so many organizations struggle to be innovative. The second main section describes how the practice perspective encourages rather than prohibits the work of innovation and explains how people can enact and manage the roles, learning processes, and underlying nature of the actual work of innovation. The practice perspective also explains four fundamental principles of organizing for ongoing innovation across multiple projects and business units. The chapter ends with a brief conclusion.

THE ACTUAL WORK OF INNOVATION By definition, innovation means bringing new products, services, and programs into use. Innovation encompasses the whole process of conceptualizing, developing, designing, manufacturing, marketing, and distributing new products. To simplify, the term “product” is used to refer to services and programs as well. Going well beyond coming up with clever ideas, innovation brings new products into being, then brings them into use as part of customers’ processes of creating value for themselves, and finally redefines products for ongoing use as customers change and their value creation processes evolve. The field of innovation management has developed deep understandings of what the everyday work

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of innovation entails. Innovation research explores the knowledge innovators need to continually create, combine, and recombine to generate new products (Leonard-Barton 1995), delves into how people can apply all this knowledge (Dougherty 1992b; Bechky 2003), identifies challenges and how to overcome them (Van de Ven et al. 1999; Leifer et al. 2000), and develops ways to build innovation strategies and supporting capabilities in research and development (R&D), manufacturing, and marketing. Books by Day (1990), Cooper (1998), and the Product Development Management Association (PDMA), to mention only a few, detail numerous tools, techniques, processes, and procedures for effective product innovation. To highlight how well defined product innovation is, Bobrow produced a Complete Idiot’s Guide to New Product Development in 1997, almost 20 years ago (Bobrow 1997). The PDMA’s surveys find that 90 percent of new products are incremental (at best), and many are minor adjustments only (Markham and Lee 2013). However, major challenges in the 21st century, such as improving health care, developing drug therapies for serious diseases such as Alzheimer’s, PTSD, or malaria, and addressing poverty and climate change, are complex innovation systems (Dougherty 2016). Complexity adds new wrinkles to the actual work of innovation. Complex systems are made up of a large number of parts that have many interactions (Simon 1977). In complex systems, all the parts interact unpredictably, because relations between cause and effect are not, or not yet, well understood. The problems of complex innovation go beyond computational complexity, or calculating many interactions among known alternate moves with parameters for evaluating them. Complex systems involve what Grandori (2010) calls “Knightian” or epistemic uncertainty, in which relevant alternate moves and parameters for evaluating them are unknown, and thus cannot be calculated. Likewise, Snowden and Boone (2007) argue that, in complex contexts, right answers cannot be ferreted out and must be allowed to emerge. Complex innovations are also nonlinear, so small changes in a product can have huge and surprising effects on product functionality. The following subsections synthesize all these insights into the work of innovation in three categories: the work roles of innovators; the learning processes that enable innovators to create, combine, and recombine knowledge into novel products, services, programs, and policies; and, finally, the underlying nature of the work itself. The Work Role of Innovators: Multidisciplinary Team Players Successful innovators do not adopt the role of independent geniuses. Innovation work can be compared to a “team sport” (Tushman and O’Reilly 1997), so innovators’ work role is that of a team player who actively and heedfully contributes their own unique insights and abilities to the collective process of innovating (Dougherty and Takas 2004). Successful new products are developed by multifunctional teams who delve into user needs and link them with technological and other organizational knowledge to design, build, manufacture, and distribute a product (Souder 1987; Bacon et al. 1994). To work effectively on innovation, all team members need “T”-shaped skills (Iansiti 1993), or a deep understanding of their own area of expertise combined with intimate knowledge of how that expertise fits with others’ expertise to create value. Team members also need the ability to anticipate problems people in other departments might have, and to appreciate their constraints as they carry out their own work (Clark and Fujimoto 1991).

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Dougherty (2016) suggests that complexity requires additional skills for the work role of innovators. Work roles cannot be understood as separate steps that are abstracted out of the overall work context, since breaking processes down seeks to reduce rather than embrace complexity. Rather, work roles in complex systems encompass a holistic set of steps. To embrace a more holistic view of work, innovators need to develop and contribute to a collective mind, defined by Weick and Roberts (1993) as patterns of heedful interrelations in a social system. Heedfulness refers to the way behaviors are assembled: carefully, creatively, purposefully, and vigilantly. Heedful interrelating means that people construct their own actions (contributing) while envisioning a system of joint action (representing) and interrelate their action with the actions of others (subordinating). When people work collectively in a heedful manner, they interrelate their activities with more care, and together can comprehend more of a complex reality than is possible for one person acting alone. The Work of Innovation: Learning by Creating, Combining, and Recombining Knowledge into Novel Configurations The multifunctional innovation teams work together to create, combine, and recombine knowledge about the products’ possible functionalities and uses into novel configurations (Leonard-Barton 1995). Teams of product innovators generate certain knowledge content by using certain knowledge generating processes. Market–technology linking represents the knowledge content for new products (Dougherty 1992a; 2006). Technology is knowledge, or what we know and can do, and as such goes beyond being a simple artifact. Understanding user needs is also knowledge. Technologies provide possible solutions that need to be matched with problems or customer needs. Technology knowledge involves a deep appreciation of the possibilities that technologies can deliver, and how to achieve these possibilities. Technology knowledge also encompasses insights into diverse possibilities and how the trajectories of technology emergence may point at future possibilities. Technology can include manufacturing, supply chain, and distributions systems. On the market side, innovators need to understand what users want, how they would perceive possible solutions, and how any product would fit into their activities. Market knowledge also includes an understanding of the potential size of the market, how to reach users, what market channels to rely on, how to sell the product, and how customers and uses might emerge and evolve over time (Douglas and Hargadon, Chapter 10, this volume; Dewald and Truffer, Chapter 37, this volume). Dougherty (1992a) defined four aspects that underlie market–technology linking: visceralizing, assessing feasibility, figuring out fit with the firm, and connecting to trends. Visceralization is based on Schon’s (1983) discussion of how the codified aspects of professional practice can be enlivened so students can learn from experienced workers. Visceralizing emphasizes “gut feel” and experience, but also seeing the product in use to gain a sense of the nuances of user problems and how the technology can solve those problems. Innovators imagine the product in use, project how customers perceive value, appreciate what customers’ preferences and decision-making processes are, and aim to understand how the technologies they can bundle together will meet customer needs. For example, marketing cannot give the R&D department much help to make a product easy to use, because engineers need to visceralize the product in use.

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The three additional aspects of learning build on and reinforce visceralization. Feasibility involves demand forecasts and assessments of technical capabilities along with people’s insights into the possibilities. Innovators consider whether technology problems can be solved and manufacturing capabilities developed in time. They also consider the size of the market, how fast users will be able to figure out the new applications and what the competition is doing. Understanding fit with the firm identifies critical synergies with the firm’s capabilities and competitive strengths to enhance the new product’s functionality and competitive differentiation. Assessing emerging trends considers what might be in the technology and marketplace, and how the new product fits with those possibilities. Assessing trends adds important understandings about how technologies might evolve and what to anticipate and plan for, and also how markets might emerge around particular new products or applications. Complexity adds new wrinkles to the market–technology linking learning process. The knowledge in complex innovation systems is partial, fragmented, and widely dispersed among agents and agencies. Despite simplifying notions of science as an objective, dispassionate process of testing hypotheses to confirm theories (Knorr Cetina 1999; Grinnell 2009), learning at the frontiers of science relies on a discovery, not confirmatory, style of research (Nightingale 2004; Héraud, Chapter 4, this volume). Discovery research is situated in experimental tinkering, and in epistemic cultures that are based on distinct machineries for creating, warranting, and validating knowledge (Knorr Cetina 1999). Scientists draw heavily on a learned tacit background of knowledge that provides a “constellation of beliefs, values, techniques, and methods that are shared by a community of practitioners” (Turro 1986: 886). This machinery of knowing enables scientists to recognize and understand patterns in nature, to extrapolate and codify those patterns, and to also apply these new understandings to innovation. In a study of drug innovation, Dunne and Dougherty (2016) find that abductive learning routines apply the discovery style of emergent learning to the process of complex innovation. Abductive reasoning provides a way to create, combine, and recombine knowledge into viable new products, processes, and strategies despite the inherent complexity (Dougherty 2016). Abduction, first articulated by Charles Peirce and other pragmatic philosophers, is the deliberate reasoning that leads to scientific discoveries (Nesher 2001). According to Peirce, abduction is the best answer we have to problems of discovery, since abduction alone among the forms of reasoning originates possible explanations and introduces new ideas. Locke et al. (2008: 907) quote Peirce to explain: “[d]eduction proves that something must be; induction shows that something actually is operative; abduction merely suggests that something may be” (emphasis in original). Abduction “is the process of reasoning in which explanatory hypotheses are formed and evaluated” (Magnani 2001: 18). Dunne and Dougherty (2016) identify three abductive learning routines that innovators in complex systems cycle through repeatedly to create complex new products. Building on Feldman and Pentland (2003), routines are recognizable, repetitive patterns of interdependent action. But people do not carry out routines by rote, like standard procedures. Rather, people adapt their routines to the particular contexts they are in, and to the actions of people with whom they are working. In this way, routines are generative, or continually absorb contextual variations that help people adapt and continually create shared understandings and social actions.

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The first abductive learning routine is using clues to imagine a configuration of interdependencies that would constitute the product. For example, in drug discovery, innovators would imagine a configuration of interactions among molecular compounds, the disease process in question, and the rest of human biology that might constitute an effective therapy. Innovators construct a coherent story about how their emerging product will behave in the context of use. Clues, configurations, and imagination all synthesize useful information despite the fragmented and noisy nature in complex systems, so formulating hypotheses in this manner captures available information and variation in the problem space, and makes them meaningful. The second abductive learning routine is to evaluate the imagined configuration by elaborating and narrowing around the envisioned interdependencies. Innovators empirically inquire into the actual effects of their hypothesized configuration in order to assess the nature of the mechanisms that govern the interdependencies they imagine. In so doing, innovators surface new and deeper insights about how a product possibility might work in the context of use to generate value. Since so much is unknown in complex innovation systems, evaluating burrows into the mechanisms to explore how and why the configuration might work, what else may be going on, and what are the limits and contingencies. Innovators use the hypothesized configuration to sift through all the noisy information as they open up around possibilities to explore them, and then narrow down to situated aspects of interdependencies. The third abductive learning routine is reframing the hypothesized configuration by iteratively integrating across disciplines and experimental situations to accumulate and synthesize information. Reframing enables the innovators to holistically assess what they know so far and what they have learned. Different people see different aspects of the product possibility and how it might function in the context of use. Iteratively integrating helps to overcome competency traps, push ideas, cross-check possibilities, and generate a joint representation. The innovators are not simply searching, they are actively configuring (Glückler and Bathelt, Chapter 8, this volume). They drop some alternatives, develop new performance parameters, and adopt new consequences that seem more promising based on their collective learning. Reframing cycles back with a new hypothesis of the configuration of interdependencies to be evaluated again. Together these three abductive learning routines enable complex innovation because they build on available information despite the noise; they generate new meaning and new categories of knowledge; they keep the whole in mind; and they attend to the central unknowns in complex systems – that is, the interdependencies. Dougherty (2016) applies these abductive learning routines to all four subsystems of a complex innovation system: the projects, the knowledge development, the strategic, and the institutional. The Nature of Innovation Work: Emergent, Situated, and Concrete The work roles and learning processes indicate the underlying nature of the work: a process that is emergent, problem focused, situated rather than abstract, and hands-on. The work of innovation is an integrated flow of activities across all corporate functions. Creating, combining, and recombining all this knowledge requires that innovators situate their learning in actual contexts of technologies in use and in the actual contexts of users using technologies. Since the product is new, customers cannot specify its specific features

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and functions ahead of time. Rather, they need to try it out and learn how to use it. And since technologies are new or have not been used in this particular way before, innovators cannot know ahead of time exactly how technologies might work and what else is needed to ensure that the product will work as planned, or indeed as users expect it to. How Conventional Views of Work Prohibit the Actual Work of Innovation Unfortunately, the views of work that are embedded in business school curricula, in management textbooks and theories, and in the everyday practices of managing and organizing innovation, in many industries may actually prohibit the work of innovation (Schon 1983; Tsoukas 2005; Weick 2005; Weick and Roberts 1993; Dougherty 2001; 2006). This subsection summarizes how conventional hierarchical organizing and command and control management can prohibit, slow down, or divert the three categories of the actual work of innovation. First, conventional managing and organizing build on a simplified understanding of work roles. Workers are understood as interchangeable cogs in the big machine of the firm. Work roles are focused on separate steps that have been hierarchically decomposed into particular, abstracted work. Work roles are individualized so that workers can be held accountable for their work and managers can supervise each individual. Work itself is understood as an abstracted slice of operations that is predefined and measured apart from anything that anyone else was doing, so that workers can be held accountable for their part (Schon 1983; Brown and Duguid 1991; Barley 1996). Rewards focus on individual performance over the short term, and often on measurable outputs – all of which squash creativity. Second, conventional managing and organizing presupposes that the problems of work are defined ahead of time by managers, so emergent learning does not occur except outside the normal bounds. Most workers are presumed to engage in normal operations, or in carrying out the work of the firm as specified and following standard operating procedures. They do not engage much in learning. The learning that occurs would be incremental and a small extension of existing knowledge: categorizing problems as something already known, finding already known solutions that fit the category, and implementing them for this particular problem (Grandori 2010). Workers do not routinely create, combine, and recombine new insights and understandings. Such learning activities would be separated into special “skunkworks” (i.e. experimental laboratories). Third, the underlying nature of work in the conventional view of managing and organizing is predetermined, not emergent, decontextualized, not situated, and abstract, not hands-on (Tsoukas 2005). Decontextualization means that people cannot see the details of actual situations that might define the problem at hand more realistically and deeply, or help explain what needs to be done (Weick 2005). People therefore tend to insist that problems be defined in a way that allows them to apply their expertise in a general way, rather than be expected to understand how to apply their expertise to the problems as they really are (Leonard-Barton 1995). Bechky (2003) suggests that different functions may decontextualize the entire product innovation process by concentrating on only their part, so integrating the functions requires recontextualizing the functional knowledge into the whole process. This view of work overlooks the continuous flows of activities that

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comprise innovation work by focusing on outcomes rather than on the activities through which people produce those outcomes. My own research highlights the dominance of the conventional views of managing and organizing during the 1980s and 1990s (Dougherty 1992b; Dougherty and Heller 1994; Dougherty and Hardy 1996). By the early 1990s, most product innovators that I interviewed had understood that they needed to work on multifunctional teams and appreciate everyone’s insights and constraints on the team in order to create a viable new product. But their organizations prevented them from doing so. Most of these organizations were designed as bureaucracies, based on the hierarchical decomposition of the holistic practice of work into separate steps. Each step or function was managed and optimized separately, and communications were flowing up and down, not sideways. The result was a decontextualization of each function from collective work. From this I drew the conclusion that managers need to organize the practice of innovation, not just the different steps in the process, and they need to organize the entire firm around the practice of innovation (Dougherty 2001; 2006).

THE PRACTICE PERSPECTIVE EXPLAINS HOW TO CARRY OUT THE WORK OF INNOVATION The practice perspective explains how people throughout an organization can routinely (as Feldman and Pentland (2003) define the term) engage in the actual work of innovation, and still work in an organized and systematic manner. Workers are not cogs in a big machine, rather they are “professional practitioners” like physicians, nurses, or lawyers. Dougherty (2001) argues that all members of the organization, not just a select few, need to understand their work role as that of an innovator who works with teams to create, combine, and recombine knowledge into viable new products. Some people in all the functions might not always work directly on innovation teams, but they would nonetheless work to support and enable innovation. First, regarding work roles, the role of the “professional” is a familiar metaphor that most people understand. Professionals do not work by rote or standard procedures, or at least we assume not. Rather, we observe our physicians, auto mechanics, plumbers, and other professionals in our daily lives applying their expertise to our particular situations, drawing on their experience to figure out the actual problems and develop workable solutions. Barley (1996), Orr (1996), and Bechky (2003), among many others, demonstrate that even lowly technicians work as professional practitioners, while at the same time their managers inhibit work as practice. People do not require simplified jobs, and although they may be paid very poorly and managed badly, people do work as professional practitioners and leverage the emergent, situated knowledge in their practice that enables them to do a good job (Barley 1996). A related role metaphor is that of the “craftsman” (Sennett 2008), related to people who work knowledgably yet in the context, applying their capabilities and also pulling in situated knowledge to figure out actual problems. Second, the practice perspective depends on collective learning. Schon (1983) argues that professional practitioners across an entire practice reflect-in-action collaboratively. And Nightingale (2004) suggests that scientists working at the frontier of knowledge collaboratively use the discovery style of inquiry based on active experimental intervention.

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Rather than isolate a particular option, scientists and other knowledge professionals create something new to learn from by intervening to test specific mechanisms. These interventions build up understandings and inform judgments about what might be working. With this discovery style of inquiry, innovators do not ask if something works, but rather how it works. Reflection-in-action enables professional practitioners to experience surprise and puzzlement and see the unexpected. Practitioners surface and criticize their initial understanding of the phenomenon that now seems surprising or unstable, construct a new description of it, and test that new description using an on-thespot experiment. Reflective practitioners step into a problem situation, impose a frame on it, and follow the implications of the disruption thus established while remaining open to the situation’s back talk. Other uses of the practice perspective also highlight knowing-in-doing, and learning in a situated, contextualized fashion (Tsoukas 2005). Situated activities would include forming relationships with clients to elicit insights that might not otherwise be revealed, interacting with colleagues over the situation, considering subtle differences in the appearance of material (e.g. cancerous cells; see Barley 1996) or in equipment displays (e.g. readings in an intensive care unit), and improvising to surface problems. The skills for knowing comprise the “artful competence” (Schon 1983) of applying principles of the profession to unique situations, and making do with the resources available (Lave and Wenger 1991; Orlikowski 2002). Knowledge from practice is produced continuously in situated action, as people draw on their physical presence in a social setting, on their cultural background and experience, and on sentient and sensory information. Practicebased knowledge does not exist independently of social action and its content does not necessarily mean the same thing to all people. Finally, the underlying nature of work as practice emphasizes the emergent, problemfocused, hands-on nature of the work. Schon (1983) emphasizes that when situations are complex, unique, and fraught with value conflict, ends are not fixed and clear but rather are confused and conflicting, and there is no clearly identifiable problem to be solved. Professional practitioners must first construct the problem from the materials of problematic situations which are puzzling, troubling, and uncertain. Problem-setting is the process by which innovators select what they will treat as “things” of the situation, set the boundaries of their attention to it, and impose upon it a coherence which allows them to say what is wrong and in which directions the situation needs to be changed. It is a process in which, interactively, innovators name the things to which they will attend and frame the context in which they will attend to them. Dougherty (2004) studied innovation in professional service firms to dig into the practice-based nature of innovation in a setting where innovators are professional practitioners (IT specialists, civil engineers, trainers). In professional services, the market–technology knowledge is embedded in the ongoing practice of delivering services. Dougherty found that while most service providers could fit their knowledge to particular user needs, they struggled with articulating the very situated market–technology linkages into a more general service innovation that they could sell to more users. Their practices of working with customers tended to remain tacit, so Dougherty (2004) found that service innovators had to articulate their practices of working with them. To bring their practices out of the background and articulate them, the study suggests that service innovators can collectively enact three kinds of activities in their everyday work: interweaving

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designing and using (or routinely doing their particular designing activity in terms of its impact on using, not apart from it), participating in the whole flow of designing and using (or routinely doing their part in terms of its relationship to the whole, not apart from the whole), and reflecting in action (or routinely iterating from emergent knowing to articulating that knowledge). These activities constitute a common ground of social action, so people can engage in situated learning and make sense of what they learn in similar ways across the organization. The Practice Perspective on Organizing for Innovation The practice perspective suggests fundamental principles for organizing the firm to support the process of innovation (Orlikowski 2002). An organization is a system of practices. Since practice-based knowledge is embedded in work activities, the activities themselves must be replicated across the organization to recreate the knowledge. Dougherty (2001; 2006) develops a theory of organizing based on the principle of defining work as practice. Once the work is understood as practice, three fundamental principles of organizing ensue. Rather than differentiate work into separate vertical functions, work should be differentiated into holistic horizontal flows of four practices, each focusing on solving a major innovation problem. Rather than integrating the differentiated work grouping with hierarchy, the communities of innovation practice should be integrated strategically by iterating across communities to define and shape emerging directions for innovation. Rather than control the organization with top-down directives, it should be controlled with rules and resources that foster innovation. Other studies of innovation find support for this approach to organizing (Markham and Lee 2013), or are consistent with it (Van de Ven 1986; Gibson and Birkinshaw 2004; Foss et al. 2011). Innovation work can be differentiated into four distinct communities of practice that each emphasize an essential innovation problem to be continually set and solved: the project practice, the knowledge or capability practice to support ongoing innovation, the business management practice to match up innovations with markets, and the strategic management practice to marshal resources to support multiple innovations and capability developments. PDMA surveys (Adams 2004; Markham and Lee 2013) show that the more effective innovative businesses are organized around these four practices. The project community of practice of innovation encompasses all those people who work on innovation projects. This community of practice involves pulling together disciplinary, organizational, and industry information into market–technology linking for individual new products. Since an organization of any size harbors multiple new product projects, each perhaps going off in a different direction, the organization also must develop knowledge capabilities to support all these activities. The second community of innovation practice involves all those people who develop capabilities in R&D, marketing, manufacturing, and other functions. This community develops long-term capabilities that continually support all the innovation projects by anticipating and developing needed technologies, market know-how, supply chain possibilities, and so on, by maintaining and building up the expertise in people, and by ensuring that people work effectively on project and capability teams. The third community of innovation practice involves business managers who bundle the innovations together with other resources and match those up to markets for revenue generation. Rather than fixate on current functioning, if businesses

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must adapt to market and technology changes, they must adopt new products. Finally, the fourth community of innovation practice involves corporate managers who marshal the long-term investments to support and direct ongoing capability development, define the firm’s overall practice so that businesses can stay aligned, and continually leverage the experiments in all three other practices regarding market–technology linking. Work as the practice of innovation lies at the heart of all four communities of practice, and provides a common logic. Business and strategic managers integrate all the innovation projects by connecting them with markets and by directing the kinds of innovation thrusts that are desired. Capability managers integrate innovations as well by providing a common core of technologies and marketing and other capabilities, and by managing people within their areas to engage continually in the practice-based role of innovation. Specific rules and resources also integrate all four communities of innovation practice and help control and shape the collective work. Many scholars in technology emphasize a few simple rules that drive a system (Jelinek and Schoonhoven 1990; Brown and Eisenhardt 1997). Giddens (1979) draws on the idea that humans use rules to “go on” in social life. We have many rules for parenting, working, being a good neighbor, and so on, so rules are familiar. Resources in structuration theory are the media through which power is exercised. Giddens’ (1979) ideas of structuration emphasize the everyday constitution and reconstitution of social life as regularized practices. In structuration theory, social structure refers to the set of rules and resources that are instantiated in recurrent practice. According to Giddens (1979: 66): “[t]o study the structuration of a social system is to study the ways in which that system, via the application of generative rules and resources, and in the context of unintended outcomes, is produced and reproduced in interaction.” Structuration underpins the practice perspective. Feldman and Orlikowski (2011) provide more insight into rules and resources in theory. Dougherty et al. (2005) apply the theory of structuration via rules and resources to innovation. They find that people in more innovative organizations operate with three basic rules that support organization-wide innovation practices, and that generate three essential resources to enable innovation. The three rules are (i) look for opportunities to add value by exploring options and alternatives; (ii) take responsibility for the whole project, and expect everyone to contribute to the innovation throughout its life cycle; and (iii) all knowledge is valuable so respect expertise and make your own expertise accessible to others. The innovators and managers interviewed all seemed to work with these basic rules in mind. The rules generated important social resources for innovation. The first rule of looking for opportunities and exploring options provides the resource of a variety of existing options and alternatives that others have explored to address problems. The second rule of taking responsibility for the whole project provides people with the resource of others’ time and attention to their project. The third rule of valuing knowledge and making it accessible to others provides people with the resource of extensive and diverse knowledge for innovation. In contrast, people in non-innovative organizations operated with other basic rules that generate the conventional management approach: (i) work to eliminate problems; (ii) separate responsibility and foster autonomous action; and (iii) focus on outcomes and results. The resources are the ability to limit the actions of others, control over one’s own domain, and control over how one’s expertise is applied – all of which preclude interactive, integrated efforts of collective innovation.

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These fundamental principles for organizing and controlling from the practice perspective provide the foundation for organizing. Once they are developed, managers can create and implement the particular kinds of formal, quasi-formal (Jelinek and Schoonhoven 1990), and semi-formal (Brown and Eisenhardt 1997) principles that fit particular business units and competitive situations. All the tools, techniques, procedures, and processes developed to support innovation matter, because they capture necessary steps and knowledge. Innovation as practice provides the intelligent actions through which people can carry out all these procedures. The two always go together.

CONCLUSION Understanding the actual work of innovation as practice explains how people can collectively do all the challenging work of innovation effectively and routinely. Practice explains how people can know by doing and by situating. The practice perspective is grounded in a broader practice lens adopted by some organizational theorists to study the everyday activity of organizing (Feldman and Orlikowski 2011). Practice approaches highlight human actors and agency, and see people’s everyday actions as consequential in producing the structural contours of social life. Professional practitioners possess extensive experience and background knowledge. Working as practitioners enables people to use all this knowledge intelligently when they confront tricky, ambiguous challenges like product innovation. Work as practice is enabling, expanding, engaging, and empowering. The practice perspective also provides a clear, holistic understanding of the principles of organizing for innovation. These principles enable other inputs such as intellectual capital, certain cultural norms and values, and visionary leadership to function effectively as innovation enablers. Practice also provides the connective tissue among the diverse community of innovation practice that needs to be carried out within organizations, and among organizations in infrastructures of complex innovation. Researchers and managers are encouraged to develop the practice of innovation more fully and richly in additional domains of innovation.

REFERENCES Adams, M. (2004) PDMA Comparative Performance Assessment Study. Chicago IL: PDMA Foundation. Andrew, J., J. Manget, D. Michael, A. Taylor, and H. Zablit (2010) ‘Innovation 2010’. BCGperspectives.com. Bacon, G., S. Beckman, D. Mowery, and E. Wilson (1994) ‘Managing Product Definition in High Technology Industries: A Pilot Study’. California Management Review, 36: 34–56. Barley, S. (1996) ‘Technicians in the Workplace: Ethnographic Evidence for Bringing Work into Organization Studies’. Administrative Science Quarterly, 41: 404–441. Bechky, B. (2003) ‘Shared Meaning Across Occupational Communities: The Transformation of Understanding on a Production Floor’. Organization Science, 14: 312–330. Bobrow, E. (1997) The Complete Idiot’s Guide to New Product Development, New York: Alpha Books. Brown, J. and P. Duguid (1991) ‘Organizational Learning and Communities of Practice’. Organization Science, 2: 40–57. Brown, S. and K. Eisenhardt (1997) ‘The Art of Continuous Change: Linking Complexity Theory and TimePaced Evolution in Relentlessly Shifting Organizations’. Administrative Science Quarterly, 42: 1–35. Clark, K. and T. Fujimoto (1991) Product Development Performance, Boston MA: Harvard Business School Press. Cohendet, P., G. Parmentier and L. Simon (2017) ‘Managing Knowledge, Creativity and Innovation’. In

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H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton MA: Edward Elgar Publishing, 197–214. Cooper, R. (1998) Product Leadership: Creating and Launching Superior New Products, Reading, MA: Perseus Books. Day, G. (1990) Market Driven Strategy: Processes for Creating Value, New York: Free Press. Dewald, U. and B. Truffer (2017) ‘Market Formation and Innovation Systems’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton MA: Edward Elgar Publishing, 610–624. Dougherty, D. (1992a) ‘A Practice-Centered Model of Organizational Renewal through Product Innovation’. Strategic Management Journal, 13: 77–92. Dougherty, D. (1992b) ‘Interpretive Barriers to Successful Product Innovation in Large Firms’. Organization Science, 3: 179–202. Dougherty, D. (2001) ‘Re-imagining the Differentiation and Integration of Work for Sustained Product Innovation’. Organization Science, 12: 612–631. Dougherty, D. (2004) ‘Organizing Practices in Services: Capturing Practice-Based Knowledge for Innovation’. Strategic Organization, 2: 35–64. Dougherty, D. (2006) ‘Organizing for Innovation in the 21st Century’. In S. Clegg, C. Hardy, T. Lawrence, and W. Nord (eds), Handbook of Organization Studies, 2nd edn, London: Sage, 598–617. Dougherty, D. (2016) Taking Advantage of Emergence: Productively Innovating in Complex Innovation Systems, Oxford: Oxford University Press. Dougherty, D., D. Dunne, and H. Barnard (2005) ‘The Rules and Resources that Generate the Dynamic Capability for Sustained Product Innovation’. In K. Elsbach (ed.), Qualitative Organizational Research, Greenwich CT: Information Age Publishing, 37–74. Dougherty, D. and C. Hardy (1996) ‘Sustained Product Innovation in Large, Mature Organizations: Overcoming Innovation to Organization Problems’. Academy of Management Journal, 39: 1120–1153. Dougherty, D. and T. Heller (1994) ‘The Illegitimacy of Successful Product Innovation in Established Firms’. Organization Science, 5: 200–218. Dougherty, D. and H. Takas (2004) ‘Heedful Interrelating in Innovative Organizations: Team Play as the Boundary for Work and Strategy’. Long Range Planning, 37: 569–590. Douglas, Y. and A. Hargadon (2017) ‘Domesticating Innovation—Designing Revolutions’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton MA: Edward Elgar Publishing, 152–164. Dunne, D. and D. Dougherty (2016) ‘Abductive Reasoning: How Innovators Navigate in the Labyrinth of Complex Product Innovation’. Organization Studies, 37(2): 131–159. Feldman, M. and W. Orlikowski (2011) ‘Theorizing Practice and Practicing Theory’. Organization Science, 22: 1240–1253. Feldman, M. and B. Pentland (2003) ‘Reconceptualizing Organizational Routines as a Source of Flexibility and Change’. Administrative Science Quarterly, 48: 94–118. Ferraro, F., D. Etzion, and J. Gehman (2015) ‘Tackling Grand Challenges Pragmatically: Robust Action Revisited’. Organization Studies, 36: 363–387. Foss, N., K. Laursen, and T. Pedersen (2011) ‘Linking Customer Interaction and Innovation: The Mediating Role of New Organizational Practice’. Organization Science, 22: 980–997. Gibson, C. and J. Birkinshaw (2004) ‘The Antecedents, Consequences, and Mediating Role of Organizational Ambidexterity’. Academy of Management Journal, 47: 209–226. Giddens, A. (1979) Central Problems in Social Theory: Action, Structure, and Contradiction in Social Analysis, Berkeley CA: University of California Press. Glückler, J. and H. Bathelt (2017) ‘Institutional Context and Innovation’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton MA: Edward Elgar Publishing, 121–137. Grandori, A. (2010) ‘A Rational Heuristic Model of Economic Decision Making’. Rationality and Society, 22: 477–504. Grinnell, F. (2009) The Everyday Practice of Science, New York: Oxford University Press. Héraud, J.-A. (2017) ‘Science and Innovation’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton MA: Edward Elgar Publishing, 56–74. Iansiti, M. (1993) ‘Real-World R&D: Jumping the Product Generation Gap’. Harvard Business Review, May– June: 138–147. Jelinek, M. and C. Schoonhoven (1990) The Innovation Marathon: Lessons from High Technology Firms, Oxford: Basil Blackwell. Knorr Cetina, K. (1999) Epistemic Cultures: How the Sciences Make Knowledge, Cambridge MA: Harvard University Press.

Innovation in the practice perspective

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Lave, J. and E. Wenger (1991) Situated Learning: Legitimate Peripheral Participation, Cambridge: Cambridge University Press. Leifer, R., C. McDermott, G. O’Connor, L. Peters, M. Rice, and W. Veryzer (2000) Radical Innovation: How Mature Companies Can Outsmart Upstarts, Boston MA: Harvard Business School Press. Leonard-Barton, D. (1995) Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Boston MA: Harvard Business School Press. Locke, K., K. Golden-Biddle, and M. Feldman (2008) ‘Making Doubt Generative: Rethinking the Role of Doubt in the Research Process’. Organization Science, 19: 907–918. Magnani, L. (2001) Abduction, Reason, and Science: Processes of Discovery and Explanation, New York: Kluwer Academic/Plenum Publishers. Markham, S. and H. Lee (2013) ‘Product Development and Management Association 2012 Comparative Performance Assessment Study’. Journal of Product Innovation Management, 30(3): 408–429. Nembhard, S., J. Alexander, T. Hoff, and R. Ramanujam (2009) ‘Why Does the Quality of Healthcare Continue to Lag? Insights from Management Research’. Academy of Management Perspectives, 23(1): 24–42. Nesher, D. (2001) ‘Peircian Epistemology of Learning and the Function of Abduction as the Logic of Discovery’. Transactions of the Charles S. Peirce Society, 37: 23–57. Nightingale, P. (2004) ‘Technological Capabilities, Invisible Infrastructure and the Un-social Construction of Predictability: The Overlooked Fixed Costs of Useful Research’. Research Policy, 33: 1259–1284. Orlikowski, W. (2002) ‘Knowing in Practice: Enacting a Collective Capability in Distributed Organizing’. Organizational Science, 13: 249–272. Orr, J. (1996) Talking About Machines, Ithaca NY: ILR/Cornell University Press. Pisano, G. (2006) Science Business: The Promise, the Reality, and the Future of Biotech, Boston MA: Harvard Business School Press. Schon, D. (1963) ‘Champions for Radical Innovation’. Harvard Business Review, March–April: 77–86. Schon, D. (1967) Technology and Change, Oxford: Pergamon. Schon, D. (1983) The Reflective Practitioner: How Professionals Think in Action, New York: Basic Books. Sennett, R. (2008) The Craftsman, New Haven CT: Yale University Press. Simon, H. (1977) Models of Discovery and Other Topics in the Methods of Science, Dordrecht: D. Reidel Publishing. Snowden, D. and M. Boone (2007) ‘A Leader’s Framework for Decision Making’. Harvard Business Review, November: 69–76. Souder, W. (1987) Managing New Product Innovations, Lexington MA: Lexington Press. Tsoukas, H. (2005) ‘Noisy Organizations: Uncertainty, Complexity, Narrativity’. In H. Tsoukas (ed.), Complex Knowledge: Studies in Organizational Epistemology, Oxford: Oxford University Press, 338–377. Turro, N. (1986) ‘Geometric and Topological Thinking in Organic Chemistry’. Agnew Chemistry, International Edition English, 25: 882–901. Tushman, M. and C. O’Reilly (1997) Winning through Product Innovation, Boston MA: Harvard Business School Press. Van de Ven, A. (1986) ‘Central Problems in the Management of Innovation’, Management Science, 32: 590–607. Van de Ven, A., D. Polley, R. Garud, and S. Venkataraman (1999) The Innovation Journey, New York: Oxford University Press. Weick, K. (2005) ‘Organizing and Failures of Imagination’. International Public Management Journal, 8: 425–438. Weick, K. and K. Roberts (1993) ‘Collective Mind in Organizations: Heedful Interrelating on Flight Decks’. Administrative Science Quarterly, 38: 357–381.

10. Domesticating innovation—designing revolutions Yellowlees Douglas and Andrew Hargadon

INTRODUCTION In the first three days after it unveiled its iPhones 6 and 6 plus, over ten million consumers lined up to buy Apple’s latest offering, topping the record five million units of the iPhone 5 and nine million units of the 5c and 5s sold in 2012 and 2013, respectively (Hutchinson 2014). Few, if any, of the 14 million users who handed over their plastic for the latest iPhones would have been aware of debates by the technorati, bloggers, and business analysts over whether Apple’s latest technical and OS design tweaks were yet another example of Apple getting exactly right the look and feel consumers crave. Depending on which side of the argument one stands, Apple has fostered a cultish following by convincing a substantial customer base that its design is the epitome of cool (Thompson 2013). Or, in a similar if more jaded viewpoint, Apple’s design offers a superficial difference in a market filled with less-expensive products that operate just as speedily and efficiently, if not identically, to Apple’s (Gruber 2013a). In both cases, Apple’s product design is as crucial to consumer demand and willingness to pay higher price points as the same quality design is to Mercedes and Porsches, brands that deliver a product with the same functionality as a Kia, more or less. However, the current debate over design as the great differentiator, especially with digital technologies, misses a key point. Certainly, design figures heavily in our decisions on which smartphone to purchase or which tablet or laptop we covet. But design plays a far more crucial role in our adoption and use of technology than the pundits acknowledge— or, quite possibly, even realize. Design creates the commodity in the first place. In fact, without the right sort of design features, innovations can languish for decades without mass adoption (Hargadon and Douglas 2001). Before design differentiates, design domesticates, translating everything that is foreign in new technologies into terms we can understand, inviting us to use something entirely unfamiliar to support long-established routines and needs. Even as Remington Rand’s UNIVAC promised to transform business computation by replacing the established punch card systems, beginning within the insurance industry, IBM’s far more conventional approach won greater traction because it involved building computing components that fit discretely both within the physical line of punch card machines and within the organizational units overseeing them (Yates 1999). We may think that innovation proceeds in revolutionary, disruptive leaps but instead it inches forward by nudging users with evolutionary steps that first conform entirely to the routinized processes already in place. Nevertheless, ultimately, as Hughes (1987) noted, innovation cycles through non-discrete stages, from invention, development, innovation, and diffusion, to growth, competition, and consolidation—in stages that are seldom sequential but most often involve overlaps, loops, and backtracking (see also Cohendet and Simon, Chapter 3, this volume). At key points in this cycle, and under-appreciated in the current literature on innovation, design 152

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plays key roles in (1) invention and development, where design domesticates a novel offering to its intended users, and (2) in innovation, growth, and competition, where design differentiates an offering in the eyes of those same users. To date, the innovation literature has largely ignored the complementary yet opposing cycle invoked by these two faces of design. As this chapter’s cases demonstrate, robust design balances between the opposing and rapidly changing market needs for domestication and differentiation. Domestication makes users comfortable with an innovation, which then enjoys not only widespread and fast diffusion but also engenders competition, as new entrants to the market identity opportunities to follow and improve on the initial invention. Users, once domesticated, rapidly adapt to such innovations and become free to consider these newer and more advanced versions. To subsequently maintain its market share, the innovation’s originators must now differentiate themselves from similar products offered by emerging competitors. This differentiation, in turn, often triggers new uses or imports yet other new technological innovations that then require yet more domestication. Thus domestication and differentiation chase one another in a cycle that can evolve with dazzling rapidity, especially accelerated when the innovations involve not physical objects but, instead, software and code. We explore the specific way in which this cycle of domestication–differentiation drives innovation and competition alike. Innovations gain traction from the ways in which they evoke embedded understandings while extending our reach and range. This understanding of innovation, driven by design, represents a purposeful invoking of behavior both congruous and incongruous with consumers’ prior habits, at the same time as this understanding overturns common assumptions that new technology wins because of the economic or performance advantages it offers consumers. Today, much of the literature on innovation and adoption focuses on differentiation and competition. Surprisingly, it neglects the ways in which design first domesticates its users, taming the unrecognizable by mimicking long-understood objects and deeply embedded practices—even as it seeks to transform the ways in which we interact with the world around us.

DESIGN AS DIFFERENTIATING AND DOMESTICATING The more noticeable and widely discussed purpose of design is to set products apart from their competition and, more broadly, the threat of commodification. This role of design dominates discussions with each new model year in traditional design-driven industries such as fashion, automobiles, cinema, and furniture—where new offerings must constantly set themselves apart from old. The oxymoronic “new and improved” labels on consumer goods tell us that, even if the container looks identical to the one we bought last year, the contents are different. The automotive and fashion industries constantly draw our attention to a few inches difference here, a sharper angle there, minute changes in industries that thrive on obsolescence. Even if the products themselves remain stubbornly durable, consumers must feel the object they already own merits discarding in favor of the minute differences that make this year’s version clearly superior or trendier. As psychologists who study consumer behavior have noted, the right level of schema incongruity— some discontinuity with our perceptions of how an object should look and feel—creates novelty (Myers-Levy and Tybout 1989, p. 40). This novelty, in turn, elicits arousal, with

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moderate incongruities regarded as more interesting and valuable than objects that hew closely to existing schemas (Mandler 1982, p. 22). In fact, to maintain satisfaction or pleasure, consumers will seek out moderate amounts of novelty, preferring this mild jolt of differentiation over completely familiar or extremely novel items (Venkatesan 1973, p. 376). In other words, the most successful products nudge our understandings and uses of them along incrementally—a far cry from the leaps we equate with the truly innovative, which users have long misunderstood as being sharply discontinuous and utterly novel, compared to what preceded it. The notion that innovations succeed by being evolutionary, not revolutionary, flies somewhat in the face of the public perception of inventions. To a public accustomed to switching on a light or picking up a telephone, inventions succeed because of their dazzling discontinuity with the technologies that preceded them. Yet, as scholars long ago demonstrated, users remain blind to the utility of innovations unless they fit within tightly circumscribed schemas that enable them to perceive an object’s purpose and to understand the precise methods for making use of it (Schank and Abelson 1977; Rumelhart 1986). Technologies that enjoy rapid adoption also rely on the ability of design to evoke familiar understandings and patterns of use, what Donald Norman has dubbed “affordances” (1998, p. 123) and George Basalla, “skeuomorphs” (1988, p. 107). Norman’s affordances describe the metaphors that invoke a familiar schema, like the knob for switching on electric lights that mimicked the same knob users twisted to adjust gas flames (Conot 1979) while Basalla’s skeuomorphs refer to the particular and concrete design features involved in ensuring novelty fits comfortably within an already-established framework for understandings and use. We can think of skeuomorphs as the physical evocations of familiar habits and features while affordances invite us to interpret what we see as consistent with the practices we have just abandoned. Computers gained their first footholds in the consumer market when Apple replaced line-command systems with affordances that relied on a desktop metaphor, complete with file folders and a trash can—all staples of users’ everyday physical offices.

EDISON’S INCANDESCENT LIGHTING SYSTEM Our earlier work considered the ingenious ways in which Edison deliberately sought to hide the novelty of his electric light under the mantle of schemas familiar to users of gas lighting (Hargadon and Douglas 2001). Edison relied on both skeuomorphs—the physical embodiments of connections to past technologies and ways of using them—and on affordances—the more institutional, or symbolic, affordances connecting users to past understandings to make sense of current innovations (see Glückler and Bathelt, Chapter 8, this volume). For example, Edison relied on entirely superfluous shades for his incandescent bulbs, which required no shelter from air currents to keep the light from flickering (Israel 1998, p. 186). Moreover, Edison introduced electric lights with the same weak, yellowish 12-watt light, inadequate for close work, that was typical of gas lamps—despite having created bulbs capable of shedding three times that light during his development of its prototype (Israel 1998, p. 186). And Edison doggedly pursued the development of a circuit enabling users to turn on and off individual lights independently, exactly as with gas lighting, despite the significant technical challenges required of the electric technology of the time.

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Indeed, the design of his entire system relied on a central generating plant and distribution mains as a means to hide the complexity of electric systems and simply mimic the customer’s experience with gas lighting, in which engagement with the technology was limited to turning on and off the lamps and paying the utility bill. This was costly in several senses. First, he had to develop a means of centrally transmitting electricity— and an entire system that accompanied it—when he was already selling isolated electric systems into the homes of affluent clients, including J.P. Morgan (whose offices provided the highly public debut of the Edison Electric Company’s signal invention). Second, in a more symbolic twist, Edison incorporated under gas company statutes, enabling him to lay his lines underground, rather than above it, exactly like gas mains, despite the space above New York City streets being darkened with a web of overhead wires (Silverberg 1967, p. 173). Third, the laying of expensive copper wiring accounted for a third of Edison Electric’s capital expenditures and also forced him to develop the means to insulate those wires from the surrounding streets (Hughes 1983, p. 39). Fourth, he paid high premiums for laying his electric wiring, mostly because the gas companies were firmly in the pockets of Tammany Hall, supposedly the most corrupt administration in New York City history (Granick 1975). And, fifth, Edison invoked gas companies’ methods for measuring consumption by metering usage, rather than flat rate billing, despite his having to invent a meter, which took six months and relied on a solution that froze during the winter (Conot 1979, p. 198). With these design choices, Edison made electricity seem indistinguishable from gas, even though he would swiftly move to differentiate his system of lighting from gas lighting once he had introduced it successfully. This domestication-by-design strategy has proven highly successful in many key innovations of the last century, including corporate computers, personal computing, and smartphones. Edison spelled out this strategy explicitly in one of his notebooks: “Object, Edison to effect exact imitation of all done by gas so as to replace lighting by gas with lighting by electricity” (Basalla 1988, p. 48).

IBM AND THE INSURANCE INDUSTRY The advent of general-purpose computers during World War II led to their development for and introduction into corporate America in the early 1950s. The first such computer was the Remington Rand UNIVAC 1, the direct successor of the ENIAC system funded by the U.S. Government and developed by J. Presper Eckert and John Mauchly of the University of Pennsylvania. The UNIVAC 1 was introduced to the market in 1951. IBM quickly followed, announcing their IBM 650 computer in 1953. As an early supplier of punched card technology for storing actuarial data to insurance companies, IBM had an advantage in knowing the customers and almost immediately dominated its competitor, Remington Rand, by minimizing the differences between the new computer and old punch card system. For example, by relying on two 80-column punched cards for its input and outputs, it would have looked nearly identical to the cards used for tabulation and storage in 1930 (Yates 1999). Unusually, IBM’s domestication-by-design strategy was largely dictated by the insurance companies, which stressed the stability and reliability of data input, verification, and retrieval, and insisted on maintaining tabulator cards, even as Remington Rand’s UNIVAC computer made faster and more efficient storage

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on magnetic tape possible. Instead, the insurance industry relied on card-to-tape and tape-to-card converters, as well as on high-speed printers. At the same time, the industry took delivery of the first commercial computers that could address labor shortages, shortages of storage space, and pressures to make operations more efficient. Despite Remington Rand attempts to reassure customers that UNIVAC stood for “Unchanging Need Is for Volumes of Additional Clerks” (Yates 1999, p. 15), IBM outsold Remington Rand in computers in the insurance industry by situating its card- and magnetic-drumbased system as a means of tabulating, maintaining, and printing records, rather than mechanizing actuarial calculations. Unsurprisingly, given the very nature of its business, the insurance industry was reluctant to risk a wholesale conversion of reliable processes to new and unfamiliar technology. IBM’s use of decades-old punch cards increased efficiency by combining onto only three cards what might have previously existed in ten or more files (Yates 1999, p. 10) but stopped well short of computerizing the actuarial side of the insurance industry and the more radical changes promised by Remington Rand’s UNIVAC system.

THE APPLE MACINTOSH Fast-forward thirty years, and the Apple Macintosh enters the scene. The 1984 Macintosh was a microprocessor with a single, built-in disk drive and 64K of RAM, a rectangle with the handle built into its plastic case that charmingly suggested luggability and the square, greyish monitor that likewise invoked mid-1960s cathode-ray televisions that also sported grey, square screens and handles atop a box that would require a strongman to heft it. But the Macintosh, far from being the revolutionary product touted in its famous 1984 Superbowl commercial was, in fact, a purely evolutionary object. Its most prominent feature was the graphical user interface (GUI) that required no knowledge of command-line interfaces or directory-level file management and was just a virtual replication of the desktop complete with documents, file folders, and a trash can. This design approach was not novel. In a 1968 public demo, Douglas Engelbart had introduced the mouse, cursor, point-and-click editing, and elements of the GUI (Ashman and Simpson 1999). However, Xerox, ironically, invented the item that domesticated personal computing by creating the menu bar, an icon-driven interface, and, most importantly, the desktop skeuomorph with its 1981 Star interface (Card 1996). In fact, one of the authors recalls discussing Xerox PARC’s Star system with a former manager who worked at PARC prior to Apple’s appropriation of the interface’s primary elements into its Lisa and Macintosh. Far from thinking he had been working with something innovative, he merely saw Star as an interface with dramatically limited use, particularly for a company that focused on photocopiers (Douglas 1988). However, the impact on personal computing for home users was dramatic: the computer was simply an automated, tidier version of their own physical desktop, and the handful of early applications created for the Macintosh, including MacWrite, MacPaint, and Lotus 1-2-3 boasted features familiar to their physical, non-binary counterparts. MacWrite, for example, included the now-familiar tab bar across the top of the page with a helpful ruler, establishing the physical constraints of the virtual page, in addition to the typeface, Courier, that precisely resembled the type produced by a typical typewriter (Lewis and Rieman 1993).

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However, Apple soon discovered, just as Edison had earlier, that once design domesticates the novelty of an innovation, competitors quickly move in. And, true to the phases for the growth of systems outlined by Hughes (1987), if innovation translates into growth with an increase in demand, then competition rapidly follows. And, with competition comes the need for differentiation to hang onto the market these companies invented—and domesticated. Through the 1980s and 1990s, the so-called personal computing market was actually anything but personal; corporate demand made up roughly 75 percent of its sales (Thompson 2013). A variety of manufacturers increasingly cranked out undistinguished boxes that ran on kludgy Windows platforms. What the corporate sector wanted was uniformity, predictability, compatibility, and cost efficiency. All these demands fit quite nicely with the personal computer (PC), a recognizable descendant of UNIVAC that competed on differentiation based not on design but on performance specifications and price points. No one cared what the computers themselves looked like—they were all stowed on the floor beneath a desk. No one cared that PCs were difficult to install or their networks hard to configure, since in-house or hired techies typically handled their installations. And no one cared that users adapted their workflows to a GUI that was, at the end of the day, a thinly veiled line-command system with a few icons for window-dressing. In the 1980s, only computer geeks owned PCs. Even into the 1990s, only workaholics and outliers regularly used a PC at home.

CYCLING BETWEEN DOMESTICATION AND DIFFERENTIATION Consumers aren’t necessarily chasing the next new thing, at least not initially. Instead, once robust design domesticates the strange (Hargadon and Douglas 2001), consumers end up facing more familiar decisions, choosing between efficiency, effectiveness, and price points of technologies to determine which product or service they adopt. As they watch a sudden demand for an innovation spring up, competitors seek a slice of the newly emerging and, usually, lucrative market—part of Hughes’ (1987) cycle of the development of innovations. Competitors can end up stealing away the hard-won markets created by the likes of Edison and Apple by introducing improvements to efficacy, as in refinements to the alternating current (AC) system that brought it into direct competition with direct current (DC) (McNichol 2006) and the introduction of Microsoft’s Windows that offered users the same features of the initial Macintosh Operating System (OS), albeit with a clumsy and unreliable interface. However, both Tesla’s AC system, adopted by Westinghouse, and the availability of Windows to manufacturers of low-cost PCs created serious competition with Edison and with Apple.

EDISON AND THE LIMITATIONS OF DC POWER Edison’s system of electric lighting was shaped by his desire to introduce as evolutionary what was a revolutionary new technology. At the same time, it was also shaped by the limitations of the technology at the time, particularly DC. Ironically, Edison’s success in

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domesticating electricity created the conditions for others to introduce novel technologies, like Westinghouse’s AC dynamos, transformers, and motors, that while irrelevant in isolated electric systems became superior in the centralized generation systems of Edison’s utility design. Put simply, the DC system operated at the same voltage level throughout, relying on 110 volts to match the amount of voltage high-resistance carbon filament lamps could withstand (McNichol 2006). However, DC relied on generating plants like Pearl Street feeding heavy distribution conductors, with customer lighting tapping off the current. In addition, resistance of the system conductors was so high that generating plants lost significant voltage. As a result, generating plants could only distribute power within a mile of its customers. Moreover, higher voltages, which could increase the distances across which a plant could provide power, could not function readily within the DC system because no low-cost technology could step high transmission voltage down to the lower voltages tolerated by Edison’s incandescent bulbs (McNichol 2006). In this case, Edison, with so many other innovations staked to the DC system, all envisioned when he introduced the incandescent bulb—from elevators to sewing machines (Israel et al. 1998, p. 505)—remained the champion of the DC system in which his company and investors had sunk substantial capital (despite Edison having patented the design for an alternative AC system) (Israel 1998). Moreover, Edison stubbornly now attempted to differentiate himself from Westinghouse and AC power by claiming that DC power was inherently democratic, since local municipalities could build electric plants to suit their needs and not rely on monopolies to supply their electricity (Brands 1995). Significantly, in both public opinion and economics, Westinghouse and Tesla emerged the clear-cut victors, despite Consolidated Edison continuing to supply DC to customers until 2007 (Lee 2007)—most strikingly in the New Yorker Hotel where Tesla spent his waning years and died in 1943 (Blalock 2006).

MANAGING THE DOMESTICATION–DIFFERENTIATION CYCLE The most intriguing aspect about the domestication–differentiation cycle is that the very innovators who successfully domesticated one device fail to grasp the subtleties behind differentiation-by-design on introducing subsequent innovations. Perhaps, having introduced an innovation that enjoys rapid adoption and profitability, innovators forget the role played by design in domesticating consumers. Certainly no single case illustrates the full, competitive, and ultimately limiting nature of the domestication– differentiation cycle better than the smartphone. As we will see below, the very features that invite consumers to seize on an innovation as immediately useful can sustain it nicely through the competition phase of innovation, as innovators make incremental and occasionally mildly discontinuous improvements to its design to differentiate it from similar products—falling into the category of mild incongruity with existing schemas that excite interest and continue to drive adoption. However, ultimately, these same features will outlive their usefulness to consumers as their sophistication outgrows the affordances and skeuomorphs that initially made a product like the smartphone seem so immediately useful and later become indispensable.

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THE IPHONE: EXEMPLAR OF THE DOMESTICATION– DIFFERENTIATION CYCLE An early ad for the Apple Macintosh was simplicity itself: a hand reaching into a bag and hauling the 1984 Macintosh out by its convenient handle—a skeuomorph that suggested a computer was something you could both take with you and would need somewhere other than an office. In contrast, Apple introduced its Newton to the public with a two-page spread in national magazines topped by a giant headline that posed a question that was to prove ominously prescient of the public’s response: “What is Newton?” Beneath the headline, the ad listed the Newton’s primary capabilities and benefits (Marcus 1994, p. 41). Clearly, the public needed to be educated on how to use a computer now small enough to fit in your hand. The problem, however, was that nothing in the Newton’s design suggested how users might easily interact with it. Instead, the design was an uncomfortable mashup of general-purpose display, limited-use computer, and communication device. Nothing in its outward appearance or interface suggested the two key elements central to the Technology Acceptance Model (Umesh et al. 2007, p. 65): perceived usefulness and ease of use, or what we have earlier categorized as schematic fit. Unsurprisingly, the Apple Newton proved one of Apple’s more conspicuous failures (Grudin 2012, pp. 62–63). In 1996, U.S. Robotics, perhaps mindful of the crash-and-burn reception of the Newton, introduced an innovation altogether more modest, on introduction, but with ultimately the same endpoint as the Newton. The PalmPilot, later acquired by 3Com and called simply “Palm,” on its debut presented itself as a modest, simple replacement for what had become a staple of business in the 1980s and 1990s, the Filofax. Able to fit into an actual palm and weighing just 5 ounces, the original PalmPilot covered the basic functions familiar to everyone who had ever lugged around a bulging Filofax: contacts, calendar, notepad, and to do list, with a calculator thrown in. Redundancy, moreover, made these functions easy to access, either via buttons labeled with icons on the bottom of the PalmPilot, or via the stylus screen, where users could click on icons for a menu, applications, or the find function. Users could write the names of contacts, using the stylus screen to find them. However, instead of frustrating users with primitive handwriting recognition software, the PalmPilot offered a simpler, more intelligible solution: synchronization with a computer, enabled by a one-touch HotSynch button (Grieve 1997). In this way, the PalmPilot represented an incremental nudge along a schematic continuum that ran from the address book to the status-item Filofax. Subsequent innovations to the design of the Personal Digital Assistant (PDA), then renamed Palm, eventually included flash memory, a backlit screen, serial port, and Internet connectivity (Jackson 1998, p. 42). Eventually, PDAs morphed into proto-smartphones like the Nokia 9000 Communicator (launched in 1996), the RIM Blackberry (launched in 2003), and the Palm Treo 700w (launched 2005), a device that, despite glowing reviews, failed to gain the significant market share the Palm initially had (King 2006; Cha 2008). Then, in June 2007, Apple released its first iPhone (Cohen 2007). However, Apple had preceded its release, weeks earlier, with an announcement at its Worldwide Developers’ Conference that the iPhone would support third-party applications, accessible via the Safari browser engine on the iPhone (Dowling 2007). As a result, at the iPhone’s debut, unlike the Macintosh’s arrival more than thirty years prior, third-party applications were already available for use on the new device (Kim 2007). When Apple launched its first

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generation iPhone, it sold over six million units in the first five quarters, putting it on par with other established competitors. By the time Apple rolled out its 2013 iterations of the iPhone, nine million consumers purchased an iPhone in the first weekend alone (Gruber 2013b). With the iPhone, Apple used several key features that had made its iPod, which similarly debuted into an mp3-player market crowded with products, a runaway success. Rapidly after its debut, the iPod surged ahead of its competitors—and stayed so firmly fixed there that it achieved the status of a killer technology. Before the iPod, audiophiles and even casual listeners were aware of the trade-off between sound quality and portability. But, notably, before the introduction of the iPod, the quality of the mp3 files themselves were dubious, consisting of illegal downloads and file-sharing platforms like Napster that stocked listeners’ meager libraries—collections that held only 2GB of data even on the original iPod—with music containing omitted beginnings, skips transferred from ripped CDs exhibiting aural wear-and-tear, and the occasional hiccup, like the song that terminated half-way through. The iPod, however, soon integrated with iTunes and an increasingly growing library of music that offered uniform quality, ease-of-use with immediate synching with an Apple computer, and legal downloads at a time when users were beginning to suffer prosecution for digital piracy. This integration of purchases— hardware and content—paved the way for Apple’s App Store, which integrated hardware, software, and content, ultimately all synched effortlessly and seamlessly across up to five computers and as many Apple devices as one could afford. Moreover, the stunning simplicity of the design of both the iPod and iPhone contrasted starkly with the other offerings on the market. The iPod had a screen, a click wheel, directional keys, and a button, which rapidly gave way to a virtual wheel, with mere gestural indicators to stand in for buttons in the successors to the original iPod that Apple rapidly introduced. Similarly, the iPhone was sleekly elegant, reduced to the look and feel of a slender, minute monolith with its lone on/off button accessible on its top edge, minuscule buttons for volume tucked unobtrusively onto one side, and its front dedicated to screen real estate and a single central button. The device suggested, from the outset, discontinuity with every phone that had preceded it, eschewing an actual keypad in favor of a virtual one, number keys and phone functions for on-screen representations of them, and dedicated, singular functionality for plasticity. With full Internet access via a standard browser, familiar to Safari users from their computers, the iPhone delivered the web and, on its debut, applications that promised to make life more organized, unified, and streamlined. In addition, the touch screen successfully incorporated gestural commands into the interface that relied on long-established understandings of physical gestures in the West for basic functions. Moving items or screens forward or backward involved finger flicks in those directions. Ditto, up and down. To increase the size of an object for a better view, spreading fingers increased the pixel size and, then, as the microprocessor adjusted to the command, it rapidly sharpened the resolution of the image, rather than retaining the fuzziness of enlarged pixels. To shrink objects, users pinched their fingers. Tellingly, some of these same metaphors for navigation had debuted with the failed Newton (Marcus 1994; Norman 2010, p. 6). Signally, the iPhone was the epitome of robust design, a concept we explored in our initial evocation of the term (Hargadon and Douglas 2001). Users who merely wanted an easy-to-use device to enable them to phone, text, retrieve email, and web surf could

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now do so easily, with a larger screen that enabled users to zoom in on details they wished to see. Even though initial adopters of the iPhone had third-party applications like the shopping list One Trip available to them via Safari immediately after purchasing the device (Kim 2007), the App store provided access to over 500 third-party applications, of which one-quarter were free (Ricker 2008). The store, available via iTunes, enabled users to seamlessly choose, download, and use applications ranging from games and entertainment to educational and productivity software, enabling users essentially to have what Apple had envisioned with its Newton, a computer that lived with them, not on their desktops. In the case of the iPhone, Apple had domesticated the computer until it fit comfortably in pockets and served a more central function to daily lives, fitting in with schemas for having small mobile phones with users wherever they ventured. Moreover, users obviously needed no instruction or suggestions on how to use this integration of product and design, with over ten million downloads recorded during the store’s first weekend in operation (Pocock and Pope 2008). Ultimately, Apple’s domesticationby-design and differentiation-by-design helped it survive competition from similar smartphones launched by Samsung, LG, Motorola, and HTC, using interfaces and online stores that aped the successful Apple model. By late 2013, Apple replaced Samsung as the leading vendor of smartphones in the U.S., in addition to capturing over a third of the overall market for mobile phones (Arnold 2013). While the rivalry for the dominance of the smartphone market turned to Apple after its launch of the iPhones 5c and 5s, phones carrying the Android and Windows operating systems hardly lagged far behind.

DESIGN MAY BE INADEQUATE FOR DIFFERENTIATION Edison’s incandescent lighting system might have fallen by the wayside when improvements to the design of the AC motor and system proved too competitive and redressed inefficiencies of Edison’s original system. Nevertheless, his original vision of everything but the power generation survives virtually intact to this day, thanks to his largely robust design. However, with digital technology, the speed of competition increases dramatically, initially courtesy of Moore’s Law dictating increases in computing power and speed every two years that has also been linked to exponential increases in memory capacity and even to pixels in displays (Grudin 2012). Furthermore, with digital technologies, additional improvements to operating systems and applications also spell faster rollouts, lowered capital costs, and fewer risks for companies with competing products. The result: significant increases in the differentiation phase for product design, made still more rapid by the domestication of users already comfortable with not only the hardware but also most uses of the software. Finally, the initial features like the integrated applications and products available via an online store, Google Play, for applications are now available for Android smartphones, offering nearly half a million applications less than four years after the Apple App store debuted (Li 2012). Already, Apple’s design strategy may well be running into the limitations of differentiation by design. From the outset, Apple relied heavily on skeuomorphic design and affordances to provide users with schemas that made sense of things as innovative as creating bytes rather than characters and moving pixels rather than segments of lines (Weiser 1994). When users moved an object to a folder or the trash, it disappeared

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(Norman 2010). But, by the time Apple rolled out the iPhone, users no longer needed leather stitching—which apparently exactly replicated the leather covering the seats in Steve Job’s Gulfstream Air—to evoke in users the idea of a calendar (Carr 2012). In line with Norman’s critique of what he dubbed “perceived affordances” (Norman 1998, p. 123), the attempt to replicate the real world via shading and textures in a non-digital environment is always misleading.

DESIGN CAN PROVE LIMITING—AND GIVE ADVANTAGES TO COMPETITORS Donald Norman argues that metaphors are always inherently limiting in technology design. While useful in giving early adopters schemas and scripts for use, ultimately metaphors bind and limit us (Norman 1998, p. 181). For users accustomed to digital technology, paging or rolling through a digital calendar month by month can be nearly as limiting as flicking through the pages of a physical calendar—and entirely unnecessary for users long accustomed to the idea that the virtual calendar has replaced the one that decades ago sat on a desktop. Similarly, navigating via a virtual map, even with flicks of our fingers and zooms in and out, is inherently limiting (Stenovec and Smith 2012). In comparison, Google Maps personalizes users’ navigation, based on previous searches, identifies sites businesses users have previously visited, and brings up reviews of the businesses users search (Kosner 2013). Similarly, Nokia’s Here Maps lets users save their search histories, share their destinations, organize their navigation directions and destinations into collections, and monitor traffic (Metz 2012). Ultimately, the most crucial stage in innovation involves not revolutionizing the way we view and interact with the world but nudging consumers forward via well-trammeled routines and understandings. Good design always domesticates. And, with domestication comes ingrained understandings that could ultimately stop any innovation well short of ultimately revolutionizing our worlds quite as thoroughly as Edison’s vision of a world where incandescent light was merely part of a system that powered electric sewing machines, cigar lighters, and gramophones. The best domestication occurs via robust design that enables consumers to readily grasp the utility of something that should appear entirely novel but seems oddly familiar. However, that short-term acceptance is merely the first stage in designing what ultimately becomes a revolution, albeit one that occurs in minute stages, via stealth. Robust design inevitably nudges users’ understandings and schemas forward as their experiences and use evolve. With that evolution, demands for newer, broader uses emerge. Ultimately, the best revolutions are designed deliberately—and sufficiently robustly to survive the rough and tumble cycle of innovation’s domestication and differentiation.

REFERENCES Arnold, N. (2013) ‘Apple’s U.S. smartphone market share gets twice as nice in September,’ Wall Street Cheat Sheet. Online. Available HTTP: (21 October 2013).

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Ashman, H. and Simpson, R.M. (1999) ‘Computing surveys’ electronic symposium on hypertext and hypermedia: editorial,’ ACM Computing Surveys (CSUR), 31(4es): 1. Basalla, G. (1988) The Evolution of Technology. New York: Cambridge University Press. Blalock, T. (2006) ‘Powering the New Yorker: a hotel’s unique direct current system,’ IEEE Power and Energy Magazine: IEEE, 4(1): 70–76. Brands, H.W. (1995) The Reckless Decade: America in the 1890s. New York: St. Martin’s Press. Card, S.K. (1996) ‘Pioneers and settlers: methods used in successful user interface design,’ in S.K. Card (ed.), Human–Computer Interface Design: Success Stories, Emerging Methods, and Real-World Context. San Francisco, CA: Morgan Kaufmann Publishers, 122–169. Carr, A. (2012) ‘Will Apple’s tacky software design philosophy cause a revolt?’ Fast Company, 11 September 2012. Online. Available HTTP: (11 September 2012). Cha, B. (2008) ‘Where have all the PDAs gone?’ CNET.com, 19 October 2008. Online. Available HTTP: (1 August 2013). Cohen, P. (2007) ‘Apple updates iTunes for the iPhone,’ MacWorld, 29 June 2007. Online. Available HTTP: (1 October 2013). Cohendet, P. and Simon, L. (2017) ‘Concepts and models of innovation,’ in H. Bathelt, P. Cohendet, S. Henn, and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 33–55. Conot, R.E. (1979) A Streak of Luck. New York: Seaview Books. Douglas, Y. (1988) Personal conversation with Avon executive, Windsor, UK. Dowling, S. (2007) ‘iPhone to support third-party Web 2.0 applications,’ Press release, Apple, Inc. Glückler, J. and Bathelt, H. (2017) ‘Institutional context and innovation,’ in H. Bathelt, P. Cohendet, S. Henn, and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 121–137. Granick, H. (1975) Underneath New York. New York: Fordham University Press. Grieve, R. (1997) ‘U.S. Robotics announces two new models of the best selling pilot connected organizer,’ U.S. Robotics Press Release, 10 March 1997. Gruber, J. (2013a) ‘The trend against skeuomorphic textures and effects in user interface design,’ Daring Fireball, 29 June 2013. Online. Available HTTP: (9 August 2013). Gruber, J. (2013b) ‘Design quality and customer delight as sustainable advantages,’ Daring Fireball, 9 August 2013. Online. Available HTTP: (15 October 2013). Grudin, J. (2012) ‘Punctuated equilibrium and technology change,’ Interactions, 19(5): 62–66. Hargadon, A.B. and Douglas, Y. (2001) ‘When innovations meet institutions: Edison and the design of the electric light,’ Administrative Science Quarterly, 46: 476–501. Hughes, T.P. (1983) Networks of Power. Baltimore, MD: Johns Hopkins University Press. Hughes, T.P. (1987) ‘The evolution of large technological systems,’ in T.P. Hughes, W.E. Bijker, and T. Pinch (eds), The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology. London: MIT Press, 51–82. Hutchinson, L. (2014) ‘iPhone 6 first weekend beats last year’s iPhone 5 sales, sets record,’ Ars Technica, 22 September 2014. Online. Available HTTP: (21 June 2017). Israel, P.B. (1998) Edison: A Life of Invention. New York: Wiley. Israel, P.B., Nier, K.A., and Carlat, L. (1998) The Papers of Thomas A. Edison: The Wizard of Menlo Park, 1878. Baltimore, MD: Johns Hopkins University Press. Jackson, D.S. (1998) ‘Palm-to-Palm combat,’ TIME, 151(10): 42. Kim, A. (2007) ‘iPhone application example: OneTrip,’ MacRumors.com, 1 November 2007. Online. Available HTTP: (20 October 2013). King, G. (2006) ‘Palm Treo 700w smartphone,’ Techgage.com, 21 December 2006. Online. Available HTTP: (1 July 2013). Kosner, A.W. (2013) ‘The new Google maps is a social network in disguise,’ Forbes, 1 June 2013. Lee, J. (2007) ‘Off goes the power current started by Thomas Edison,’ New York Times, 14 November 2007. Lewis, C. and Rieman, J. (1993) Task-Centered User Interface Design: A Practical Introduction. Shareware book. Available HTTP: (21 June 2017). Li, A. (2012) ‘100K Android apps in Google Play are ìsuspiciousî,’ Mashable.com, 5 November 2012. Online. Available HTTP: (8 August 2013). Mandler, G. (1982) ‘The structure of value: accounting for taste,’ in M.S. Clark and S.T. Fiske (eds), Affect and Cognition. Hillsdale, NJ: Lawrence Erlbaum, 3–36. Marcus, A. (1994) ‘Metaphor mayhem: mismanaging expectation and surprise,’ Interactions, 1(1): 41–43.

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McNichol, T. (2006) AC/DC: The Savage Tale of the First Standards War. San Francisco, CA: Jossey-Bass. Metz, R. (2012) ‘You are the real winner of the mobile maps wars,’ MIT Technology Review, 13 December 2012. Online. Available HTTP: (31 July 2013). Meyers-Levy, J. and Tybout, A.M. (1989) ‘Schema congruity as a basis for product evaluation,’ Journal of Consumer Research, 16(1): 39–54. Norman, D.A. (1998) The Invisible Computer: Why Good Products Can Fail, the Personal Computer Is so Complex and Information Appliances Are the Solution. Cambridge, MA: MIT Press. Norman, D.A. (2010) ‘Natural interfaces are not natural,’ Interactions, 17(3): 6–10. Pocock, J. and Pope, S. (2008) ‘iPhone App Store downloads top 10 million in first weekend,’ Press release, Apple Computer, Inc. Ricker, T. (2008) ‘Jobs: App Store launching with 500 iPhone applications, 25% free,’ Engadget.com, 10 July 2008. Online. Available HTTP: (12 July 2013). Rumelhart, D.E. (1986) ‘Schemata: the building blocks of cognition,’ in R.J. Spiro, B.C. Bruce, and W.F. Brewer (eds), Theoretical Issues in Reading Comprehension: Perspectives from Cognitive Psychology, Linguistics, Artificial Intelligence, and Education. Hillsdale, NJ: Lawrence Erlbaum, 33–59. Schank, R. and Abelson, R.P. (1977) Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum. Silverberg, R. (1967) Light for the World: Edison and the Power Industry. Princeton, NJ: Van Nostrand. Stenovec, T. and Smith, C. (2012) ‘Google maps for iPhone: company reportedly developing new app for iOS 6,’ The Huffington Post, 26 September 2012. Online. Available HTTP: (30 June 2013). Thompson, B. (2013) ‘What Clayton Christensen got wrong,’ Stratechery.com, 22 September 2013. Online. Available HTTP: (29 June 2013). Umesh, U.N., Jessup, L., and Huynh, M.Q. (2007) ‘Getting ideas to market: current issues faced by technology entrepreneurs,’ Interactions, 50(10): 60–66. Venkatesan, M. (1973) ‘Cognitive consistency and novelty seeking,’ in S. Ward and T.S. Robertson (eds), Consumer Behavior: Theoretical Sources. Englewood Cliffs, NJ: Prentice-Hall, 334–384. Weiser, M. (1994) ‘The world is not a desktop,’ Interactions, 1(1): 7–8. Yates, J. (1999) ‘The structuring of early computer use in life insurance,’ Journal of Design History, 12(1): 5–24.

11. Innovation and lock-in Uwe Cantner and Simone Vannuccini

INTRODUCTION The concept of lock-in can certainly be listed among those weighing most heavily in the conceptual toolbox used by scholars of innovation and evolutionary economics (Cohendet and Simon, Chapter 3, this volume). Processes of competitive diffusion, or choice between alternatives of ‘unknown merit’ (Arthur, 1989), are known to generate lock-in, that is, inflexible outcomes, and this finding has critical implication for the study of economic dynamics. In fact, the very existence of lock-in outcomes relies on the acceptance of non-equilibrium, non-optimal, and history-dependent processes. However, it is somewhat ironic that an evolutionary-inspired notion describes a situation that resembles that of rest, a structural equilibrium of a dynamic system. This seemingly paradoxical situation emerges from the fact that lock-ins are outcomes, rather than determinants, of processes (of adoption, or of choice). Therefore, to focus one’s analysis only on lock-ins limits the understanding of the processes unfolding over time that may lead to them. Focusing on the whole process and set of conditions that generate lockedin situations may shed further light on the inflexible nature of certain technological and market outcomes, and on the inescapable attraction of some states of the world compared to other, competing ones. In this chapter, we summarize what is known in the economic literature about the nature of lock-in, and we discuss if lock-ins are really inescapable, especially when innovation is concerned. Also, we address the question as to whether lock-in is a well-defined concept at all. To offer a fresh view on lock-in, and to tackle the issues just raised, we employ the replicator dynamics model (Metcalfe, 1994). The replicator model is traditionally used in economics to represent the Schumpeterian ‘competition for the market’ (Cantner, 2009) and market share dynamics, but it can also consistently capture the evolution of the frequencies of given competing alternatives (technologies, products, etc.) over a given alternative space to assess if the dynamical system converges towards states of monopolization or dominance of one alternative. We make a parallel between monopolization and lock-in, and we show that the convergence of a system to such dominance of a single alternative does not have to be inescapable, and it is strongly dependent on the regime and parameters characterizing the competition. To support this view, we offer a critical view on historydependent processes based on the insights of recent contributions to the literature. These contributions highlight the need for a more precise demarcation of the conceptual boundaries of lock-in and path dependence, from both the formal and the empirical side, and suggest that further structural features – for example, user heterogeneity – may play a relevant role in affecting the outcome of dynamic allocation and competition processes. The concept of lock-in is deeply interconnected with that of path dependence, given that one is the cause of the other; the direction of causality varies according to the particular characterization followed for the lock-in. Therefore, in what follows, we will refer to 165

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both of them together most of the time. The two terms are not to be considered synonyms (because they are not), but a separate treatment of path dependence and lock-in risks leaving aside some of the multifaceted dimensions of the phenomenon of interest. The chapter proceeds as follows: in the second section, we define lock-in, we relate it to path dependence, and we overview the fields in which the notion has been used more successfully. In the third section, we discuss if lock-in is an inescapable state of affairs or just a transitory situation. Factors that make lock-in unlikely are discussed, with a prominent role reserved to the introduction of novelties into the competition between alternatives. In the fourth section, we use replicator dynamics to model the interaction between selection-driven increasing returns and alternative-specific improvements with decreasing returns in order to allow a system to diverge from monopolization outcomes; we interpret this as additional evidence that lock-ins are not inescapable. The fifth section concludes.

LOCK-IN IN THE LITERATURE Lock-in: Feedbacks and Incontestability The concept of lock-in owes its fortune in economic theorizing to David (1985) and Arthur (1989), who succeeded where many economists failed before: breaking with the abstract and a-historical view of economic processes to remind (and, in some cases, convince) fellow economists that ‘history matters’. In a nutshell, lock-ins can be considered as ‘inflexibilities’ of outcomes. As Arthur (1989) points out, inflexibility is one of the properties of dynamic allocation problems – such as that of choices between competing technological alternatives – featuring (dynamic) increasing returns (or positive feedbacks). Increasing returns – that is, a situation in which an increase of an action, for example consumption, investment, or technology adoption, by x % yields returns (e.g. utility, profits, gains from technology use, efficiency) of more than x %, or in other words to more than proportional positive feedbacks – are known to generate multiple equilibria, non-predictability and potential inefficiency of outcomes. Small, accidental events, driven by chance, can be ‘magnified’ by positive feedbacks so much as to make history – that is, the path of allocations or choices – relevant and to drive the dynamical system to one or another of its possible equilibria. Putting this all together, ‘once an outcome (a dominant technology) begins to emerge it becomes progressively more “locked in”’ (Arthur, 1989, p. 117), meaning that the more history unfolds, the more possible worlds and trajectories do not maintain the same ex ante probability of happening; by this the system becomes less and less flexible, and one of the outcomes eventually is selected even if it may not be the ‘superior’ one. Although the very definition of superiority of one alternative with respect to the others can be a subject of debate (especially with respect to the criteria used to identify superiority), the bottom line of the story is clear: a recipe combining random accidents, increasing returns and choice over alternatives that unfolds in time creates the conditions for lock-in to occur. A caveat is in order here before proceeding further with the analysis. The ‘recipe’ just mentioned is dissected and discussed in the following at a rather abstract level of analysis, to gain advantage of the formal representation of lock-in and path dependence. It goes

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without saying that lock-ins at the level of technologies, actors, and whole systems are always the emergent outcome of networks of interaction that endogenously set the conditions for outcomes’ inflexibility and prevalence. In turn, such networked interactions are deeply rooted in unique (environmental, social, cultural) contexts that certainly play a role in affecting the rate, direction, and result of the process’ object of analysis (Glückler and Bathelt, Chapter, 8, this volume; Dougherty, Chapter 9, this volume). Such embeddedness cannot be trivially introduced in the model proposed in this chapter. Hence, in what follows, it is left in the background. However, that does not mean that a contextual embeddedness of path-dependent processes does not exist at all; the environmental, social, and cultural roots of technological and system emergent outcomes have to be included in the analysis any time scholars pass from formal analysis to policy implications. Increasing returns may arise either on the supply side of a market as a result of learning effects (all the ‘learning-by’ concepts such as learning-by-doing or learning-by-using) or on the demand side as a result of positive network or agglomeration externalities/effects (Klemperer, 2008) that raise the benefits of a technique, product, or location for each user as the total number of users increases. As one alternative, due to chance, gets a head start in diffusion, for example by passing a certain threshold of users, increasing returns narrow the degree of freedom for the system to switch to another or to significantly change the direction of the current trajectory, irrespective of the ‘goodness’ of the trajectory taken. Another way to see inflexibility is as incontestability; here one of the alternatives is so prevalent that others cannot contest it. According to David (1985; 1987), prevalence is affected not just by dynamic increasing returns (due to the mentioned learning or network effects), but also by the technical interrelatedness of system components and quasi-irreversibility of investment – both of which can be expressed more generally, in terms of switching costs. The technical interrelatedness of a system appears to be an aspect very much out of the economic realm: chemical and physical laws, as well as engineering types of relationships, presumably determine which kinds of technologies fit together, which ones may be substituted, and which complementarities cannot easily be challenged. Such systems often require high investments, which in turn are characterized by quasi-irreversibility, and quasi-irreversibility implies that changes are related to – often very high – switching costs. This translates the previous argument of systemic interrelations into economic cost terms. For example, the switching costs related to the resources required to explore new chemical and physical laws or engineering relationships, which allow for breaking up the existing interrelations, are very (if not infinitely) high. In other cases, it is the systemic dimension of the supply of the goods and services related to a certain technology which protects against the challenges of new invader technologies – the combustion engine for automobiles and the accompanying system of fuel stations and fuel logistics being a point in case. Combined with these investments are mutual dependencies – not only of a technical nature but also in terms of relative prices – which contribute to the prevalence of the core technology. As long as relative factor price changes remain in a certain range, switching costs to new alternatives prevail and, hence, secure the persistence of the existing technology. Farrell and Klemperer (2007) explore in detail the role network externalities and switching costs play in an industrial organization framework that is concerned with firms’ entry and exit dynamics, pricing, contracting, competition, efficient scale, and so on. While for the authors neither of the two elements is problematic by definition for market

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dynamics, their very existence requires at least policy attention to avoid coordination failures. In fact, the main issue that the notion of lock-in suggests has to do with losses of efficiency: users may be better off under the alternative state of the world, but the historydependent process makes the ‘best’ scenario unfeasible. Lock-in: The Chicken-and-Egg Problem Conceptually, while from the Arthur and David papers introduced above lock-in appears as an ex post outcome produced by a specific property of dynamic allocation problems with increasing returns, that is, inflexibility, other scholars consider lock-in as a cause for path dependence. For example, Page (2006), defining path dependence, suggests that: A survey of the literature on path dependence reveals four related causes: increasing returns, self-reinforcement, positive feedbacks, and lock-in. Though related, these causes differ. Increasing returns means that the more a choice is made or an action is taken, the greater its benefits. Self-reinforcement means that making a choice or taking an action puts in place a set of forces or complementary institutions that encourage that choice to be sustained. With positive feedbacks, an action or choice creates positive externalities when that same choice is made by other people. Positive feedbacks create something like increasing returns, but mathematically, they differ. Increasing returns can be thought of as benefits that rise smoothly as more people make a particular choice and positive feedbacks as little bonuses given to people who already made that choice or who will make that choice in the future. Finally, lock-in means that one choice or action becomes better than any other one because a sufficient number of people have already made that choice. (Page, 2006, p. 88)

Besides the useful clarification provided by Page on how increasing returns, selfreinforcement, positive feedbacks, and lock-in can be distinguished, and despite that here lock-in is considered one of the causes, rather than an outcome of path dependence, the common feature of lock-ins is that they are forms of inflexibility. This general property has been considered key by scholars to understand the establishment of certain alternatives over others in disparate fields of technological competition: the QWERTY keyboard (David, 1985), VHS, nuclear power reactors (Cowan, 1990), electric vehicles (Cowan and Hultén, 1996), fossil fuels (carbon)-based energy systems (Unruh, 2000), eco-innovation (Cecere et al., 2014), just to name a few and to offer a non-exhaustive list. The inflexible and especially inefficient (read inferior) nature of some of the mentioned cases has been questioned recently (e.g. Kay, 2013), and we discuss that in the next section. However, in general, the concept of lock-in offers a neat guiding principle to understand that technological competition without policy ‘supervision’ can generate undesired outcomes. At the same time, it suggests the possibility that policy intervention itself may generate those small historical events capable of driving the economic system off of a given path and onto another – inefficient – one without any further right of appeal – a case of ‘government failure’. Lock-ins: Beyond Technological Competition The idea of inflexibility suggested by the concept of lock-in has seen a spectrum of applications and developments ranging far beyond the domain of technological competition. At the micro level, behavioral lock-ins are in general variations on the theme

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of informational cascades and herd behaviors (Bikhchandani et al., 1992), where choices tend to weigh external signals, conveying information of others, more than internal ones (such as intrinsic motivation) until one of the possible viable alternatives become inescapable. Informational effects work in this case in the same direction as network effects, with the only difference being that in the first case the information about a benefit is conveyed, while in the second case the benefit is directly obtained through adoption (Easley and Kleinberg, 2010). At the macro level, more than lock-in, it is path dependence that has gained widespread usage in the literature. More precisely, the concept of path dependence has a parallel in the related – but not exactly equal – notion of hysteresis (Setterfield, 2009; Göcke, 2002). Hysteresis is defined as the ‘permanent effects of a temporary stimulus’ (Göcke, 2002); the concept, inspired by studies on magnetism, is mostly applied in the study of macrodynamics (e.g. ‘strong’ hysteresis in the labor market, or the persistence of natural rates like the non-accelerating inflation rate of unemployment, NAIRU) and to inform econometric analysis of the historical component of the data generating process underlying some given variable. According to Setterfield (2009), hysteresis can be considered a special case of path dependence, where the latter serves more as an ‘organizing concept’ and its explanatory power does not relate only to persistence, but rather to the specific path of choices, decisions, or adoptions taken through history. Addressing the meso level, path dependence and lock-in often overlap in usage with the notions of a standard, dominant design, and platforms (see e.g. Gallagher, 2007); while all these concepts are defined in different ways and refer to different objects, phenomena, or fields of analysis, they all share the nature of ‘stable configurations’ implied by the inflexibility property of lock-ins. Therefore, they can be considered as representing constellations of lock-in. Path dependence and lock-in found application in industry studies, in regional studies, and in development studies as well. Starting from the latter, development and structuralist economics recognizes path dependence and lock-in as fundamental categories to understand the success or failure of catching-up processes. This holds, on the one hand, at the country level, where countries can rest on a path-dependent trail of underdevelopment leading to one of the many ‘traps’ waiting to slow down the process of growth and structural change. On the other hand, path dependence matters at the firm level, where ‘bygones are rarely bygones’ and companies combine bounded rationality, routines, institutional frameworks, cooperation and competition, and bundles of resources and capabilities to build up their unique evolutionary path and their dynamic capabilities (Cimoli and Porcile, 2015). The progressive sedimentation of firm-specific characteristics increases market heterogeneity which, combined with selective processes, give rise to restless industrial dynamics (Cantner, 2009). In regional studies, and even more critically in evolutionary economic geography (Martin and Sunley, 2006), path dependence and lock-in have been used to explain the success or failure of specific regions and clusters as well as patterns of regional diversification and resilience (Boschma, 2015). Juxtaposing lock-in and path dependence with the complementary concepts of path creation, path renewal, path dissolution, and place dependence (that is, dependence stemming from location rather than history), and complementing this perspective with notions and insights derived from the related fields of system transition and strategic niche management, evolutionary economic geography has set up the most ready-to-use conceptualization of

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lock-in at hand for policy makers (Boschma et al., 2016). Finally, industry studies such that of Bergek and Onufrey (2014) inductively derive from the observation of patenting activity in the lighting industry the richer and kaleidoscopic nature of path-dependent processes. To explain the existence of the multi-technology company and the co-existence of different technological alternatives, one has to, in fact, delve more deeply into the very concept of path: while firms (or technological systems, at a higher level of aggregation) show the typical inflexibility of locked-in situations (persistence and the presence of positive feedbacks), such inflexibility may be grounded at a finer-grained level of detail in the co-evolution of parallel trajectories. The latter contribution suggests that the emergence of a lock-in situation is far from being fully understood. The complexity of real-world phenomena that display competition for dominance between alternatives and inflexibilities reminds us that the concept of lock-in has a valuable use for illustrative purposes, while – as we discuss later in the chapter – it yet lacks the conceptual elaboration to make it a well-defined formal tool. Finally, as we already mentioned briefly, the concepts of path dependence and lock-in have served to back competition policy and the claim that in the presence of network effects tendencies of welfare-reducing monopolizations have to be contrasted with regulation (Liebowitz and Margolis, 1995). Lock-ins are therefore a form of dynamic market failure. However, recently the role played by (indirect) network effects has been found to be more complex, especially in so-called two-sided and multi-sided markets (Armstrong, 2006), where the interaction between different users with interdependent utilities mediated by network effects and by the existence of competing platforms providing an interface between the market sides may generate benefits thanks to lock-ins.

LOCK-IN – IS IT REALLY INESCAPABLE? IS IT REALLY AN ISSUE? Lock-in is usually conceived as a deadlock of technological competition or economic dynamics, where one of the competing alternatives – not always the superior one – becomes uncontestable. While such deadlocks do not have to be always welfare reducing (the very idea of standards is that they are sort of welfare-enhancing lock-ins that reduce coordination and compatibility costs), the shrinking of the set of choices – especially if the alternative over which the system is locked into is inferior – may not be a desirable property of history-dependent processes. The related questions at stake that we deal with in this section are two: first, if lock-ins are inescapable inflexible outcomes, and if the inflexibility they generate is only a temporary tendency; second, in any case, if lock-in is a well-defined concept at all. Overcoming Lock-ins: The Role of New Alternatives The debate on the real ‘pressure’ exerted by path dependence and lock-ins on technological and economic dynamics is still going on. For example, a ‘revisionist’ approach to the emergence of the QWERTY keyboard (Kay, 2013) suggests that QWERTY was already superior since the very beginning of typewriting, if the process is seen from a problem-solving perspective. Kay claims that re-running the tape of history we would

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have seen QWERTY winning over and over again against competing alternatives such as the DSK keyboard. Hence, path dependence and lock-in are criticized from their very foundational example; initial conditions, small events, and accidents along the technology evolution path may play a less relevant role than previously thought. Along the same line, already Witt (1997) pointed out that the results of David’s analysis and the Arthur model were strongly dependent on the design and the nature of the modeling strategy used, namely a generalization of the Polya urn scheme. In fact, such models assume as a starting configuration a ‘virgin market condition’, which is rarely the framework under which technological competition takes place; furthermore, the inescapable feature of lockins has no real counterpart in reality, where novel technologies continuously threaten, contest, and displace existing alternatives. In a nutshell, innovation may be the reason behind the possibility to escape lock-in. Witt suggests using an alternative modeling strategy (the master equation) featuring an incumbent-entrant race, and the existence of critical masses as threshold values that, when overcome, allow the new alternatives to reverse lock-ins. The role played by ‘diffusion agents’ in arranging coordination over the new alternative is crucial to unlock lock-ins, and the switch to a new alternative is easier the lower the critical mass thresholds required to induce migration of users from one technology or product to the other are. Andreozzi (2004) adjusts and complements Witt’s view including the feature of compatibility between technologies, suggesting that ‘the selection process will favor not so much efficient technologies, but rather technologies that are compatible with the already established alternative’ (Andreozzi, 2004). This line of argumentation can be linked with the literature on niches management and system transition (Schot and Geels, 2007) that posits that emerging technologies can be nurtured in closed market niches before becoming able to play a transformative role in the economic system. The issue of compatibility (or recombination) brought forward by Andreozzi is discussed, in a different context, by Bresnahan (2012) when analyzing the possible patterns leading to the emergence of general purpose technologies. Although not explicitly mentioning lock-in, the literature on general purpose technologies explores a rather similar territory: that of coordination and allocation failures in the making of a pervasive and dominant technology. According to Bresnahan (2012), a novel technology whose expected value, if considered in isolation, is not worth the cost of inventing it, can become viable if combined with other general purpose or specific technologies. However, the returns expected from the recombination depend on, besides contractual arrangements (e.g. with respect to intellectual property rights), the degree of entrepreneurial and market knowledge, that is, respectively, the knowledge available to the individual inventors and knowledge that can be captured on the market. From a path dependence and lock-in perspective, provided that enough knowledge is available, the possibility of recombining innovations may ease the emergence of a novel alternative capable of building, in the words of Witt (1997), a critical mass of consumers. Recombination of novelties can therefore create the conditions for lock-in breakup; by opening the way for the establishment of a new dominant general purpose technology, however, such a process also induces the generation of new locked-in trajectories of technological development. Loch and Huberman (1999) also suggest the possibility of lock-in breakup; their model describes the competition between an old and a new technology in a setting featuring performance improvements (learning) and network effects generated by the size of the user base. Potential adopters of the new technology evaluate its performance at discrete

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intervals and decide if to switch or not. The jump from one fixed point of the system to the other (dominance of the old or the new alternative) – that the authors interpret as a case of ‘punctuated equilibria’ (Mokyr, 1990) – depends on the values of the parameters modeling learning and network effects and, also, on users’ expectations regarding the technology’s performance; furthermore, user heterogeneity with respect to technology evaluation is present and plays a role in determining punctuated equilibria, stressing once again the fact that the distribution of user characteristics can affect lock-in. Cantner and Vannuccini (2016) argue in a similar direction; they model technological competition in the case of vertical relationships between upstream technologies and a continuum of downstream (user) industries/applications. Given the distribution of comparative (relative) costs and benefits of adoption of an established and a new upstream technology over the continuum of downstream industries, and given the laws of motions of such relations (that are affected both by network effects and by the resistance of the established technology), the authors identify the constellations under which the established upstream technology maintains its dominance – thus, the system is locked-in in the old alternative – and those that lead the new upstream technology to ‘acquire purposes’ and penetrate the downstream market. Finally, Marengo and Zeppini (2016) propose a variant of the Polya urn scheme that allows for the ‘arrival of the new’, namely for the entry of new competing alternatives. Their model is able to combine the role of innovation without abandoning the very analytical framework supposed to lead to inflexible outcomes. The reason identified here for the occurrence of lock-ins is the ‘closed world’ nature of the urn model; in their setting, one of the balls usually composing the urn setting acts as a ‘mutator’ that, once selected, introduces a new variant of choice (e.g. a new color) in the game. In a sense, innovation continuously reshuffles the cards in the deck of path-dependent processes. The idea behind this approach is also represented in models not directly interested in lock-in, but that illustrate similar dynamics. For example, Silverberg and Lehnert (1993) use a Lotka–Volterra predator– prey setting, adapted to model economic cycles and the run-up between wage rate and employment rate in the style of Goodwin (1982) and enriched with evolutionary features (the same replicator dynamics as used in this contribution) to illustrate the competition between capital vintages (techniques) in an economy and how this produces fluctuations comparable to the Long Waves well known to Schumpeterian economists (Freeman and Louçã, 2001). The dynamic behavior of the system is rejuvenated at random intervals by the arrival of new techniques that restore the competition for market dominance among the competing vintages, spur a new long wave in the evolution of macro prices (unemployment rate, wages), and, in a sense, break tendencies towards lock-in. Unlikely Lock-ins: Population Heterogeneity Bassanini and Dosi (2006) show formally, while retaining the Polya urn modeling framework, and in absence of innovation, how technological domination does not always occur with probability one, even under the conditions of the Arthur model, namely unbounded increasing returns with a random order of adopters: Unbounded increasing returns to adoption are neither necessary nor sufficient to lead to the emergence of technological monopolies. . . . Arthur’s result applies only when returns are linearly

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increasing and the degree of heterogeneity of agents is, in a sense, small. . . . More generally the emergence of technological monopolies depends on the nature of increasing returns and their relationship with the degree of heterogeneity of the population. Given a sufficiently high heterogeneity amongst economic agents, limit market sharing may occur even in the presence of unbounded increasing returns. (Bassanini and Dosi, 2006, pp. 25–26)

Adopters’ heterogeneity plays a fundamental role in keeping the system of technological competition far from monopolization. In a sense, users’ heterogeneity is implied also in Witt’s (1997) model, given the different behavior of the diffusion agent with respect to the other users. Shy (1996, p. 799) elaborates in a similar direction in his combination of technological ‘revolutions’ with network externalities; he finds that: for a given product, an improved technology will be adopted by the consumers that treat quality and network as substitutes and rejected by those who treat the two components as complements. . . . [W]hen a new technology is introduced, the market for the product splits between two types of consumers: those who treat the two components as substitutes . . . and therefore adopt the new technology; and those who treat the two components as complements . . . and do not adopt the new technology.

Here, heterogeneity is defined in terms of user preferences with respect to product quality or network size; already this basic distinction generates patterns of adoption that can contrast the technological monopolization implicit in path-dependent processes and lock-in outcomes. Unlikely Lock-ins: Degrees of Path Dependence The idea that lock-ins are inescapable structural equilibria has spurred an extensive debate on the real-world implication of path dependence, increasing returns, feedbacks, and lock-in itself. Liebowitz and Margolis (1995) were among the first to point out how the theoretical definition of path dependence rests on shaky grounds, and that its empirical counterpart does not make a good job of supporting the theory. To support their claim, they distinguish between first-, second-, and third-degree path dependence. First-degree path dependence is one under which ‘sensitivity’ to initial conditions does exist, but generates no harm to the process’ efficient unfolding. Second-degree path dependence generates outcomes that are inefficient ex post, but were not foreseeable ex ante, due to uncertainty and limited knowledge of alternative paths and their related wealth gains. Therefore, they cannot be considered real inferior outcomes. Finally, third-degree path dependence is a form of sensitivity to initial conditions leading to inefficient outcomes that were instead avoidable ex ante. While the case of third-degree path dependence implies that, at least theoretically, cases of selection of inferior alternatives may occur, the distinction between different degrees of path dependence suggests that the importance of the notion should be downplayed, especially for what concerns empirical relevance. Unlikely Lock-ins: Conception of Path Dependence Finally, the most critical systematization of the conceptualization of path dependence and lock-in is the one provided by Page (2006). Page starts by pointing out how path dependence emerged as a common framework to explain diverse phenomena occurring

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in diverse fields, often unrelated or non-comparable. As we already claimed, there seems to be path dependence at work in cases that range from economic dynamics to technological evolution, from micro-level choice to the meso selection of institutions, from the patterns of regional specialization and diversification to macro persistence of shocks and stimuli. However, there may be several identifiable forms of history dependence. Page distinguishes between path dependence, where the path of previous outcomes matters, state dependence where the paths can be partitioned into a finite number of states which contain all relevant information, and . . . phat dependence where the events in the path matter, but not their order . . . between early and recent path dependence, and perhaps most importantly, between processes in which outcomes are history-dependent and those in which the equilibria depend on history. (Page, 2006, p. 89)

The label ‘phat’ is a clever choice of Page to define processes dependent on a whole history, but not on the sequential order of choice: the word ‘phat’ appears quite similar to ‘path’, thus suggesting a retaining of the broader meaning and structure of the process, but swaps the order of the letters, thus suggesting that the order of choices does not matter, as instead it does for path dependence. As in Bassanini and Dosi (2006), increasing returns are found to be not a sufficient condition for path dependence and lock-in to occur or persist, as all the competing alternatives might be showing increasing returns. Most importantly, the focus on increasing returns detracts the attention from what is the true cause of path dependence, namely negative feedbacks and constraints in the not-chosen alternatives. By proposing a series of variants of the Polya urn process, Page shows the inner complexity of historical dependent processes, of which path dependence is just one case. In fact, the literature on path dependence tends to conflate concepts that from a formal viewpoint describe different phenomena, for example path-dependent outcomes and path-dependent equilibria, where the first notion indicates that the outcome in a period depends on past outcomes, while the second describes a process in which the longrun (limiting) distribution over outcomes depends on past outcomes. Similarly, another misunderstanding is the one between early path dependence and sensitivity to initial conditions. The latter concept is usually mentioned in the literature; it is a deterministic concept that describes how the equilibrium of a system is determined by the initial conditions. However, the consensus conceptualization of path dependence and lock-in is stochastic in nature. More appropriate is the concept of early path dependence, where early accidents shape the probability distribution of future histories. More importantly, many of the processes of competition studied and reported as path dependent do not necessarily have to be path dependent, but only phat dependent. In those cases, the history of choices still matters, but their order does not. To sum up, the initial idea of inescapable lock-in can be questioned from many perspectives: first, path-dependent and phat-dependent processes are stochastic, rather than deterministic – meaning the convergence is towards limiting distribution of outcomes rather than towards specific outcomes; second, increasing returns and positive feedbacks may not be sufficient to generate lock-ins, especially if all the alternatives are experiencing them; third, the heterogeneity of agents – namely the existence of diffusion agents willing to nurture a critical mass, or a distribution of preferences between technological performance (or product quality) and network effects – can produce outcomes other than monopolization; fourth, the arrival of new alternatives – that is, innovation – can

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continuously refresh technological competition and provide a way out from lock-ins. From our overview, the lesson to take home is that lock-in meant as the monopolization outcome of technological competition or incontestability of a dominant alternative is a transitory rather than a permanent phenomenon. This, however, does not reduce its relevance for policy or the fact that the notion contributes to a needed historical and evolutionary view of economic and technological dynamics. We proceed now to explore the argument mentioned before, according to which negative feedbacks are a fundamental determinant of the outcomes of history-dependent processes.

LOCK-IN: A NEO-SCHUMPETERIAN ILLUSTRATION In this section, we employ the replicator dynamics model to illustrate in a simple, dynamic, and non-stochastic setting how the (inevitable) outcome of lock-in can be overcome (or reinforced) by innovation, meant here not as the entry of novel alternatives, but as progressive improvements of the ‘merit’ – value or performance – of the competing alternatives. We explore the respective dynamics of share allocation and reallocation in a Neo-Schumpeterian perspective. While the replicator model has been used in NeoSchumpeterian economics mostly to study the competition for the market (Metcalfe, 1994; Cantner, 2009) between firms, we consider it flexible enough to capture the essence of the competing technologies problem. Indeed, as already mentioned, the model of Silverberg and Lehnert (1993) also does exactly that, adapting the replicator dynamics to model the competition between capital vintages, which in turn correspond to techniques active into the economy. Furthermore, it has been suggested (Dosi et al., 2015, p. 16) that, from the mathematical viewpoint, the stochastic version of the replicator dynamics is equivalent to a generalized Polya urn scheme; this supports our choice to employ the replicator dynamics in order to capture some feature of history-dependent processes of technological competition. In a nutshell, the replicator dynamics compares the ‘fitness’ of a given technology with that of its reference population, as it relies on the philosophy of ‘population thinking’ (Metcalfe, 2008). Doing that, the model adapts to the economic realm the Darwinian natural selection, or the principle of the ‘survival of the fittest’. In the literature, the fitness f of one technology, or firm, or agent, is usually represented by a proxy whose rationale is sound from the viewpoint of economic thinking: unit cost, productivity, and product quality are examples in this sense. The fitness of the population f , in turn, is represented by the share-weighted average fitness of all the agents active in the market, industry, or environment of interest (thus we have f 5g i si fi , where i indexes the alternatives, and s tracks the share of each alternative in a given period – we dropped the time index t for f # simplicity). Hence, the standard replicator equation, that takes the form si 5 si l ( fi 2 f ), describes the change of the frequency (share) of each actor (represented by a dotted variable in the continuous case – formally it is the derivative of an agent’s market share with respect to time) as a function of the relation (positive or negative) between her fitness and f # that of the reference population. The parameter λ is usually called s ‘speed of selection’ and captures the efficiency through which the advantage (disadvantage) of having a superior (inferior) fitness translates in gains (losses) of shares. While empirical tests of selection return at best ambiguous results (Cantner et al.,

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2016), the replicator model remains a useful tool to study the conditions under which monopolization occurs. In fact, different scenarios of competition for the market can be explored (Mazzucato, 1998). In its simplest formulation, actors have fixed fitnesses, there is no entry or exit (in a way similar to the ‘virgin market condition’ setup criticized by Witt) of alternatives, and what drives the dynamics of the system is only the continuous reallocation of shares to alternatives that have higher fitness, which in turns changes the level of the share-weighted average fitness. The necessary outcome under this setting is monopolization – lock-in. The speed of transition to lock-in depends on initial conditions (the distribution of market shares at the setup of the model) and on the speed of selection λ (that helps to calibrate the model to the specificities of different contexts), but the outcome is unsurprising: the fittest survives. The dynamics are only driven by positive feedbacks in the selection process, in the sense that the average fitness f is changing step by step in favor of the (in the end) dominating alternative respectively impairing the inferior alternative. Figure 11.1 plots as an example the evolution of market shares for five technologies when the fitness is the (negative) unit cost under the conditions just f described: shortly after t 5 100 the system locks-in around technology one, the one with the highest fitness since the very beginning. However, the essence of path dependence and lock-in is that ‘inferior’ outcomes can prevail due to stochastic shocks intervening throughout history; contrariwise, the simulation just shown indicates that lock-in happens, but always favorss# the superior alternative. An extended replicator model should, therefore, account for more elaborated forms of competition, where inferior alternatives can also become uncontestable. In this case, the issues at stake become two: if the prevalence of inferior outcomes occurs, and if such an outcome is inescapable. Mazzucato (1998) introduces the possibility for competing alternatives to improve their fitness by engaging in innovative activities under different scale returns scenarios. This introduces an additional (positive or negative) feedback mechanism into the #competition, f in form of (different types of) returns to scale acting as improvements at the level of the individual fitness, being the result of innovation. The two feedback mechanisms, one via #

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Figure 11.2 Replicator dynamics with two competing technologies and negative feedbacks selection and the other via innovation, interact either by reinforcing or by dampening each other. Formally, individual improvements are modeled relating the changes in fitness to the current share owned by an alternative. In the generic case, the laws of motion read as follows: # f# 5 gfi , for constant returns f# 5 g fi si , for increasing returns (positive feedbacks of share to fitness), and f 5 gfi (1 2 si) , for decreasing returns (negative feedbacks of share to fitness), (

where γ is a parameter assumed to be uniform across all actors that captures exogenous improvements. From an innovation viewpoint, what we introduced here is process pi innovation, rather than product innovation; while product innovation – or ‘the arrival of i thep new’, (as discussed in the third section above – modifies the set of alternatives, process innovation affects the value/fitness of the alternatives. Superiority or inferiority as meant j in the lock-in literature is therefore treated as a variable rather than a parameter (that is, aj given feature of the technology), and it is endogenously determined by the model. p ij Constant and increasing dynamics returns reinforce the selection dynamics and hence the speed of arriving at a lock-in, while decreasing dynamics returns provide the most j interesting result for the objective of this chapter, where continuous catching-up and leapj frogging is taking place between the dominant alternative and the competing ones. Figure 11.2 shows the dynamic allocation problem under decreasing returns. In the example provided, after enough time, monopolization does not occur, and the system stabilizes far j from the corner solutions implied by lock-in. d As already suggested by Page (2006), a competition between alternatives all characterized by increasing returns may not display lock-ins. Decreasing returns are in this sense a more interesting case provided by the replicator model. The interaction of negative feedbacks at dthe level of the individual alternative and selection (with positive feedbacks) going

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in the opposite direction constrain the wannabe-dominant alternatives as soon as they get close to becoming uncontestable. While for Page negative feedbacks are meant as bounds trapping some alternatives and leaving the door open for dominance to those less affected by decreasing returns, the replicator dynamics tells a somehow different story: negative feedbacks moderate selection and lead to (at least early) instability and uncertainty over the winner of the race to dominance among alternatives. Lock-in in presence of negative feedbacks, even without the ‘arrival of the new’, might not be inescapable after all. The replicator model can be used to include additional elements among those highlighted in our literature review. For example, the compatibility issue raised by Andreozzi (2004) can be added to the replicator setting by modeling a chain of connected technologies. Cantner et al. (2016) explore this possibility, but with a different aim: to capture the effect of value chain relations on selection dynamics. Despite different premises, however, the phenomena under analysis are structurally rather similar, and the findings may hold as well: the existence of a chain of compatible and interdependent components (that is, a complex technology meant as a near-decomposable and hierarchical architecture of subtechnologies) – especially when components are matched randomly – can produce at the same time the success of inferior alternatives (as path dependence studies suggests) and an even stronger turbulence in technological competition, with continuous takeover of leadership. With respect to the aims of this chapter, however, our claim is that the replicator dynamics can be a useful tool to study how lock-in emerges and can be escaped under different regimes of competition between alternatives. What this kind of models shows is that scholars have just started to scratch the surface of the complex dynamics leading to inflexible outcomes. Other classes of models, for example percolation models of technological diffusion (Silverberg and Verspagen, 2005), in which a technology diffuses by ‘percolating’ through a lattice or landscape, endogenously activating new areas willing to adopt it, in a cascade-like process, may further enrich our understanding of the processes leading (or not) to lock-in.

CONCLUSION In this chapter, we provided a particular vision of lock-in and path-dependent processes. Our contribution has been to incorporate recent critiques into the conceptualization of history-dependent dynamics as they are employed so far to explain cases of (technological) competition that may be affected by small historical events. While lock-in is conceptually considered as an inescapable outcome, we argued in the opposite direction: critical literature highlights how innovation, diffusion agents achieving critical masses of adopters, responses of established/dominant technologies, and adopters’ heterogeneity may keep the dynamical problem of allocation between alternatives far from monopolization and locked-in situations of rest. We employed the replicator dynamics model – a modeling strategy alternative to the standard Polya urn setting used in lock-inrelated literature, that however retains its mathematical properties – to reinforce the claims about the absolute inflexibility of lock-ins, and suggested that negative feedbacks, when combined with selection processes displaying positive reinforcement, may play a pivotal

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role in influencing dynamic allocation problems among alternatives. In sum, it seems that lock-in is not always inescapable, and policy makers should be aware of this property of history-dependent processes in order to design intervention in a more ‘catalytic’ manner (Cantner, 2015; Cantner and Vannuccini, 2012). More precisely, Research and Innovation policies designed to focus on the direction, rather than on the rate of innovative activities, should create protected arenas for ‘experimentation’ of technologies before and next to the main arena of the market. Furthermore, policy support to one or the other technological trajectory has to acknowledge what has been suggested earlier on with respect to competition between complex, system and platform technologies, where outcomes of prevalence have to be assessed considering their compositional nature, with historydependent processes running at the level of components/sub-systems and aggregating up to the level of the technology of interest. The latter point highlights also what could be a promising research avenue in the ambit of history-dependent processes: to elaborate a generalized theory including the factors discussed in this chapter: heterogeneity of adopters, complexity of the technology, nested path and phat dependence. Finally, the chapter was also meant to provide an answer to the question as to whether lock-in is a well-defined concept at all. Building on our claims at the end of the third section, our tentative reply is that, in the universe of real-world technological competition, where the ‘virgin market condition’ is only an ideal-type and complex multi-dimensional and multi-level interactions take place, lock-in meant as inflexibility of outcomes is predominantly a transient, rather than an equilibrium property. Lock-in may not be an ill-defined concept, but it relies on an ill-defined understanding of history-dependent processes that should be amended by economists and scholars of technological change in the direction of a theory of flexible, rather than inflexible, outcomes. The fact that processes of allocation of choices, resources, and market shares between alternatives might not end up trapped forever in inferior outcomes despite the existence of non-constant returns does not imply that transitory effects of path and phat dependence do not require corrective measures at all. After all, economic life is what happens in the transition between (temporary – due to innovation) fixed points.

REFERENCES Andreozzi, L. (2004) A note on critical masses, network externalities and converters. International Journal of Industrial Organization, 22(5), 647–653. Armstrong, M. (2006) Competition in two-sided markets. RAND Journal of Economics, 37(3), 668–691. Arthur, W. B. (1989) Competing technologies, increasing returns, and lock-in by historical events. Economic Journal, 99(394), 116–131. Bassanini, A. and Dosi, G. (2006) Competing technologies, technological monopolies and the rate of convergence to a stable market structure, in: C. Antonelli, D. Foray, B. H. Hall and W. E. Steinmuller (eds.), New Frontiers in the Economics of Innovation and New Technology: Essays in Honour of Paul A. David. Cheltenham and Northampton, MA: Edward Elgar, pp. 23–50. Bergek, A. and Onufrey, K. (2014) Is one path enough? Multiple paths and path interaction as an extension of path dependency theory. Industrial and Corporate Change, 23(5), 1261–1297. Bikhchandani, S., Hirshleifer, D. and Welch, I. (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. Boschma, R. (2015) Towards an evolutionary perspective on regional resilience. Regional Studies, 49(5), 733–751. Boschma, R., Coenen, L., Frenken, K. and Truffer, B. (2016) Towards a theory of regional diversification. Papers in Evolutionary Economic Geography #16.17, Utrecht University.

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Bresnahan, T. F. (2012), Generality, recombination and re-use, in: J. Lerner and S. Stern (eds.), The Rate and Direction of Inventive Activity Revisited, Chicago: University of Chicago Press, pp. 611–656. Cantner, U. (2009) Competition in innovation, in: A. Pyka, U. Cantner, A. Greiner and T. Kuhn (eds.), Recent Advances in Neo-Schumpeterian Economics: Essays in Honour of Horst Hanusch. Cheltenham and Northampton, MA: Edward Elgar, pp. 13–33. Cantner, U. (2015) Challenges and expectations for today’s innovation support, Keynote at the TAFTIE 2015 Annual Conference ‘Complex Innovation: New Challenges, Requirements and Approaches for Research and Innovation Support Programmes’, Berlin, 10 June 2015. Cantner, U., Savin, I. and Vannuccini, S. (2016) Replicator dynamics in value chains: explaining some puzzles of market selection. Jena Economic Research Papers, 10, no. 2016-018. Cantner, U. and Vannuccini, S. (2012) A new view of general purpose technologies, in: A. Wagner and U. Heilemann (eds.), Empirische Makroökonomik und mehr: Festschrift zum 80. Geburtstag von Karl Heinrich Oppenländer. Stuttgart: Lucius and Lucius Verlag, pp. 71–96. Cantner, U. and Vannuccini, S. (2016) Competition for the (downstream) market: modeling acquired purposes, mimeo. Cecere, G., Corrocher, N., Gossart, C. and Ozman, M. (2014) Lock-in and path dependence: an evolutionary approach to eco-innovations. Journal of Evolutionary Economics, 24(5), 1037–1065. Cimoli, M. and Porcile, G. (2015) What kind of microfoundations? Notes on the evolutionary approach (No. 37758). Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL). Cohendet, P. and Simon, L. (2017) Concepts and models of innovation, in: H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 33–55. Cowan, R. (1990) Nuclear power reactors: a study in technological lock-in. Journal of Economic History, 50(03), 541–567. Cowan, R. and Hultén, S. (1996) Escaping lock-in: the case of the electric vehicle. Technological Forecasting and Social Change, 53(1), 61–79. David, P. A. (1985) Clio and the economics of QWERTY. American Economic Review, 75(2), 332–337. David, P. A. (1987) Some new standards for the economics of standardization in the information age, in: P. Dasgupta and P. Stoneman (ed.), Economic Policy and Technological Performance. Cambridge: Cambridge University Press, pp. 206–239. Dosi, G., Pereira, M. C. and Virgillito, M. E. (2015) The footprint of evolutionary processes of learning and selection upon the statistical properties of industrial dynamics, LEM Working Papers Series 2015/04. Dougherty, D. (2017) Innovation in the practice perspective, in: H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 138–151. Easley, D. and Kleinberg, J. (2010) Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge: Cambridge University Press. Farrell, J. and Klemperer, P. (2007) Coordination and lock-in: competition with switching costs and network effects, in: M. Armstrong and R. H. Porter (eds.), Handbook of Industrial Organization, Vol. 3. Amsterdam: North-Holland, pp. 1967–2072. Freeman, C. and Louçã, F. (2001) As Time Goes By: From the Industrial Revolutions to the Information Revolution. Oxford: Oxford University Press. Gallagher, S. (2007) The complementary role of dominant designs and industry standards. IEEE Transactions on Engineering Management, 54(2), 371–379. Glückler, J. and Bathelt, H. (2017) Institutional context and innovation, in: H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, pp. 121–137. Göcke, M. (2002) Various concepts of hysteresis applied in economics. Journal of Economic Surveys, 16(2), 167–188. Goodwin, R. M. (1982) A growth cycle, in: Essays in Economic Dynamics. London: Palgrave Macmillan, pp. 165–170. Kay, N. M. (2013) Rerun the tape of history and QWERTY always wins. Research Policy, 42(6), 1175–1185. Klemperer, Paul. (2008) Network goods (theory), in: S. N. Durlauf and L. E. Blume (eds.), The New Palgrave Dictionary of Economics. Second Edition. London: Palgrave Macmillan. Available at The New Palgrave Dictionary of Economics Online, (13 September 2016). Liebowitz, S. J. and Margolis, S. E. (1995) Path dependence, lock-in, and history. Journal of Law, Economics and Organization, 11(1), 205–226. Loch, C. H. and Huberman, B. A. (1999) A punctuated-equilibrium model of technology diffusion. Management Science, 45(2), 160–177. Marengo, L. and Zeppini, P. (2016) The arrival of the new. Journal of Evolutionary Economics, 26(1), 171–194. Martin, R. and Sunley, P. (2006) Path dependence and regional economic evolution. Journal of Economic

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Geography, 6(4), 395–437. Mazzucato, M. (1998) A computational model of economies of scale and market share instability. Structural Change and Economic Dynamics, 9(1), 5–83. Metcalfe, J. S. (1994) Competition, Fisher’s principle and increasing returns in the selection process. Journal of Evolutionary Economics, 4(4), 327–346. Metcalfe, J. S. (2008) Accounting for economic evolution: fitness and the population method. Journal of Bioeconomics, 10(1), 23–49. Mokyr, J. (1990) Punctuated equilibria and technological progress. American Economic Review, 80(2), 350–354. Page, S. E. (2006) Path dependence. Quarterly Journal of Political Science, 1(1), 87–115. Schot, J. and Geels, F. W. (2007) Niches in evolutionary theories of technical change. Journal of Evolutionary Economics, 17(5), 605–622. Setterfield, M. (2009) Path dependency, hysteresis and macrodynamics, in: P. Arestis and M. Sawyer (eds.), Path Dependency and Macroeconomics. London: Palgrave Macmillan, pp. 37–79. Shy, O. (1996) Technology revolutions in the presence of network externalities. International Journal of Industrial Organization, 14(6), 785–800. Silverberg, G. and Lehnert, D. (1993) Long waves and evolutionary chaos in a simple Schumpeterian model of embodied technical change. Structural Change and Economic Dynamics, 4(1), 9–37. Silverberg, G. and Verspagen, B. (2005) A percolation model of innovation in complex technology spaces. Journal of Economic Dynamics and Control, 29(1), 225–244. Unruh, G. C. (2000) Understanding carbon lock-in. Energy Policy, 28(12), 817–830. Witt, U. (1997) ‘Lock-in’ vs. ‘critical masses’ – industrial change under network externalities. International Journal of Industrial Organization, 15, 753–777.

12. Patents and open innovation Julien Pénin

INTRODUCTION: THE “SECOND BEST” THEORY OF PATENTS Standard economic theory highlights the incentives properties of the patent system, which by granting exclusive rights to innovators, encourage research and development (R&D) investment, thus contributing to increasing innovation, growth and social welfare. Actually, patents have a double incentive role: 1. 2.

They induce firms to invest in R&D and to innovate, that is, to invest in the economic valorization of inventions (Nordhaus 1969; Kitch 1977); They induce firms to disclose their inventions, that is, help disseminate technical knowledge within the economy and to fight secrecy (since the description of the patented invention is automatically published) (Encaoua et al. 2006).

In other words, the patent instrument provides an elegant solution to the Arrow dilemma (1962), which highlights the difficulty to achieve both optimal incentives to produce knowledge and an optimal level of dissemination of the produced knowledge. In his seminal paper, Arrow (1962) concluded that: To sum up, we expect a free enterprise economy to under invest in invention and research (as compared with an ideal) because it is risky, because the product can be appropriated only to a limited extent and because of increasing returns in use . . . Further, to the extent that a firm succeeds in engrossing the economic value of its inventive activity, there will be an underutilization of that information as compared with an ideal allocation.

As regard to this dilemma, patents both encourage knowledge production and diffusion. In the long run, patents have hence an unquestionable positive effect on economic growth, that is, they are an instrument of dynamic efficiency. Yet, in the short run, patents give market power to inventors, thus inducing monopoly deadweight loss and static inefficiency (patents allow inventors to charge prices above marginal cost). Hence, due to this short-run inefficiency, patents do not lead to a “first best” but only to a “second best” solution. As reminded by Schumpeter (1942), static inefficiency is the price to pay in order to induce innovation and dynamic efficiency. As a result, policy makers, when designing the characteristics of the patent system, must try to balance those to opposing effects, that is, provide a maximum level of incentives without reducing too much the efficiency in the short run. Even though this vision of patents has the merit of consistency and simplicity, a growing number of researchers tend to oppose it more or less frontally (Kingston 2001; Bessen and Meurer 2008; Hilaire-Pérez et al. 2013). On the one hand, some studies question the positive effect of patents on incentives (see Bessen and Meurer 2008 for a survey) and, rather, highlight their possible dynamic inefficiency. For instance, patents could 182

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harm innovation by promoting negative, “hold-up” like, strategic behaviors (“trolling”) (Shapiro 2001), helping create situations of anti-commons (“royalty stacking”) (David 2011), and introducing transaction costs in the sequential process of innovation (Scotchmer 1991). In the light of those problems, prominent scholars have recently called for either major reforms of the system (Jaffe and Lerner 2004; Bessen and Meurer 2009) or even its abolition (Boldrin and Levine 2008). On the other hand, other studies have highlighted alternative potential benefits of the patent system which, paradoxically, could promote inter-firm collaborations, external interactions and markets for technology (Arora et al. 2001). For these authors, patents could encourage open innovation strategies, that is, strategies in which firms open up their boundaries and interact with other firms, public research organizations, users and so on (Chesbrough 2003). Indeed, it seems that to open up its boundaries can be risky and that often patents can help to mitigate some of the risks and difficulties associated with open innovation (West 2006; Alexy et al. 2009; Zobel et al. 2016; Vanhaverbeke, Chapter 6, this volume). The aim of this chapter is therefore to review this recent literature in order to go beyond the simple “second best” view of the patent system and to present patents within an open innovation framework, that is, to study how patents can foster open innovation but also the dangers that they entail and what could be done, from a policy perspective, to make sure that patents support open innovation. In any case, before doing this, it is essential to remember that, as regards the consequences of patents on innovation, important differences across sectors can be expected. Bah and Le Bas (2011) talk of sectoral systems of intellectual property (SSIP) in order to characterize the fact that the economic impacts of intellectual property (IP), including patents, depend on the sector. In particular, the technological regime of the industry strongly affects the way firms use their patents (Cohendet and Pénin 2011). Differences in the technological regime across sectors can thus explain why the “second best” view of patents is particularly well suited to pharmaceuticals but much less to other sectors such as electronics, information and communication technology (ICT) or software, where the complex nature of technologies increases the risk of tragedy of the anti-commons and the possibility of trolling. The chapter is divided as follows. First it focuses on the coordination function of patents and explains why patents can be structuring elements of open innovation. The next section explores the role of patents in markets for technology. This is followed by a specific case of complex technologies in which patents may induce a possible tragedy of the anti-commons. Then we analyze the role of patents when innovation is sequential. The final section concludes by discussing how changes in patent laws could contribute to improve patents’ support of open innovation.

FROM EXCLUSION TO COORDINATION DEVICES: PATENTS AND OPEN INNOVATION The traditional Arrovian framework considers innovation as an individual and isolated act and therefore puts the emphasis on the importance of patents to exclude imitators (Arrow 1962). It neglects the collective dimension of innovation, which often requires economic

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actors to interact and collaborate. It is hence critical to defocus from the traditional economic framework and to embrace a wider framework, which would consider more explicitly the properties of knowledge and innovation (Cohendet and Pénin 2011; Cohendet et al., Chapter 13, this volume). In such a renewed framework, a double role for patents clearly emerges: to increase incentives to innovate but also to mitigate the specific coordination difficulties linked to collective invention. In a knowledge-based perspective patents must be considered as strategic instruments which serve not only to exclude potential infringers but also to ensure the coordination among heterogeneous actors and to structure innovation activities. In other words, in most cases the main role of the patent system is not to effectively exclude rivals but to “include” all the different stakeholders in the innovation process (Cohendet and Pénin 2011). Patents contribute to facilitating interactions among actors in the innovation process because they hold two important properties concurrently: they both disclose and protect an innovation. In particular, the disclosure property (the fact that patents are public documents available to everybody) transforms patents into signaling instruments. Patents contribute to the creation of a public database that contains technological knowledge but also information concerning the “know-who” (who is doing what), which is often essential to find partners, suppliers, financers and so on. In other words, patents signal information to the environment of the patent holder and thus often enable firms to establish contacts in fields in which the multiplicity of small and heterogeneous actors coming from highly differentiated domains may complicate the identification of partners. Thanks to the coupling of these two properties, patents can ease interactions among the actors of innovation at two different levels: (1) they can facilitate technology transfer through the exchange of licenses in markets for technology; and (2) they can be important instruments to frame non-market collaborations and alliances (formal and informal). Market coordination: Patents to favor technology trading. Patents help technology and knowledge trading in markets for technology (Arora et al. 2001; Arora and Gambardella 2010). Indeed, the combination of the two properties of protection and knowledge disclosure help in solving the Arrow’s paradox (Arrow 1962): on the one hand, the disclosure of knowledge allows technology sellers to signal and to advertise their products; on the other hand, the protection granted by the patent system prevents free riding from buyers. Therefore, paradoxically, strong patents often favor knowledge transfer in markets for technology and markets for ideas and inventive solutions (crowdsourcing, for instance). We will come back to the implications of markets for technology in the next section. Non-market coordination: Patents to collaborate and form alliances. Patents can intervene fairly early in the innovation process and their role can go beyond a mere perspective of allocation of existing resources. They can help to structure more or less formal collective modes of knowledge creation (networks, research consortium, research joint venture, informal exchanges, etc.). During the process of collaboration between different organizations, we distinguish several steps at which a patent can help. First, as stated before, in the early stages of collaboration, patents can allow the actors to signal their competences, thus mitigating the problems of incomplete information and facilitating the search for a partner. They also tend to reduce the risks linked to cooperation due to free riding by one of the partners (Ordover 1991), therefore increasing the incentives to participate in the venture (“good fences make good neighbors”). Patents

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can also play a key role during the determination of the terms of the collaboration. They allow the assessment of the competences of each partner (they provide a benchmark that enables firms to compare their relative competences), that is, they are often central devices to determine the bargaining power of each part. Patents can also help to share the outcome of the collaboration, in particular when the latter is indivisible (Hagedoorn and Ridder 2012). Patents, in such cases, may encourage the collective process of innovation by facilitating the sharing of the dividends of collaborations. Finally, patents, all along the collaboration, help the coordination between the different participants because they represent a common language that can be understood by all of them (public labs, big multinationals, consulting agencies, financing organizations, etc.). To sum up, in parallel to the traditional role of patents as instruments dedicated to increasing incentives, we see an equally important second role emerging: to ensure coordination between actors of the innovation process. From the vision of industrial property aimed at rewarding the independent innovator, we end up with a conception of industrial property as a structuring element of open innovation. For instance, Bureth and Pénin (2007) illustrate how the patent system helps the coordination of very heterogeneous actors involved in the development of genetically engineered vaccines. They show how the patent system is used in a logic of co-opetition, that is, how it helps firms to exclude imitators and to collaborate with complementators.

STRATEGIC USES OF PATENTS IN MARKETS FOR TECHNOLOGY: FABLESS FIRMS, BROKERS AND TROLLS As seen in the above section, patents favor the development of markets for technology in which firms can sell and buy technologies. In turn, markets for technology support division of labor and specialization, that is, they allow the emergence of fabless or technological firms that specialize upstream in the production of new technology that they then transfer to manufacturing firms located downstream on the value chain, which embody those technologies in their products (Arora et al. 2001; Arora and Merges 2004; Arora and Gambardella 2010). Patents are often central for this division of labor between technological and manufacturing firms because, without them, manufacturing firms would have, to some extent, the possibility to free ride and to gain the knowledge for free, which would undermine the incentives of technological firms to invest in R&D (Arora and Merges 2004). This new industrial organization, based on markets for technology, has important positive normative implications: it allows each firm to specialize where it is most efficient. It also supports a better distribution of the technologies, ensuring that innovations are used by those who are the most capable of generating value from them. Finally, it prevents costly duplication of research. However, even with the presence of patents, markets for technology are clearly imperfect, that is, technology deals are often subject to significant transaction costs. This has provoked the arrival of new players, namely technology brokers, whose role is to reduce these transaction costs and facilitate technology transfers (Benassi and Di Minin 2009; Dushnitsky and Klueter 2011; Hagiu and Yoffie 2013). These market intermediaries are firms (consulting companies) composed of experts in IP evaluation, transaction and litigation and specialized in technology transfer, and more specifically in IP transfers.

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Their primary role is to assist the transfer of intellectual assets from technological to manufacturing firms. Concretely, patent brokers can take two forms: they can buy patents and other intellectual assets from technological firms and then sell them to other firms (mostly manufacturing firms), but, more often, they do not buy the patents they valorize but just take in charge their financial evaluation and their marketing via specialized web platforms, and they secure their transfer. The most well-known examples of these kinds of brokers include Yet2.com, Oceantomo, Avenium (a CEA spinoff), BTG (a former UK National Research Development Corporation, specialized in pharmaceuticals), F2T (France Technology Transfer), TEchTransferOnline.com, and Innocentive (mainly focused on crowdsourcing). Patent brokers play a critical role in fostering the emergence of markets for technology. In a context where information about inventions is far from perfect, where uncertainty about the value of inventions is high and where knowledge transactions are complex, they help identify promising technologies and transfer them to the firms who want them the most. They also reinforce incentives for technological firms to invest in promising technologies. Indeed, by specializing in technology transfer they remove the burden of IP litigation from R&D companies, which are rarely specialists in litigations processes. Without patent brokers, technological firms, not experienced with IP, may hence fear free riding behaviors from manufacturing firms, thus lowering their incentives to invest in R&D. The role of patent brokers is therefore to provide a credible threat of litigation in case manufacturing firms try to free ride (McDonough 2006). In this sense they still increase by one step the division of labor between technological and manufacturing firms. They specialize in technology transfer, R&D firms specialize in technology development and manufacturing firms specialize in production and distribution. In other words, patent brokers have a positive effect on innovation and the economy (Pénin 2012). However, if, on the one hand, patents have favored the development of technological firms, helped by technology brokers, on the other hand, they have also contributed to introducing new strategic patenting behaviors, based on hold-up strategies, and performed by the so-called patent trolls. Indeed, although similar to patent brokers at first glance, the strategy of patent trolls is very different. While patent brokers look for licensees and try hard to grant licenses (they don’t hide), patent trolls keep their patent portfolios hidden and want to be infringed (Henkel and Reitzig 2008; Reitzig et al., 2010). In a sense they speculate on patent litigation. Specifically, trolling behaviors consist of a company (usually non-manufacturing firms, also known as NPEs (“non- practicing entities”) or PAEs (“patent assertion entities”)) trying to hide some of its patents in order to provoke infringement and to place infringing firms in hold-up situations (Pénin 2012). The word “troll” precisely reflects this dissimulation behavior in order to provoke irreversibility and hold-up. In Scandinavian mythology, trolls are ugly monsters who hide in the forest and wait for merchants to cross the forest in order to rob them. Trolling behavior thus means to delay the attack in order to be sure that merchants are buried deep in the forest and cannot go back. Interestingly one can note that patent trolling amounts in a sense to radically hijacking the patent system. The latter was created precisely to avoid imitation whereas trolls use it in order to be infringed. For a troll a patent has no value if it is not infringed. Trolls’ business model is based on infringement. They want to be infringed. A firm may be trapped in a hold-up situation when it has undertaken sunk investments

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(for instance when building a new factory, a new production line or implementing a major advertising campaign for the launch of its new product). As it has already invested, the company can hardly stop its activity if it is accused of infringement (sunk investments commit firms over the long term), which puts it in a very uncomfortable position to negotiate licenses on the infringed patents. Patent trolls thus try to provoke hold-up situations by hiding their patents and refusing to grant licenses before potential infringers (usually manufacturing firms) have made sunk investments. The strategic manipulation of patents in order to provoke hold-up situations may also occur during the implementation of standards (Tassey 2000). In areas where the technology is complex, the multi-component nature of technology raises important needs for standardization in order to facilitate compatibility between components and to benefit from network externalities. This is particularly the case for ICT (Maskus and Merill 2013). Furthermore, for a given standard, some patents are more important than others. They are called essential patents (Bekkers et al. 2011). Hence, in the process of establishing a standard, firms which own essential patents may have an interest in concealing them in order to increase their value thereafter, when the standard becomes effective and the patents included in it, essential (Shapiro 2001; Berger et al. 2012). To prevent those strategic uses of patents, antitrust authorities and major industrial stakeholders usually try to agree on some shared rules such as Fair, Reasonable and Non-Discriminatory (FRAND) licensing agreements, for instance (Lemley and Shapiro 2013). This strategy of trolling allows patent holders to capture a share of the value much higher than the intrinsic value of the technology they own (Lemley and Shapiro 2007; Farrell et al. 2007; Shapiro 2010). In other words, the value of their technology ex post, once it has been infringed, is much higher than its value ex ante, if it is not infringed. It is this prospect which encourages patent trolls to hide their patents and not to approach manufacturing firms in order to grant them licenses. According to a recent report by the White House (White House patent report, June 4: Executive Office of the President 2013), disputes caused by trolls tripled between 2011 and 2013 from 29 percent of patent litigation in the United States to 62 percent. According to the report, in 2012, trolls would have accused approximately 100,000 companies in the United States of infringement. The problem with trolls is that they have a clear adverse effect on social welfare (Pénin 2012). Bessen et al. (2011) tried to quantify the damage caused by trolls and concluded that: we find that NPE [Non-Practicing Entity] lawsuits are associated with half a trillion dollars of lost wealth to defendants from 1990 through 2010, mostly from technology companies. Moreover, very little of this loss represents a transfer to small inventors. Instead, it implies reduced innovation incentives and a net loss of social welfare.

But beyond the costs of litigations provoked by trolls, the most disastrous consequence of trolling is likely to stem from the deterring effect on innovative firms. Trolls introduce uncertainty on the freedom to operate of innovative firms. Anticipating the encounter with a troll, innovative manufacturing firms may become reluctant to invest in R&D and to bring new products to the market (Pénin 2012). These reservations may also directly impact technology firms, which will find it more difficult to sell their technology to innovative manufacturers. Furthermore, Tucker (2013) shows that patent trolls, by provoking litigations on new technologies, can have a negative effect on the diffusion of

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these technologies and harm their incremental process of improvement. Indeed, during a trial, legal uncertainty put on the technology is likely to reduce other firms’ incentives to develop improvements and complementary products to this technology. To sum up, the rise of patent trolling illustrates clearly the multiple possible strategic uses of patents and the ambivalence of patenting strategies. On the one hand, patents allow technological firms to valorize their R&D investments in markets for technology; on the other hand, they contribute to creating trolling behaviors.

PATENTS AND COMPLEX TECHNOLOGIES: A POSSIBLE TRAGEDY OF THE ANTI-COMMONS? In the patent literature an important distinction is drawn between complex and simple technologies. A complex technology is multi-component, that is, it is necessary to combine many components (many technologies) in order to be able to put a product in the market. Sectors such as ICT, semi-conductors and electronics fit this definition of complex technology. Conversely, in the case of a simple technology, one (or a limited number of) technology leads to a product which can be sold in the market. It is not necessary to combine this technology with many others. The pharmaceutical sector typically fits this definition. A major implication is that there are fewer problems of freedom to operate in the case of simple technologies than in the case of complex ones. In sectors where the technology is complex, firms must therefore sometimes combine up to thousands of different technologies in order to bring a new product to the market. Yet, many of these technologies can be patented, which forces the innovator to negotiate permission with each patent holder. This leads to an increase in transaction costs and may induce a problem of “royalty stacking”, which is to say that the addition of fees for accessing the various components can ultimately make the innovation unprofitable, thus reducing its diffusion and use. Although individual royalties represent only a small amount, once “stacked” they can become substantial (David 2011). In particular, the price increase induced by the fragmentation of ownership and the proliferation of patents is due to the well-known principle of multiple marginalization. The proliferation of patents on complementary technologies may thus lead to an under-utilization of these technologies, which explains the use of the term “tragedy of the anti-commons” (Heller and Eisenberg 1998). The expression “tragedy of the anti-commons” refers to the wellknown expression “tragedy of the commons” (Hardin 1968). Common resources, that is, non-appropriable but rival resources, may be used too intensively (above their capacity for regeneration), which may lead to their disappearance. In this case, the absence of property rights induces a utilization of the resource, which is too intensive as compared to an ideal situation. In contrast, in the case of the “tragedy of the anti-commons”, too many property rights (i.e. the fragmentation of the ownership of a single resource) induce a use of the resource which is too low compared to the optimum. To our knowledge, very few studies managed to measure empirically the anti-commons problem. An exception is the work of Murray and Stern (2007), who, based on patent citations, found a small but significant anti-commons effect. Von Graevenitz et al. (2011; 2013) also proposed a method to measure the “patent thicket” and concluded that the problem may be acute in some cases.

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Some practices, such as cross-licensing agreements and patent pools, can to some extent allow firms to “navigate the patent thicket” (Shapiro 2001). Compulsory licensing may also contribute to softening problems of anti-commons. However, this type of licensing is difficult to apply in practice and its use is limited to the case of public health in most countries (Martinez and Guellec 2004). For example, crosslicensing is a strategy which enables firms to ensure freedom to operate. Cross-licensing agreements may include entire technological fields (“field of use”) and even patents not yet filed. Yet, it is important to mention that if cross-licensing practices could constitute an efficient ex post strategy to avoid being denied the use of a technology, they could also contribute to increasing the proliferation of patents and the risk of anticommons by encouraging firms to multiply patent applications to protect themselves in case they are accused of infringement (defensive patenting strategies, Grindley and Teece 1997). “Patent pools” are also institutions that contribute to restoring to some extent the dynamic efficiency of the patent system. The idea of a patent pool is to gather all the patents which are relevant to using a given technology in a single structure in order to unify the intellectual property related to this invention and thus facilitate its dissemination (Merges 2001). Users of the invention, instead of having to negotiate with a myriad of patent holders, each in a situation of a local monopoly, only have to bargain with a single owner, namely the manager of the patent pool. This obviously facilitates access to the technology, reduces the number of transactions that have to be handled and, most of all, lowers the price of the technology since patent pools, by unifying property rights, solve the problem of multiple-marginalization. Lerner and Tirole (2004) showed that social welfare is enhanced by the formation of pools when patents gathered in the pool are more complementary than substitutable. When patents are strictly complementary, patents pools increase both firms’ profits and consumers’ surplus. Furthermore, they can reduce incentives to invest resources in inefficient duplications, which is also positive for social welfare. Thus, it is easy to understand why, due to the increasing proliferation of patents in certain sectors, which greatly complicates access to technology, patent pools are a growing phenomenon and are likely to continue to develop in the near future. Nowadays we even have examples of firms which form pools of patent pools (den Uijl et al. 2013). However, the formation of patent pools is not free of problems. Companies can sometimes be tempted to use patent pools strategically in order to reduce competition by including in the pool non-essential and rather substitutable patents. In this case, the formation of patent pools can be anticompetitive and reduce social surplus (Lerner and Tirole 2004; Brenner 2009; Layne Farrar and Lerner 2011; Lévêque and Ménière 2011). To sum up, if the problems of “anti-commons” and “royalty stacking” can be serious, we have also seen that ways for firms to mitigate them exist, by forming patent pools or engaging in cross-licensing agreements. However, it is not certain that these strategies always work successfully and, even if that were the case, it remains that these practices are costly. In a sense they illustrate well the social costs of patents. In the absence of the patent system, resources engaged in patent pools and cross-licensing could be used elsewhere.

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THE CASE OF SEQUENTIAL INNOVATIONS It is now widely accepted that, in many areas, innovation proceeds sequentially, that is, today’s innovations feed tomorrow’s innovations. Inventors do not start from scratch but sit on the “shoulders of giants” (Scotchmer 1991; Bessen and Maskin 2009). It is possible to identify several types of sequential innovations (Scotchmer 2004). For example, firms that undertake applied research must use more upstream and fundamental knowledge. Or they must use and combine different research tools in order to advance their research and develop their products. Or inventors must improve an existing product on which they base their research. In any of these examples there is a sequence since research in a first step feeds research in a second step. A key element in the case of sequential innovations is that first-generation innovations are necessary for second-generation innovations. The latter cannot be created without the former. This raises the question of the accessibility of first-generation innovation and thus the question of the role of patents. How do patents manage to ensure the distribution of profits between the different generations of innovators (Scotchmer 2004)? The absence of patents or too weak patents, that is, too easy to circumvent, may offer few incentives to first-generation innovators. Conversely, too strong patents, that is, too protective, increase the cost of accessing first-generation innovations and therefore may reduce incentives to produce second-generation innovations. Scotchmer (1991) showed that there is no way of “fine tuning” the patent system to solve this dilemma. This analysis stresses that patents, in making it harder to use patented technologies, may hinder the dynamics of innovation. Patent holders may oppose the reuse of their technologies and slow down technological progress. However, contract-friendly people may ask why first-generation inventors, who are perfectly rational and profit maximizers, would refuse to grant licenses to second-generation innovators? It would be more profitable for them to license their technologies and to obtain royalties. Patents should therefore not oppose the reuse of existing technologies, they should not affect the global efficiency of the process, but they should just impact the distribution of profits between the different generations of inventors. Yet, it is possible to object to least three things in this “contract-friendly” argument (Menell and Meurer 2013): (1) high transaction costs may prevent the completion of mutually beneficial transactions; (2) firms operate in highly uncertain environments and risk aversion can prevent the execution of transactions (the required premium may be too high); and, finally, (3) the risk of hold-up and patent holders’ strategic behaviors may also reduce the incentives for second-generation innovators. It is hence possible that patents in some contexts prevent the realization of mutually profitable technology deals, thus harming the dynamics of innovation. More generally, this point addresses the questions put forward by the evolutionary literature, which is almost entirely focused on the dynamics of technical progress and on the fact that innovation is a sequential process in which history, that is, past choices, matters. This literature insists on the fact that innovation follows technological paradigms and trajectories which, once developed, are difficult to reverse (Nelson and Winter 1982). Innovation is therefore largely path dependent and initial decisions may soon become irreversible. In this context the risk is therefore that patents interfere in the choice of a technological trajectory and provoke a lock-in on a sub-optimal technology. In particular,

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the proliferation of patents may block the construction of the common knowledge base which is necessary for the emergence of the new paradigm (Winter 1993; Freyermuth et al. 2012). This evolutionary view emphasizes the importance of past choices and the fact that patents may contribute to guiding the economy on a bad technological trajectory, that is, selected not due to its technological properties but for reasons that have to do with patents, from which it becomes difficult to deviate.

CONCLUSION: HOW TO TRANSFORM PATENTS INTO ACCELERATORS OF OPEN INNOVATION? Beyond the “second-best” view, which reduces patents to an instrument of exclusion aiming at restoring appropriation and ensuring a minimum level of diffusion of knowledge, we have presented here a view of patents as being possibly structuring elements of open innovation, that is, as helping to coordinate the different economic actors involved in the innovation process. This additional role of patents makes this system very central in the knowledge-based economy. However, some problems remain. In addition to the static, short-run, monopoly deadweight loss highlighted by the “second-best” view, we have identified three potentially important sources of dynamic inefficiency caused by the patent system: patents can impede the sequential process of innovation, they can provoke costly hold-up situations (induced by patent trolls) and they can lead to anti-commons problems. This work thus emphasizes the importance, but also the dangers, of the patent system for the process of open innovation. It is therefore important to try to limit those problems in order to make sure that patents are instruments that foster dynamic efficiency. For Le Bas and Pénin (2014), most of the problems induced by the patent system today have two main roots: too many patents and the bad quality of patents’ information. It is indeed the proliferation of overlapping patents which induce possible anti-commons situations and which facilitate the possibility to hide some patents. It is the fact that patents are difficult to understand and their borders difficult to draw, which increases transaction costs and favors trolling (Mulligan and Lee 2012; Menell and Meurer 2013). In a world in which patent information is perfect (the frontiers, i.e. what is exactly protected by each patent, would be common knowledge), trolls could not exist and transaction costs would be at a minimum. In order to make sure that patents encourage open innovation it may thus be necessary to limit the number of patents. Today millions of patents are in force, which contributes to increase problems of freedom to operate and to blur the signaling roles of patents. Le Bas and Pénin (2014) discuss the possibility of increasing patent fees, introducing some discrimination in patent fees or changing the remuneration scheme of patent examiners in order to limit the number of patents. They also suggest raising patent acceptation criteria (or at least to respect the existing ones) in order to avoid patents on things which are only marginally new and inventive. In addition, they also call for important changes in the mentality of policy makers and top managers and the way in which they consider patents. In particular, they emphasize the confusion which exists today in the minds of many stakeholders between the ends (more innovation) and the means to reach these ends. Patents are only the instrument, not the final goal. Economies and firms do not

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necessarily need more patents, but they do need more ideas, inventions and innovations (Macdonald 2004). Most of all, if patents are to encourage open innovation it will be necessary to improve the quality of patents’ information and to fight against any form of secrecy and dissimulation. Open innovation requires being able to delimit technological neighbors, that is, to know exactly what is one’s property, what is others’ property and what is public domain. If “good fences make good neighbors” it is unlikely that bad fences also make good neighbors, but rather the contrary. And without good information about what is protected by patents and what is not, patents are equivalent to bad fences. Ideally, one should therefore be able to do with patents what has been done with land property, that is, build a public register which indicates without ambiguity the property of each citizen. If, due to the immateriality of knowledge, this ideal cannot be reached for patents on new technologies, one should at least try to minimize as much as possible problems of information. For instance, patents should be published as soon as possible (at the moment of patent application), patent descriptions should be improved in order to reach a minimum level of consensus as to what exactly they protect, at least among experts, and patents with unclear borders should be automatically rejected. Without any improvement in the quality of patents’ information, and if the number of patents is not reduced, transaction costs will always impede the reuse of existing technologies, trolls will always be at the corner of the street and anti-commons will always threaten cumulative innovation. To improve patent information radically, to make it as readable and understandable as possible, is without doubt the next challenge that will have to be overcome by the patent system. Acknowledgements The sections on patents and complex technologies and sequential innovations are largely drawn from Le Bas and Pénin (2014).

REFERENCES Alexy O., Criscuolo P., Salter A. (2009), “Does IP strategy have to cripple open innovation?”, MIT Sloan Management Review 51(1), 71–77. Arora A., Fosfuri A., Gambardella A. (2001), Markets for Technology: The Economics of Innovation and Corporate Strategy, MIT Press, Cambridge, MA. Arora A., Gambardella A. (2010), “Ideas for rent: an overview of markets for technology”, Industrial and Corporate Change 19, 775–803. Arora A., Merges R. (2004), “Specialized supply firms, property rights and firm boundaries”, Industrial and Corporate Change 13, 451–475. Arrow K. J. (1962), “Economic welfare and the allocation of resources for invention”, in National Bureau of Economic Research, The Rate and Direction of Inventive Activity: Economic and Social Factors, Princeton University Press, Princeton, NJ, 609–625. Bah M., Le Bas C. (2011), “Un nouveau cadre d’analyse des fonctions du brevet: l’hypothèse des systèmes sectoriels de propriété intellectuelle (SSPI)”, in Corbel P., Le Bas C. (eds.), Les nouvelles fonctions du brevet. Approches économiques et managériales, Economica, Paris, 23–44. Bekkers R., Bongard R., Nuvolari A. (2011), “An empirical study on the determinants of essential patent claims in compatibility standards”, Research Policy 40, 1001–1015. Benassi M., Di Minin A. (2009), “Playing in between: patent brokers in markets for technology”, R&D Management 39(1), 68–86.

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Berger F., Blind K., Thumm N. (2012), “Filing behaviour regarding essential patents in industry standards”, Research Policy 41, 216–225. Bessen J., Maskin E. (2009), “Sequential innovation, patents, and imitation”, RAND Journal of Economics 40(4), 611–635. Bessen J., Meurer M. (2008), “Do patents perform like property?”, Boston University School of Law, Working Paper 08-08. Bessen J., Meurer M. (2009), Patent Failure: How Judges, Bureaucrats, and Lawyers Put Innovators at Risk, Princeton University Press, Princeton, NJ. Bessen J. E., Meurer M., Ford J. L. (2011), “The private and social costs of patent trolls”, Boston University School of Law, Law and Economics Research Paper No. 11-45, 19 September 2011. Boldrin M., Levine D. (2008), Against Intellectual Monopoly, Cambridge University Press, Cambridge. Brenner S. (2009), “Optimal formation rules for patent pools”, Economic Theory 40, 373–388. Bureth A., Pénin J. (2007), “Modular innovations and distributed processes: the case of genetically engineered vaccines”, European Journal of Economic and Social Systems 20(2), 251–274. Chesbrough, H. (2003), Open Innovation: The New Imperative for Creating and Profiting from Technology, Harvard Business School Press, Boston, MA. Cohendet P., Parmentier G., Simon L. (2017) “Managing knowledge, creativity and innovation”, in Bathelt H.,  Cohendet P., Henn S., Simon L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham, Northampton, MA, 197–214. Cohendet P., Pénin J. (2011), “Patents to exclude versus include: rethinking the management of intellectual property rights in a knowledge-based economy”, Technology Innovation Management Review, December, 12–17. David P. (2011), “Mitigating anticommons harms to science and technology research”, SIEPR Discussion Paper 10-030. den Uijl S., Bekkers R., de Vries H. (2013), “Managing intellectual property using patent pools: lessons from three generations of pools in the optical disc industry”, California Management Review 55(4), 31–50. Dushnitsky G., Klueter T. (2011), “Is there an eBay for ideas? Insights from online knowledge marketplaces”, European Management Review 8(1), 17–32. Encaoua D., Guellec D., Martinez C. (2006), “Patent systems for encouraging innovation: lessons from economic analysis”, Research Policy 35(9), 1423–1440. Executive Office of the President (2013), “Patent assertion and US innovation”, White House, June 2013. Farrell J., Hayes J., Shapiro C., Sullivan T. (2007), “Standard setting, patents and hold-up”, Antitrust Law Journal 74, 603–670. Freyermuth J., Pénin J., Cohendet P. (2012), “Appropriation strategies and endogenous technological regime: towards a dynamic theory of the role of patents”, in Burger-Helmchen T. (ed.), Economics of Creativity: Ideas, Firms and Markets, Routledge, London, 151–166. Grindley P., Teece D. (1997), “Managing intellectual capital: licensing and cross-licensing in semi-conductors and electronics”, California Management Review 39, 8–41. Hagedoorn J., Ridder A.-K. (2012), “Open innovation, contracts, and intellectual property rights: an exploratory empirical study”, UNU-Merit Working Paper 2012-025. Hagiu A., Yoffie D. (2013), “The new patent intermediaries: platforms, defensive aggregators, and superaggregators”, Journal of Economic Perspectives 27, 45–66. Hardin G. (1968), “The tragedy of the commons”, Science 162, 1243–1248. Heller M. A., Eisenberg R. S. (1998), “Can patents deter innovation? The anticommons in biomedical research”, Science 280, 698–701. Henkel J., Reitzig M. (2008), “Patent sharks”, Harvard Business Review 86(6), 129–133. Hilaire-Pérez L., MacLeod C., Nuvolari A. (2013), “Innovation without patents: an introduction”, Revue économique 2013(64), 5–8. Jaffe A., Lerner J. (2004), Innovation and Its Discontents: How Our Broken Patent System Is Endangering Innovation and Progress and What to do about It, Princeton University Press, Princeton, NJ. Kingston W. (2001), “Innovation needs patent reform”, Research Policy 30, 403–423. Kitch E. (1977), “The nature and function of the patent system”, Journal of Law and Economics 20, 265–290. Layne Farrar A., Lerner J. (2011), “To join and not to join: examining patent pool participation and rent sharing rules”, International Journal of Industrial Organizations 29, 294–303. Le Bas C., Pénin J. (2014), “Patents and innovation: are the brakes broken, or how to restore patents’ dynamic efficiency”, Working Paper BETA 2014-2. Lemley M. A., Shapiro C. (2007), “Patent hold-up and royalty stacking”, Texas Law Review 85, 1991–2009. Lemley M., Shapiro C. (2013), “A simple approach to setting reasonable royalties for standard-essential patents”, mimeo. Lerner J., Tirole J. (2004), “Efficient patent pools”, American Economic Review 94, 691–711. Lévêque F., Ménière Y. (2011), “Patent pool formation: timing matters”, Information Economics and Policy 23, 243–251.

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Macdonald S. (2004), “When means become ends: considering the impact of patent strategy on innovation”, Information Economics and Policy 16(1), 135–158. Martinez C., Guellec D. (2004), “Overview of recent changes and comparison of patent regimes in the United States, Japan and Europe”, Chapter 7 in Patents, Innovation and Economic Performance, OECD Conference Proceedings, OECD, Paris Maskus K., Merrill S. (2013), Patent Challenges for Standard-Setting in the Global Economy: Lessons from Information and Communication Technology, Board on Science, Technology, and Economic Policy; Policy and Global Affairs; National Research Council. McDonough J. F. (2006), “The myth of the patent troll: an alternative view of the function of patent dealers in an idea economy”, Emory Law Journal 56, 188–228. Menell P., Meurer M. J. (2013), “Notice failure and notice externalities”, Journal of Legal Analysis 5(1), 1–59. Merges R. P. (2001), “Institutions for intellectual property transactions: the case of patent pools”, in Dreyfuss R., Zimmerman D.L., First D. (eds.), Expanding the Boundaries of Intellectual Property, Oxford University Press, Oxford, 123–166. Mulligan C., Lee T. B. (2012), “Scaling the patent system”, N.Y.U. Annual Survey of American Law 68, 289–317. Murray F., Stern S. (2007), “Do formal intellectual property rights hinder the free flow of scientific knowledge? An empirical test of the anti-commons hypothesis”, Journal of Economic Behavior and Organization 63, 648–687. Nelson R. R., Winter S. G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA. Nordhaus W. D. (1969), “An economic theory of technological change”, American Economic Review 59(2), 18–28. Ordover J. A. (1991), “A patent system for both diffusion and exclusion”, Journal of Economic Perspectives 5, 43–60. Pénin J. (2012), “Strategic uses of patents in markets for technology: a story of fabless firms, brokers and trolls”, Journal of Economic Behavior and Organization 85, 633–641. Reitzig M., Henkel J., Schneider F. (2010), “Collateral damage for R&D manufacturers: how patent sharks operate in markets for technology”, Industrial and Corporate Change 19, 947–967. Schumpeter J. (1942), Capitalism, Socialism and Democracy, Harper, New York. Scotchmer S. (1991), “Standing on the shoulders of giants: cumulative research and the patent law”, Journal of Economic Perspectives 5, 29–41. Scotchmer S. (2004), Innovation and Incentives, MIT Press, Cambridge, MA. Shapiro C. (2010), “Injunctions, hold-up, and patent royalties”, American Law and Economic Review 12, 280–318. Shapiro C. (2001), “Navigating the patent thicket: cross licenses, patent pools, and standard setting”, in Jaffe A., Lerner J., Stern N. (eds.), Innovation Policy and the Economy, MIT Press, Cambridge, MA, 119–150. Tassey G. (2000), “Standardization in technology-based markets”, Research Policy 29, 587–602. Tucker C. (2013), “Patent trolls and technology diffusion”, mimeo. Vanhaverbeke W. (2017), “Broadening the concept of open innovation”, in Bathelt H., Cohendet P., Henn S., Simon L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham, Northampton, MA, 87–98. von Graevenitz G., Wagner S., Harhoff D. (2011), “How to measure patent thickets – a novel approach,” Economics Letters 11, 6–9. von Graevenitz G., Wagner S., Harhoff D. (2013), “Incidence and growth of patent thickets – the impact of technological opportunities and complexity”, Journal of Industrial Economics 61(3), 521–563. West J. (2006), “Does appropriability enable or retard open innovation”, in Chesbrough H., Vanhaverbeke W., West J. (eds.), Open Innovation: Researching a New Paradigm, Oxford University Press, Oxford, 109–133. Winter S. G. (1993), “Patents and welfare in an evolutionary model”, Industrial and Corporate Change 2, 211–231. Zobel A. K., Balsmeier B., Chesbrough H. (2016), “Does patenting help or hinder open innovation? Evidence from new entrants in the solar industry”, Industrial and Corporate Change 25(2), 307–331.

PART III INNOVATION AND CREATIVITY

13. Managing knowledge, creativity and innovation Patrick Cohendet, Guy Parmentier and Laurent Simon

INTRODUCTION Confronted with an ever more complex and ever changing socio-economic environment, and challenged by the acceleration of technology, firms are still looking to find efficient ways to organize innovation. The development in the past 30 years of the knowledgebased approaches of the firm (resource-based view, competence-based view, evolutionary approaches, etc.) has progressively highlighted the central role of knowledge management for conducting innovation processes. As underlined by Leonard-Barton (1995), since firms are knowledge institutions, or well-springs of knowledge, they compete on the basis of creating and using knowledge for succeeding in their innovation processes: “managing a firm’s knowledge assets is as important as managing its finances, and all aspects of product or process development must be viewed in terms of knowledge management and growth.” The pioneering work of Nonaka and Takeuchi (1995) posits that knowledge creation in organizations is the central tenet of innovation, while Adler (1995) considers that “knowledge creation reaches into the heart of the process of technological innovation”. The recent and active debates in the management literature on the notion of dynamic capabilities (Teece, 1996, 2007; Eisenhardt and Martin, 2000), seen as the capabilities of an organization to purposefully adapt and exploit the organization’s resource base, have confirmed the strategic coupling between knowledge management and innovation processes. The new dynamic capabilities framework for corporate strategic management, especially in terms of organizational knowledge processes, has become the predominant paradigm for the explanation of innovation strategy. While the strategic relationships between the processes of knowledge management and the processes of innovation have been progressively unveiled, this has also revealed a “blank spot” in the understanding of the innovation processes and value-chain: the intermediate role of creativity and creative processes. The place and role of managing creativity in this organizational framework appears to be growing concern among scholars as well as practitioners. A recent world survey conducted by IBM Global Business Services (2010) confirms that to accelerate and improve innovation, the key management challenge that will be faced by companies in the coming years is how to manage creativity in order to make deeper internal changes in their operations, and to experiment with drastic, sometimes disruptive evolutions of their business model to achieve their strategic intentions. The aim of this chapter is to situate and analyze how managing creativity should fit into the organizational framework orchestrated by the interactions between the management of knowledge and the management of innovation. In this contribution, we question the traditional view that places creativity at the preliminary stage of the innovation process. Following pioneering works on the management of creativity (Drazin et al., 1999; Mednick, 1962; Woodman et al., 1993), we suggest in the following that managing creativity is equivalent to managing ideas, and argue that the main theoretical obstacle is 197

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that at the present stage ideas are mostly “black boxes” in innovation theories (Birkinshaw et al., 2011). In an effort to “open this black box” (see “Managing ideation processes in organizations”), we come to the conclusion (see “Managing the tension between the ideation and the innovation process”) that a major change of perspective is needed in management: instead of viewing the management of ideas as an initial stage of the innovation process, we propose an integrated framework where the processes of ideation and innovation are not sequential but coupled, and where these strategic interactions are mediated by knowledge-management processes. Such a change of perspective suggests drastic impacts on the ways to manage organizations, which are discussed in the conclusion of this chapter.

MANAGING IDEATION PROCESSES IN ORGANIZATIONS For a long time, the analysis of idea generation was the exclusive domain of psychologists who focused on the cognitive styles of individuals, on their cognitive capacities and their personality traits. Over time, organizational creativity gained attention in management research as firms’ capacity to create new ideas and knowledge has been increasingly recognized as a strategic challenge. The studies expand on the contexts and tools favouring the individual (Amabile et al., 1996), groups (Taggar, 2002) and organizational creativity (Drazin et al., 1999; Woodman et al., 1993). Fed by and based on ideas, creativity in the organization can be defined as “the production of novel and useful ideas by an individual or small group of individuals working together” (Amabile, 1998). From these perspectives, the literature started to unveil the complex process of ideation, from the initial generation of ideas to a rich and dense concept activating the generative potential of intersecting knowledge bases. After an historical perspective on the origins of the concept of creativity in organization, we expose the four main steps supporting the ideation processes: the intention, the “spark”, the “social construction”, and the “landing”. A key issue remains the place and role of this process of ideation in the strategic framework of organization. The literature in the 1990s tended to limit the role of ideas to the beginning of the innovation process, thus considering that the ideation process is just a preliminary stage among others that lead to innovations viewed as the result of successful implementation of creative ideas within organizations and markets (Amabile, 1988; Staw, 1990). In such a perspective, as for any stage in the innovation process, the role of knowledge management is limited in supporting the different ideation process activities (codifying, storing, recording, etc.). We will strongly question this perspective and argue that creativity is both an input to the innovative outcome and a part of the innovation process. Both creation and innovation are the process and the outcomes, and they interact in the complex social system of the organization. Unveiling the Ideation Process: An Historical Perspective Following research initiated by Poincaré, Wallas and Csikszentmihalyi, the creative process has been first conceptualized at the individual level as the iteration of short cognitive loops between idea generation and idea selection, starting from problem identification, and strongly driven by the motivation and creative skills of the crea-

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tive worker (Runco and Chand, 1995). These pioneer authors describe the ideation process – from the initial generation of an idea to a mature concept having the potential to be implemented in an innovation – as a long and intricate process. Wallas based his vision on the Poincaré story that relates the process of the discovery of the Fuchsian functions (Poincaré, 1908). He models the creative process in four steps: preparation, incubation, illumination and verification (Wallas, 1926). More recently, other authors, analyzing artistic processes, point out a series of very short back-and-forth movements between the generation of an idea, its development and its evaluation (Doyle, 1998; Getzels and Csikszentmihalyi, 1976). Lubart analyzed in further detail the sub-processes that support the production of creative ideas (Lubart, 2001). For instance, at the step of preparation, the sub-steps of identification, formulation and reformulation of problems are frequently mobilized in the creative work (Getzels and Csikszentmihalyi, 1976). Other authors include under the term “problem finding”, prior to problem solving, the steps of discovery, construction, expression, positioning, definition and identification of the problem, without specifying a specific sequence of action (Runco and Row, 1999). The quality of the output of creative work depends on the ability to correctly and intensely engage in processes of creativity, especially to define the problem (Getzels and Csikszentmihalyi, 1976), to activate the divergent and convergent thinking (Basadur et al., 1982), and to use the ability to combine and reorganize the information into new categories that are going to drive the ongoing evaluation process (Mumford et al., 1991). Similarly, the intensive use of analogy and bisociation seems to be common to all creative types, inventors, artists and scientists alike (Koestler, 1964; Weisberg, 1986, 1993). In the mid-1950s, the literature started considering that the creative processes can be deployed at the collective level. At the group level, Osborn based his creative method of problem solving on a process with six steps: the Objective finding, the Fact finding and the Problem finding to understand and define the problem and the objective, the Idea finding to generate ideas about the problem and the Solution finding and the Acceptance finding to find, design and act on the best solutions (Osborn, 1953). A similar creative process is proposed by Amabile: problem identification, preparation, idea generation, idea validation and assessment (Amabile, 1998). At the organizational level, in change and development organization, the creative activity is conceptualized as a circular process: ideas begin with problem generating, followed by problem formulating, solution developing and solution implementing, and finally organization reacts to this implementation solution, generating new problems, and the process begins anew (Basadur et al., 2012). For Basadur, each stage of this process requires specific attitudinal, behavioural and cognitive skills in order for it to be successfully completed (Basadur et al., 2012). Furthermore, faithful to the Amabile’s componential model, the creative performance of an individual depends on her relevant knowledge of the domain, her creative skills and her intrinsic motivation. The positive action of intrinsic motivation in creativity has been confirmed by other researchers (Dewett, 2007; Ford, 1996; Woodman et al., 1993). However, extrinsic motivation can also have a positive impact on creative endeavours (Eisenberger and Rhoades, 2001; Friedman, 2009), and Amabile points out a motivational synergy between intrinsic and extrinsic motivation (Amabile, 1993). Intrinsic motivation is central in Amabile’s componential model because the creative potentialities of domain-relevant knowledge and creativityrelevant skills can only be fully expressed and exploited when the intrinsic motivation is

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high, where the motivation is determined by the degree to which it emanates from the self (Ryan and Deci, 2000). According to the Cognitive Evaluation Theory (Deci and Ryan, 1985), the contextual characteristics affect two aspects of intrinsic motivation – informational and controlling – and thus also impact creativity. The impact of social context in creativity, in interaction with personal characteristics, was addressed in multiple theories. Woodman et al.’s central contribution draws up a multilevel, interactionist, and integrationist model of creativity in which creativity is influenced by both situational and dispositional factors (Woodman et al., 1993). In the interactionist model, creativity is the result of the interaction between individual, group and organizational variables. Social influence and context (working context) facilitate or inhibit the potential of the individual, acting on his or her behaviour in the group, which determines the creative performance of the organization. Following Woodman et al., Taggar (2002) looked for empirical validation of part of this model. Taggar notably examined the effect of personality on the creativity of the group using the components of the Five Factor Model of personality (Taggar, 2002). The evolutionist model of individual creative action extends the interactionist model and integrates the psychologist perspective (focused on the individual) and the sociologist perspective (focused on the social, cognitive and organizational context) within the evolutionist perspective (focused on the variation, retention and selection of ideas) (Ford, 1996). This model analyzes the factors that lead an individual to intentionally engage in a creative action, and the contextual and organizational factors that foster and facilitate creative action at the individual and collective level. At the organizational level, the model considers that creative and conformist actions are constantly in competition, facilitated or constrained by the frameworks of thoughts in permanent construction in the organization (see also Drazin et al., 1999). In Ford’s model, the creative commitment is thus dependent of the construction of meaning, motivation (objectives, responsiveness to standards of action, confidence in his or her abilities, emotion), and knowledge and skills (knowledge in the domain, social ability, creative ability). In a complementary model, Drazin et al. (1999) developed the sensemaking factors of an evolutionist approach, where creativity is the process of sensemaking leading to involvement in a creative act whatever the nature of its result, as long as it is new, useful and feasible. This multilevel model of creativity focuses on the identification of the multiple factors that mediate, favour or inhibit the creativity in the group and the interaction between personality, knowledge, cognitive skills and social context. However, these models do not account for the organized creative process in groups and organizations. In the creative process, individuals do not activate the same cognitive skills at all steps and the context probably does not have the same effect on different creative workers at different steps of the process. So, we posit that for any individual, each step of a creative endeavour calls for a specific level of action, exploration and experimentation activities, cognitive artifacts and cognitive activities. In the following section, we engage in an in-depth inquiry of the idea development and management process. According to the literature review of the creative process, idea management is a long, complex and highly strategic process for organizational creativity, which is fed and structured in large part by the knowledge-management system and processes.

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The Main Phases of the Ideation Process: The Intention, the Spark, the Social Construction and the Landing At the beginning, the intention of the creator triggers the building of the motivational, informational and knowledge context that favours the identification of a problem or challenge. Triggered by their internal or external motivation, people engage in a creative process either on open problems (the problem is fuzzy and the method for solving the problem must be designed) or closed problems (the problem is well identified and the method for solving the problem is known) (Unsworth, 2001). Fuelled by intrinsic motivation, the creative activity can be autonomous, self-directed and proactive. In this case, the intention to solve a problem depends on individual motivation without organizational solicitations. Nevertheless, the organizational climate can have a strong impact on this type of creativity. Conversely, the creative activity can be a response to a problem presented by the organization. However, the interpretation of the organization’s intention biases the creative work. The identification and framing of the problem is a crucial step in the creative process. The creative problem-solving process often involves an ill-defined problem (Mumford et al., 1991) and the creative worker must often re-engage with the problem-finding process to discover a problem that is relevant for both him/her and the organization (Getzels and Csikszentmihalyi, 1976). For example, the lead user creates innovations based on a use problem that he identifies earlier than other users (von Hippel, 1986). Because the lead user uses a product or service intensively, he or she is motivated to identify an important use problem before the others. It is an individual ongoing process based on collecting information at a conscious or unconscious cognitive level. Understanding a problem includes framing and reframing the issue, collecting and combining information, and formulating several possible questions (Lubart, 2001; Treffinger, 1995). The trigger of the process could be serendipity, the continuous observation of a repeated issue, or an insightful analysis that can lead to the identification and formulation of an interesting problem worth investigating and working on. Serendipity could be defined as “the art of making an unsought finding” (Van Andel, 1992); the way an individual analyzes and interprets an unusual phenomenon according to its objectives (Weisenfeld, 2009). A lot of great innovations are based on the observation of a surprising event, which is associated by analogy with other phenomena to formulate a new question or resolve a problem already identified. The identification of the problem or challenge leads to the initial phase: the “creative spark” or idea generation. This phase is exploratory and aims at generating new insights through knowledge association and recombination. It can involve free exploration or a more disciplined approach using specific methods to generate new ideas – brainstorming (Osborn, 1948), creative problem solving (Osborn, 1953; Parnes, 1967), lateral thinking (De Bono, 1971), the TRIZ method (Altshuller, 1984), the C/K method (Le Masson et al., 2010; see also Le Masson et al., Chapter 18, this volume) – or involve capturing new ideas from the inside out and from usages – user toolkits for innovation (von Hippel and Katz, 2002), crowdsourcing (Howe, 2008), design thinking (Brown, 2009). The effective execution of idea generation by the creative worker is based on cognitive processes and abilities (Mumford et al., 2009): divergent and convergent thought processes (Guilford, 1950, 1967); and the ways to handle, combine and synthesize the information through

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association (Mednick, 1962); bisociation (Koestler, 1964); lateral thinking (De Bono, 1971); andanalogy and metaphor (Weisberg, 1993). After the spark, the road ahead aims at maturating, challenging, enriching and validating insights. This conversion of the idea requires an investment in time, resources and efforts in order to clearly identify, actualize and extract the potential value of the idea. Throughout the literature, many researchers insist on the importance of transformation, conversion, maturation and “valuation” for the development of ideas in innovative organizations (Christensen and Raynor, 2003; Furr and Dyer, 2014; Govindarajan and Trimble, 2005). The Actor Network Theory provides an interesting framework for empirically analyzing processes in organizations (Whittle and Spicer, 2008). It sees organizations as networks of heterogeneous actors gathered together in more or less stable associations or alliances (Law, 1991). This theory has been used to study the functioning of innovation in organizations (Akrich et al., 2002a, 2002b; Callon, 1986; Harrisson and Laberge, 2002). In this model, the success of an innovation is explained by the ideator’s capacity to interest and engage people that can be involved at different evaluation and valuation moments, or even become co-developers of the idea. After the idea generation, the original “ideators” try to convince others of the newness, relevance and value of the idea. At the same time, they need to foster reactions, comments and criticisms from more and more partners to challenge and enrich the idea. Ideas are more likely to be implemented when ideators have strong networking skills and a large number of ties in the organization (Baer, 2012). The idea moves from the firm to the market through a process of progressive “translations” in which it gradually changes as it is diffused beyond the limited circle of original ideators, and comes into contact with the interests of those who are going to exploit it or use it. In this translation process, the firm’s ideators are not the only actors, for some of the process takes place beyond the firm’s borders. Everything depends on finding the right spokespersons, those “who will interact, negotiate to give shape to the project and to transform it until a market is built” around the idea (Akrich et al., 2002b). In this approach, many studies point out the role of knowing communities in this process of conversion of the idea, where the idea would interest more and more actors until it is finally legitimated enough to be adopted by the firm (Harvey et al., 2015). In the video game industry, for instance, the members of the internal communities at Ubisoft have at the same time one foot in the cognitive construction of new ideas and another one in the innovative projects of the firm (Cohendet and Simon, 2007, 2015). They enrich the ideation processes (exploration) with the knowledge acquired in the project development of video games (exploitation). Other external communities, such as virtual user communities and brand communities, can also be a locus of idea generation, conversion and validation. In these online communities, the open spaces of collaboration facilitate knowledge collaboration and recombination of knowledge (Burger-Helmchen and Cohendet, 2011; Parmentier, 2015), and in opening these boundaries and the products and services for co-creation, the firm can capture valuable ideas (Parmentier and Mangematin, 2014). Moreover, these communities include lead users as spokespersons of the market, who are capable of altering and turning the ideas in a direction that will subsequently interest a broader public (Lilien et al., 2002). These communities act as an active device of exploration, exploitation and renewal of the “creative slack”, a reservoir of potential new ideas (Cohendet and Simon, 2007), that will influence the strategic innovation pathways of the organizations in the future.

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Two typically overlooked artifacts also seemingly play a key role at this conversion phase: the manifesto and the codebook. The manifesto, explicit or not, asserts a strategic positioning in differentiation and values, and favours collective enrolment. It provides the creative collective with an agreement on the orientation of efforts, focusing on shared meaning and on a well-understood and accepted common purpose. Manifestos can be found, for instance, in the unfolding of the cubist movement (Sgourev, 2013; Cohendet et al., 2014), in “techno-emotional” cuisine in the form of the synthesis of elBulli cuisine (Capdevila et al., 2015; Svejenova et al., 2007), or in blogposts claiming the Trackmania spirit in the creative user community of an online racing video game (Parmentier, 2015). What appears as a shared orientation in the symbolic dimension is completed by a systematic, more concrete effort to define the ways the idea is going to be used and exploited; its “grammar of use” is laid out in the codebook. The codebook generally emerges from the projection of the creative intention into the realm of users: what they need to know and do in order to fully benefit from the new idea, once it has been concretized into a new product, service or process. Often, prototyping will help in designing and refining the codebook. Both artifacts, the manifesto and the codebook, act as powerful complements to foster understanding and acceptance of the idea by employees, peers and the hierarchy. Finally, idea conversion is a process of both sensemaking and “intéressement” (Akrich et al., 2002a) that creates collective meanings in connecting the idea to the knowledge bases and values of actors that could be involved in supporting and contributing to the idea. At this stage of the ideation process, we must identify the active units in the idea-generation and conversion processes. Generating and converting ideas is essentially a socio-cognitive process and construction. If the original spark is more than often individual, the first validation and valuation of the idea comes from a small, situated group of informal “partners in crime”, invited by the first “ideator” to react, comment and contribute to the idea. At the next step, when an idea reaches a sufficient degree of maturity (i.e., there is an understanding of its possible functioning and potential value) and is validated and supported by the hierarchy, the question at stake is its execution. Executing an idea entails organizing its “landing” in pre-existing structures and processes. Hierarchy has a fundamental role to play in giving the “green light” to an idea when it reaches a certain level of ripeness. Officially endorsing the idea and starting a formal innovation process means keeping up with the enrichment, concretization and valuation of the idea. The idea will benefit from internal as well as external contributions, consciously channelled, managed, evaluated and selected by management. Differing from the vision and metaphor of the innovation “funnel”, ideas should not be considered only as quasi-material inputs to feed the innovation process. In this regard, many innovative projects have encountered difficulty – when taking a sequential perspective – in recognizing, evaluating, transferring and exploiting the new pieces of knowledge generated from the process. Generally, these insights are at worst forgotten, or at best recaptured in complex intellectual property models, to be eventually franchised to external actors. Focusing on the idea-generation, conversion and execution process allows emphasis not only on the expected outputs (i.e., the deliverables and their exploitation/valuation model), but also on the outcomes (i.e., the potentially useful knowledge produced from the exploration/experimentation process itself). Hargadon and Sutton (2000), for instance, in analyzing the specific internal functioning of IDEO, the world-renowned design firm, insisted on the contribution of

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those “secondary” ideas to the sparking and fuelling of new innovative initiatives and projects. Crutzen et al. (2014) came to a similar understanding through their analysis of a creative consulting firm, where the accumulation of knowledge through experience in often-failed endeavours nurtured the success of subsequent projects. In the following section, we introduce a framework for idea management based on this vision of an ideation process. Table 13.1 synthetizes the components and activities involved at the four stages of the idea-development process. The starting point is to acknowledge that the ideation process in organizations should be considered as an unfolding, openended process that needs to be managed in four main steps: 1) intention building, 2) generation of the idea, 3) conversion of the idea (i.e., looking for its consolidation and validation/valuation) and 4) execution of the idea through the mobilization or organizational resources and processes. The activities at the four stages differ significantly. The first and second stage are mostly exploratory and aim at generating new insights through knowledge association and recombination. They can involve free exploration or a more disciplined approach using specific methods. The third stage is essentially social and aims at convincing other actors to contribute to the validation and consolidation of the idea. The fourth stage aims at translating the idea into a value proposition relevant for the organization, to convince the hierarchy to endorse the idea, and to reformat organizational structures and processes to support the actualization of the idea. Ideation process generates formal ideas that nourish the process of innovation. However, the innovation process does not necessarily use all the ideas, but often ideas are rejected or stay in the ideation process for refinement in order to build a more robust concept. The challenge is thus to find the right method to evaluate the idea and to identify a way to store the ideas not used by the innovation process. Dean et al. (2006) identified the four most important criteria: newness, feasibility, relevance, and the specificity of the ideas. The newness of an idea can be estimated from its degree of originality and its paradigm relatedness. The feasibility of an idea can be estimated from its social acceptability and the technical and organizational ability to implement it. The relevance of an idea can be estimated from its applicability to the problem and its effectiveness in solving the problem. The specificity can be estimated from its implicational explicitness and the completeness of its description. However, in organizations, the technical feasibility, market potential and product uniqueness are the most frequently used criteria (Hart et al., 2003). In fact, the importance of these criteria is dependent on the phase of the innovation process. In the concept-testing phase, the strategic fit and the customer acceptance are the most important criteria to evaluate the ideas (Carbonell-Foulquié et al., 2004). Finally, separating the idea generation and idea evaluation can be counterproductive to actually generating value from the idea for the organization. The evaluation, or more precisely the valuation, is a generative process that shapes and guides collective and organizational creativity (Harvey and Kou, 2013). Harvey and Kou (2013) show that, in creative sessions, the evaluation-centered process, moving from parallel to iterative/sequential interactions, allows the creation of more elaborated ideas. In the iterative mode, when the group moves back and forth between ideas and group goals, the group often combines multiple ideas, refines the problem framework, and validate or invalidate the ideas in light of this framework. In communities, the ideas are often evaluated by the peers and combined before getting adopted by a larger number of members. Creative and knowing communities appear as efficient social groups

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to evaluate the ideas. Their fundamental role in organizational creativity shouldn’t be overlooked and should be integrated into the reconsideration of the stage-gate process. The trajectory of ideas in organizations is not a smooth process. Each step presents some risks that the idea will not get validation and will be rejected. Before becoming a concept, an idea can go through many back-and-forth movements: checking an insight, re-evaluating a hypothesis, exploring a way through prototyping, starting again from a different angle and so on. During this process, the original question is often re-examined, debated and reframed. The socialization of the idea often fosters the evolution of the initial concept, which, when combined with other elements of knowledge, can sometimes lead to a major reformulation of the problem. The result of the ideation process is uncertain and unpredictable. Sometimes, the individuals and the creative groups generate a lot of interesting and valuable ideas in a burst of inspiration, but at other times long periods can pass without a significant idea. Managing this process with a traditional hierarchical management, a formal project mode and a linear perspective can prove counterproductive. The four-stage process is aligned with Teece’s interpretation of a firm’s dynamic capabilities for innovation (Teece, 2009), where the first issue for the organization is to generate some relevant insights, then to assess their value and select the most relevant one, and finally to reformat the idea as a formal project that must be implemented in the pre-existing set of organizational resources and processes, thus reconfiguring the organization to allow for the concrete development and actualization of the idea as a new product, service or process. Mastering the ideation process is probably a robust way to build creative capabilities to support the dynamic capabilities of a firm. However it is not sufficient because the ideation process is in interaction with both the innovation process and the knowledge-management process. Managing innovation therefore means setting up and balancing those three families of processes.

MANAGING THE TENSION BETWEEN THE IDEATION AND INNOVATION PROCESSES Questioning the Sequential Perspective Whatever the complexity of the ideation process, the traditional view in management is a “sequential perspective” which places the ideation process at the initial stage of the innovation process. This view for instance is clearly implicit in the recent and growing literature on the “fuzzy front end” (Koen et al., 2002). This stream of research argues that in the process of development of innovation in organizations, the earliest phase, the fuzzy front end, is chaotic, unpredictable and insufficiently structured, and thus offers significant opportunities to improve the overall efficiency of the management of innovation. In contrast, when decomposing the innovation process within organizations in three main phases from upstream to downstream (the fuzzy front end, new product and process development (NPPD), and commercialization), the last two phases appear as well structured and formalized (through well-known procedures such as stage-gating). The fuzzy front end is precisely the phase of emergence and construction of ideas, which requires informal exchanges between peers, interactions between a diverse set of knowing

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communities, absorptive capacities to capture ideas from the environment, and the recognition that these exploratory activities cannot occur in a specific order. The traditional vision in management approaches new ideas as preformatted “black boxes” (which can come either from outside or inside of the organization) containing well-described pieces of knowledge. What matters for the organization is the potential economic value of the new ideas. These hypotheses on value guide the selection procedures of managers at each step of the stage-gate process (Cooper, 1990). In practice, the first stage, the pre-conception stage (or fuzzy front end), is dedicated to the process of idea generation and construction. More precisely, in the traditional view, this first step generally aims at gathering the maximum number of ideas (using methods such as brainstorming). At the end of this phase, ideas are put in competition with each other: “no go ideas” that are not mature enough are generally discarded, and only a small number of “go ideas” pass the various gates before being transformed into some innovative output for the organization. The ideas that are not selected are definitively discarded, and forgotten. Then, through a sequence of stages and gates, an irreversible process of reduction of the variety of available options starts: the process of innovation per se follows different phases (conception, prototyping, demonstration, production, etc.). Even if this approach proved its efficiency in terms of control of costs and respect of deadlines, it has, with regards to creativity, severe drawbacks: it aims at concentrating “thematic” creativity at the early stages of the process and discourages significant creativity at the later stages. With regard to ideas evaluation, the classical stage-gate process entails two major risks: the first risk is to definitively discard an idea that did not seem mature enough at the moment of the decision, but that eventually would have had the potential of being a real breakthrough after additional enriching work and feedback. The second risk is to select and commit to an idea that eventually will prove to be a poor one. Often, in such cases, it is too late to reconsider a process that has taken an irreversible path. To some extent, it is as if, at the end of the so-called fuzzy front end, once the process of ideation comes to the gate, that ideas lose their creative power, and cannot be further developed or integrated with other ideas. Moreover, this sequential perspective implies that, once the process of ideation comes to its end, there is no possible feedback and mutual cross-fertilization between the parallel building of ideas and the process of innovation. In this view, for instance, the implementation phase of innovation does not necessitate new ideas. It is also admitted that none of the lessons learned or none of the creative inspirations that emerged from day-to-day management of projects can contribute to nurture the ideation processes. Through this approach, many potentially creative ideas, which did not have time to mature, are definitely eliminated. In other words, the risk of killing creativity in pursuit of short-term efficiency is high. For all these reasons, our view is that the sequential perspective should be strongly questioned and challenged: the creation of complex and radical innovation requires solving problems with creative ideas at all phases of the innovation process. Thus, instead of viewing the management of ideas as an initial stage of the innovation process, we propose an integrated framework where the processes of ideation and innovation are not sequential but parallel and coupled (Figure 13.1). We argue that the ideation and innovation process are intertwined, and that ideation process nourishes the innovation at all stages of its conception and the innovation process fosters new questions and generates new ideas. For example, in creative industries (Pixar, Ubisoft, Google, etc.), the two

Managing knowledge, creativity and innovation Processes of innovation

Idea

Coupling mechanisms • Communities • Boundary spanners • Knowledge brokers • Boundary objects • KM platforms • Modularity, etc.

UPPERGROUND

Build business case

Gate 1: Idea screen

Development

Gate 2: Go to development

Processes of knowledge management

Processes of ideation

Figure 13.1

207

Gate 3: Go to tests

Testing & valuation

Launch

Gate 4: Go to launch

MIDDLEGROUND

UNDERGROUND

Coupling processes of ideation and processes of innovation

processes clearly run in parallel; they constantly mutually feed each other: “Exploitation and exploration tend to be unfolded in an organically intricate and complementary way where they constantly fuel each other” (Cohendet and Simon, 2007). This raises challenging questions for management. The Role of the Manager in Coupling Ideas, Knowledge and Innovation In this dynamic view, managers must orchestrate the link between the ideation process and the innovation process to ensure the implementation of sustainable creative processes in the organization. These main processes need subtle coupling and decoupling activities with the knowledge-management process. During the intention phase of ideation, the tacit and formal knowledge are necessary to bring out the problem and questions. At the spark phase, idea generation is fed by different frames of reference. The idea itself carries an amount of explicit and tacit knowledge. The ideation activity requires an environment where tacit and formal knowledge, from different frames of reference, circulate freely and are in perpetual evolution, collision and recombination. Here, the main challenge for knowledge management is to ensure a dynamic relationship between two heterogeneous processes: ideation and innovation. On the one hand, processes of ideation are often informal, merely divergent and somehow chaotic, which implies that the classic means of control, such as contractual schemes of incentives, are mostly irrelevant. On the other hand, classic innovation processes, which are based on project teams and consequently mostly managed by the hierarchy, focus on the convergence on value generation and actualization. These are mostly formal, sequential and linear processes. To be consistent, the dynamic of these creative powerhouses supposes that both processes are to be constantly mutually enriched. This role mostly belongs to management, who are in charge

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of implementing various socio-cognitive transversal practices and processes to harness the idea generation dynamic to innovative projects. In the wide array of options possible, we can mention encouraging boundary spanners and knowledge brokers, designing technical cognitive platforms, and fostering and supporting communities. In other words, looking at the active units of ideation, to deal with coordination issues in innovation processes, managers usually have to articulate the interactions between creative individuals and collectives, formal project teams, and the hierarchy. To integrate the parallel ideation processes, we suggest taking into close consideration another type of active unit: knowing communities. Knowing communities appear to be one of the most efficient socio-organizational devices for knowledge creation validation, and circulation. Their role as a source of creativity for the firm is becoming widely acknowledged in the literature (Brown and Duguid, 1991; Amin and Roberts, 2008; see also Roberts, Chapter 21, this volume). These communities take multiple forms: epistemic communities, communities of practice, communities of creation, communities of innovation, occupational communities, user communities and brand communities. Harvey et al. (2015) bring together these communities under the umbrella term of knowing communities. Knowing communities share, challenge and assemble bits and pieces of knowledge around a common object of interest, be it a practice, an emerging paradigm or the construction of a new frame of understanding in a creative field. They act as an active repository of cognitive and practical resources that feeds not only the exploratory capabilities of the firm, but also its exploitation activities. Creative processes may emerge from the negotiation between competing interests of different groups and communities within the organization (Drazin et al., 1999). These communities can be within the firm with external links such as the occupational communities of Ubisoft (Cohendet and Simon, 2007; Harvey et al., 2015) or outside the firm with internal links such as user communities (Jeppesen and Frederiksen, 2006; Parmentier and Gandia, 2013). Communities use coat-tailing mechanisms for coordination and cooperation which align individual actions and collective activities (Hemetsberger and Reinhardt, 2009). What matters for agents involved in these ideation processes is the recognition of their contribution to the building of ideas (reputation), and intrinsic motivation. Essentially, nurtured by the creative communities, the fundamental element of the ideation process is the building of a creative reservoir. Ideas are circulated between the members, bisociated and combined, and are sometimes stored in prototypes, tinkering, formal concepts or just dormant insights. The remarkable characteristic of the process is the formation of a creative reservoir viewed as a “repertoire of creative opportunities” that contributes by guiding the choice of future projects for the growth of the firm. The creative reservoir is shaped by the culture of the firm and is essentially understandable through the jargon of the organization. This parallels the analysis of Penrose, in which previously utilized managerial resources become “slack”, and these “unused productive services are, for the enterprising firm, at the same time a challenge to innovate, an incentive to expand, and a source of competitive advantage” (Penrose, 1959). In line with Penrose’s vision, the firm that has accumulated a creative reservoir is better prepared than any other organization to derive a benefit from the creative potential of the reservoir. Because of these idiosyncrasies, it is much cheaper to valorize the reservoir within the firm that holds it than through any other organization (including through any isolated communities). Some may argue that the creative reservoir appears as a cushion of redundancy that is costly

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Table 13.1 Untangling the idea-development process The intention

The spark

The social construction

The landing

Focus Active units

Goal definition Individuals

Idea conversion Social group Knowing communities

Idea execution Organization Hierarchy Formal Project

Main activities

Identification of problems, incidents, surprises, irregularities, singularities, etc. ● Monitoring ● Information searching ● Responses to organizational solicitation Existing knowledge and experience Creative brief

Idea generation Individuals and informal groups and communities Looking for insights ● Creative session ● C/K method ● Design thinking

“Intéressement” and sensemaking ● Sharing the idea ● Looking for allies ● Seducing ● Convincing ● Valuating ● Building legitimacy ● Manifesto ● Codebook ● Boundary objects ● Prototype

Sensemaking ● Translating the idea into a value proposition for the organization ● Actualizing value hypothesis

Cognitive artifacts

Cognitive activities

Serendipity

Organizational activities

Sensing

● Post-it sessions ● Mood board ● Mind maps ● Empathy maps ● Value analysis ● C/K diagram Bisociation Effectuation Divergent thinking Combining Bisociating Valuating

Identity construction Value construction Seizing

● Evaluation criteria ● Value proposition ● Business model

Seizing and reconfiguring

to maintain. The specific conditions of formation of the creative reservoir in creative companies rely on the functioning and interactions of quasi-autonomous communities that naturally produce and conserve the knowledge in their domain of specialization at negligible costs. They offer strong guarantees of the efficiency of maintaining the creative reservoir at low costs. The reservoir is not “possessed by the firm”. It is essentially “delegated” to the communities. The challenge remains for the management to recognize the potential of this reservoir, to activate its exploration, and to channel its exploitation. This strongly pleads for a profound reconsideration of the role of the managers with regard to the coordination, balancing and integration of the parallel processes of ideation and innovation. In particular, a specific attention should be given to the role of knowing communities and the dynamic interactions between them, and to more formal processes, such as project management and stage-gating. Innovation management could be redefined not only as the mastery of innovation projects, but also as the development of specific capabilities in ideation management and community management. This opens an extremely rich research agenda for academics and practitioners alike.

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CONCLUSION We have suggested in this contribution that managing the coupling between ideation processes and innovation processes is a central issue for firms under a growing pressure to innovate. While our observation is that firms belonging to the so-called creative industries are already engaged in such new forms of organization, it appears more challenging for traditional firms that are presently historically focused on conducting traditional innovation processes. What has been learned from the careful observation of firms belonging to the creative industries (Cirque du Soleil, Pixar, IDEO, Ubisoft, etc.) is that managing this coupling implies a reconsideration of most of the principles and practices of management, impacting human resources management, project management, intellectual property and so on. We address hereafter some examples. From a human resources management perspective, most of the employees of these firms process knowledge with a dual orientation, aiming both at exploration and exploitation. On the one hand, in their day-to-day current (exploitative) activities, they work in a given innovation process with classical responsibilities and tasks determined by the hierarchy of the firm. On the other hand, they also interact with members of their community of specialists and engage in regular and continuous meetings, discussions or exchanges on social networks, sensing and seizing (exploring activities) the new trends, new technologies and new modes of usage that will influence their domain of expertise. They have “one foot” in innovation processes (exploitation) and “one foot” in ideation processes (exploration). As a result, their incentives are twofold: on the one hand, classical incentives based on performance in the conduct of innovation processes; on the other hand, reputational incentives based on their involvement in their creative endeavours and interaction with knowing communities. The implications for the human resources department are, for instance, to conceive specific dual mechanisms for motivating, recruiting and rewarding employees. From the perspective of project management, a reconsideration and reconfiguration also seem necessary. Project management in creative industries shares common features with classical project management styles in more traditional industries, but also exhibits some specific traits in order to nurture the fundamental creativity in these industries. As an example, in the domain of the video game, Cohendet and Simon (2007, 2015) observed that the form of project management relies on the design of two hierarchical dimensions: a common cognitive architecture of the project (the “script”, the “scenario”, the “shared vision” or “shared meaning”) and the definition of the associated standardized component interfaces (codified prescriptions and constraints to be respected by the participating groups). From this hierarchical structure, each component can be designed independently and simultaneously by a specialized team or community, which can express creativity provided that it respects the standardized interfaces. The script is the cognitive reference that glues together the different communities of specialists that work in modules specializing in the different domains of knowledge related to a video game project. Modules use different pools of knowledge, specific jargons and specific understandings of the project requirements (Langlois, 2002), which rely on different specialized generic skills (game design, level design, 2D and 3D graphic arts, various levels of software programming and integration, sound design, tests, etc.). The consequences of this are that

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project management needs to focus at the same time on 1) the script definition and the coordination of engagement of different communities around the script; 2) capabilities development inside each distinctive community of specialists engaged in multiple projects and with other internal and external communities. In terms of management of property rights, while the conduct of innovation processes requires traditionally strong classical property rights (patents, licenses, etc.), the conduct of ideation is more subtle and calls increasingly for new forms of property rights such as creative commons, that recognize who is at the origin of the idea, but which are more flexible and less costly than traditional rights in a creative context (see for instance Pénin’s chapter, Chapter 12, this volume). To sum up, many dimensions of management are challenged in order to cope with the coupling of ideation and innovation processes. As said above, if these new forms and practices of management could be observed in the creative industries, we posit that in the long run all traditional industries and firms will have to consider adopting such practices and rules. There are already numerous examples of companies (including Procter & Gamble, Philips, Whirlpool, Renault, Decathlon) that are developing various forms of platforms and informal communities to facilitate the coupling of ideation processes and innovation processes. The fundamental issue at stake for companies is the ability to reconcile efficiency and creativity for sustainable innovation.

REFERENCES Adler, P.S. (1995) The dynamic relationship between tacit and codified knowledge, in G. Pogorel, J. Allouche (ed.), International Handbook of Technology Management. Amsterdam, North-Holland, pp. 110–124. Akrich, M., Callon, M. and Latour, B. (2002a) The key to success in innovation Part I: The art of interessement. International Journal of Innovation Management 6 (2), 187–206. Akrich, M., Callon, M., Latour, B., Monaghan, A. (2002b) The key to success in innovation Part II: The art of choosing good spokespersons. International Journal of Innovation Management 6 (2), 207–225. Altshuller, G.S. (1984) Creativity As an Exact Science. Gordon & Breach, New York. Amabile, T.M. (1988) A model of creativity and innovation in organizations. Research in Organizational Behavior 10, 123–167. Amabile, T.M. (1993) Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review 3 (3), 185–201. Amabile, T.M. (1998) How to kill creativity. Harvard Business Review 76 (5), 76–87. Amabile, T.M., Conti, R., Coon, H., Lazenby, J., Herron, M. (1996) Assessing the work environment for creativity. Academy of Management Journal 39 (5), 1154–1184. Amin, A., Roberts, J. (2008) Knowing in action: Beyond communities of practice. Research Policy 38, 353–369. Baer, M. (2012) Putting creativity to work: The implementation of creative ideas in organizations. Academy of Management Journal 55 (5), 1102–1119. Basadur, M.S., Basadur, T., Licina, G. (2012) Organizational development, in M.D. Mumford (ed.), Handbook of Organizational Creativity. Academic Press, London, pp. 667–703. Basadur, M., Graen, G.B., Green, S.G. (1982) Training in creative problem solving: Effects on ideation and problem finding and solving in an industrial research organization. Organizational Behavior and Human Performance 30 (1), 41–70. Birkinshaw, J., Bouquet, C., Barsoux, J.L. (2011) The 5 myths of innovation. MIT Sloan Management Review 52 (2), 43–50. Brown, J.S., Duguid, P. (1991) Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science 2 (1), 40–57. Brown, T. (2009) Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. HarperCollins, New York. Burger-Helmchen, T., Cohendet, P. (2011) User communities and social software in the video game industry. Long Range Planning 44 (5/6), 317–343.

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Callon, M. (1986) The sociology of actor-network: The case of the electric vehicle, in M. Callon, J. Law, A. Rip (eds), Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World. Macmillan Press, London, pp. 19–34. Capdevila, I., Cohendet, P., Simon, L. (2015) Establishing new codes for creativity through haute cuisine: The case of Ferran Adrià and elBulli. Technology Innovation Management Review 5 (7), 25–33. Carbonell-Foulquié, P., Munuera-Alemán, J.L., Rodríguez-Escudero, A.I. (2004) Criteria employed for go/ no-go decisions when developing successful highly innovative products. Industrial Marketing Management 33 (4), 307–316. Christensen, C.M., Raynor, M.E. (2003) Why hard-nosed executives should care about management theory. Harvard Business Review 81 (9), 66–74. Cohendet, P., Grandadam, D., Simon, L., Capdevila, I. (2014) Epistemic communities, localization and the dynamics of knowledge creation. Journal of Economic Geography 14 (5), 929–954. Cohendet, P., Simon, L. (2007) Playing across the playground: Paradoxes of knowledge creation in the videogame firm. Journal of Organizational Behavior 28 (5), 587–605. Cohendet, P., Simon, L. (2015) Introduction to the special issue on creativity in innovation. Technology Innovation Management Review 5 (7), 5–13. Cooper, R.G. (1990) Stage-gate systems: A new tool for managing new products. Business Horizons 33 (3), 44–54. Crutzen, N., Moreau, C., Pichault, F. (2014) InnovAddict: une entreprise “malade” de la créativité, in D.  Chabault, A. Hulin, R. Soparnot (eds), Cas d’innovations en entreprise – Organisation et stratégie. Editions Management & Société (EMS), pp. 77–86. De Bono, E. (1971) Lateral Thinking for Management. Penguin Books, Harmondsworth. Dean, D.L., Hender, J.M., Rodgers, T.L., Santanen, E.L. (2006) Identifying quality, novel, and creative ideas: Constructs and scales for idea evaluation. Journal of the Association for Information Systems 7 (10), 646–698. Deci, E.L., Ryan, R.M. (1985) Intrinsic Motivation and Self-Determination in Human Behavior. Plenum, New York. Dewett, T. (2007) Linking intrinsic motivation, risk taking, and employee creativity in an R&D environment. R&D Management 37 (3), 197–208. Doyle, C.L. (1998) The writer tells: The creative process in the writing of literary fiction. Creativity Research Journal 11 (1), 29–37. Drazin, R., Glynn, M.A., Kazanjian, R.K. (1999) Multilevel theorizing about creativity in organizations: A sensemaking perspective. Academy of Management Review 24 (2), 286–307. Eisenberger, R., Rhoades, L. (2001) Incremental effects of reward on creativity. Journal of Personality and Social Psychology 81 (4), 728–741. Eisenhardt, K.M., Martin, J.A. (2000) Dynamic capabilities: What are they? Strategic Management Journal 21 (10/11), 1105–1121. Ford, C.M. (1996) A theory of individual creative action in multiple social domains. Academy of Management Review 21 (4), 1112–1142. Friedman, R.S. (2009) Reinvestigating the effects of promised reward on creativity. Creativity Research Journal 21 (2/3), 258–264. Furr, N., Dyer, J.H. (2014) The Innovator’s Method: Bringing the Lean Startup into Your Organization. Harvard Business School, Boston, MA. Getzels, J., Csikszentmihalyi, M. (1976) The Creative Vision: A Longitudinal Study of Problem Finding in Art. Wiley Intersecience, New York. Govindarajan, V., Trimble, C. (2005) Building breakthrough businesses within established organizations. Harvard Business Review 83 (5), 58–68. Guilford, J.P. (1950) Creativity. American Psychologist 5, 444–454. Guilford, J.P. (1967) The Nature of Human Intelligence. McGraw-Hill, New York. Hargadon, A., Sutton, R.I. (2000) Building an innovation factory. Harvard Business Review 78 (3), 157–166. Harrisson, D., Laberge, M. (2002) Innovation, identities and resistance: The social construction of an innovation network. Journal of Management Studies 39 (4), 497–521. Hart, S., Jan Hultink, E., Tzokas, N., Commandeur, H.R. (2003) Industrial companies’ evaluation criteria in new product development gates. Journal of Product Innovation Management 20 (1), 22–36. Harvey, J.-F., Cohendet, P., Simon, L., Borzillo, S. (2015) Knowing communities in the front end of innovation. Research Technology Management 58 (1), 46–54. Harvey, S., Kou, C.-Y. (2013) Collective engagement in creative tasks: The role of evaluation in the creative process in groups. Administrative Science Quarterly 58 (3), 346–386. Hemetsberger, A., Reinhardt, C. (2009) Collective development in open-source communities: An activity theoretical perspective on successful online collaboration. Organization Studies 30 (9), 987–1008. Howe, J. (2008) Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. Random House, New York.

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IBM Global Business Services (2010) Capitalizing on Complexity: Insights from the Global Chief Executive Officer Study. IBM, New York. Jeppesen, L.B., Frederiksen, L. (2006) Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organization Science 17 (1), 45–63. Koen, P.A., Ajamian, G.M., Boyce, S., Clamen, A., Fisher, E., et al. (2002) Fuzzy-front end: Effective methods, tools and techniques, in P. Belliveau, A. Griffen, S. Sorermeyer (eds), Toolbook for New Product Development, 2nd edn. Wiley, New York. Koestler, A. (1964) The Act of Creation. Macmillan, New York. Langlois, R.N. (2002) Modularity in technology and organization. Journal of Economic Behavior and Organization 49 (1), 19–37. Law, J. (1991) Introduction: Monsters, machines and sociotechnical relations, in J. Law (ed.), A Sociology Of Monsters: Essays on Power, Technology and Domination. Routledge, London, pp. 1–25. Le Masson, P., Hatchuel, A., Weil, B. (2017) Design theories, creativity and innovation, in H. Bathelt, P. Cohendet, S. Henn, L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham, Northampton, MA, pp. 275–306. Le Masson, P., Weil, B., Hatchuel, A. (2010) Strategic Management of Innovation and Design. Cambridge University Press, Cambridge. Leonard-Barton, D. (1995) Wellsprings of Knowledge: Building and Sustaining the Source of Innovation. Harvard Business School Press, Boston, MA. Lilien, G.L., Morrison, P.D., Searls, K., Sonnack, M., von Hippel, E. (2002) Performance assessment of the lead user idea-generation process for new product development. Management Science 48 (8), 1042–1059. Lubart, T.I. (2001) Models of the creative process: Past, present and future. Creativity Research Journal 13 (3/4), 295–308. Mednick, S. (1962) The associative basis of the creative process. Psychological Review 69 (3), 220–232. Mumford, M.D., Hunter, S.T., Byrne, C.L. (2009) What is the fundamental? The role of cognition in creativity and innovation. Industrial and Organizational Psychology 2 (3), 353–356. Mumford, M.D., Mobley, M.I., Uhlman, C.E., Reiter-Palmon, R., Doares, L.M. (1991) Process analytic models of creative capacities. Creativity Research Journal 4 (2), 91–122. Nonaka, I., Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York. Osborn, A.F. (1948) Your Creative Power: How to Use Imagination to Brighten Life, to Get Ahead. Charles Scribner’s Sons, New York. Osborn, A.F. (1953) Applied Imagination: Principles and Procedures of Creative Problem Solving. Charles Scribner’s Sons, New York. Parmentier, G. (2015) How to innovate with a brand community. Journal of Engineering and Technology Management 37, 78–89. Parmentier, G., Gandia, R. (2013) Managing sustainable innovation with a user community toolkit: The case of the video game Trackmania. Creativity and Innovation Management 22 (2), 195–208. Parmentier, G., Mangematin, V. (2014) Orchestrating innovation with user communities in the creative industries. Technological Forecasting and Social Change 83, 40–53. Parnes, S.J. (1967) Creative Behavior Guidebook. Scribners, New York. Pénin, J. (2017) Patents and open innovation, in H. Bathelt, P. Cohendet, S. Henn, L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham, Northampton, MA, pp. 182–194. Penrose, E. (1959) The theory of the growth of the firm. Oxford University Press, Oxford. Poincaré, H. (1908) L’invention mathématique. Bulletin de l’Institut Général Psychologique 8 (3), 2–15. Roberts, J. (2017) Community, creativity and innovation, in H. Bathelt, P. Cohendet, S. Henn, L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Edward Elgar Publishing, Cheltenham, Northampton, MA, pp. 342–359. Runco, M.A., Chand, I. (1995) Cognition and creativity. Educational Psychology Review 7 (3), 243–267. Runco, M.A., Row, G. (1999) Problem finding, in M.A. Runco, S.R. Pritzker (eds), Encyclopedia of Creativity. Academic Press, London, pp. 433–435. Ryan, R.M., Deci, E.L. (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist 55 (1), 68–78. Sgourev, S.V. (2013) How Paris gave rise to Cubism (and Picasso): Ambiguity and fragmentation in radical innovation. Organization Science 24 (6), 1601–1617. Staw, B.M. (1990) An evolutionary approach to creativity and innovation, in M.A. West and J.L. Farr (eds), Innovation and Creativity at Work: Psychological and Organizational Strategies. Wiley & Sons Ltd, Chichester, pp. 287–306. Svejenova, S., Mazza, C., Planellas, M. (2007) Cooking up change in haute cuisine: Ferran Adriã as an institutional entrepreneur. Journal of Organizational Behavior 28 (5), 539–561.

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Taggar, S. (2002) Individual creativity and group ability to utilize individual creativity ressources: A multilevel model. Academy of Management Journal 45 (2), 315–330. Teece, D.J. (1996) Firm organization, industrial structure, and technological innovation. Journal of Economic Behavior and Organization, 31(2), 193–224. Teece, D.J. (2007) Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28 (13), 1319–1350. Teece, D.J. (2009) Dynamic Capabilities and Strategic Management. Oxford University Press, Oxford. Treffinger, D.J. (1995) Creative problem solving: Overview and educational implications. Educational Psychology Review 7 (3), 301–312. Unsworth, K. (2001) Unpacking creativity. Academy of Management Review 26 (2), 289–297. Van Andel, P. (1992) Serendipity: “Expect also the unexpected”. Creativity and Innovation Management 1 (1), 20–32. von Hippel, E. (1986) Lead users: A source of novel product concepts. Management Science 32 (7), 791–801. von Hippel, E., Katz, R. (2002) Shifting innovation to users via toolkits. Management Science 48 (7), 821–833. Wallas, G. (1926) The Art of Thought. Harcourt Brace, New York. Weisberg, R.W. (1986) Creativity, Genius and Other Myths. W.H. Freeman & Co, New York. Weisberg, R.W. (1993) Creativity: Beyond the Myth of Genius. Freeman, New York. Weisenfeld, U. (2009) Serendipity as a mechanism of change and its potential for explaining change processes. Management Revue 20 (2), 138–148. Whittle, A., Spicer, A. (2008) Is actor network theory critique? Organization Studies 29 (4), 611–629. Woodman, R.W., Sawyer, J.E., Griffin, R.W. (1993) Toward a theory of organizational creativity. Academy of Management Review 18 (2), 293–321.

14. Urban diversity and innovation Pierre Desrochers, Samuli Leppälä and Joanna Szurmak

INTRODUCTION While much traditional urban economic analysis continues to revolve around agglomeration (dis)economies, the unique context urban areas provide for creative and innovative activities has attracted interest over the course of the last two decades (Scott 2006). Despite a few exceptions (Gordon 2013), regional development scholars have paid scant attention to the outcomes of research on human creativity. Hall (2000, p. 642) justifies this apparent oversight by claiming that “virtually none of [the creativity literature] addresses the question of location. Psychologists and psychoanalysts treat it almost exclusively in terms of the individual personality; so do students of management, who have looked at company innovation. Few studies mention the social context; even fewer are specific.” Similarly, the recent work on the “creative class” (e.g., Florida 2005), while more contextualized, studies how the agglomeration of inherently creative people in particular industries is a necessary foundation for urban innovation and growth rather than explains how urban economic diversity fosters the creativity of people across all industries. Contra Hall, we believe that we can make significant gains from an exchange of ideas between the literatures of “geography of innovation” and creativity in the particular case of “Jacobs spillovers” (i.e., interindustrial knowledge spillovers between geographically proximate but different lines of work). While the concept suggests a positive relationship between innovation and urban diversity and has provided the starting point of numerous empirical (i.e., econometric) tests, its underlying processes have rarely been made explicit. A better understanding of how a diverse environment fosters the creation of new combinations of existing ideas and technologies in human minds can, we suggest, shed more light on this issue (see also Cohendet et al., Chapter 13, this volume). This chapter is structured as follows. We first discuss the origins and shortcomings of the literature on Jacobs spillovers before putting them into the larger context of urban economics analysis. While unsurprisingly a few other scholars have anticipated or independently made this argument, we illustrate that the connection between urban diversity and innovation remains vague. We then endeavor to make that connection more explicit with some creativity literature insights. We finally summarize some of our work on the actual mechanisms underlying Jacobs spillovers and discuss the role of geographical proximity and urban spaces in this context through additional examples.

THE BIRTH DEFECTS OF JACOBS SPILLOVERS In one of the most cited urban economics articles of the last three decades, Glaeser et al. (1992, p. 1126) posited that knowledge spillovers – that is, knowledge that, 215

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once discovered or created, easily comes to the attention of and is absorbed by other individuals – are “particularly effective in cities, where communication between people is more extensive.” They identified and tested three broad competing, if sometimes overlapping, perspectives on the issue. The first two, named respectively for the insights of economists Alfred Marshall, Kenneth Arrow and Paul Romer (MAR spillovers) and management theorist Michael Porter (Porter spillovers), stress the greater importance of knowledge spillovers within an economic sector (also known as intraindustrial spillovers). The other perspective, credited mainly to urban theorist Jane Jacobs (Jacobs spillovers), emphasizes spillovers between sectors (interindustrial spillovers). Glaeser et al. (1992) also briefly discussed the work of urban historian Paul Bairoch and economic historian Nathan Rosenberg alongside that of Jacobs, even at one point referring to the “Jacobs-Rosenberg-Bairoch model” (p. 1151), a perspective rarely mentioned in the literature that subsequently built on their framework. Among the spillover models, the MAR perspective favors local monopolies because they restrict the flow of ideas whereas Jacobs and Porter view a large number of smaller firms and intense local competition as more desirable. Using employment growth in the 170 largest American cities between 1956 and 1987 as their main proxy, Glaeser et al. (1992) observed that their results were “negative on MAR, mixed on Porter, and consistent with Jacobs” (p. 1129), thus suggesting that “crossfertilization of ideas across industries speeds up growth” and that if “Jane Jacobs is right, the research on growth should change its focus from looking inside industries to looking at the spread of ideas across sectors” (p. 1151). As acknowledged by Glaeser et al. (1992) though, their “evidence on externalities is indirect” and their findings could be explained “by a neoclassical model in which industries grow where labor is cheap and demand is high” (p. 1151). Furthermore, instead of identifying the mechanisms through which interindustrial knowledge spillovers took place, the authors merely quoted earlier literature that indicated spillovers across industries. Indeed, their key quote on Jacobs spillovers was actually from historian Paul Bairoch (1988, p. 336), who stated that the “diversity of urban activities quite naturally encourages attempts to apply or adopt in one sector (or in one specific problem area) technological solutions adopted in another sector.” In later years several researchers conducted empirical tests of all kinds in order to identify correlations between unrelated inputs (greater or lesser regional diversity or specialization of large or small firms) and outputs (such as R&D expenditures, patents, job creation, economic growth per capita, and innovation counts). Contradictory results were found depending on the unit of analysis, indicator, time period, industry life cycle and institutional setting examined. In the words of van Oort and Bosma (2013, p. 214): Since Glaeser et al. (1992), it has become more apt to analyse growth variables using employment in cities, suggesting a relationship between agglomeration and economic growth, coining the possibility that urban increasing returns are working in a dynamic, rather than static, context. Sector-specific localization economies, stemming from input–output relations and transport cost savings of firms, human capital externalities and knowledge spillovers, are generally offset against the earlier customary measured general urbanization economies (Henderson 2003). A large literature builds on this conceptualization of agglomeration economies, reflected in three recent overview and meta-studies (Melo et al. 2009; Beaudry and Schiffauerova 2009; De

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Groot et al. 2009). These studies show that the relation agglomeration-growth is ambiguous and indecisive on either specialization or diversity being facilitated by (sheer) urbanization as context. The ambiguity is fuelled by measurement issues and heterogeneity in terms of scale of time and space, aggregation, growth definitions, and the functional form of the models applied.

A further difficulty in attempts to capture Jacobs spillovers by these means is that there exist various ways, known more generally as urbanization economies, through which diversity affects growth variables. Through the use of industrial classification codes, Frenken et al. (2007) studied this issue by distinguishing between “related” and “unrelated” variety, where the former aims to capture Jacobs spillovers and the latter the portfolio effect of diversification. Related variety subsumes both Jacobs spillovers and the idea that spillovers occur more readily between industries that use similar technologies. Discounting the fact that industrial classifications are based on outputs rather than inputs, it is plausible that incremental innovations are hence diffused more easily between similar industries. However, this does not hold for radical innovations that are typically built on spillovers between previously unrelated industries. Evidence for the connection between unrelated variety and radical innovations was subsequently found by Castaldi et al. (2015), which demonstrates that the multifaceted effects of diversity on innovation confound the measurement of Jacobs spillovers. As van Oort (2015) restated more recently, the main problems with the Glaeser et al.-inspired literature are its “weak conceptualisation and [the] limited theoretical underpinning of the concepts” used. The way forward, he suggests, is to “focus more on the transfer mechanisms of knowledge and knowledge spillovers” for even though they are “implicitly suggested in econometric agglomeration models that aim to capture specialisation and diversity,” in truth “none of the models actually captures these flows and networks of relatedness.” As Beaudry and Schiffauerova (2009, p. 320) put it more succinctly, the “exact spillover mechanism is not yet fully understood and documented” and there is even “no direct proof of the existence of knowledge spillovers.” In light of these problems, many researchers have argued for the need to reconsider the specialization–diversity debate. We will now suggest, however, that a better understanding of the relationship between the creative process and its physical setting provides a way to address current conceptual and empirical problems.

URBAN DIVERSITY, CREATIVITY AND ECONOMIC DEVELOPMENT Cities have long been hosts to clusters of similar and related activities. To give but one illustration, in 1850 the journalist Henry Mayhew described the leather and hides trades of the Bermondsey district (south London) in the following terms: On every side are seen announcements of carrying on of the leather trade . . . The signboards announce, in thick profusion, dealers in bark, tanners, curriers, French tanners and curriers, leather-dressers, morocco and roan manufacturers, leather-warehousemen, leather factors, leather dyers, leather enamellers, leather sellers and cutters, hide salesmen, fellmongers, tawers, parchment makers, wool factors, woolstaplers, wool warehousemen, wool dealers, wool dyers, hair and flock manufacturers, dealers in horns and hoofs, workers in horn, glue makers, size makers, and neat’s-foot oil makers. (quoted in Atkins 2012, p. 92)

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While most commentators have long explained the existence of such “industrial districts” by localization economies such as shared inputs and labor market pooling, a few also discussed the importance of knowledge creation. Arguably the most famous quote on the subject is Marshall’s (1920, non-paginated) observation that: When an industry has thus chosen a locality for itself, it is likely to stay there long: so great are the advantages which people following the same skilled trade get from near neighbourhood to one another. The mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously. Good work is rightly appreciated, inventions and improvements in machinery, in processes and the general organization of the business have their merits promptly discussed: if one man starts a new idea, it is taken up by others and combined with suggestions of their own; and thus it becomes the source of further new ideas [our emphasis]. And presently subsidiary trades grow up in the neighbourhood, supplying it with implements and materials, organizing its traffic, and in many ways conducing to the economy of its material.

As hinted earlier in this chapter, Marshall wrote this passage in a discussion of “the concentration of specialized industries in particular localities” and, through no fault of his own, probably led many subsequent scholars to ignore the fact that significant innovative advances are always and everywhere the results of new combinations of previously unrelated know-how, skills, ideas, processes, materials and artifacts (Sternberg 1999; Gassman and Zeschky 2008). For example, patent data clearly highlight the combinatorial nature of invention (Youn et al. 2015). Indeed, while there are many perspectives on creativity, researchers usually agree that it involves “the development of a novel product, idea, or problem solution that is of value to the individual and/or the larger social group” (Hennessey and Amabile 2010, p. 572) as the result of “crossing the boundaries of domains” through the work of individuals who “love to make connections with adjacent areas of knowledge” (Csikszentmihalyi 1997, p. 9). This being said, several creativity scholars are still debating the taxonomy of creative processes and their relationship to internal and external stimuli such as those present in work settings and urban environments (Hennessey and Amabile 2010). We will return to the dynamics of social processes in innovation, reflectively reframed by other researchers, once we have briefly examined the (re)combinatory nature of technological innovation. When Jacobs (1969, pp. 61–62) argued that “when new work is added to older work, the addition often cuts ruthlessly across categories of work, no matter how one may analyze the categories” and economic classification systems “interfere with our understanding” of how “old work leads to new,” she was merely restating the most ancient truth about innovation. To give but a few historical illustrations of “interindustrial” technology transfers, it is believed that the bow-drill, which was used for drilling holes and starting fires, led to the bow (McNeil 1996). The concept of a production chain was adapted over several decades in flourmills, slaughterhouses and machine tool works as well as canning, railroad car and auto assembly factories (Hounshell 1991). Lasers have been used for some time in, among other things, printers, telecommunication equipment, navigational instruments, textile machinery, surgery, precision measurement, weapon systems, sound systems and cash registers (Lipsey et al. 1998). Similarly, countless manufacturers have long expanded their product line(s) across different industries. For example, numerous buggy, railroad, toy, agricultural equipment, firearms and sewing machine manufacturers turned to the production of bicycles during the 1890s’ “bicycle craze” (Hounshell 1991), while New York’s shipbuilding manufacturers diversified their operations to include

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carriage, steam engine and locomotive production in the first half of the nineteenth century (Lichtenberg 1960). Not surprisingly, numerous students of technological change wrote about its (re)combinatory nature long before Jacobs. The polymath Charles Babbage (1832, p. 206) thus observed almost two centuries ago that the “power of inventing mechanical contrivances, and of combining machinery, does not appear, if we may judge from the frequency of its occurrence, to be a difficult or a rare gift.” A century later, Carter (1939, p. 24) wrote that “one of the most frequent methods of employing inventive talent” is for an “expert in one branch of technology [to] intelligently investigate another field with the objective of discovering some application for his specialized knowledge.” In Ayres’ (1943, p. 113) words, “the history of every material is . . . one of novel combinations of existing devices and materials in such a fashion as to constitute a new device or a new material or both.” Barber (1952, p. 194) defined inventions as “those imaginative combinations which men make of previously existing elements in the cultural heritage and which have emergent novelty as combinations.” Fores (1979, p. 853) described the main thrust of an engineer or a creative technician as “gather[ing] knowledge from diverse places in order to help solve technical problems.” Interestingly, Marshall was well aware of the interindustrial nature of manufacturing technologies. As he observed in his classic Industry and Trade: Modern work is more narrowly specialized, in so far as the number and variety of the operations performed by a modern worker are on the average less than those of elementary skilled handicraftsman; but it is less narrowly specialized, in the sense than an operative, who has mastered the accurate, delicate and prompt control of machinery of any kind in one industry, can now often pass, without great loss of efficiency, to the control of similar machinery in an industry of a wholly different kind, and perhaps working on different material. (Marshall 1923, p. 10)

At a more conceptual level, social scientists have addressed the interindustrial diffusion of technological know-how through frameworks and concepts such as “technological convergence,” “technoeconomic paradigms,” “general purpose technologies” and “recombinant growth” (Lipsey et al. 1998; Weitzman 1998), while students of human creativity have written much on “associative ability,” “bisociation,” “lateral thinking” and “analogical reasoning,” among others (Koestler 1964; de Bono 1992; Weber and Perkins 1992; Csikszentmihalyi 1997; Sternberg 1999; Berkun 2007). Recently, Johnson (2010, p. 153) stressed that technological innovation often “lay not in conceiving an entirely new technology from scratch, but instead from borrowing [Johnson’s emphasis] a mature technology from an entirely different field, and putting it to work to solve an unrelated problem.” Reaching for an evolutionary biology analogy, Johnson (2010, pp. 153–154) called this process exaptation, after Stephen Jay Gould’s and Elisabeth Vrba’s first use of this term in 1971 to describe a situation when “[a]n organism develops a trait optimized for a specific use, but then the trait gets hijacked for a completely different function.” Acknowledging (or not) the importance of the combinatory process is also probably at the heart of how one views humanity’s future prospects. Machlup (1962, p. 156) thus distinguished between the “retardation school” of technological change whose proponents believed that “the more that has been invented the less there is left to be invented” and the “acceleration school” according to which “the more that is invented the easier it becomes to invent still more” because “every new invention furnishes a new idea for

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potential combination with vast numbers of existing ideas” and the “number of possible combinations increases geometrically with the number of elements at hand.” More recently, Jones (2009) argued that the accumulation of knowledge creates an increasing educational burden, which may only be overcome by further division of knowledge and expertise and by collaboration between individuals. How do one individual’s social and economic environments affect his propensity and capacity to combine existing things in a new way? A few researchers have addressed the issue in the context of multidisciplinary teams and corporate rotational programs in which specialists are asked to join projects for which their expertise is not obvious. As innovation managers have long known, these contexts help individuals overcome the blinders created by their particular expertise (Schroeder et al. 1989). Twiss (1980, p. 69) thus explained that such groups bring together people working within different mental constraints. An extreme case of this comes from a large American research organization where one of the most creative members is a former theologian. Inevitably many of his ideas cannot be translated into practical terms, but occasionally he does come up with a proposal which would not have resulted from the normal thought processes of his technological colleagues and yet proves to be technically feasible. Another striking illustration of these benefits was given nearly two decades ago by the electronics pioneer Raymond Kurzweil, who explained how his firm relied on the contributions of experts as diverse as linguists, signal-processing experts, very large scale integration (VLSI) designers, psycho-acoustic experts, speech scientists, computer scientists, human-factor designers, and experts in artificial intelligence and pattern recognition. His main challenge, he observed, was “to provide a climate in which people with different expertise can work together toward a common goal and communicate clearly with one another” for [e]ach one of these fields has very different methodologies and different terminologies. Very often a term in one field means something else entirely in another field. Sometimes we even create our own terminology for a particular project. So, enabling a team like that to communicate and solve a problem is a significant challenge. If you look at the entire company, you bring in even more disciplines: manufacturing, material-resources planning, purchasing, marketing, finance, and so on. Each of these areas has also developed sophisticated methodologies of their own that are as complex as those in engineering. (Brown 1988, pp. 243–244)

In their study of urban idea generation as measured by patents issued, Packalen and Bhattacharya (2015, p. 12) noted that teams of inventors consistently used more recent ideas than inventors working on their own: We find that teams of inventors are much more likely to apply fresh knowledge than lone inventors. An intriguing avenue for future work is examining to which extent this result arises because each inventor in a team brings in their own knowledge of existing ideas to the team and to which extent the result arises because inventors working in teams can solve the mysteries of new ideas faster than lone inventors through a vigorous debate on the new ideas’ merits.

Ultimately, these research findings, while emphasizing the complexity of behaviors contributing to creativity, have not been able to distil the essence of creative processes taking place in groups. One hint of group dynamics contributing to the kind of fertile innovation climate outlined by Kurzweil (Brown 1988) and observed by Packalen and

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Bhattacharya (2015) may come from Hennessey and Amabile (2010), who empirically linked reflective and complex communication and collaboration methods, an evolving set of processes cycling through the reservoir of diversity and knowledge resident in the group, to sustained group creativity. Johnson (2010) has provided useful conceptual handles for examining the environmental constraints to the flow of ideas and expertise in a work setting. He envisioned the total set of possible innovations as being limited by the physical and conceptual properties of their setting, controlled by the workflow, expertise levels and other variables. Johnson called this innovation potential “the adjacent possible” (Johnson 2010, p. 31). Work settings in which the adjacent possible could grow in stable and productive ways enabled the rise of liquid networks that create “a more promising environment for the system to explore the adjacent possible” (Johnson 2010, p. 53). Johnson’s illustration of liquid networks exploiting the adjacent possible included an analysis, originally conducted by the psychology researcher Kevin Dunbar, of workflow patterns in a McGill University laboratory. Even in such a confined context as a research lab, the most productive paths were those that involved the maximum interaction of diverse players in a dynamic setting. As Johnson observed, conference tables were thus more conducive to the development of new ideas than lab benches, echoing the hypothesis of Packalen and Bhattacharya (2015, p. 12) that “inventors working in teams can solve the mysteries of new ideas faster than lone inventors through a vigorous debate on the new ideas’ merits.” Another set of circumstances that is said to increase creativity or expand the adjacent possible is experiencing cultural diversity. Although there is some support in the literature for team creativity being enhanced through the collaboration of culturally diverse members (Hennessey and Amabile 2010), living and working abroad provides the most profound way of fostering innovation (Maddux et al. 2010). While merely visiting another culture is not sufficient to change an individual’s creative potential, increasing one’s multicultural learning experience by engaging in active and sustained cultural comparisons has the effect of literally re-wiring one’s brain. Maddux et al. (2010) showed that cultural learning experiences changed the cognitive processes of the participants, affecting how they approached problem-solving and generating new ideas. As a result of their work, Maddux et al. (2010, p. 738) found that: Learning within and about a foreign culture – in particular, learning that certain behaviors one has long grown accustomed to as natural and inevitable can suddenly have very different functions in a different cultural environment – may help individuals perceive and understand why cultural differences occur. These experiences then seem to enhance cognitive complexity and flexibility, heightening the ability to approach problems from new and multiple perspectives and ultimately enhancing the creative process.

Here is, thus, an experimentally validated mechanism of innovation enhancement through a change of setting and interactional parameters for working individuals, a set of circumstances that, in our opinion, is also replicated to some extent when employees move between firms, institutions and corporate cultures. Seen in this light, the downside of a somewhat homogeneous local economic environment is obvious. As the economic geographer Malcom Keir (1919, p. 47) observed nearly a century ago:

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From the point of view of employees, [geographically-specialized] localization is bad because it also tends toward narrowing the minds of the townspeople. A young man brought up in Fall River [Massachusetts], say, has but little choice of occupation; he must become a weaver or a loom-fixer or some other artisan connected with cotton manufacture, because by upbringing, education and example he is forced into that path, and furthermore he goes to work at an early age. It may happen that many a square peg is rammed into a round hole in this way, or a life constricted which might under better conditions have expanded. There is something deadening to the human mind in uniformity; progress comes through variation, therefore in a town of one industry a young man loses the stimulus for self-advancement.

The counter-innovative effect of local economic and cultural uniformity was also independently rediscovered by Michael Porter (2000, p. 24) several decades later when he commented on the possible downsides of specialized clusters: Under certain circumstances, however, cluster participation can retard innovation. When a cluster shares a uniform approach to competing, a sort of groupthink often reinforces old behaviors, suppresses new ideas, and creates rigidities that prevent [the] adoption of improvements. Clusters also might not support truly radical innovation, which tends to invalidate the existing pools of talent, information, suppliers and infrastructure. In these circumstances, a cluster participant might be no worse off, in principle, than an isolated firm (because both can outsource), but the firm in an established cluster might suffer from greater barriers to perceiving the need to change and from inertia against severing past relationships that no longer contribute to competitive advantage.

Contradicting the endorsement of economic specialization that dominated the writings of urban economists and regional scientists, Jacobs (1969, p. 59) suggested that urban diversity was a key foundation of economic development because “the greater the sheer numbers and varieties of divisions of labor already achieved in an economy, the greater the economy’s inherent capacity for adding still more kinds of goods and services. Also the possibilities increase for combining the existing divisions of labor in new ways.” In other words, in a diverse city made up of numerous small firms active in different lines of work, creative individuals are constantly faced with new problems and are given more opportunities to address them, including the possibilities of interacting more meaningfully with people who possess different and complementary expertise. By offering a greater number and variety of problems to be solved, as well as a much wider pool of expert knowledge and other resources, a diversified city can only increase the probabilities of new combinations. Of course, large cities also host temporary events such as conferences and trade shows that further provide opportunities for individuals to expand their intellectual horizons and connect with knowledgeable people in other lines of work (Bathelt et al. 2014; Bathelt, Chapter 31, this volume). Not surprisingly, the “Jacobs spillovers” argument was anticipated or restated independently by a few other writers. For instance, the economist Simon Kuznets (1960, pp. 328–329) discussed the “interdependence of knowledge of the various parts of the universe in which we human beings operate” where, for instance, “discoveries and inventions in the field of tensile strength of metals contribute to discoveries and inventions in the field of electric currents.” He further suggested that “creative effort flourishes in a dense intellectual atmosphere, and it is hardly an accident that the locus of intellectual progress (including that of the arts) has been preponderantly in the larger cities, not in the bucolic surroundings of the thinly settled countryside.” This was attribut-

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able to the “existence of adequately numerous groups in all fields of creative work” and the “possibility of more intensive intellectual contact, as well as of specialization, afforded by greater numbers.” Similar comments were made by Aitken (1985, pp. 15–16) in his history of the radio: A hypothesis worth testing is that the points of confluence of information flows define the social locations where there is a high probability of new combinations being made . . . Such an approach avoids determinism: it gives no warrant for asserting any kind of necessity in the process. But neither are we thrown back into blind chance. It is a matter of probabilities: the probabilities of new combinations being formed is higher at the points of confluence than it is elsewhere.

Building in part on Jacobs’ work, Johnson (2001) similarly suggested that the city offers the perfect arena for emergent systems since they require a degree of complexity and randomness. A city builds up the adjacent possible and offers varied pathways for exaptation, or interdisciplinary borrowing, to arise via fluid yet sufficiently stable liquid networks. As Johnson (2001, p. 92) put it: The brilliance of [The] Death and Life [of Great American Cities] was that Jacobs understood – before the sciences had even developed a vocabulary to describe it – that those interactions [between strangers] enabled cities to create emergent systems. She fought so passionately against urban planning that got people “off the streets” because she recognized that both the order and the vitality of working cities came from the loose, improvised assemblages of individuals who inhabited those streets.

In his later work, Johnson (2010, p. 162) once again aligned his ideas with those of Jacobs, pointing to the large urban center as the locus of exaptation within liquid networks: “Both [Claude] Fischer and Jacobs emphasize the fertile interactions that occur between subcultures in a dense city center, the inevitable spillover that happens whenever human beings crowd together in large groups.” Johnson’s characterization of cities as ideal settings for diffusion plays up the value of cultural diversity that, in turn, contributes to exaptation potential (Johnson 2010, p. 162): Cities, then, are environments that are ripe for exaptation, because they cultivate specialized skills and interests, and they create a liquid network where information can leak out of those subcultures, and influence their neighbors in surprising ways. This is one explanation for superlinear scaling in urban creativity. The cultural diversity those subcultures create is valuable not just because it makes urban life less boring. The value also lies in the unlikely migrations that happen between the different clusters. A world where a diverse mix of distinct professions and passions overlap is a world where exaptations thrive.

We now turn to our own attempt to illustrate these processes more concretely with a few additional examples.

CASE STUDIES To gain a better understanding of the circumstances through which local economic diversity can facilitate the development of new combinations of artifacts, ideas and skills, two of us set out to study Canadian individual inventors. Our rationale was that it

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is typical for such creative individuals to frequently move between different lines of work and/or borrow ideas from fields other than their current one. Desrochers and Leppälä (2011) provides a more detailed description of our method, samples and conclusions, which we summarize here. From our review of the literature and our field work, we identified three broad, although not mutually exclusive, sets of circumstances through which individuals found new uses or applications for existing products and created new combinations of existing products, processes and materials: 1) by adding to, switching or adapting specific know-how to other lines of work; 2) by observing something in another line of work and incorporating it into one’s own line of work; and 3) through formal and informal multidisciplinary teams working towards the creation of new products and processes. We are reasonably confident that, despite the obvious geographical and size limitations of our empirical study, these basic processes are at the roots of virtually all cases of Jacobs spillovers. The following will provide relevant illustrative examples. Adding to, Switching or Adapting Specific Know-How to Other Lines of Work Benefiting from knowledge gained from previous jobs and tasks was one of the main knowledge spillover mechanisms observed, particularly in light of the very diverse professional backgrounds of the individuals interviewed. A representative case is that of an individual who regularly moved to other fields in search of new challenges and who ended up working in the electronics, digital devices and early IT and telephone technologies. Another example is that of an industrial technician who worked in the steel, chemical, aeronautical and armament industries before launching his own ceramic-making business. This widespread pattern of employment across different lines of work seems to be attributable at least in part to the fact that technically creative individuals tend to get bored very quickly with routine work. Job mobility obviously facilitates the spontaneous transfer of know-how across otherwise seemingly unrelated lines of work. For example, one interesting case involved the transfer of some basic know-how from the newspaper printing business to asphalt production. A recurring problem in the latter line of work was the question of how to clean up residual asphalt found sticking to the inside of tanks after long periods of inactivity. At a particular asphalt firm, people actually climbed into the tank to scrape the residual off, a laborious and equipment-damaging process. After noticing this, the individual interviewed pointed out to his then new employer that, in the printing business where he had previously been employed, large tanks were cleaned by pouring hot water into them. This technique was tried and eventually proved to be successful, saving the company a significant amount of resources. Observing Know-How and Materials and Incorporating Them in a Different Production Setting Sometimes observation and subsequent learning can be sufficient for the creation of Jacobs spillovers. For instance, a shower brush was inspired by a car wash brush; a mouse pad arm rest combined with an office chair was inspired by some classroom furniture; a controllable sled was inspired in part by the movements of ice skates; a production

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shop for a new type of foldable baby carrier was inspired by the division of labor in a restaurant kitchen; and a device to conduct time studies drew on ideas from chess clocks, stop watches and computers, among others. Multidisciplinary Team Made Up of Individuals Possessing Different Skills The interpersonal and interdisciplinary aspect of creativity has long made firms interested in promoting cross-functional new product teams. On other occasions, though, multidisciplinary teams can be composed of individuals working for different employers and collaborating or providing input on a project, either formally or informally. One such case was the development of a moveable outdoor bicycle rack. The inventor we interviewed originally got the idea from a friend who pointed out that no such thing existed yet. The reasons for this soon became obvious. The rack needed to be light enough to be carried, heavy enough to hold the bicycles and prevent thefts, have a nice design, be maintenance-free, and suitable for four bicycles (two adult and two children’s bicycles), be they road or mountain bikes. Finally, it should be affordably priced. A metallic structure would have met most of these requirements, but would have been too heavy to carry. Aluminum was a lighter option, but was too expensive. The inventor then thought of using plastic, but realized quickly that it would be too light. He contacted an industrial draughtsman with whom he had worked in the past on a specially designed water container for long-distance running. His former collaborator suggested that the rack should be made by blowing (i.e., filling a plastic mold) rather than casting. That way the rack would be empty inside, thus light enough to carry, but heavy enough to hold the bicycles in place after it was filled with water. This solution would finally prove to be the best one. Local Economic Diversity and the Fostering of Jacobs Spillovers The interindustrial nature of the three mechanisms is obvious, but the case is less clear with respect to the underlying role of local economic diversity. The problem is that assessing the specific impact of large and diversified metropolitan areas on interindustrial knowledge spillovers is not as straightforward as documenting the existence and importance of knowledge spillovers in highly specialized industrial districts. Indeed, in most cases, the new combinations we documented could probably have been developed in any number of large urban agglomerations, especially when inspired by “non-local” observations such as watching television or traveling. Nonetheless, a number of recurring observations and patterns can be identified from our cases. The first is that Jacobs spillovers are only one facet of the economic advantages of large and diverse agglomerations. Indeed, just as Jacobs (1969) herself had strongly emphasized, traditional urbanization economies, especially the widespread availability of a broad range of supply goods, were judged crucial by inventors. Individuals who had lived in both a large urban agglomeration and a highly specialized one were especially adamant on this point. The greater facility of face-to-face interaction between individuals possessing different expertise made possible by close physical proximity was also striking. The reasons given in this respect ranged from traditional ones, such as establishing trust and jointly addressing

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innumerable hurdles in development phases, to one that is more specific to Jacobs spillovers, that is, making sure that individuals with different expertise truly understood each other and that the final product reflected the vision of the project leader. Interestingly, while our interviews spanned a decade which saw drastic improvements in information and communication technologies, electronic communication and the Internet did not seem to affect inventors’ strong preference for face-to-face interactions and local suppliers. While technologies can sustain formal collaboration across distances, geographical proximity to people possessing different expertise remains beneficial with respect to informal, spontaneous and temporary discussions as well as the initial establishment of more formal and lasting forms of collaboration (see also Rallet and Torre, Chapter 26, this volume). Another recurring theme was, not surprisingly, that a large urban agglomeration provides many unplanned learning opportunities by spontaneously allowing creative individuals to observe processes and ways of doing things in different contexts. We described this feature of urban environments as Johnson’s (2010, p. 31) “the adjacent possible,” with its potential fully engaged through “liquid network” mobility. While the Internet connects one into an enormous collection of information, this is a source of knowledge of a very different kind and independent of geographical location. By contrast, being located in a diverse urban setting has the added benefit that as a by-product of one’s daily life one becomes exposed to other people’s ideas, problems and solutions without a deliberate search. One way to think of the benefits of a large diverse city is by analogy with what we now call “crowd-sourcing.” A famous case is InnoCentive.com that anonymously advertises difficult problems encountered by (mostly) large corporations. According to Lakhani et al. (2007, p. 7), the strongest and most significant effect for the likelihood of a problem being solved “relates to the presence of heterogeneous scientific interests amongst scientists submitting solutions.” Indeed, “the more heterogeneous the scientific interests attracted to the solver base by a problem, the more likely the problem is to be solved.” Lakhani et al. (2007, p. 7) further observed that “[m]ost organizations have limited access to such a range of heterogeneous problem solving perspectives and algorithms.” Our case study highlighted job mobility as the main channel in terms of transferring know-how between different lines of work. Obviously, a large and diverse metropolitan area gives creative individuals the possibility to switch jobs without having to relocate their family or lose their friends and social networks. Admittedly, the expertise and capacities possessed by an individual influence the number of available job opportunities, but while many companies are limited to a specific sector or a few end products, many industrial capabilities are generic in nature and can be applied in different contexts. When not constrained by family circumstances or personal preferences, individuals may look for jobs in other cities, but diverse cities provide more opportunities for immigrants as well. Nee and Sanders (2001) caution us, however, about assuming uniform workforce participation from immigration even when, as is true in some cases, most new immigrants settle in cities (de Guibert-Lantoine 1992). This being said, immigration does contribute to growth in diversity via socio-economically stratified processes (Nee and Sanders 2001). Many immigrants settle into local ethnic economies where their economic contributions are less globally visible, yet still potentially able to spill into urban liquid networks; those immigrants whose family socio-economic capital can be profitably traded within their new setting attain work status within the mainstream economy and add directly to workplace diversity (Nee and Sanders 2001). As complex as its effects are, immigration does create

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new connections between individuals and may further add to the diversity of know-how and subsequently to the creative and innovative activities of urban areas, recent evidence of which has been discovered by Cooke and Kemeny (2017).

CONCLUSION The fact that all innovations are the result of new combinations of pre-existing and different know-how, skills, ideas, processes, materials and artifacts has long been known and discussed by students of both human creativity and technological innovation. The importance of multidisciplinary teams in promoting the cross-fertilization of ideas is also taken for granted by numerous writers on the creative process. Indeed, as we have shown through the work of Johnson (2010) and Hennessey and Amabile (2010), there is empirical validation and growing research interest in all aspects of diversity leading to innovation. Far from being controversial, Jacobs’ take on the source of city growth seems eminently sensible, and has been supported and championed by big-picture students of innovation like Johnson (2001; 2010). We also suggest that, though less visible, local circumstances are perhaps even more crucial for interindustrial than for intraindustrial knowledge spillovers, especially in an age where communication between professionals possessing similar backgrounds or keeping up to date with direct competitors’ newest developments is easier than ever before. Because regional economic specialization presents fewer opportunities for combining unrelated things in new ways while increasing the risks of lock-in, regional development policies that aim to promote innovative behavior should arguably avoid the temptation of providing additional support to established clusters. Of course, urban economic diversity is a geographical attribute that in itself does not guarantee meaningful interactions between individuals possessing different knowledge bases. Attempts to foster such interactions on the part of students of urban development, however, will in our opinion require a more serious effort to understand human creativity than has so far been the case.

REFERENCES Aitken, H.J. (1985) The Continuous Wave: Technology and American Radio, 1900–1932, Princeton, NJ: Princeton University Press. Atkins, P. (1992) Animal Cities: Beastly Urban Histories, London: Ashgate Publishing. Ayres, C.E. (1943) The Theory of Economic Progress, Chapel Hill, NC: University of North Carolina Press. Babbage, C. (1832) On the Economy of Machinery and Manufactures (2nd ed.), London: Charles Knight. Bairoch, P. (1988) Cities and Economic Development: From the Dawn of History to the Present, Chicago, IL: University of Chicago Press. Barber, B. (1952) Science and the Social Order, Glencoe, IL: Free Press. Bathelt, H. (2017) ‘Trade fairs and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 509–522. Bathelt, H., Golfetto, F. and Rinallo, D. (2014) Trade Shows in the Globalizing Knowledge Economy, Oxford, UK: Oxford University Press. Beaudry, C. and Schiffauerova, A. (2009) ‘Who’s right, Marshall or Jacobs? The localization versus urbanization debate’, Research Policy, 38(2): 318–337.

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Berkun, S. (2007) The Myths of Innovation, Sebastopol, CA: O’Reilly Media, Inc. Brown, K.A. (1988) Inventors at Work: Interviews with 16 Notable American Inventors, Redmond, WA: Tempus Book of Microsoft Press. Carter, H.D. (1939) If You Want to Invent, New York, NY: The Vanguard Press. Castaldi, C., Frenken, K. and Los, B. (2015) ‘Related variety, unrelated variety and technological breakthroughs: An analysis of US state-level patenting’, Regional Studies, 49(5): 767–781. Cohendet, P., Parmentier, G. and Simon, L. (2017) ‘Managing knowledge, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 197–214. Cooke, A. and Kemeny, T. (2017) ‘Cities, immigrant diversity, and complex problem solving’, Research Policy, 46(6): 1175–1185. Csikszentmihalyi, M. (1997) Creativity: Flow and the Psychology of Discovery and Invention, New York, NY: Harper Perennial. de Bono, E. (1992) Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas, New York, NY: Harper Business. de Groot, H.L.F., Poot, J. and Smit, M.J. (2009) ‘Agglomeration externalities, innovation and regional growth: Theoretical perspectives and meta-analysis’, in R. Capello and P. Nijkamp (eds) Handbook of Regional Growth and Development Theories, Cheltenham and Northampton, MA: Edward Elgar Publishing, 256–281. de Guibert-Lantoine, C. (1992) ‘Permanence and diversification of immigration in Canada’, Population (Paris), 47(1): 47–83. Desrochers, P. and Leppälä, S. (2011) ‘Opening up the “Jacobs Spillovers” black box: Local diversity, creativity and the processes underlying new combinations’, Journal of Economic Geography, 11(5): 843–863. Florida, R. (2005) Cities and the Creative Class, London: Routledge. Fores, M. (1979) ‘The history of technology: An alternative view’, Technology and Culture, 20(4): 853–860. Frenken, K., van Oort, F. and Verburg, T. (2007) ‘Related variety, unrelated variety and regional economic growth’, Regional Studies, 41(5): 685–697. Gassman, O. and Zeschky, M. (2008) ‘Opening up the solution space: The role of analogical thinking for breakthrough product innovation’, Creativity and Innovation Management, 17(2): 97–106. Glaeser, E.L., Kallal, H.D., Scheinkman, J.A. and Shleifer, A. (1992) ‘Growth in cities’, Journal of Political Economy, 100(6): 1126–1152. Gordon, P. (2013) ‘Thinking about economic growth: Cities, networks, creativity and supply chains for ideas’, Annals of Regional Science, 50(3): 667–684. Hall, P. (2000) ‘Creative cities and economic development’, Urban Studies, 37(4): 639–649. Henderson, J.V. (2003) ‘Marshall’s scale economies’, Journal of Urban Economics, 53(1): 1–28. Hennessey, B.A. and Amabile, T.M. (2010) ‘Creativity’, Annual Review of Psychology, 61: 569–598. Hounshell, D. (1991/1984) From the American System to Mass Production, 1800–1932, Baltimore, MD: Johns Hopkins University Press. Jacobs, J. (1969) The Economy of Cities, New York, NY: Random House. Johnson, S. (2001) Emergence: The Connected Lives of Ants, Brains, Cities and Software, New York, NY: Scribner. Johnson, S. (2010) Where Good Ideas Come from: The Natural History of Innovation, New York, NY: Penguin. Jones, B. (2009) ‘The burden of knowledge and the “death of the Renaissance man”: Is innovation getting harder?’, Review of Economic Studies, 76(1): 283–317. Keir, M. (1919) ‘The localization of industry: How it starts; why it grows and persists’, Scientific Monthly, 8(1): 32–48. Koestler, A. (1969/1964) The Act of Creation, London: Hutchinson of London. Kuznets, S. (1960) ‘Population change and aggregate output’, in National Bureau of Economic Research Demographic and Economic Change in Developed Countries. Princeton, NJ: Princeton University Press, and National Bureau of Economic Research, 340–367. Online. Available HTTP: (12 March 2014). Lakhani, K.R., Jeppesen, L.B., Lohse, P.A. and Panetta, J.A. (2007) The Value of Openness in Scientific Problem Solving, HBS Working Paper 07-050. Online. Available HTTP: (12 March 2014). Lichtenberg, R.M. (1960) One-Tenth of a Nation: National Forces in the Economic Growth of the New York Region, Cambridge, MA: Harvard University Press. Lipsey, R.G., Bekar, C. and Carlaw, K. (1998) ‘What requires explanation?’, in E. Helpman (ed.) General Purpose Technologies and Economic Growth, Cambridge, MA: MIT Press. Machlup, F. (1962) ‘The supply of inventors and inventions’, in National Bureau of Economic Research The Rate and Direction of Inventive Activity: Economic and Social Factors, Princeton, NJ: Princeton University Press, 143–167. Maddux, W.W., Adam, H. and Galinsky, A.D. (2010) ‘When in Rome. . . Learn why the Romans do what they

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do: How multicultural learning experiences facilitate creativity’, Personality and Social Psychology Bulletin, 36(6): 731–741. Marshall, A. (1920/1890) Principles of Economics (8th ed.), London: Macmillan. Online. Available HTTP: (12 March 2014). Marshall, A. (1923) Industry and Trade, London: Macmillan. McNeil, I. (1996/1990) ‘Introduction: Basic tools, devices and mechanisms’, in I. McNeil An Encyclopedia of the History of Technology, London: Routledge, 1–40. Melo, P.C., Graham, D.J. and Noland, R.B. (2009) ‘A meta-analysis of estimates of urban agglomeration economies’, Regional Science and Urban Economics, 39: 332–342. Nee, V. and Sanders, J. (2001) ‘Understanding the diversity of immigrant incorporation: A forms-of-capital model’, Ethnic and Racial Studies, 24(3): 386–411. Packalen, M. and Bhattacharya, J. (2015) Cities and Ideas, NBER Working Paper No. 20921. Online. Available HTTP: (11 June 2017). Porter, M. (2000) ‘Location, competition, and economic development: Local clusters in a global economy’, Economic Development Quarterly, 14(1): 15–34. Rallet, A. and Torre, A. (2017) ‘Geography of innovation, proximity and beyond’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 421–439. Schroeder, R.G., Van de Ven, A.H., Scudder, G.D. and Polley, D. (1989) ‘The development of innovation ideas’, in A.H. Van de Ven, H.L. Angle and M.S. Poole (eds) Research on the Management of Innovation: The Minnesota Studies, New York, NY: Harper and Row, 107–134. Scott, A.J. (2006) ‘Creative cities: Conceptual issues and policy questions’, Journal of Urban Affairs, 28(1): 1–17. Sternberg, R.J. (ed.) (1999) Handbook of Creativity, Cambridge, MA: Cambridge University Press. Twiss, B.C. (1980/1974) Managing Technological Innovation, London: Longman. van Oort, F. (2015) ‘Unity in variety? Agglomeration economics beyond the specialization–diversity controversy’, in C. Karlsson, M. Andersson and T. Norman (eds) Handbook of Research Methods and Applications in Economic Geography, Cheltenham and Northampton, MA: Edward Elgar Publishing, 259–271. van Oort, F. and Bosma, N. (2013) ‘Agglomeration economies, inventors and entrepreneurs as engines of European regional productivity’, Annals of Regional Science, 51(1): 213–244. Weber, R.J. and Perkins, D.N. (1992) Inventive Minds: Creativity in Technology, New York, NY: Oxford University Press. Weitzman, M.L. (1998) ‘Recombinant growth’, Quarterly Journal of Economics, 113(2): 331–360. Youn, H., Strumsky, D., Bettencourt, L.M.A. and Lobo, J. (2015) ‘Invention as a combinatorial process: Evidence from US patents’, Journal of the Royal Society Interface, 12(106): 1–8.

15. Innovation and the cultural economy Andy C. Pratt

INTRODUCTION Innovation is generally regarded as a universal and positive element of economic life. However, it is an apparent paradox that not all innovations are good, nor are they necessarily an improvement on those which they replace. We have all experienced the ‘novelty’ gadget that is quickly forgotten, or the new special-effects-heavy, multi-milliondollar, blockbuster film that fails at the box office. We cannot dismiss this as the fickleness of taste – there are a number of well-documented examples of the inferior technology ‘winning’: the classic case in video recording technology is the VHS format replacing the technically superior Betamax (Cusumano et al. 1992). Put simply, the lesson is that neither technical nor artistic superiority consistently bear a simple or direct relationship with economic or cultural success. The important point is that the value of innovation is not universal but is established in context. This applies more generally, but in the field of the cultural economy the situation is more critical than in other fields. In the cultural economy, the ‘value’ is the product or practice. Moreover, social or cultural values change, serving to ‘re-value’ an object or practice, at any stage in the production process between ideation and use. In this chapter I explore how such instability and change in the ‘value’ of innovations may need to be re-positioned at the centre of our analyses, challenging what we find in mainstream analyses of innovation where such rogue characteristics are regarded as ‘exceptional’ or peripheral. I conclude that insights from innovation processes in the cultural economy should prompt us to re-frame our analyses not only of the cultural economy but of the rest of the economy as well. The first question I pose concerns the assumption that all industries operate essentially in the same way with regard to the market in terms of allocation and price setting. Is innovation any different in the cultural economy, or to any other industries? If so, why and how? If the cultural economy was ‘different’, this characteristic would potentially be the causal variable. The normative perspective is that the cultural economy is different: this chapter challenges this. Paradoxically, I will argue that innovation is no different in the cultural economy. However, more disruptively, I will argue that it is our conceptualisation of innovation, based upon mass production industries, that leads us to view the industries of the cultural economy as ‘different’. The problem lies in how we conceive of knowledge and innovation. The foundational intervention is to conceptualise knowledge as relational: that it shapes, and is shaped by, context and agency. The values (cultural and economic) of an innovative product or practice are only temporarily fixed in each interaction. Exploring innovation in the cultural economy discloses many problematic assumptions about innovation in the normative literature; sociologists and economic geographers have begun to query these norms. These assumptions may have been appropriate to a particular period of mass production of commodities; however, it can be argued that the assumptions no longer 230

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hold. Rather than being assumptions, they may have to be moved analytically centre stage: to become ‘what is to be explained’. In sum, I want to reverse normative arguments on the basis of three challenger claims. First, I will argue that our expectations of innovation have been limited by a specific experience of industrial transformation of mass production and a particular division of labour (see also Glückler, Chapter 17, this volume). I will argue further that the cultural economy should be the ‘new normal’. Second, I also argue against generalisation, recognising that all industries have empirical differences, both within and between what we term ‘industries’. In short, the situated nature of innovation is critical to our understanding of it. Finally, I am led to ask fundamental questions about what knowledge is. Accordingly, this chapter is divided into three parts. First, I review normative innovation practices and their relationship to the philosophy of science. Second, I argue that due to normative assumptions about knowledge, the focus of analysis and empirical investigation is on the transfer of knowledge. The third part proposes that a more helpful focus, namely the translation of knowledge, is one that expresses the generative, relational and situated nature of knowledge making. The normative model of the ‘leaky pipe’ analogy of knowledge transfer is where the very formation of knowledge is assumed to be concerned with incremental change (that is, minimally innovative) (Godin 2006). By contrast, I want to offer the notion of ‘making in translation’, which is conceived of as a constructive and a constitutive practice: one that is focused on radical change.

NORMAL INNOVATION, NORMAL SCIENCE Controlled Innovation Innovation is a deceptively simple term; we commonly view it as a technique or an outcome (but less commonly do we note that it is not absolute, such that something is only innovative ‘in relation to’ something else) to produce something ‘new’. The model that we often have in mind is to ‘build a better mousetrap’, an incremental improvement to an established need. The problem and the parameters are assumed or fixed, an incremental iteration is what is defined as innovation. Our common understanding of innovation comes from science, where we term a discovery as a ‘natural fact’ – akin to that which we commonly conceive of as if new knowledge were a pebble on a beach, simply waiting to be discovered, or picked up (its meaning is intrinsic and not related to time or place, let alone social and cultural settings). Inside the laboratory, discovery is a codified and ordered process – insulated from the social world – that is tried and tested to confirm or deny ‘newness’. Innovation, discovery and innovation, or newness, are profoundly socially, culturally and organisationally embedded. However, in normal science, or normal innovation, we ‘bracket out’ these ‘contextual’ factors. This strategy does have utility when wider social and economic processes are stable. However, in periods of social and economic transformation their explanatory powers are weakened (see also Cohendet and Simon, Chapter 3, this volume; Héraud, Chapter 4, this volume). The paradox here is between the model (philosophy) of science and the practice of ‘normal science’, that is, science that can only produce incremental and not revolutionary innovation. Such a (normative) process relies upon a stable value system of both facts

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and interpretation. We know that the ‘value’ of an innovation is not always stable. The instability – or indeterminacy – is clearest in science when one moves away from the strictly applied, and from the confines of the laboratory: ‘blue skies research’ is knowledge that does not have a ready application, but at some point it may. Even in normal manufacture a product innovation may be ‘new’, but may not find a market nor use. As I note below, this relates to a major question in the philosophy of science and how revolutionary it is, or is not. There are other ways to catch mice; moreover, we might change our perspective to see mice as the solution, not the problem. A revolutionary innovation may do away with the very need for a mousetrap. Thus, ‘normal’ innovation is both a method that may be limiting, and one that is – or aspires to be – a-social, or a-contextual. Both aspects are problematic for anything other than incremental change (which we generally might not consider to be ‘real innovation’) within fixed and non-variable environments. What makes an innovation ‘different’, or ‘interesting’, let alone useful or important, is only disclosed by its social and cultural value: its relational value. What is innovative today, may be normal and uninteresting tomorrow; what is ‘world changing’ for one group of people, or in a particular place, may be uninteresting in another. Normal science, that is the standard model of science that we are familiar with, is based upon incremental change. It is effective in contexts where a paradigm (in this case a market, set of values and technology) is fixed and limited. Incrementalism is the characteristic of ‘mature’ products at the middle of their life cycle (that is, when innovation is at a low point) (Balland et al. 2013). It was Kuhn (1962), a prominent philosopher of science, who contrasted notions of normal and revolutionary science. Normal science is path-dependent which means that scientists do not have to think about the big questions of meaning and knowledge as long as they follow the small rules of the scientific method. Kuhn pointed out that the logical flaw in such an incremental method is to produce ‘true’ innovations. In much of the work on the sociology of scientific knowledge since Kuhn, fundamental questions of logic, and of meaning, now undermine the normative scientific method as any adequate account of scientific practice. A number of economic geographers and sociologists have sought to challenge these assumptions (Amin and Cohendet 2003; Ibert 2007), but little of this insight has found its way into normative analyses of innovation. Innovation in the Wild The cultural economy is a sub-section of all production, and is normally further constrained in definition to be identified as a particular group of industries that have as their output ‘cultural products’. It seeks to point to a wider cultural economy defined by processes as well as output or input. Recent practice has been to use the notion of the cultural economy to indicate this diversity of product, process or social context. Traditional industrial classifications that are composed of taxonomies of final product, and not always process, can be limiting. Hence, the term cultural or creative economy is used to capture this more holistic view; one that encompasses the whole production system from ideation, production and distribution to consumption and archiving. This is usually referred to as the cultural economy ecosystem (Pratt 1997). These definitions and conceptualisations have been developed to capture the actually existing process of cultural production, something that is neglected by normative

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taxonomies and analyses that only frame ‘cultural occupations’, or final producers of cultural products. In the example of film making this would be like: (a) only considering the actors and director but excluding the ‘below the line’ technical talent (the huge list of names we see in the credits of a film); and (b) only considering film production companies (and ignoring the finance and administration, the special effects, the distribution, sales and advertising, etc.). Quite literally, much of the innovation ‘falls between the cracks’ of normative conceptualisations. This contrasts with the cultural economy ecosystem conceptualisation which holds open the fact that there is considerable empirical variation within the industries that comprise the cultural economy. Put simply, film is empirically different to theatre, and to fine art; however, it shares some important organisational characteristics, including the same risk profile and market structures. The contrast of the cultural economy with the normative (manufacturing) economy is important if we are to appreciate that concepts of innovation are built on the assumptions, and empirical regularities, of the normative economy (see also Leslie and Rantisi, Chapter 16, this volume). The linear, atomistic and truncated production process that echoes a Fordist production line is but one type of production, and a particular innovation system has been developed to satisfy its characteristics (marginal improvements in technology, different product styling and decorative effects). However, cultural production is better characterised through the exploration of its ecosystem (a more comparatively variegated and heterogeneous field). Moreover, as an organisational system cultural production tends to a ‘missing middle’ form: there tend to be a small number of very large companies, and many micro-companies and freelancers. Compared to the normative pyramid structure of much of the economy, the cultural economy has a lack of medium-sized companies. In addition, the micro-enterprises which dominate the sector add a distinct organisation element: they work on the basis of project-based activities, where a project, and a company, may exist for only 6 months (Pratt 2007), at which point the company may be disbanded and recombined into another company/project with others. Generally, the cultural economy is organised to solve the challenges that accompany working with an unstable notion of ‘value’, both economic and cultural. Values change faster than new product innovation can keep pace: literally with fashion seasons, or the charts. Accordingly, product development cycles are very short, and product life can be equally brief. As such the market characteristics of these industries are (a) that they are very risky (that is, there is a high failure rate, and uncertainty as to what a success will be), (b) that there is a ‘winner takes all’ structure (that is, the product that is a success can achieve monopoly profits). What may appear to be peculiar organisational forms and practices from a normative perspective are in fact innovative responses to particular conditions. Likewise, the field of regulation is intrinsic to the form of the economy. Regulators are concerned with both content and competition: censorship and monopolies. There is commonly a complex relationship between these. Historically, state ownership has been a dominant form. In the UK, as with many states, regulatory changes have changed the organisation of the cultural economy. For example, the shift from in-house, fully integrated production of programmes to the BBC being mainly a ‘publisher’ has led to the emergence of a fragmented and horizontal organisational structure where risks are outsourced from ‘publisher’ to ‘producer’ (Pratt and Gornostaeva 2009). Other regulation can affect the structure of the industry in different ways. For example, the rules in Formula

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One racing are changed annually to destabilise technologies and team structures; in effect to increase economic risk, and generate innovation. So, as with Formula One racing, innovation is always judged in relation to a set of rules, regulations or structures: it is not simply to go faster, but to win. Regulation literally constructs and shapes the market and defines the terms of competition and value (Pinch et al. 2003). Finally, there are two further ways in which the cultural economy contrasts with normative expectations of industrial production. These are linear and uni-directional. This is well illustrated by the case of advertising. Advertising – itself a member of the cultural economy – is deployed in all areas of manufacture to not just inform the market of a product’s existence, but to create a demand for it. Simply, there was no expressed need previously: advertising manufactures demand. A well-documented example is that of the development of the personal music player, the first iteration of which was Sony’s Walkman (Du Gay 1997), itself a development of the transistor radio, but this time personalised. It is not only manufacture that works in this way: artists have agents and galleries who perform the same task with a nascent ‘star’ (White and White 1993). Interviews and background briefings provide the interpretive context, and create a ‘buzz’ and a desire for the art. This process of non-linearity, multi-directionality and feedback has been necessarily taken to new levels by the cultural economy (Pratt and Jeffcutt 2009). The marketing of music is but one example. The organisational form of the ‘charts’ not only signals availability, but ‘what other people are buying’, and equates that with a social, cultural and economic value. People purchase the new recording, or see the new film, either sight unseen, or on the basis of information that constructs its value (for example, advertising: the paid form; news reports: a non-paid form of value construction). In the cultural economy demand has to be created, to drive demand for an existing supply of goods that the market did not know it needed. The conditions of a highly regulated mass production system create certain situated values on which territory competition is fought out. In turn, these shape the ‘innovation process’, and are valued in terms of their role in competition. To reflect back on arguments about the product cycle, these are all efforts to avoid the high-risk situation of creating a new ‘class of products’ for which demand is unknowable and unknown. The work involved is often one of convincing consumers that the old product is no longer innovative, and that the new one is; moreover, for consumers to replace the old with the new (even though it is still functional) is a risky and expensive decision. Hence, we can see lots of reasons why in manufacture there is sometimes an inbuilt (anti)-innovation. As many authors have pointed out, we are currently experiencing a wider ‘culturalisation’ of the economy (Lash and Urry 1993). What this means is that the processes that are familiar throughout the cultural industries increasingly shape more ‘utilitarian’ products like a laptop or a toaster. Market differentiation is produced by the ‘valuing’ of technologies (which may or may not ‘really’ make a difference), or simply design (it looks good; a judgement that is of course cultural and relational) (Lash and Lury 2007). Perhaps the best example is car design in the late 1950s with the sculpting of bodywork that signified that year’s product, and hence encouraged product turnover (even without the built-in obsolescence which was also notorious). Or, today, the sales of computers, and the role of companies such as Apple whose sales pitch is based on the design characteristics rather than ‘raw processing power’. Of course, ‘raw processing power’ is – like with engine specifications in cars – a relational term, that appears factual: bigger

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is better. It all depends on the use and the relationship between other components and software. Whilst one may seek to dismiss the ‘cultural clothing’ of the product, if it is that value which is the differentiator between two products, then the relatively small cultural value will translate into a big economic value. In this case, a true winner takes all: the final differentiation is sale or no sale. To summarise thus far, the process of creation has been black boxed and isolated. Normative processes are used to create change; but overwhelmingly these are targeted at incremental change. It is assumed that the intrinsic value of the product will win a market when it leaves the lab. Even ‘alternatives’ referred to as open innovation, which appear to break down the walls of the ‘black box’ of innovation, in fact retain all of the normative characteristics of standard innovation; the openness is a facet of network configuration, not of logic, nor the conception of knowledge (Trott and Hartmann 2009). By contrast, as we have seen, particularly in the cultural field, but increasingly (and less acknowledged) in the wider product field, the relational value, and the cultural sign, is a critical generator of ‘value’ (not all technical). Even when the apparent proportion of ‘cultural value’ in a product is small, the market impact may be total. Hence, we need to look more carefully at relational value construction. Clearly, this is front and centre of any consideration of the cultural economy, but one that applies to all industries. In a previous period, the weighted balance was to a ‘locked in’ intrinsic value, but the unbundling of this value in the current period has made all industries more like the cultural industries. The problem is that the model we have for understanding innovation is based on a rather limited version of practice in manufacture that privileges technology and reduces market and knowledge to givens (for example, neo-classical models). What we need is a model that positions these issues centrally, not peripherally.

TRANSFER In the normative conception of innovation, whether in the laboratory or the studio, the artist or the practitioner generates the product as a self-formed and self-referential object. Knowledge is produced as a product and is fixed at creation (the ‘Eureka!’ moment). The process of knowledge transfer is viewed as a separate process, albeit one beset by many barriers, usually expressed as broken linkages or leaky pipelines hindering the passage to the audience or consumer. The process is linear, and non-reversible; the challenge is defined as that which will overcome the obstacles in the way of a smooth pattern of diffusion: from high concentration to low, from supply to demand. The dominant assumption is of an un-differentiated audience, who all demand, or value an innovation in the same way: precisely not the characteristics of a cultural audience. These conditions may apply more or less to all industrial production, but in the case of industries being transformed, not only by new production processes, but also by the relationship of production to society, the process itself has to be transformed, not simply adapted to greater quantity: for example, mass customisation, active consumption, and ‘pro-sumption’ (these are all terms that refer to the dissolution of traditional boundaries, and the direction of process, between producers and consumers) (Tapscott and Williams 2006). The cultural economy represents the leading edge of such practices, where the value or content of knowledge varies as well as its modes of communication and transport.

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The current organisational forms of the cultural economy have evolved in response to such conditions: the transformation of market or audience relations (or the identity of the ‘consumer’), as well as fluid value systems (offering an expanded choice to satisfy any particular demand; or, indeed to encourage multiple purchases of single products – in different customisations, or colourways). However, the a-social and a-organisational perspectives we encounter in many studies are the main reasons why normative innovation theory offers less insight when applied to the cultural economy. I will highlight three dimensions in which we need to modify our perspectives: (a) spatial, (b) organisation and scale, and (c) knowledge. Spatial The normative literature leaves the technical aspects of innovation to philosophers and scientists, leaving it in a black box of innovation (Latour 2005); the only aspect of the process that is open to manipulation and interpretation is the movement of knowledge: that is what is to be explained. The models of knowledge transfer are rooted in the physical analogy of diffusion models: a physical process of transfer from higher to lower concentrations, based on a tendency towards entropy in closed energy systems (Easton 1992). In such a conception, the knowledge object is separate from the transport mechanism. This latter issue – the transport mechanism empirically joining supply and demand – is also assumed in traditional economic theory as being the invisible hand of the market: it is assumed that it will happen ‘naturally’. However, this issue – the movement of labour and goods – has fascinated geographers concerned with what they politely call the spatial and technological ‘friction’ of distance; put more boldly, this is literally the actually existing structure and economy of transport systems that create an irregular ‘cost surface’ as opposed to the ‘perfect demand curve’ found in micro-economics text books (Smith 1981). In these models, price alone is used as an analogue of value, and economic equilibrium is hypothesised as the mechanism of transfer of ‘goods’, albeit modified by transport issues. Interestingly, debates about digitisation and the hypothesised reduction of transport costs to zero, have led some to claim the irrelevance of geography to knowledge transfer: the death of distance (Cairncross 1998), a claim that was not sustained by evidence (Pratt 2000). Subsequently, literature on industrial location has attended to the various unrealistic, or unfeasible, assumptions in both the economic theories of location and the interactions of industrial production. Research has indicated the role of the social organisation of the production process over space, where transfer costs may be internalised and when new technologies change transport costs. At other periods innovation in the production process and/or social organisation, or changes in regulation, may lead to externalisation of not only transportation, but also research and development. However, in normative approaches these are all externalities. We should question a model’s utility when the residuals dominate the equation. Organisation and Scale The analysis of the role of organisation is undermined in some studies by their neoclassical economy assumptions in which organisation is not a variable. However,

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institutional approaches to both economics and sociology have highlighted organisation as the complementary other side of the coin to transportation. Within the multi-plant and multi-function production process there may be economies of scale and useful ‘waste’ (Grabher 1993). That is, complex processes may be costed and audited over a longer time period, or over multiple sites and profit centres. Some slack or ‘redundancy’ can offer useful opportunities for residence and sustainability, and for the maintenance of economies of scale (which may be lost in more ‘efficient’ organisations). Put concretely, the return on an investment is neither intrinsic, nor separate from, the organisation and governance regime it is embedded in it. An investment will, if audited at the end of month one, be in deficit; after year ten it may, however, be in profit. Likewise, a research facility may have a number of failed outcomes before a success. The principle is the same: the context or setting can frame profitability, not the process. If audited individually it might never, if governed by an over enthusiastic accountant, achieve the innovative gains. Furthermore, a small organisation devoting only a minimal resource budget to innovation may not achieve the economies of scale that a large one may do. The transitions in the history of economic organisations that have occurred between large multifaceted facilities where economies of scale are maximised have often been contrasted with the diversity of outcomes and flexibility, so-called economies of scope that is a common outcome of network enterprises (Lundvall 1992). Thus, the location, distribution and diffusion – the transport – of knowledge is not independent of the social organisation. In short, innovation is not reducible to space, technology or indeed social organisation: it is a hybrid. Knowledge A variant on the organisational aspects of innovation is to take account of the institutional embedded nature of knowledge, whilst still retaining an atomistic notion of knowledge and its creation: to search for the ‘essence’, or most ‘intense’ manifestation in the organisation or place; or which are the most ‘innovatively productive’ occupations or activities (measured by added economic value). Conceptualising this is a very small step away from the discredited notion of ‘pure’ innovation, or creativity, that is commonly associated with artistic or scientific genius (Pratt 2008). In this literature, even the nominally institutional approach to innovation is undercut by the assumption of a single or finite ‘source’ of innovation. A parallel argument has been applied to the cultural economy with respect to creativity. In part inspired by Richard Florida’s (2002) analyses of the ‘super-creative’ occupations in the creative class (which in his case apply particularly to ‘creative jobs’ in ‘non-creative industries’), it has been argued that a measure of ‘creative intensity’ that produces the most added value (not simply patents, nor non-profit-earning outputs) if identified could also be used to target potential intervention (Bakhshi et al. 2012). Where immaterial products are concerned, legal codification seeks to domesticate products as if they were objects that had eternal and unchanging parameters, regardless of context: for example, a patent. In traditional analyses, patents are used as a proxy for innovation (Acs et al. 2002). This is problematic: a patent is only a potential innovation, untested and not verified in its own terms, let alone within the context of a particular market or audience. There is no guarantee, in fact it is statistically unlikely, that it will be

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successful; only a minute proportion of patents get made into products, and fewer still successful products. A patent, or an idea, is merely one part of an extended innovation system that is required to validate, and value, an object and its relations to the world, let alone getting the ‘world’ to appreciate it. Albeit expressed in slightly different terms, this conception is underpinned by the same model of the innovation system: a variant in organisation, transport and transfer mechanisms. It is still a network in which nodes, or what flows, is assumed, and connection and volume of interaction is assumed as causal. This is an inherent problem of traditional social network analyses (Murdoch 1997). The focus is still on the velocity and volume of flows, and the technical or rational efficiency of networks to maximise the transfer in an ideal space. What is overlooked are the means by which an idea is translated into practice, and the means by which it is ‘valued’, and ‘revalued’, at each interaction. As hinted above, this dimension is particularly relevant to accounting for the cultural economy: a (truly) relational model. I want to argue that despite some theoretical progress and some more nuanced empirical work, two points remain unanswered: first, how is knowledge created; and second, how to divide good from bad knowledge? It is interesting that these are considered as a priori assumptions, not worthy of analysis, or self-evident fact, in neoclassical analyses. On the contrary, I would argue that they are important and relevant to our understanding of innovation. On the second point, in the experimental process, if the outcome is a binary (good or bad), this will give us one definitive answer. But, as we have already argued, even good (or indeed bad) outcomes can be re-valued outside the lab, or when the artist enters society. We are still left with the fundamental deceit at the core of science that Kuhn identified: ‘normal science’ will not, cannot, produce revolutionary, paradigm changing, events. In other words, normal science is additive and deductive; but in the end, it is limited by its own caution – it is not inductive, and it cannot make a leap into the unknown (deductive processes are based on logical deduction from two known facts to arrive at a composite, or additive, fact). A variant of this problem in a more practical manner is the theory of innovation that suggests that the ‘product life cycle’ is akin to separate paradigms: eventually, after the new idea creates a market, it matures and no further innovation takes place; it is replaced by a new paradigm (in classical analysis, this is ‘caused’ by substituting a new technology). The philosophical ‘trick’ is that a new product provides the new paradigm, without explaining where the new product/idea came from. In summary, much of our conception of knowledge creation is about knowledge transfer. Knowledge is ‘black boxed’, or wrapped as if it is a parcel; the question of how the parcel was unpacked and how the contents were (re-)interpreted is not addressed; simply that it moved from A to B. Moreover, the ‘value of an innovation is assumed to be fixed and indexed by technological and economic reductivism, and atomism. The process of knowledge creation is displaced to the philosophy of science, and practice thus follows the binary rules and protocols of the laboratory; knowledge and meaning is ‘assumed away’ or simply ignored for the purposes of economic, or spatial and social analyses. However, these assumptions, and this philosophy, can and must be questioned: at very least the logic based on the normative mass manufacture and its innovation processes. In the modern cultural economy, these assumptions are now variables.

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TRANSLATION In the previous section I criticised the exclusive focus on transfer mechanisms; the other side of the innovation coin is the ‘what is transferred’: knowledge and ‘newness’/ innovation. As I have also noted in the first section of the chapter, the science model codifies the production of knowledge as a logical, rule-governed and technical process. It is created in the laboratory; the challenge is to migrate it to a user. Normative models have a particular conception of knowledge. In the process of reconstructing the understanding of innovation, a potential line of critique emerges from studies of craft processes, and the subtler innovation that they produce. The scientific method, and the laboratory, produce a singular output. The attention to craft skill highlights the tension between learning and doing; or between tacit and codified knowledge. Normative concepts of innovation solely concern codified knowledge, in words and numbers, legal terms, or physical objects. The notion of tacit knowledge opens up a realm of non-codified, practical knowledge and the varied process of learning and doing (Polanyi 1957). Tacit knowledge is always and already embodied and embedded in place and organisations; whereas codified knowledge can be transferred (more like the ‘ideal’ knowledge discussed above). Tacit knowledge is often seen as ‘soft’ knowledge, both in the boundary-less indefinability, but also in value judgements (and accordingly not judged as valuable as real science). A common interpretation is that these two forms of knowledge exist in parallel realms; another is that codified knowledge – which applies to a limited class of knowledge – can be quantified and hence reduced to a common score. This produces an additive notion of knowledge, and it obscures the ‘valuation’ and its reductivism via quantification. A similar problem underpins network analyses, even those that stress the ‘relational’ dimension. Although more informative, embedded and nuanced, they still fall foul of the basic assumption of network analysis that is to measure flows, not their (co-)creation. The relative value of knowledge is dictated by the network structure, one that is more or less efficient at diffusion (Bathelt and Glückler 2011). They do not fully question the additive notion of knowledge(s), nor the implicit accumulation and rational assumptions of normative network analyses. By contrast, translation theories of innovation use a different notion of knowledge – a generative one. In such an approach two knowledge inputs do not simply add or subtract from one another; instead, they produce contestation, which may lead to the revaluation of both inputs, and/or a completely novel resolution, thus challenging the binary between the knowledge and the transfer which is the research object of traditional analyses (and which ‘locks up’ value questions). Translation analyses begin with a different ontology: they view the whole process as actors, things and networks all co-defining one another. As the name suggests, the literature that underpins this approach is Actor Network Theory (ANT). It is especially relevant that ANT has its roots in the Sociology of Scientific Knowledge (SSK), that is, the sociology of experimentation and ‘knowledge making’. A seminal example is based on the re-interpretation of laboratory practice (Latour and Woolgar 1986), although recent work has extended the process of ‘valuation’ and ‘justification’ practices into what are cognate areas for those interested in the economy, in financial dealing and markets (Callon 1998; Knorr Cetina and Preda 2006). Generative notions of knowledge explore how knowledge is created via interaction

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and justificatory disputation, not by simple addition. A good example is the way that a play or music performance is developed in front of an audience via the feedback and the live experience of the performance. The technical practice is modified via not just the interpretive actions of the director and performer, but by their assessment of audience reaction, and their own aspirations and values. This is but one example, or a multiple example, of interactive and heuristic feedback. Then comes the process of problem solving and how and which issues to resolve (Pratt 2015). We can immediately see the weakness of a transfer model of the practice in the example of how the composer’s idea of the music (based on their education, training and interaction with previous musicians and audiences) is transcribed as notes on a page (which is an interpretation of the ‘music’ in his or her head). Moreover, the performers must interpret these notes. We know that there are many ways to perform the same eight notes (even guided by musical notation); moreover, this still may not accord with what the composer ‘heard’ in his or her head. Clearly, the crucial moments are in the translation of ideas from the composer to the manuscript, and by the ensemble of musicians and conductor. This actor-network, not the laboratory, or composition studio, is a vital part of ‘making’ music. The importance of this social element of knowledge making, and the rejection of dualistic ontologies (the artificial divide between making and transfer, and between codified and tacit), opens up a new realm for our studies. Critically, the role of embedding is also different from normative analyses; the context is now seen as co-constitutional. Commonly, ‘communities of practice’ (Wenger 1998) writing is embedded in a binary of text and context, and tacit and codified knowledge; however, the notion of the social creation of value – if founded in a relational ontology of knowledge – can offer a more sympathetic framework for the analysis of cultural practice (Amin and Cohendet 2003; Ibert 2007; Cohendet et al., Chapter 13, this volume). Interestingly, this social making of knowledge is precisely the opposite of the ‘laboratory’ model which seeks to isolate the innovation. Traditionally, science and arts are considered as different modalities of knowledge creation (or more usually, ranked in a hierarchy of knowledge creation: science above the arts). If the laboratory is the norm, then inevitably the arts process is seen as inferior. But reverse the situation, making arts the model, for a successful outcome we might seek to entertain a variety of cultural practices, which may or may not be controlled (or curated) or managed or constrained; in this sense the science model seems deficient. I simply want to argue in favour of an admission of the potential value of multiple sources and varieties of knowledge (artistic or scientific: although as noted above, I find such dualistic formulations unhelpful). Again, useful examples can be drawn out from the literature on music scenes as communities of practice (Straw 2001; Webb 2008). Such cases explore and exemplify the multiple flows and various justifications of (embedded) musical value, and social and cultural value. What I have discussed in this section is how a fixed version of knowledge (although it may be multiple) can be considered to be embedded in our innovation discourse. By contrast, a number of authors have stressed the relational nature of knowledge, although this is a restatement of a traditional network analysis. In normative network analyses, value is intrinsic in a node associated with the number of connections. In a relational network analysis, value is constituted by organisation spatial position, and the assets accessed, in a network: it is a quality not a quantity. It is in effect to ‘build a better network’ (not just a mousetrap) and to redirect the flow of (quality) knowledge across nodes. This seems like

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progress. However, I introduced a different notion of relational knowledge here, one that is commonly and obviously found in the cultural economy (although it has increasingly been discussed as part of financial transactions), namely translation, a socially constructed notion of knowledge(s) that is/are situated and embedded in communities of practice, but more generally communities of ‘learning’/‘knowledge’/‘critique’. Such a notion is radical in the sense that not only does it erode the boundaries and stability of knowledge, but crucially it transcends the dualism of transport and knowledge. The normative ‘lost in translation’, a combination of diffusion and loss (using a mechanical analogy), has to be reconfigured as ‘gained in translation’. In fact, translation, disputation and instability, rather than being interference and loss, are the very essence of innovation. Translation can, and does, occur at all points on a network.

CONCLUSION The aim of this chapter has been to open up the problem of ‘innovation’ with respect to the cultural economy. Much of the chapter has been taken up with showing how existing analyses obscure rather than clarify the analytical lens trained on innovation. Analyses of the cultural economy point to a number of problems with the normative or standardised assumptions of economic analyses. Fundamentally normative approaches to economics and management (and their derivative disciplines) present innovation as linear, atomistic, a-social, and technically and economically reductive. Moreover, and more difficult to discuss, they make heroic assumptions about the nature of knowledge itself. Consequentially, I have sought to ‘un-pick’ our understanding of knowledge, particularly that borrowed from scientific discourse. Many of the chapters in this volume point to dimensions of these approaches to innovation and their limiting factors. The analytical point that I want to make is that the underlying principles of these models need to be fundamentally challenged; simply recalibrating them is not sufficient. I have sought to take a bolder approach in this chapter, one that challenges normativity and incrementalism; one that is properly innovative. Central to my analysis has been the question of what knowledge is, and what we mean by knowledge transfer. I pointed out that this construction of the problem, and the dualism it is founded upon, is the fundamental challenge. I have argued here that these limiting factors may not be troubling in the analysis of manufacturing innovation and High-Fordism. However, the lens becomes a distorting one when focused on other sectors, times and places. The cultural economy, I argued, is an instructive exemplar of these issues. In many respects, viewed through the normative lens, the creative economy is ‘exceptional’. I pointed out that more fundamentally the assumptions of normative studies are – in the case of the cultural economy – what need to be explained. Hence, normative approaches are relatively ‘blind’ to innovation in the cultural economy. The chapter argued that in the cultural economy ‘value’ is ‘live’; that is, it is in a state of becoming. Its translation to ‘being’ is a relational achievement, a complex interaction of various actors, institutions and networks, and objects. By contrast, normative theories are primarily concerned with knowledge and its movement, more or less effectively or efficiently, from one ‘stage’ to another. Critical approaches have stressed the situated and embedded nature of networks; however, this chapter has argued the need to take a thoroughgoing relational approach,

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one that not only looks at connections, but also at the relational co-construction of meaning and values. This is the potential of translation approaches: knowledge is remade in contexts, its value changing between situations. This is the core idea of ‘making in translation’, a creative and generative event. We can contrast this to the normative notion of ‘lost in translation’, or ‘lost in transit’, where additive, or subtractive, notions of knowledge are deployed. To be sure, analyses of the cultural economy benefit from this perspective; but they also suggest that the rest of the economy may also benefit from the application of a similar revolutionary science.

REFERENCES Acs, Z. J., L. Anselin and A. Varga (2002). ‘Patents and innovation counts as measures of regional production of new knowledge’. Research Policy, 31(7): 1069–1085. Amin, A. and P. Cohendet (2003). Architectures of Knowledge: Firms, Capabilities, and Communities. Oxford, Oxford University Press. Bakhshi, H., A. Freeman and P. Higgs (2012). A Dynamic Mapping of the UK’s Creative Industries. London, NESTA. Balland, P.-A., M. De Vaan and R. Boschma (2013). ‘The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007’. Journal of Economic Geography, 13(5): 741–765. Bathelt, H. and J. Glückler (2011). The Relational Economy: Geographies of Knowing and Learning. Oxford, Oxford University Press. Cairncross, F. (1998). The Death of Distance: How the Communications Revolution Will Change Our Lives. Boston, MA, Harvard Business School Press. Callon, M. (ed.) (1998). The Laws of the Markets. Sociological review monograph series. Oxford; Malden, MA, Blackwell Publishers/Sociological Review. Cohendet, P., G. Parmentier and L. Simon (2017). ‘Managing knowledge, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham; Northampton, MA, Edward Elgar Publishing: 197–214. Cohendet, P. and L. Simon (2017). ‘Concepts and models of innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham; Northampton, MA, Edward Elgar Publishing: 33–55. Cusumano, M. A., Y. Mylonadis and R. S. Rosenbloom (1992). ‘Strategic maneuvering and mass-market dynamics: The triumph of VHS over Beta’. Business History Review, 66(1): 51–94. Du Gay, P. (1997). Doing Cultural Studies: The Story of the Sony Walkman. London, Sage in association with The Open University. Easton, G. (1992). ‘Industrial networks: A review’, in B. Axelsson and G. Easton (eds), Industrial Networks: A New View of Reality. London, Routledge: 2–27 Florida, R. L. (2002). The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. New York, NY, Basic Books. Glückler, J. (2017). ‘Services and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham; Northampton, MA, Edward Elgar Publishing: 258–274. Godin, B. (2006). ‘The linear model of innovation: The Historical construction of an analytical framework’. Science, Technology and Human Values, 31(6): 639–667. Grabher, G. (1993). The Embedded Firm: On the Socioeconomics of Industrial Networks. London; New York, Routledge. Héraud, J.-A. (2017). ‘Science and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham; Northampton, MA, Edward Elgar Publishing: 56–74. Ibert, O. (2007). ‘Towards a geography of knowledge creation: The ambivalences between “knowledge as an object” and “knowing in practice”’. Regional Studies, 41(1): 103–114. Knorr Cetina, K. and A. Preda (2006). The Sociology of Financial Markets. Oxford; New York, Oxford University Press. Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago; London, University of Chicago Press. Lash, S. and C. Lury (2007). Global Culture Industry: The Mediation of Things. Cambridge; Malden, MA, Polity.

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Lash, S. and J. Urry (1993). Economies of Signs and Space. London, Sage. Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford; New York, NY, Oxford University Press. Latour, B. and S. Woolgar (1986). Laboratory Life: The Construction of Scientific Facts. Princeton, NJ, Princeton University Press. Leslie, D. and N. M. Rantisi (2017). ‘Innovation and cultural industries’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham; Northampton, MA, Edward Elgar Publishing: 244–257. Lundvall, B.-Å. (1992). National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning. London; New York, NY, Pinter Publishers. Murdoch, J. (1997). ‘Towards a geography of heterogeneous associations’. Progress in Human Geography, 21(3): 321–337. Pinch, S., N. Henry, M. Jenkins and S. Tallman (2003). ‘From “industrial districts” to “knowledge clusters”: A model of knowledge dissemination and competitive advantage in industrial agglomerations’. Journal of Economic Geography, 3(4): 373–388. Polanyi, K. (1957). The Great Transformation. Boston, MA, GowerBeacon Press. Pratt, A. C. (1997). ‘The cultural industries production system: A case study of employment change in Britain, 1984–91’. Environment and Planning A, 29(11): 1953–1974. Pratt, A. C. (2000). ‘New media, the new economy and new spaces’. Geoforum, 31(4): 425–436. Pratt, A. C. (2007). ‘The state of the cultural economy: The rise of the cultural economy and the challenges to cultural policy making’, in A. Ribeiro (ed.), The Urgency of Theory. Manchester, Carcanet Press/Gulbenkin Foundation: 166–190. Pratt, A. C. (2008). ‘Innovation and creativity’ in J. R. Short, P. Hubbard and T. Hall (eds), The Sage Companion to the City. London, Sage: 266–297. Pratt, A. C. (2015). ‘Do economists make innovation; do artists make creativity? The case for an alternative perspective on innovation and creativity’. Journal of Business Anthropology, 4(2): 235–244. Pratt, A. C. and G. Gornostaeva (2009). ‘The governance of innovation in the film and television industry: A case study of London, UK’, in A. C. Pratt and P. Jeffcutt (eds), Creativity, Innovation and the Cultural Economy. London, Routledge: 119–136. Pratt, A. C. and P. Jeffcutt (2009). ‘Creativity, innovation and the cultural economy: Snake oil for the 21st century?’, in A. C. Pratt and P. Jeffcutt (eds), Creativity, Innovation and the Cultural Economy. London, Routledge: 1–20. Smith, D. M. (1981). Industrial Location: An Economic Geographical Analysis. New York, NY, John Wiley & Sons. Straw, W. (2001). ‘Scenes and sensibilities’. Public, 22–23: 245–257. Tapscott, D. and A. D. Williams (2006). Wikinomics: How Mass Collaboration Changes Everything. New York, NY, Portfolio. Trott, P. and D. Hartmann (2009). ‘Why “open innovation” is old wine in new bottles’. International Journal of Innovation Management, 13(4): 715–736. Webb, P. (2008). Exploring the Networked Worlds of Popular Music: Milieu Cultures. New York, NY, Routledge. Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge, Cambridge University Press. White, H. C. and C. A. White (1993). Canvases and Careers: Institutional Change in the French Painting World, Chicago, IL, University of Chicago Press.

16. Innovation and cultural industries Deborah Leslie and Norma M. Rantisi

INTRODUCTION Cultural industries encompass the production, distribution, marketing and display of cultural products, including visual art, literature, magazines, performing arts, music, video games, newspapers, film, radio, television, architecture, fashion and advertising (Pratt 1997). These industries involve the production of goods and services with an aesthetic or semiotic content. Products are typically valued for their subjective and experiential qualities, rather than their functional characteristics (Scott 2000). Cultural industries have seen tremendous growth in recent years. Culture is now a major source of revenue and employment (Brandellero and Kloosterman 2014: 62; Pratt 2008). Their economic significance has been attributed to the direct contribution they make through the production and distribution of cultural goods, and in terms of the role they play in place-branding a city for tourism and cultural consumption (Scott 2000; Molloy and Larner 2013). As Scott (2000: 2) points out, ‘capitalism itself is moving into a phase in which the cultural forms and meanings of its outputs are becoming critical if not dominating elements of productive strategy, and in which the realm of human culture as a whole is increasingly subject to commodification’. Cultural industries have continued to expand, even throughout the economic crisis of 2008, outpacing the performance of many other sectors (EYGM 2014; Jakob 2013). In light of the growing interest, there has been a burgeoning literature on the nature of cultural industries and the key elements that contribute to creativity and innovation in these industries (e.g. Power and Scott 2004; Pratt and Jeffcut 2009). In this chapter, creativity refers to new ideas and inventions, while innovation denotes the commercial exploitation and implementation of new ideas, including the creation of new products or services, or modifications to existing goods, processes or experiences (Brandellero and Kloosterman 2014: 67; Pratt and Jeffcutt 2009; Pratt, Chapter 15, this volume). Like other knowledge-intensive activities (e.g. biotechnology), cultural industries are marked by rapid product life cycles and extreme market volatility, that is, a general interest in the ‘new’. However, the aesthetic content of the cultural product is a distinguishing feature that informs the innovative dynamics (Scott 2000; Santagata 2004). In fashion, for instance, innovation takes the form of a new style, as embodied in a fashion garment’s cut, colour and fabric (Rantisi 2002). For contemporary circus, innovation can take the form of a new act, for example combining juggling with contemporary dance (see Leslie and Rantisi 2011). Owing to the artistic dimension, such industries rely on knowledge of the tastes of the day, or what Asheim and Gertler (2005) term ‘symbolic knowledge’. As the production of such knowledge requires exposure to a range of cultural currents, one that is facilitated by a wide network of relations, the innovation process assumes a collective and interactive form (Power and Scott 2004). In this chapter, we examine the unique nature of innovation in cultural industries and 244

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the key elements that shape it as a collective process, identifying elements that have been, to date, under-examined in the current literature. At the same time, we also consider some of the key challenges that firms and workers in these industries face, particularly in light of recent structural and technological changes, and the delicate balance between aesthetic and commercial considerations that must be attained. We highlight initiatives that can help to foster more conducive settings for creativity and innovation, with particular emphasis on forms of support and spaces that can mediate rising commercial and technological pressures. We highlight how different institutional configurations may shelter aesthetic workers from risk, and thereby encourage processes of innovation. Innovation as the Production of Symbolic Knowledge As noted above, an integral feature of the innovation process for cultural industries is a reliance on symbolic knowledge. For Aspers (2006), such knowledge is by definition ‘contextual’ knowledge, in that it is time- and place-specific. Its production entails a process of continuous interpretation and understanding, rather than the mere processing of information. In contrast to an explicit object or a design that can be readily transferred, symbolic knowledge assumes a tacit form; it is difficult to verbalize or write down, and often exists at an unconscious level, acquired through personal experience. The fact that it is ‘hidden’ makes it a potentially valuable resource for generating new creations, and as Asheim et al. (2007) suggest, tapping into this knowledge requires a deep understanding of the habits, norms and everyday life of specific social groups. Industry actors must be culturally embedded and sensitive to idiosyncratic qualities. Viewing symbolic knowledge as a contextual phenomenon, geographers have taken an interest in examining the process by which this knowledge is developed and articulated. In particular, Asheim and Gertler (2005) have highlighted the need for both ‘know-how’ and ‘know-who’ for the production of symbolic knowledge. ‘Know-how’ refers to the practical skills that can enable a creator to exploit information and signs from different internal and external sources on a continuous basis. ‘Know-who’ refers to the knowledge of potential collaborators that is acquired through informal relations in a professional network. Asheim and Gertler (2005) suggest that ‘know-who’ is even more important than ‘know-how’ for industries that rely on symbolic knowledge, and there are two reasons for this. First, symbolic knowledge production is inherently a social process, as exchange between different actors is critical for the articulation of nascent (as of yet, unspoken) insights, as well as exposure to new ones. Consequently, a large network of contacts can provide access to a diverse set of competencies and outlooks, which is integral to learning (Cohendet and Llerena 1997; Grabher 2005). Second, the nature of work in cultural industries is often organized on a project basis; teams are assembled for short periods of time in order to complete a particular task (Grabher 2002; Christopherson 2002; Ross 2008). Thus, networks are important for identifying people with whom to collaborate; they help to establish who is appropriate for a given project and who is available. Today, such networks are vital for cultural workers who must chart their own career paths (Ross 2008; McRobbie 2016). In acknowledgement of the socialized nature of the innovation process, much of the literature on cultural industries presents art and culture as the product of an interdependent

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system of actors, which includes artists and creators, marketers, critics and dealers (Becker 1982; Hirsch 1972; Bourdieu 1993). Such a view challenges a traditional notion of an individual star creator of genius and posits creativity as a process that occurs along a value or commodity chain, which ranges from conception to consumption. Moreover, the chain is characterized by a reflexive relationship between producers and consumers, as opposed to a unidirectional one, since symbolic goods are positioned within a wider system of aesthetic judgement and taste and an appreciation of consumer trends is required (Brandellero and Kloosterman 2014: 63). Within this view, cultural actors have the task of developing a concept, but a variety of other actors, who provide inputs and information, are important in translating the concept into a marketable good or service. Cultural innovations thus emerge within particular networks, often referred to as ‘art worlds’ or ‘cultural fields’ (Becker 1982; Bourdieu 1993). Studies pertaining to cultural industries and the production of symbolic knowledge foreground not only the social nature of innovation but also its spatiality (Asheim and Gertler 2005; Weller 2007). More specifically, this literature highlights the importance of proximity as a key ingredient. These studies suggest that trust and a common cognitive frame, on which meaningful exchange depend, are more likely to develop when interactions are face-to-face and occur on a frequent basis. Proximity promotes the development of trust and localized learning between actors – or what Storper and Venables (2004) term ‘buzz’ – by facilitating formal and informal encounters and the social monitoring (i.e. observation and gossip) that comes from ‘being there’ (Gertler 2003). In particular, the spatial concentration of diverse, yet cognitively proximate actors, which is typically associated with urban settings, is viewed as most conducive to new knowledge production (Grabher 2005; Stolarick and Florida 2006). Indeed, for Weller (2007), symbolic knowledges are embedded in places where complex, socially constructed tacit knowledges circulate. Within dense urban centers, cultural workers can draw upon varied influences to forge new innovations. Processes of innovation are thus based on the filtering of ideas within localized communities (Weller, 2007: 42). Over time, these communities become identified as leading centers of cultural production and this leads to further agglomeration (Molotch 1996; Scott 2000; Weller 2008). Cultural industries are thus characterized by a particular creative ecology marked by dense interactions along a chain or network of interrelated actors (Grabher 2002). In the next section, we discuss some research areas that have garnered recent attention in an effort to broaden out the analysis of innovation in cultural industries. More specifically, we focus on relations that cross the boundaries of the traditional commodity chain, material culture as a contributor to innovation, and production-related actors along the chain.

EXPANDING THE ANALYSIS OF CULTURAL INNOVATION Integrating Influences outside the ‘Chain’ As noted, past research on the social basis of cultural production has tended to emphasize relations along a commodity ‘chain’ by focusing on the sequential activities or ‘nodes’ through which a commodity must pass (e.g. conception, production, marketing,

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distribution and consumption) (Hirsch 1972; Gereffi and Korzeniewicz 1994; Van Assche, Chapter 45, this volume). In such studies, relations tied to the direct material flow of the commodity are privileged and cultural production is depicted as occurring in a linear fashion. More contemporary analyses have sought to trouble such an approach by depicting the more fluid and iterative nature of relations that constitute a chain and by viewing the chain as ‘leaky’ rather than self-contained (Leslie and Reimer 1999: 402; Hughes and Reimer 2004; Weller 2008; see also Jackson 1999; Cook and Crang 1996). With a preference for the terms ‘network’ or ‘circuit’ over ‘chain’, such studies foreground the open-ended nature of ties and move beyond a focus on intra-chain relations as the key set of relations implicated in the realization of value to consider how external actors bleed into and shape the constitution of a given node. Positing the node as a permeable and dynamic site of activity opens up the potential range of influences that can shape the creation of cultural products. In the case of contemporary circus, for instance, the incorporation of artistic practices from fields such as dance, theatre and music, as well as new technological innovations, are integral to the creation of a hybrid art form that seeks to marry gymnastic acts with performance and media arts. Today, the major circus training schools and companies, such as Cirque du Soleil, hire dancers and theatre directors as in-house creative directors to work alongside other coaches who specialize in sports and traditional circus fields (Leslie and Rantisi 2011; Rantisi and Leslie 2015). Indeed, for Stolarick and Florida (2006), such interactions across traditional industry lines (what they term ‘industry spillacrosses’) are more likely to generate innovation than those that occur within industries, since they can trouble established conventions and encourage new forms of exploration (see also Grabher 2005). Extra-chain relations are also implicated in the marketing and consumption of cultural products. In a study on fashion, for example, Weller (2008) foregrounds the shared symbolic associations and representational benefits that arise when designer fashion is cross-marketed with other luxury goods, such as cosmetics or champagne at fashion shows. Such linkages have also been illustrated in relation to the furniture industry, which has sought to accelerate the fashion cycle for furniture products through interactions with established fashion institutions (such as Ralph Lauren, Roots and Calvin Klein) (Leslie and Reimer 2003). In examining the realm of consumption, Cook and Crang (1996) suggest that symbolic meaning and knowledges of commodities at a given node of a commodity chain are shaped by the linkages that constitute that specific node (or ‘site’). They illustrate, for example, how representations of non-local food at the site of consumption are shaped by localized tropes about their origins and biographies (Cook and Crang 1996). Similarly, Leslie and Reimer (1999) show how the home as the final site of consumption contributes to the representation of home furnishing commodities. Integrating the Role of ‘Material Culture’ Recent studies on cultural production have also sought to broaden the scope of analysis through greater consideration of the role of material culture – or the ‘non-human’ – in aesthetic innovation. Much of this work is inspired by actor-network theory, which not only foregrounds innovation as a networked process, but one that entails non-human, as well as human, actants (Callon 1986; Latour 1987). It looks at how the coming together of

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actants, who are the products of diverse time periods and spatial contexts, can shape the nature and direction of action, enabling certain sets of practices while impeding others. This is linked to the fact that these actants qua cultural artifacts will often reflect a set of collective values or outlooks. For example, with regard to the significance of the built environment for aesthetic innovation, Molotch (2003: 187) states: the look and functionality of the city influences designers as they do their work, producers as they figure out what to make, and consumers as they develop their wants. The built environment and its accessories – directional signs, shop design, advertising regulations, window displays, street hardware – provide durable evidence to people of the kind of place they are in, of how things are done, of what is appreciated and what is devalued.

Thus, material culture, as an assemblage of actants past and present, has agency within the cultural production process. The ways in which material culture contributes to cultural innovation are varied, and are determined in association with other actants in the cultural production network. In the case of music, for instance, Gibson (2005) examines the material surfaces and technologies that are embedded in music recording studios and their affective influence. He illustrates how sound waves bounce off surfaces and behave in different ways due to the configuration and construction of materials used in rooms (Gibson 2005: 197). Watson et al. (2009: 871) further assert that the creative uses of older warehouses and factories can turn cracks within the built environment into ‘lived spaces and imaginative landscapes’ for musicians. For graphic designers and visual artists, the lighting and aesthetics of the studio space – and their adapative re-use – can inspire new design outcomes, and in turn feed into new representations of the studio as material culture (Rantisi and Leslie 2010; Sjoholm 2013). In such accounts, material culture is not a fixed actant, but like other elements within a network, it is fluid, always ‘becoming’ in relation to other cultural actants. Integrating the Role of Production-Related Actors A third way in which recent studies of innovation are seeking to broaden the analysis of cultural production is by integrating the ‘production’ side of the cultural production process. To date, much of the contemporary research on innovation in cultural products industries has centered on activities along the commodity chain that are viewed as directly shaping consumer preferences and aesthetics, with particular emphasis on design, marketing and retail (Rantisi 2004; Jansson and Power 2010; Molloy and Larner 2013). This consumption-oriented focus within the literature is associated with the privileging of immaterial labour (the production of symbols and signs) over material labour (the manual production of things) as a key source for product differentiation in an increasingly global marketplace (Lazzarato 1996; Gill and Pratt 2008). However, a focus on immaterial labour, often associated with ‘high-skilled’ or ‘creative’ individuals, comes at the expense of viewing cultural products innovation as a truly social and networked process, that is, one that involves the actors who are tasked with the materialization of the symbols and signs. While often overlooked in the cultural industries literature, there is growing recognition of the centrality of production-related actors to the realization of an artistic vision of

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design (Banks 2010; McRobbie 2016). Such analyses acknowledge how production (or ‘craft’) actors give form to cultural products, and in this way, directly shape their aesthetic qualities and reception. This applies to music, where sound technicians contribute directly to the quality of production (Zendel 2014), and to contemporary circus, where stage designers, riggers and prop manufacturers furnish the context that will shape the physical and aesthetic delivery of a performance (Rantisi and Leslie 2015). Design–production relations are also critical for the fashion industry. While fashion designers have long been valorized as a source for immaterial labour, designers themselves acknowledge the critical role that production-related actors, such as the patternmakers and sewing operators, play in translating their design concepts (Rantisi 2013). The patternmaker, for example, is responsible for the technical rendering of a design, and this is done through a marrying of practical considerations (what is feasible in terms of size, material and costing) with aesthetic ones, and for ‘visualizing’ what a designer is thinking (Rantisi 2013). This implies a close working relationship, and a collective creation process where design ideas are negotiated and the role that the  production actor plays is constitutive of the creation process, not merely a by-product of it.

RISKS ASSOCIATED WITH CULTURAL PRODUCTION AND THE NEED TO SOCIALIZE RISKS Contemporary research has not only shed light on the basis for innovation within cultural industries, but also on some of the challenges that affect innovation in these sectors (Hracs 2012; McRobbie 2016). One such challenge is a constant tension between ‘art for art’s sake’ and commercial considerations (Brandellero and Kloosterman 2014: 64). Some creators forgo economic profits in order to gain legitimacy (Bourdieu 1993). However, this position is not tenable for all artists, since there is a need to make a living. There is thus a constant trade off between economic and cultural considerations, and too much commercial pressure can prevent cultural workers from developing new innovations (Brandellero and Kloosterman 2014: 62). Cultural industries require resources to help them balance commercial and aesthetic objectives and manage the risks associated with aesthetic experimentation (Frey 1999; Banks et al. 2000). This is particularly important given the growing commodification of culture, whereby culture is increasingly valued for the profits it can generate (Adorno 1975). Another risk associated with cultural industries is related to technological change. New technologies have democratized creative production, making it easier to access software for music, design and film production, and to innovate by combining different media. In the case of music, for example, Hracs (2012) discusses how recording, editing, mixing and mastering can now be done independently using a computer in a home studio. With the internet, it is easier for creators (including musicians, designers, artists, authors and filmmakers) to reach an audience and independently market and distribute their work (see also Jakob 2013). However, as Hracs (2012) points out, new technologies have also greatly expanded the range of tasks that independent musicians need to perform. This translates into less time for creative tasks (such as writing, recording and performing), and more time spent

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on non-creative work, such as editing, engineering, website maintenance, marketing, networking, accounting and booking shows (Hracs 2012). In the case of the music industry, studios used to take care of many of these tasks. However, a growing number of musicians are independent and do not have access to the resources and support of a studio. This has led to a more individualized and entrepreneurial model of creativity – a model which signals diminished possibilities for innovation and has led to a ‘corrosion of creativity’ (McRobbie 2002; Hracs 2012). New technologies have also facilitated changes in the consumption of culture. Consumers now read, view or listen to news, television, films and music online, often consuming multiple types of media at the same time. This has led to new ways of engaging with creative content (EYGM 2014: 23). These transformations have stimulated a crisis in some sectors, leaving artists and media companies searching for new ways of ensuring that they are compensated for cultural content (EYGM 2014: 23). The illegal downloading of music, movies, television and other cultural products, for example, deprives creators of much-needed revenue and makes it essential for artists to find new ways of disseminating and protecting their intellectual property (EYGM 2014: 23; Hracs 2012). If the trend toward illegal downloading continues, it will become much more difficult to earn a living in these industries. This will lead to shortened careers, or the abandonment of these industries altogether. Deprived of resources to invest in creative production, there could also be a decline in quality and innovation in television, film and music. The growth of the internet has also expanded the range and diversity of cultural offerings, creating heightened competition in the industry. It has led to individualization and market segmentation. There is greater sharing of content and information about cultural products within networks of friends and cultural communities, which often have higher levels of trust than traditional intermediaries (EYGM 2014: 23). This has consequently altered the role of conventional curators and tastemakers. The growth of the internet has also facilitated a greater role for consumers in the production, distribution and marketing of cultural products (Grabher et al. 2008). All of these shifts have altered the process of innovation in cultural industries and the distribution of value. They have also heightened the risks associated with creative production and prompted the need for supports, which reduce these uncertainties. The rise of government cultural policies, which aim to enhance the appreciation of cultural industries, are one such form of support. These policies can promote more innovative, non-commercial work through the provision of artist grants, which provide an income and reduce the need to produce more commercially viable work (Leslie et al. 2014. Such policies can also prompt investments that encourage more conservative and risk-averse firms to hire cultural producers and invest in innovation (Leslie and Rantisi 2006). The provision of design tax credits in Quebec, for example, motivates fashion and other manufacturers to invest in and upgrade the design of their products (Leslie and Rantisi 2006). Apart from more generalized cultural policies, there are also other forms of support that can, over time, foster greater innovation in cultural sectors by helping to mediate and balance artistic and commercial objectives. In the following sections, we examine three specific forms: the provision of affordable spaces, the creation of informal spaces and the institution of policies to promote inclusion.

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Affordable Spaces One way that the materiality of the city influences innovation is through land rents. In a city like Montreal, Canada, where studio and living space is readily available and relatively inexpensive, artists and other creative professionals face low barriers to entry (Rantisi and Leslie 2010). This makes it easier to start up a business, and it means that independent creators do not have to take on more conservative or mainstream clients in order to survive (if they choose to remain independent). As a result, artists can take greater risks. Lower rents enable them to more actively engage in exploration and experimentation, and also mean that more money can be put toward research and development. For example, artists can attend other art shows, performances and exhibits with the money they save on rent. This expands their field of inspiration (Rantisi and Leslie 2010). Inexpensive rents also mean that artists, designers and other creative professionals can adapt lofts and other spaces to suit their needs, balancing aesthetic activities with commercial ones. Many cultural producers, for instance, may set up retail spaces in the front, facing the street, and renovate back or upstairs rooms for administration or production (Rantisi and Leslie 2010). Low rents also enable cultural workers to innovate by promoting experimentation in other artistic fields. Many creators engage in interdisciplinary pursuits. The pursuit of other avenues or art forms (such as painting or starting an art gallery on the side) becomes more feasible when rents constitute a smaller percentage of their total expenses (Rantisi and Leslie 2010). Low rents thus provide conditions for cultural innovation, mediating the risks associated with experimentation, balancing commercial and aesthetic considerations, and enabling creators to work across fields. Government programs such as rent control and the provision of non-profit housing or live-work spaces can help to facilitate the provision of affordable spaces, freeing up resources for artists to innovate (Markusen 2006). It should be noted that while affordable spaces are an important part of the creative calculus for cultural producers, many such workers clearly make trade offs between affordable space and access to the advantages that come with locating in large urban centers. Artists and creators have historically gravitated toward more expensive global cities, such as New York or Paris. They are often willing to pay more to access these centers, which are home to large creative communities and thus offer access to diverse cultural networks and influences. Such cities also feature vibrant ‘third spaces’, including cultural centers, cafes and restaurants (Lloyd 2006). Within global cities, however, cultural producers will often inhabit the more marginal and inexpensive spaces (such as Greenwich Village in the early1900s or Bushwick today) (Zukin 2011; see also Jakob 2010; Colomb 2012). The grittiness and transgressive nature of these neighbourhoods provides a fertile terrain for experimentation. Moreover, the relative affordability of such spaces, when compared to other neighbourhoods, helps to lessen the negative effects of a locational trade off. A challenge of course remains in stemming the tide of rising rents that may eventually hit these pockets of affordability, particularly as it has been shown that arts can lead to an aestheticization of place that will consequently attract investment (Ley 2003; Mathews 2010). In the face of such trends, the availability of affordable artists’ spaces or centers and access to informal and open spaces become all the more critical.

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Informal Spaces As argued, cultural industries are dominated by symbolic knowledge, which is based on ‘know-who’ (Asheim et al. 2007); therefore, access to a diverse array of actors is critical. While affordable spaces can provide cultural workers with the economic means to seek out networking opportunities, informal spaces can facilitate the planned or chance encounters from which such links can emerge, broadening a cultural worker’s basis of support and creating new communities that can underpin new knowledge exchange and commercialization. In particular, public and open spaces enhance a sense of intimacy that is central for promoting a community that depends on low-cost, word-of-mouth networking to exchange codified information (such as employment opportunities or cultural events) as well as the more sticky, tacit kind (Stolarick and Florida 2006). As noted above, cafes, restaurants, bars and community centers, as well as the street, all serve as ‘third spaces’, that is, lightly regulated and non-hierarchical spaces situated between home and work that facilitate social interaction and creative exchange (Lloyd 2006; Watson et al. 2009; Lea et al. 2009). According to Markusen (2006), the openness of such spaces offers a setting in which cultural workers can ‘just be’, creating for their own satisfaction or sharing with their own communities. And many of these sites not only enable collaborative productions, but have flexible, multifunctional uses, including performance and exhibition (Watson et al. 2009; Rantisi and Leslie 2010). Such sites link diverse cultural actors (such as visual artists and musicians) to one another, as well as linking cultural actors to potential consumers and distributors. In cases where such informal spaces succeed in facilitating a community of actors who can explore and disseminate new cultural creations, they can be said to constitute what Cohendet et al. (2010) call a middleground. (Bathelt et al., Chapter 1, this volume; Cohendet et al., Chapter 13, this volume) According to Cohendet et al. (2010: 97), the middleground represents a community setting ‘where spontaneity is progressively structured and shaped so as to be interpreted and understood by market forces’. Such a setting is marked by shared codes that allow for a ‘synergy’ among individual creative orientations. And this coordinating function means that it provides a level within a broader creative ecology that can bridge independent, underground cultural actors with more formal, commercial upperground institutions (e.g. a corporation). One example they use to illustrate the middleground is TOHU, a circus cultural center in Montreal, which is situated next to Cirque du Soleil’s headquarters and serves as a performance and research center. It provides young, recently graduated circus artists (local and nonlocal) with the space to create and perform their acts – acts which often make their way to the upperground institutions of Cirque du Soleil and other prominent circus companies (Cohendet et al. 2010). Government policies can aid in the creation of these informal, lightly regulated spaces. For example, they can play a key role in preserving and establishing public markets, where local cultural producers can exchange ideas and sell their wares. Policies can also work to preserve green space and parks, as well as local community centers, which can be used as sites for cultural education, rehearsal and exhibition (Rantisi and Leslie 2010).

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Institutions to Ensure Inclusive Networks The significance of networks for cultural industries and aesthetic innovation is well documented (Power and Scott 2004), as elaborated earlier. For the most part, this literature tends to assume that these networks always operate positively, alleviating some of the risks associated with cultural production by connecting actors to information and employment opportunities (Christopherson 2008). However, cultural industry networks are often exclusionary, particularly along the lines of race and gender. Christopherson (2008), for example, explores how women and racial and ethnic minorities are marginalized within the film industry, which is characterized by an ‘old boys network’ (see also Gill and Pratt 2008). Advertising, architecture, new media and other cultural fields are similarly racialized and gendered (Nixon and Crewe 2004; Kelan 2007; Anthony 2001). Many cultural industries are characterized by masculine workplace cultures, sexual horseplay and inappropriate sexual interactions (Nixon and Crewe 2004). In some workplaces, women are relegated to traditional caring and serving roles, while men occupy more creative positions (Banks and Milestone 2011). With reference to visual art in Toronto, Leslie et al. (2014) find that immigrant artists and artists of colour are marginalized because of a lack of mentorship and family support, and financial constraints that limit opportunities to show work. The ability to practice as a visual artist is tied to one’s ability to develop networks and understand unwritten rules. Without access to institutional and cultural capital, outsiders remain marginalized, finding it difficult to secure shows, build collaborations and gain recognition (Bourdieu 1993). Some artists find themselves ghettoized as ‘ethnic artists’ (Leslie et al. 2014). These processes of exclusion within cultural networks illustrate the need for institutional support to help foster greater inclusiveness. Government grants to cultural producers need to support a diverse array of talent and types of work. Public-funded artist-run centers are also important. These institutions can help artists from diverse cultural backgrounds to display their work in environments that are not market driven. This provides opportunities for artists whose work is often neglected by commercial galleries, and for artists whose work is positioned as more political, conceptual or interactive. For newcomers, these centers create important entry points into the city’s arts scene (Leslie et al. 2014). Creating greater inclusion within creative networks could also be facilitated by ensuring greater representation of artists, circus performers, filmmakers, dancers and other creators from diverse backgrounds on arts councils and funding agencies. Specific programs that fund work exploring issues of identity, colonialism, migration or mobility are also beneficial. Internship and mentorship programs for new immigrants and other groups are important to securing access to creative networks. Since ‘know-who’ remains vital to the process of innovation for cultural workers, such program and funding initiatives can go a long way in ensuring the prospects for livelihood (see also McRobbie 2016).

CONCLUSION This chapter has sought to provide an overview of the nature of innovation in cultural industries, highlighting the critical role that diverse sets of association play in enabling the production and implementation of new ideas. In addition to examining the key actors

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that have conventionally defined a given cultural field, we suggest that an analysis of such associations should consider the influences from other cultural fields, non-human actors (or ‘actants’) and production-oriented actors. We also review some of the pressing challenges (commercial and technological) that cultural industry actors face in mobilizing these associations for aesthetic experimentation and innovation, and the risks that these challenges engender. And we consider the kinds of spaces, institutions and programs that could mediate such risks and widen the associations that constitute such industries. In the future, analyses of innovation within cultural industries are likely to be shaped by an emerging set of challenges. As technological changes (e.g. 3D printing, new crowdsourcing platforms) continue apace, the distribution of power along the set of associations that are implicated in the innovation process is constantly in flux suggesting that the politics of innovation will figure centrally. Similarly, while cultural industries can provide important opportunities during economic crises, these opportunities are not spread evenly. Current economic austerity measures are likely to heighten the uneven effects of opportunity, and the implication that this trend will have for the practices of those who are most marginalized remains a critical question. And finally, in an era of climate change and growing ecological concerns, the intersection of cultural production and sustainability is likely to become a significant theme (see Zammit-Lucia 2013). How will this alter the content and form of aesthetic innovation? Will cultural industries seek to build greater awareness of ecological conditions through their products? Will the process of creation be altered (e.g. the nature of work organization or the inputs employed)? Further research is needed to explore how these emerging trends are conditioning the networks and spaces of innovation. More specifically, detailed studies are warranted to establish which associations can balance the ongoing tension between aesthetic and commercial considerations, which, for the time being, remains the most distinguishing feature of innovation in cultural industries.

REFERENCES Adorno, T. (1975) Culture industry reconsidered. New German Critique, 6, 12–19. Anthony, K. H. (2001) Designing for diversity: gender, race and ethnicity in the architectural profession. Champaign, IL: University of Illinois Press. Asheim, B., Coenen, L., & Vang, J. (2007) Face-to-face, buzz and knowledge bases: sociospatial implications for learning, innovation and innovation policy. Environment and Planning C, 25(5), 655–670. Asheim, B., & Gertler, M. (2005) The geography of innovation: regional innovation systems. In J. Fagerberg, D. Mowery & R. Nelson (Eds.), The Oxford handbook of innovation (pp. 291–317). Oxford: Oxford University Press. Aspers, P. (2006) Contextual knowledge. Current Sociology, 54(5), 745–763. Banks, M. (2010) Craft labour and creative industries. International Journal of Cultural Policy, 16(3), 305–321. Banks, M., Lovatt, A., O’Connor, J., & Raffo, C. (2000) Risk and trust in the cultural industries. Geoforum, 31(4), 453–464. Banks, M., & Milestone, K. (2011) Individualization, gender and cultural work. Gender, Work and Organization, 18(1), 73–89. Bathelt, H., Cohendet, P., Henn, S., & Simon, L. (2017) Innovation and knowledge creation: challenges to the field. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (Eds.), The Elgar companion to innovation and knowledge creation (pp. 1–21). Cheltenham, Northampton, MA: Edward Elgar Publishing. Becker, H. (1982) Art worlds. Berkeley, CA: University of California Press. Bourdieu, P. (1993). The field of cultural production. New York: Columbia University Press. Brandellero, A., & Kloosterman, R. (2014) Keeping the market at bay: exploring the loci of innovation in the cultural industries. Creative Industries Journal, 3(1), 61–77.

Innovation and cultural industries

255

Callon, M. (1986) Some elements of a sociology of translation: domestication of the scallops and the fishermen of Briuc Bay. In J. Law (Ed.), Power, action, and belief (pp. 196–233). New York: Routledge. Christopherson, S. (2002) Project work in context: regulatory change in the new geography of media. Environment and Planning A, 34(11), 2003–2015. Christopherson, S. (2008) Beyond the self-expressive creative worker: an industry perspective on entertainment media. Theory, Culture and Society, 25(7–8), 73–95. Cohendet, P., & Llerena, P. (1997) Learning, technical change, and public policy: how to create and exploit diversity. In C. Edquist (Ed.), Systems of innovation: Technologies, institutions and organizations (pp. 223–241). London: Pinter. Cohendet, P., Parmentier, G., & Simon, L. (2017) Managing knowledge, creativity and innovation. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (Eds.), The Elgar companion to innovation and knowledge creation (pp. 197–214). Cheltenham, Northampton, MA: Edward Elgar Publishing. Cohendet, P., Simon, L., & Grandadam, D. (2010) The anatomy of a creative city. Industry and Innovation, 17(1), 91–111. Colomb, C. (2012) Pushing the urban frontier: temporary uses of space, city marketing, and creative city discourse in the 2000s Berlin. Journal of Urban Affairs, 34(2), 131–152 Cook, I., & Crang, P. (1996) The world on a plate: culinary culture, displacement and geographical knowledges. Journal of Material Culture, 1(2), 131–153. EYGM (Ernst and Young Global Limited) (2014) Creating growth: Measuring cultural and creative markets in the EU. France. Frey, B. (1999) State support and creativity in the arts: some new considerations. Journal of Cultural Economics, 23(1), 71–85. Gereffi, G., & Korzeniewicz, M. (Eds.) (1994) Commodity chains and global capitalism. Westport, CN: Praeger. Gertler, M. S. (2003) Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there). Journal of Economic Geography, 3(1), 75–99. Gibson, C. (2005) Recording studios: relational spaces of creativity in the city. Built Environment, 31(3), 192–207. Gill, R., & Pratt, A. (2008) In the social factory? Immaterial labour, precariousness and cultural work. Theory, Culture and Society, 25(7–8), 1–30. Grabher, G. (2002) The project ecology of advertising: tasks, talents and teams. Regional Studies, 36(3), 245–262. Grabher, G. (2005) Switching ties, recombining teams: avoiding lock-in through project organization? In G. Fuchs & P. Shapira (Eds.), Rethinking regional innovation and change: Path-dependency or regional breakthrough? (pp. 63–83). New York: Springer. Grabher, G., Ibert, O., & Flohr, S. (2008) The neglected king: the customer in the new knowledge economy of innovation. Economic Geography, 84(3), 253–280. Hirsch, P. (1972) Processing fads and fashions: an organization set analysis of culture industry systems. American Journal of Sociology, 77(4), 639–659. Hracs, B. (2012) A creative industry in transition? The rise of digitally-driven independent music production. Growth and Change, 43(3), 442–462. Hughes, A., & Reimer, S. (Eds.) (2004) Geographies of commodity chains. London: Taylor and Francis. Jackson, P. (1999) Commodity cultures: the traffic in things. Transactions of the Institute of British Geographers, 24(1), 95–108. Jakob, D. (2010) Constructing the creative neighbourhood: hopes and limitations of creative city policies in Berlin. City, Culture and Society, 1(4), 193–198. Jakob, D. (2013) Crafting your way out of the recession? New craft entrepreneurs and the global economic downturn. Cambridge Journal of Regions, Economy and Society, 6(1), 127–140. Jansson, J. & Power, D. (2010) Fashioning a global city: global city brand channels in the fashion and design industries. Regional Studies, 44(7), 889–904. Kelan, E. K. (2007) ‘I don’t know why’ – accounting for the scarcity of women in ICT work. Women’s Studies International Forum, 30(6), 499–511. Latour, B. (1987) Science in action. Cambridge, MA: Harvard University Press. Lazzarato, M. (1996) Immaterial labour. In M. Hardt & P. Virno (Eds.), Radical Thought in Italy (pp. 132–146). Minneapolis: University of Minnesota Press. Lea, T., Luckman, S., Gibson, C., Fitzpatrick, D., Brennan-Horley, C., Willoughby-Smith, J., & Hughes, K. (2009) Creative tropical city: Mapping Darwin’s creative industries. Darwin: Charles Darwin University. Leslie, D., Hunt, M., & Brail, B. (2014) Attracting and retaining artistic talent in Toronto: cosmopolitanism, cultural diversity and inclusion. In J. Grant (Ed.), Seeking talent for creative cities: The social dynamics of innovation (pp. 59–76). Toronto: University of Toronto Press. Leslie, D. and Rantisi, N. (2006) Governing the design economy in Montreal, Canada. Urban Affairs Review, 41, 309–337 Leslie, D., & Rantisi, N. M. (2011) Creativity and place in the evolution of a cultural industry: the case of the Cirque du Soleil. Urban Studies, 48(9), 1771–1787.

256

The Elgar companion to innovation and knowledge creation

Leslie, D., & Reimer, S. (1999) Spatializing commodity chains. Progress in Human Geography, 23(3), 401–420. Leslie, D., & Reimer, S. (2003) Fashioning furniture: restructuring the furniture commodity chain. Area, 35(4), 427–437. Ley, D. (2003) Artists, aestheticisation and the field of gentrification. Urban Studies, 40(12), 2527–2544. Lloyd, R. (2006) Neo-bohemia: Art and commerce in the postindustrial city. New York: Routledge. Markusen, A. (2006) Building the creative economy for Minnesota’s artists and communities. Cura Reporter, (Summer), 16–25. Mathews, V. (2010) Aestheticizing space: art, gentrification and the city. Geography Compass, 4(6), 660–675. McRobbie, A. (2002) Clubs to companies: notes on the decline of political culture in speeded up creative worlds. Cultural Studies, 16(4), 516–531. McRobbie, A. (2016) Be creative: Making a living in the new culture industries. Cambridge: Polity Press. Molloy, M. & Larner, W. (2013) Fashioning globalization: New Zealand design, working women and the cultural economy. Chichester, UK: Wiley Blackwell. Molotch, H. (1996) L.A. as design product: how art works in a regional economy. In A. J. Scott & E. Soja (Eds.), The city: Los Angeles and urban theory at the end of the 20th century (pp. 225–275). Berkeley, CA: University of California Press. Molotch, H. (2003) Where stuff comes from: How toasters, toilets, cars, computers, and many other things come to be as they are. New York: Routledge. Nixon, S., & Crewe, B. (2004) Pleasure at work? Gender, consumption and work-based identities in the creative industries. Consumption, Markets and Culture, 7(2), 129–147. Power, D., & Scott, A. J. (Eds.) (2004) Cultural industries and the production of culture. London: Routledge. Pratt, A. C. (1997) The cultural industries sector: Its definition and character from secondary sources in employment and trade, 1984–91. London: London School of Economics. Pratt, A. C. (2008) Creative cities: the cultural industries and the creative class. Geografiska Annaler: Series B, Human Geography, 90(2), 107–117. Pratt, A. C. (2017) Innovation and the cultural economy. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (Eds.), The Elgar companion to innovation and knowledge creation (pp. 230–243). Cheltenham, Northampton, MA: Edward Elgar Publishing. Pratt, A. C., & Jeffcutt, P. (2009) Creativity and innovation and the cultural economy: snake oil for the twentyfirst century? In A. C. Pratt & P. Jeffcutt (Eds.), Creativity, innovation and the cultural economy (pp. 1–20). Milton Park, Abingdon, UK: Routledge. Rantisi, N. M. (2002) The local innovation system as a source of variety: openness and adaptability in New York City’s garment district. Regional Studies, 36(6), 587–602. Rantisi, N. (2004) The designer in the city and the city in the designer. In D. Power & A. J. Scott (Eds.), Cultural industries and the production of culture (pp. 91–109). New York: Routledge. Rantisi, N. M. (2013) Revisiting the social basis of cultural production: who is privileged? Who is sidelined? What is at stake? Geoforum, 49(3), R9–R11. Rantisi, N. M., & Leslie, D. (2010) Materiality and creative production: the case of the Mile End neighbourhood in Montréal. Environment and Planning A, 42(12), 2824–2841. Rantisi, N. M., & Leslie, D. (2015) Circus in action: exploring the role of a translation zone in the Cirque du Soleil’s creative practices. Economic Geography, 91(2), 147–164. Ross, A. (2008) The new geography of work: power to the precarious? Theory, Culture and Society, 25(7–8), 31–49. Santagata, W. (2004) Creativity, fashion and market behavior. In D. Power & A. J. Scott (Eds.), Cultural industries and the production of culture (pp. 75–90). London: Routledge. Scott, A. J. (2000) The cultural economy of cities. London: Sage. Sjoholm, J. (2013) Geographies of the artist’s studio #1. London: Squid & Tabernacle. Stolarick, K., & Florida, R. (2006) Creativity, connections and innovation: a study of linkages in the Montréal region. Environment and Planning A, 38(10), 1799–1817. Storper, M., & Venables, A. (2004) Buzz: face-to-face contact and the urban economy. Journal of Economic Geography, 4(4), 351–370. Van Assche, A. (2017) Global value chains and innovation. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (Eds.), The Elgar companion to innovation and knowledge creation (pp. 739–751). Cheltenham, Northampton, MA: Edward Elgar Publishing. Watson, A., Hoyler, M., & Mager, C. (2009) Spaces and networks of musical creativity in the city. Geography Compass, 3(2), 856–878. Weller, S. (2007) Fashion as viscous knowledge: fashion’s role in shaping trans-national garment production. Journal of Economic Geography, 7(1), 39–66. Weller, S. (2008) Beyond ‘global production networks’: Australian Fashion Week’s trans-sectoral synergies. Growth and Change, 39(1), 104–122. Zammit-Lucia, J. (2013) The art of sustainability. The Guardian, 24 May 2013. http://www.theguardian.com/ sustainable-business/art-sustainability-imagination-create-change. Accessed 12 August 2015.

Innovation and cultural industries

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Zendel, A. (2014) Living the dream: Precarious labour in the live music industry (Master’s thesis). University of Toronto, Toronto, Canada. Zukin, S. (2011) The life cycle of New York’s creative districts: reflections on the unanticipated consequences of unplanned cultural districts. City, Culture and Society. 2(3), 131–140.

17. Services and innovation Johannes Glückler

INTRODUCTION Although the increasing prosperity of national economies all around the world is tightly associated with the growth of service industries (Schettkat and Yocarini 2006), extant innovation research and growth theories have focused predominantly on technological progress, productivity gains in manufacturing, and multiplier effects from commodity exports. Such a narrow lens has led to the role of intangibles and services being ignored and underestimated. However, the transition to digital technologies has sparked not only the evolution of technological progress, but also, and importantly, the rise of new services and their role in innovation (Beyers 2002). Firms like Alphabet, Apple, Amazon, Facebook and Microsoft are among the most valuable corporations in the world. Unlike traditional corporations in the chemical, pharmaceutical, petroleum and automotive industries, these firms’ successes are fundamentally based on being innovators of new service-based business models such as new forms of electronic commerce (e.g. Amazon), information (e.g. Google Search) and communication services (e.g. Gmail, Facebook), software (e.g. Microsoft Office), the provision of media content (e.g. iTunes, YouTube), or the management and storage of large amounts of data in the ‘cloud’. And yet, these success stories appear to be the tip of the iceberg because service innovation not only refers to the creation of new services but also to the role services take on in the innovation process more generally. Despite the increasing prominence of services, the literature about the relationships between services and innovation is underdeveloped. It ranges from viewing services as riding the coattails of new technologies to putting services at the core of a new growth model (Gallouj and Savona 2009). Therefore, the goal of this chapter is to contribute to overcoming the relative silence in the dialogue between technology-oriented innovation studies and service innovation research. Rather than viewing services as just an additional factor of innovation activities, it is argued that the inclusion of services also requires a revised understanding of the concepts of both ‘product’ and ‘innovation’. Hence, the line of argument begins by first revisiting what has been referred to as the ‘double ambiguity’ (Miles 2010) of the service and innovation concepts. The chapter then reviews the major positions of assimilation, differentiation and integration in the interdisciplinary evolution of research on service innovation. The argument calls for an integrative perspective adopting a more inclusive concept of innovation which acknowledges that innovation practice has become ever more pervaded by and dependent upon services. As will be argued, two processes are crucial in this respect. On the one hand, the so-called servitization of firms illustrates the interfusion of tangibles and intangibles into increasingly hybrid market offerings, and it requires firms to incorporate and create new services around existing products. On the other hand, the deepening division of knowledge-based labor among firms reflects the increasing inter-firm use of specialized services to create, improve 258

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and market new products. These two processes – servitization and the increasing division of knowledge-based labor – lead to progressive convergence between production and services both within and between firms. The final part of the chapter, then, explores the opportunities for regional economies to leverage their innovativeness and competitiveness by specializing on services as well as integrating services into the production system.

PERSPECTIVES ON SERVICE INNOVATION The Double Ambiguity of Services and Innovation The inherent ambiguity of whether innovations and services are the outcome of a process or the process themselves has been a major obstacle to interdisciplinary exchange between the fields of technological innovation studies and services research (Miles 2010). Services have long been treated as a residual category in the economy and were often assigned those economic activities that could neither be classified as agriculture nor manufacturing (OECD 2000; Aoyama and Horner 2011). In Sayer and Walker’s (1992: 62) words, services have more or less been regarded as all goods ‘that you cannot drop on your foot’. The problem of such definitions is that they specify services as an antonym to tangibles while retaining the implicit understanding of a good as a static outcome. Building on Hill’s (1977) classical proposition, however, Gadrey (2000) conceives services explicitly as a process: [A] service activity is an operation intended to bring about a change of state in a reality C that is owned or used by consumer B, the change being effected by service provider A at the request of B, and in many cases in collaboration with him or her, but without leading to the production of a good that can circulate in the economy independently of medium C. (Gadrey 2000: 375, emphasis in the original)

The core elements of this definition are represented in the so-called service triangle (Figure 17.1). In essence, a service is the transformation of a matter C, facilitated by a provider A, and mandated by a customer B. Services have been framed within rather Provider

Client Service relations Interactions

rm

n

s

tio

In

en

rv

te

In

te rv of ent ow ion ne rs hi p

B

C

Fo

A

Medium (Reality to be transformed)

Source: Adapted from Gadrey (2002).

Figure 17.1

The service triangle

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different perspectives, such as a sector (e.g. the management consulting industry), a product (e.g. installation or training services), an occupation (e.g. the accountant) or a function (e.g. self-service or volunteer work). In the remainder of this chapter, this basic definition of Gadrey (2000) is adopted, and services are viewed as commercial offerings performed by firms independent of whether these firms are part of the service or the manufacturing sector. Such an inclusive understanding integrates service firms and service-producing manufacturing firms into a single framework. Similar to the concept of services, the prevailing understanding of innovation is also ambiguous. Mainstream innovation research conforms to the well-established taxonomy of product, process, marketing and organization innovations (OECD 2005). A central problem of this taxonomy is that innovations are implicitly understood as an outcome that is always attributable to one or more owners as intellectual property. This perspective, however, neglects the interactive nature of service innovation and the fact that innovation is a process in itself. New products are often only the end result of a long, sometimes serendipitous phase of multilateral interactions among all sorts of stakeholders and other contributors. This process is often quite significant for how an innovation develops and is implemented. In particular, consulting, design, and research and development (R&D) services are supposed to provide support to clients in their respective innovation work, but are themselves rarely credited as innovators. This narrow-minded focus of innovation research has led researchers of service innovations to bemoan the existence of a gap between technological and service innovation. The past thirty years have seen the number of articles about the concept and theory of service innovation increase each year (Carlsborg et al. 2013), in diverse disciplines such as economics, business studies, marketing, operations management, geography, and the more interdisciplinary research fields of service science and innovation studies (Gadrey et al. 1995; Howells 2007; Toivonen and Tuominen 2009; Barcet 2010; Chesbrough and Davies 2010; Rubalcaba et al. 2012; Djellal et al. 2013; Agarwal et al. 2015). However, the concept of service innovation still remains broad and ambiguous in many of these studies (Snyder et al. 2016; Witell et al. 2016). As will be illustrated hereafter, research on service innovation has either assimilated to the tradition of technology- and productoriented innovation studies, or it has set itself explicitly apart from this tradition to focus exclusively on new service developments in service sectors. Only relatively recently has this dialectic opposition of assimilation and differentiation been challenged by an integrative perspective and by an effort to overcome the separation of goods and services, as well as the double ambiguity of services and innovation (Coombs and Miles 2000; de Vries 2006; Howells 2007; Gallouj and Savona 2009; Wyszkowska-Kuna 2011). Assimilation The traditional approach to service innovation has followed an assimilation perspective and primarily considers how technological change is driving the development of new services. Assimilation as used here refers to the acquisition of established concepts from mainstream innovation research that focuses on new technologies and products. This perspective builds upon an earlier taxonomy of innovation concepts, according to which services are primarily the recipients and rarely the sources of innovation (Pavitt 1984). The most popular innovation model in this tradition is the reverse product cycle (Barras

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1986). It applies to technologies that are developed in the capital goods sector before being transmitted to other user sectors. The reverse product cycle assumes three phases of service innovation. In the first phase, a technology is applied to incrementally improve the efficiency of existing services. In the case of the insurance business, for instance, the computerization of policy records as an innovation during the 1970s later led to cost reductions in administration. This phase corresponds with the late maturity stage of the traditional product-cycle model and thus justifies the label as a reverse product cycle. In the second phase, firms realize technological opportunities to enhance the quality and to raise the effectiveness of their services. In the case of the insurance business, for example, the introduction of online policy quotes has helped branch offices to be more responsive to customer needs and to become more effective in sales (Barras 1986). In the third phase, new services rather than mere improvements are being developed and offered (Gallouj 1998). To use the insurance example again, the digital integration of most business processes has enabled face-less interaction with customers in marketing, sales and settlements of insurance claims. In contrast to the conventional product cycle where new technologies are first developed and improved later, the service innovation cycle first leads to improvements of existing services before new services are introduced. This perspective is thus characterized by viewing services primarily as adopters rather than sources of innovations (Gallouj 1998). Differentiation In an attempt to overcome this passive and adaptive perspective of technology absorption, proponents of the differentiation perspective assume that service innovations are fundamentally different from technological innovations, and call for new service-specific theories of innovation (Hipp and Grupp 2005; Gallouj and Windrum 2009). This demand results from the fact that a large part of recent innovation research has overlooked key contributions of services to the innovation process (Drejer 2004). For example, firms in the hotel and catering industry rarely introduce technological innovations and are therefore ignored in conventional innovation studies. However, from a service perspective, numerous process innovations are apparent which significantly influence a firm’s success. The difficulty of measuring innovativeness is rooted in the fact that innovation in the service industry is often less formalized or explicit and often unbudgeted. The service-dominant logic (Michel et al. 2008; Vargo and Lusch 2008) informs a specific concept of service innovation that is independent from research on technological innovation. This perspective emphasizes skills, that is, knowledge, abilities and proficiencies, as central conditions for the innovation process. The central focus here is based on the interaction between service providers and customers. Transformations of the roles and procedures in this interaction are considered potential service innovations. Many studies analyze the quality of customer interaction and customer relationships as a source of new forms of services, or conduct studies about employees of service firms that act as an interface between the customers’ and their own capacity for innovation (Sundbo et al. 2015). Empirical studies suggest that cooperation with customers increases the amount of innovation activity, while cooperation with partner firms increases the degree of novelty of the innovation (Ordanini and Parasuraman 2011). Additionally, the literature shows that service firms are more open and cooperative in their search for knowledge than manufacturing firms.

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Even hybrid firms that offer bundles of services and commodities are more involved in informal cooperation than purely industrial firms (Mina et al. 2014). This differentiation perspective is particularly helpful when customer behavior is the driver of service innovation (Oliveira and von Hippel 2011). Overall, the differentiation perspective highlights the crucial importance of interaction with partners and customers for acquiring new expertise and developing new solutions. At the same time, however, this approach fails to bridge the gap between technical and services innovation research. Integration Research on service innovation has shown that an approach which exclusively focuses on the particularities of services reproduces the separation between technological and service innovations in academic research. To overcome this divide and to acknowledge the increasing convergence between manufacturing and services in today’s economy, an integrative perspective is necessary (Gallouj and Savona 2009). Such a perspective aims to build a unified framework for innovation, includes technological as well as nontechnological innovation, and does not discriminate a priori between manufacturing and services. Moreover, an integrative conceptualization requires redefining what is considered to be ‘a product’. In trying to solve this task, Gallouj and his colleagues (Gallouj and Weinstein 1997; Gallouj and Savona 2009) borrow from the characteristicsbased concept developed by Lancaster (1966) to specify a product as the sum of all characteristics that satisfy customer needs. In the case of the automobile, for instance, it is not the car that is considered to be the final product, but the bundle of characteristics that define final customer utility: comfort, speed, range, driving assistance and control systems, connectivity in information and communication, thrift, maintenance and other services, and so on. In a more formal language, then, a product is represented by a system of four vectors: the competencies of provider PC, the technology of provider PT, the competencies of customer CC and the characteristics of final outcome O (Figure 17.2). A vector, here, denotes a set of elements {n, . . ., q}, where each element represents a specific competence or characteristic. The specific composition and interrelation of these elements defines Provider competencies PC1 . . PCq

Client CC1 . . CCq competencies

O1 . . Oq

PT1 . . PTq Provider technology Source: Modified from de Vries (2006).

Figure 17.2

Characteristics-based definition of a product

Outcome characteristics

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the quality of each vector. The vectors CC, PC and PT are also linked to each other and together yield the final outcome O that includes all sorts of products, regardless of whether they are physical goods, services, or hybrid market offerings. This characteristics-based framework provides the basis for an integrative analysis of innovation where innovation describes any change that affects one or more elements of one or more of the vectors of a product. Variations in the types of changes observed in the system of vectors relate to different modes of innovation (Gallouj and Weinstein 1997; Gallouj and Savona 2009) which can be classified into four basic modes, as follows (de Vries 2006). (1) Radical innovation describes the transformation of the entire system of vectors CC, PC and PT into a new system of vectors for the production of a new product O. (2) In contrast, incremental innovation occurs when the system of vectors is only partially changed either by improving the quality of some elements of a vector (e.g. improved project management skills at the provider) or by adding new elements to a vector. The latter would be the case, for example, when a machine tool manufacturer offers additional services of remote monitoring in order to increase the efficiency of product maintenance at the customer site, thus complementing O with an additional feature. (3) Ad-hoc innovation can be viewed as the interactive development of a solution to a particular problem put forward by the customer. Ad-hoc innovation strongly depends on the interactions between the provider – or several providers (de Vries 2006) – and the customer, representing the vectors PC and CC, respectively. The outcome O tends to be customized and therefore unique. While this creates inimitable value-added for the customer, the solution and the new or improved competencies only become innovations when being codified, formalized and ‘productized’ (Valminen and Toivonen 2012). The reproducibility separates ad-hoc innovation from one-off solutions and is important to ensure its reuse in other contexts. (4) Finally, recombinant innovation includes the bundling (or unbundling) of components of one or several systems of the vectors PC, PT and CC to create a new product O without changing the architecture of the vectors or its components. An example would be the recombination of otherwise separated databases and software applications into a centralized knowledge management platform (de Vries 2006). These four modes of innovation also allow us to include the previously underestimated role of the innovation of business models into our analysis, such as crowdsourcing as a new financing solution or online auctions. In addition, all four innovation modes benefit from processes of formalization through which one or more elements (e.g. skills, competencies, technologies or methods) become standardized and are thus reproducible (Gallouj and Weinstein 1997; Drejer 2004; de Vries 2006). Although empirical research in pursuit of this perspective is largely based on case studies, the European ‘Community Innovation Survey’ (CIS) has gradually included this and evolved from a narrow technological perspective further toward an integrative perspective on innovation (Vergori 2014). Overall, this typology differs from the conventional taxonomy of product, process, organization and marketing innovations in several ways. First, the four types describe innovation modes rather than innovation outcomes and focus on the quality and novelty of the changes performed on a product. Second, it integrates services – the mandated transformation of a matter – into the innovation process independent of the actual outcome, be it a tangible good, a technology, a service or a hybrid combination of tangibles and intangibles. Third, it also explicitly appraises the interaction and

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collaboration between customers and providers and puts collective co-creation and coproduction at the center of innovation studies. A focus on interaction and collective interdependencies raises questions about the specific roles that firms take in the course of the innovation process. From a processoriented perspective of service innovation, three generic roles can be distinguished in this regard (OECD 2007). First, firms can serve as carriers of innovation by contributing to the dissemination of established solutions and existing innovations. They accelerate the innovation process and encourage the use of existing knowledge in situations that are new to the firm, the industry or the region, thus creating opportunities for further innovations by recombining knowledge. Second, firms act as facilitators of innovation when providing their expertise to co-create new solutions in close cooperation with their customers, whether through products, technologies, processes or concepts. In this case, service firms are catalysts, since the clients only develop innovations based on their participation and on productive interaction with their competencies. Third, firms can act as sources of innovation when they develop innovations in the form of their own new products, technologies, methods, designs or concepts that are implemented internally or are offered as new products or services on the market. A survey in the Bundesland Bavaria in southern Germany, which explicitly studied these roles for both service and manufacturing firms, found that, on average, service firms took the roles of innovators less frequently than manufacturing enterprises (Glückler et al. 2008). Especially knowledge-intensive service firms instead served as carriers and facilitators of innovations for their clients. These divisions of knowledge-based labor in innovation are viewed by client firms to be a key support in ensuring international competitiveness, and thus shed light on the implications of an integrative understanding of service innovation for economic growth and regional development. Building on this discussion, the following sections will address two crucial processes for which an integrative perspective of innovation is especially helpful: the servitization of manufacturing and the deepening division of knowledge-based labor between firms. Both processes reflect the increasing interrelationship of tangibles and intangibles and point at the necessity to unpack the role of services for innovation and economic growth.

SERVITIZATION AND ECONOMIC GROWTH What Role for Services in Economic Growth? The role of services in the process of economic growth has been highly disputed. Traditionally, the neo-industrial position (Cohen and Zysman 1987; Gadrey 1992) assumed that the service sector was a latecomer, following the industrial sector which acted as the engine of economic development. Even Adam Smith considered services to be unproductive labor and not suitable as a source of growth, but rather a target for the consumption of income earned in manufacturing (Illeris 2007). A second position follows the theory of cost disease (Baumol 1967) and suggests that lower increases in productivity paired with the service sector’s growing share in gross domestic product (GDP) slow down long-term economic growth and thus lead to stagnation. Many services, however, are not dissimilar to manufacturing in terms of

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their productivity gains (Kutscher and Mark 1983), and some services even outperform manufacturing industries (Wölfl 2005; Maroto-Sánchez and Cuadrado-Roura 2013). In addition, the perspective following this approach is empirically inaccurate since manufacturing and services are not independent from each other as assumed in the model, but work in tandem in the context of a broader division of labor. This is precisely why a third position argues that, under the conditions of intersectoral interdependence, services can and do spur growth in manufacturing. To achieve higher productivity gains manufacturing firms accordingly depend on the use of external service providers, and this increasing demand enables services to act as a catalyst for technological progress (Illeris 1996; Glückler et al. 2008). Fourth, the so-called post-industrial position (Gadrey 1992) emphasizes the independent role of the service sector as an engine of economic development. If services meet additional needs, they generate new business cycles and create jobs. This may apply, for instance, to new services in the field of digital business as outlined in the introduction to this chapter. The next section discusses one specific process through which services have become important for innovation processes: the servitization of manufacturing, referring to the offering of new services both as stand-alone products and in support of other products. The Rise of the Hybrid Firm The concepts of ‘servicisation of manufacturing’ (Vandermerwe and Rada 1988; Howells 2004), the ‘manuservice economy’ (Bryson and Daniels 2010), or ‘servitization’ (Roos 2015) are attempts to describe the growing tendency of manufacturing firms to offer commercial services, thus blurring the already artificial distinction between physical goods and services. Customer needs are not limited to the ownership of a tangible good, but often include services such as implementation, training, operation and maintenance of these goods. A machine tool alone, for example, may not satisfy the final customers’ needs; instead, a manufacturer buying such a tool may also demand customized software to operate the machine in order to establish communication with other machines within and beyond the firm. As of today, equipment manufacturers in Germany, Switzerland and Austria often earn only one-third of their profits through the commodities they produce and up to two-thirds through their services (Roland Berger 2014). Under global competition, manufacturing firms increasingly recognize the opportunity to attain unique competitive advantages by offering product-related services to their customers. The specific benefit of additional services is that they are more difficult to imitate than tangible products and therefore deliver specific competitive advantages. Product-related services thus serve as a ‘door opener’ for tangible goods, an effect which is also described as ‘service encapsulation’ (Howells 2004). To this end, manufacturing firms are increasingly challenged to acquire the skills necessary for developing new services (Kindström et al. 2013). Sometimes, the superior profitability of product-related services over the established tangible goods leads to the formation of hybrid corporations, that is, firms that have transformed themselves from manufacturing firms to primary service providers, such as the former Digital Equipment Corporation or Rolls Royce. Originally a producer of engines, Rolls Royce has increasingly developed into an energy services provider since 1987 (Bryson 2010). Recently, a survey covering manufacturing and service sectors in Baden-Württemberg revealed that only one-third of the firms investigated were

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either exclusively manufacturing or service companies. Two-thirds realized their sales through hybrid bundles of goods and services although all the firms were classified either as manufacturing or as service industry firms (Glückler et al. 2016). Hence, not only are manufacturing firms increasingly providing product-related services, but also service providers are expanding their portfolios by offering their own or other firms’ tangible goods to leverage their services. The increasing bundling of tangible and intangible goods is, however, subject to the ‘servitization paradox’ (Gebauer et al. 2005). Firms often encounter profitability hurdles in the process of hybridization. While the promise is real that complementary services increase profit margins, quite often, after an initial improvement in earnings, there are phases of lower profitability. Such problems occur if the management of a firm underestimates the shift to services or if the perseverance of a corporate culture is adverse to providing services, or simply if the required skills have not yet been sufficiently developed (Kastalli and Looy 2013). Previous studies therefore suggest a non-linear relationship between hybridization and corporate profitability in the sense that higher profitability can only be realized beyond a critical threshold (empirically about a 30 percent) of services share in overall sales (Fang et al. 2008; Roland Berger 2014). This critical threshold indicates that product-related services benefit from increasing returns to scale and become more competitive and sustainable when provided in a sufficiently large volume. Many scholars expect servitization to be indispensable in order to meet future customer needs, to compensate for often diminishing margins from the sales of commodities, and to increase value-added (Salunke et al. 2013; Schuh et al. 2016). Although the contribution of product-related services to firm profits is hard to quantify precisely, the margins are estimated at 20 percent and higher (Schuh et al. 2016). In Germany, an official study by the Federal Statistical Office showed that the greater the proportion of product-related services the higher the profit margins achieved by hybrid firms (Henzelmann 2006). In the automotive industry today, for example, the crucial profit driver is after-sales services. Although services only represent 20 percent of revenues, they contribute up to 80 percent of the firms’ profits. Since the majority of personal customer contacts take place only after the purchase of a car, these services are also crucial for the long-term brand loyalty of customers (Grosse-Kleimann et al. 2013). Much can be said to suggest that hybrid firms pursue greater market orientation, cooperate more closely with external partners, and are more innovative than pure manufacturing firms overall. They also use external services at a larger scale in order to work on more specific projects in comparison to what would be possible if they relied solely on in-house teams. Finally, progressive servitization validates an integrative understanding of innovation of which services have become a crucial part. Services play a two-fold role in the innovation process: on the one hand, they are themselves new and continue to be further developed in order to better satisfy new customer needs. Hence, they are innovative products in their own right (Gallouj and Savona 2009). On the other hand, services help to develop new products or processes in interactive innovation processes including partners and customers. This second aspect opens a geographical perspective and raises questions about how the increasing interaction and use of external services relates to regional economic development. The next section uses the perspective of the production system to capture the increasing social divisions of labor between manufacturing and

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services, and explores some of the interdependencies between services, innovation and regional development.

SERVICE INNOVATION AND REGIONAL DEVELOPMENT Deepening Divisions of Service Labor in the Production System Not only servitization within manufacturing, but also the increasing functional links between industry and services suggest that the traditional three-sector model of the economy is no longer appropriate for the analysis of innovation processes. In their recent 2011–2020 research agenda for the European Association for Research on Services (RESER), Bryson et al. (2012) explicitly criticize that the interface between production and services remains a much-neglected area of research that will need to be addressed in the future. In this respect, the well-known concept of the production system is a valuable tool to overcome the dualism of manufacturing and services by emphasizing the functional interdependencies between both sectors and by empirically illustrating the transition from a sectoral to a functional view of the economy (Bailly et al. 1987). The production system is composed of four basic functions (Figure 17.3): the transformation of raw materials into goods (production); the use of business services during the design, production and delivery of goods (circulation); the provision of goods to consumers (distribution); and the regulation of legal and legitimate practices to coordinate these activities (regulation). This approach not only provides empirical evidence about the shift in employment from production to distribution and especially circulation, but also shows a positive correlation between the level and/or growth of GDP and the increase in the number of people employed in circulation (Bailly et al. 1987; Bailly 1990; Gámir et al. 1989). This evidence points to the importance of business services for enhancing the competitiveness in many other sectors. Results obtained in the late 1980s should not disguise, however, that the manufacturing of new technologies and goods has continued to be of crucial importance for regional economic growth. A recent study in southern Germany thus demonstrates that the traditional three-sector model of the economy overestimates the role of

Production

Regulation Circulation

Distribution

Consumption Source: Adapted from Bailly et al. (1987).

Figure 17.3

The production system

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consumer-oriented services and underestimates the role of manufacturing in regional growth (Glückler et al. 2015): while regions with high proportions of employment in distribution (e.g. tourism and health care) experienced the lowest growth rates, regions with high proportions of employment in manufacturing and business services registered the strongest growth in GDP. As the leading region in southern Germany with the highest GDP per capita, the metropolitan region of Munich is an outstanding example of the interdependence between manufacturing and business services. Although Munich recorded a rise of 41 percent in business services over a period of 15 years, it experienced an even higher increase in industrial employment by nearly 60 percent in the same time period. As such, the labor market even shifted toward manufacturing in relative terms (Glückler et al. 2015). At the regional scale, it is difficult to say whether the shift toward services is as relentless as often postulated by proponents of a structural change from manufacturing toward services. The findings in southern Germany suggest that regional growth is driven by interdependencies and spillover effects between production and intermediate services. While regional structural analyses can only provide indications of the importance of business services for the division of labor in the innovation process, economic input–output studies demonstrate that knowledge-intensive services are indeed important sources of new knowledge that flows into clients’ products and their sectors (Camacho and Rodriguez 2010). However, the magnitude and mechanisms of these service spillovers in regional innovation processes are not fully known and call for more research. Services, Innovation and Regional Development The relationship between service innovation and regional development has not yet been explored sufficiently in the academic research. Despite the extensive literature on regional innovation systems, the role of services within such systems is still little understood (Aoyama and Horner 2011). A service-based perspective on regional innovation processes puts emphasis on the specific role of services in processes of interactive learning, on the pursuit of explicit market orientation, and on the importance of social institutions for relationships with business partners and customers (Gadrey et al. 1995; Wood 2005; Glückler and Bathelt, Chapter 8, this volume). In the wake of the deepening division of labor in management (Wood 2002), services are taking an increasingly important role in collaborative and open innovation processes (Mina et al. 2014), and in the creation of value-added for other firms and sectors within a region. While many classical studies of technological innovation have addressed the relationship between public research organizations and corporate R&D, the role of private business services for regional innovation processes has received much less consideration. A regional study of R&D firms and engineering firms in the Cambridge high-technology cluster shows how conducive knowledge-intensive business services are for innovation (Probert et al. 2013): they enable technology firms to recognize and assess innovations early on, develop in a market-oriented way, and subsequently introduce their innovations into the market. The modified export base model (Illeris 2005) emphasizes exactly this role of business-oriented services. Even though service firms need not necessarily export their services to other regions, they function as partners in innovation and strengthen the competitiveness of other basic sectors in a region by helping to solve problems and sharing

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their expertise. Public services and innovations play an additional role in the institutional adaptation of regional development conditions (Mas-Verdu et al. 2010). Apart from their role as ‘indirectly basic’ sectors (Illeris 2005: 450), many services today take the role of basic sectors in the sense of productive, driving sectors of a regional economy. Many business service firms are so specialized that their catchment areas transcend regional and national boundaries, and many knowledge-intensive and highly specialized services are so insensitive to distance that they sometimes earn more than half of their revenues from customers outside their home regions (Glückler 2007). Services have proven to offer potential for regional development as both facilitators for innovation processes in basic sectors, and as independent basic sectors themselves. Sometimes, they generate their own agglomeration effects and form service clusters. A regional analysis of the locational dynamics of services industries reveals a heterogeneous result regarding spatial concentration and decentralization processes of services (Glückler and Hammer 2011). On the one hand, some highly qualified knowledge-intensive business services grow and cluster in metropolitan regions. This corresponds with the claim that large cities offer positive externalities in creating and circulating non-routine knowledge (Leamer and Storper 2001). This has been empirically supported in urban service clusters such as finance (e.g. Clark 2002; Wood and Wójcik 2010), management consulting (e.g. Keeble and Nachum 2002; Glückler 2007), or media (e.g. Grabher 2002; Bathelt and Boggs 2003; Cook and Pandit 2007). On the other hand, less highly qualified and more routine operational business services are more dispersed in space, especially in secondary cities and rural regions (Glückler and Hammer 2011). Despite such spatial diffusion tendencies, there are also examples of operational service clusters in peripheral or less developed regions, such as the mail-order service cluster in the old industrial region of Nord-Pas de Calais in France (Schulz et al. 2004), or the growth of shared service centers and back-office clusters in Eastern Europe, South America and Southeast Asia (e.g. Glückler 2008; Lorenzen and Mudambi 2013).

CONCLUSION By bridging the divide between assimilation and differentiation approaches, the consistent integration of services in innovation research requires a new understanding of the concept of innovation (Gallouj and Weinsten 1997; Miles 2010). The convergence between the production of material goods and the provision of services, as observed in the hybridization of manufacturing firms and in the increasing functional division of labor between manufacturing and service firms, supports a new understanding of what a final product actually is. Innovation can only be evaluated appropriately if it is not understood as a product itself, but rather as a process of changes in products. This redirects our research focus to consider the quality of the processes and the roles of stakeholders in these processes. Much against the intuition of traditional theories of regional growth, new solutions to satisfy customer needs increasingly depend on services. Service innovation, then, becomes evident and important in at least three ways. First, firms increasingly develop their own services in-house by means of an increasing servitization of manufacturing and hybridization of market offerings. Due to the higher profit margins and inimitable competitive advantages that services offer, firms

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increasingly convert from manufacturing to hybrid or even pure service firms. Second, the progressive division of knowledge-based labor is expressed in the increasing specialization of operational and knowledge-intensive business services and a corresponding increase in the use of external expert services. Research informed by a functional perspective of the production system shows that the linkage between business services and manufacturing actually leverages regional innovation and growth. Third, the introduction of the commercial use of the internet and digital technologies over the past twenty years has shown how many new and unprecedented services have been created by using unpredictable business models and have come to be placed among the most valuable firms in the world. The sources of competitiveness are not only rooted in technology but also in such important skills as cooperation in R&D, market intelligence, customer interaction and relationship building, and in the capacity to develop successful business models for new products, technologies and services. Future competition primarily rests on knowledgebased service functions rather than on the mere invention of new technologies (Wood 2005; Abreu et al. 2010). For this reason alone, a revision of innovation and regional policies should be considered in order to utilize the potential of services both for their own growth and for the innovativeness of their customer industries (Rubalcaba 2006; United Nations 2011). It has been questioned whether a sector-based view of services can be a useful approach in regional innovation policies. Instead, a service-informed perspective is argued to be more appropriate for an integrated concept of services and regional innovation (Wood 2005). Given the technological bias in contemporary innovation policies, it is necessary to broaden the policy perspective in order to enhance the hidden innovation activity in services. It is necessary to support training and education in order to develop the human capital necessary to sustain creative potential (Cohendet et al., Chapter 13, this volume). Moreover, since only a minor part of the innovation activities in services are engaged in technological R&D, new metrics are needed to capture the real contributions of services to the overall innovation activities, such as expenditures for design, marketing, training and education, as well as for the creation of new market offerings and business models based on digital technologies (Abreu et al. 2010). Acknowledgements I would like to thank Anna Mateja Schmidt and Christian Wuttke for their research support in unpacking service innovation in southern Germany. Helpful suggestions by Harald Bathelt, Regina Lenz and Robert Panitz to increase the clarity of the argument are also appreciated. Financial support by Pakt Zukunft Heilbronn-Franken gGmbH is gratefully acknowledged.

REFERENCES Abreu, M., Grinevich, V., Kitson, M. and Savona, M. (2010) ‘Policies to enhance the “hidden innovation” in services: Evidence and lessons from the UK’, Service Industries Journal, 30: 99–118. Agarwal, R., Selen, W., Roos, G. and Green, R. (eds) (2015) The Handbook of Service Innovation, London: Springer. Aoyama, Y. and Horner, R. (2011) ‘Service innovation’, in P. Cooke, B. Asheim, R. Boschma, R. Martin, D. Schwartz and F. Tödtling (eds) The Handbook of Regional Innovation and Growth, Cheltenham and Northampton, MA: Edward Elgar Publishing, 363–379.

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Bailly, A. (1990) ‘Not post-industrial yet: A new look at regional economic systems in Europe’, in M. Hebbert and J.C. Hansen (eds) Unfamiliar Territory: The Reshaping of European Geography, Aldershot: Avebury, 229–235. Bailly, A., Boulianne, L., Maillat, D., Rey, M. and Thevoz, L. (1987) ‘Services and production: For a reassessment of economic sectors’, Annals of Regional Science, 21: 45–59. Barcet, A. (2010) ‘Innovation in services: A new paradigm and innovation model’, in F.Z. Gallouj and F. Djellal (eds) The Handbook of Innovation and Services: A Multidisciplinary Perspective, Cheltenham and Northampton, MA: Edward Elgar Publishing, 49–67. Barras, R. (1986) ‘Towards a theory of innovation in services’, Research Policy, 15: 161–173. Bathelt, H. and Boggs, J.S. (2003) ‘Towards a reconceptualization of regional development paths: Is Leipzig’s media cluster a continuation of or a rupture with the past?’, Economic Geography, 79: 265–293. Baumol, W.J. (1967) ‘Macroeconomics of unbalanced growth: The anatomy of urban crisis’, American Economic Review, 57: 415–426. Beyers, W.B. (2002) ‘Services and the new economy: Elements of a research agenda’, Journal of Economic Geography, 2: 1–29. Bryson, J. (2010) ‘Service innovation and manufacturing innovation: Bundling and blending services and products in hybrid production systems to produce hybrid products’, in F.Z. Gallouj and F. Djellal (eds) The Handbook of Innovation and Services: A Multidisciplinary Perspective, Cheltenham and Northampton, MA: Edward Elgar Publishing, 679–700. Bryson, J. and Daniels, P.W. (2010) ‘Service worlds: The “services duality” and the rise of the “manuservice” economy’, in P.P. Maglio, C.A. Kieliszewski and J.C. Spohrer (eds) Handbook of Service Science, New York: Springer, 79–104. Bryson, J., Rubalcaba, L. and Ström, P. (2012) ‘Services, innovation, employment and organisation: Research gaps and challenges for the next decade’, Service Industries Journal, 32: 641–655. Camacho, J.A. and Rodriguez, M. (2010) ‘How important are knowledge-intensive services for their client industries? An assessment of their impact on productivity and innovation’, in F.Z. Gallouj and F. Djellal (eds) The Handbook of Innovation and Services: A Multidisciplinary Perspective, Cheltenham and Northampton, MA: Edward Elgar Publishing, 424–449. Carlborg, P., Kindström, D. and Kowalkowski, C. (2013) ‘The evolution of service innovation research: A critical review and synthesis’, Service Industries Journal, 33: 1–26. Chesbrough, H. and Davies, A. (2010) ‘Advancing services innovation’, in P.P. Maglio, A.C. Kieliszewski and C.J. Spohrer (eds) Handbook of Service Science, Boston, MA: Springer US, 579–601. Clark, G.L. (2002) ‘London in the European financial services industry: Locational advantage and product complementarities’, Journal of Economic Geography, 2: 433–453. Cohen, S. and Zysman, J. (1987) Manufacturing Matters, New York: Basic Books. Cohendet, P., Parmentier, G. and Simon, L. (2017) ‘Managing knowledge, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 197–214. Cook, G.A.S. and Pandit, N.R. (2007) ‘Service industry clustering: A comparison of broadcasting in three cityregions’, Service Industries Journal, 27: 453–469. Coombs, R. and Miles, I. (2000) ‘Innovation, measurement and services: The new problematique’, in J.S. Metcalfe and I. Miles (eds) Innovation Systems in the Service Economy: Measurement and Case Study Analysis, Economics of Science, Technology and Innovation, Boston, MA: Kluwer Academic Publisher. de Vries, E.J. (2006) ‘Innovation in services in networks of organizations and in the distribution of services’, Research Policy, 35: 1037–1051. Djellal, F., Gallouj, F. and Miles, I. (2013) ‘Two decades of research on innovation in services: Which place for public services?’, Structural Change and Economic Dynamics, 27: 98–117. Drejer, I. (2004) ‘Identifying innovation in surveys of services: A Schumpeterian perspective’, Research Policy, 33: 551–562. Fang, E., Palmatier, R.W. and Steenkamp, J.-B.E.M. (2008) ‘Effect of service transition strategies on firm value’, Journal of Marketing, 72: 1–14. Gadrey, J. (1992) L’économie des services, Paris: La Découverte. Gadrey, J. (2000) ‘The characterization of goods and services: An alternative approach’, Review of Income and Wealth, 46: 369–387. Gadrey, J. (2002) ‘The misuse of productivity concepts in services: Lessons from a comparison between France and the United States’, in J. Gadrey and F. Gallouj (eds) Productivity, Innovation, and Knowledge in Services: New Economic and Socio-economic Approaches, Cheltenham and Northampton, MA: Edward Elgar Publishing, 26–53. Gadrey, J., Gallouj, F. and Weinstein, O. (1995) ‘New modes of innovation: How services benefit industry’, International Journal of Service Industry Management, 6: 4–16. Gallouj, F. (1998) ‘Innovating in reverse: Services and the reverse product cycle’, European Journal of Innovation Management, 1: 123–138.

272

The Elgar companion to innovation and knowledge creation

Gallouj, F. and Savona, M. (2009) ‘Innovation in services: A review of the debate and a research agenda’, Journal of Evolutionary Economics, 19: 149–172. Gallouj, F. and Weinstein, O. (1997) ‘Innovation in services’, Research Policy, 26: 537–556. Gallouj, F. and Windrum, P. (2009) ‘Services and services innovation’, Journal of Evolutionary Economics, 19: 141–148. Gámir, A., Mendez, R., Molinero, T. and Razquín, J. (1989) ‘Terciarización económica y desarrollo regional en España’, Anales de Geografía de la Universidad Complutense, 9: 123–144. Gebauer, H., Fleisch, E. and Friedli, T. (2005) ‘Overcoming the service paradox in manufacturing companies’, European Management Journal, 23: 14–26. Glückler, J. (2007) ‘Geography of reputation: The city as the locus of business opportunity’, Regional Studies, 41: 949–962. Glückler, J. (2008) ‘Service Offshoring: globale Arbeitsteilung und regionale Entwicklungschancen’, Geographische Rundschau, 60: 36–42. Glückler, J. and Bathelt, H. (2017) ‘Institutional context and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 121–137. Glückler, J. and Hammer, I. (2011) ‘A pragmatic service typology: Capturing the distinctive dynamics of services in time and space’, Service Industries Journal, 31: 941–957. Glückler, J., Schmidt, A.M. and Wuttke, C. (2015) ‘Zwei Erzählungen regionaler Entwicklung in Süddeutschland: vom Sektorenmodell zum Produktionssystem’, Zeitschrift für Wirtschaftsgeographie, 59: 133–149. Glückler, J., Schmidt, A.M., Wuttke, C. and Kirchner, P. (2016) Kompetenzentwicklung in Heilbronn-Franken: Abschlussbericht. Report commissioned by Pakt Zukunft Heilbronn-Franken gGmbH. Heilbronn. Online. Available HTTP: http://heilbronn.ihk.de/ximages/1469242_finalevers.pdf (30 October 2016). Glückler, J., Zademach, H.-M., Hammer, I., Handke, M., Frölich, F. and Latzke, P. (2008) Dienstleistungsstandort Bayern. Study commissioned by the Bavarian Ministry of Economic Affairs and the Munich Chamber of Industry and Commerce. Munich. Online. Available HTTP: http://www.stmwi.bayern.de/fileadmin/user_ upload/stmwivt/Publikationen/Dienstleistungsgutachten_Bayern.pdf (20 February 2016). Grabher, G. (2002) ‘The project ecology of advertising: Talents, tasks, and teams’, Regional Studies, 36: 245–262. Grosse-Kleimann, P., Posdorf, D., Brenner, A., Beyer, S., Kiene, R. and Hunstock, T. (2013) Maßkonfektion im Aftersales. Servicedifferenzierung entlang der Kundenwünsche. Hamburg: Roland Berger Strategy Consultants. Online. Available HTTP: https://www.rolandberger.de/media/pdf/Roland_Berger_Masskonfektion_im_After sales_20131014.pdf (20 February 2016). Henzelmann, B. (2006) ‘Dienstleistungsforschung – für wen und wofür?’, in D. Streich and D. Wahl (eds) Moderne Dienstleistungen. Impulse für Innovation, Wachstum und Beschäftigung, Frankfurt, New York: Campus, 531–538. Hill, T.P. (1977) ‘On goods and services’, Review of Income and Wealth, 23: 315–338. Hipp, C. and Grupp, H. (2005) ‘Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies’, Research Policy, 34: 517–535. Howells, J. (2004) ‘Innovation, consumption and services: Encapsulation and the combinatorial role of services’, Service Industries Journal, 24: 19–36. Howells, J. (2007) ‘Services and innovation: Conceptual and theoretical perspectives’, in J. Bryson and P. Daniels (eds) The Handbook of Service Industries, Cheltenham and Northampton, MA: Edward Elgar Publishing, 34–44. Illeris, S. (1996) The Service Economy: A Geographical Approach, Chichester, UK: Wiley. Illeris, S. (2005) ‘The role of services in regional and urban development: A reappraisal of our understanding’, Service Industries Journal, 25: 447–460. Illeris, S. (2007) ‘The nature of services’, in J. Bryson and P. Daniels (eds) The Handbook of Service Industries, Cheltenham and Northampton, MA: Edward Elgar Publishing, 19–33. Kastalli, I.V. and Looy, B.V. (2013) ‘Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance’, Journal of Operations Management, 31: 169–180. Keeble, D. and Nachum, L. (2002) ‘Why do business service firms cluster? Small consultancies, clustering and decentralization in London and Southern England’, Transactions of the Institute of British Geographers, 27: 67–90. Kindström, D., Kowalkowski, C. and Sandberg, E. (2013) ‘Enabling service innovation: A dynamic capabilities approach’, Journal of Business Research, 66: 1063–1073. Kutscher, R.E. and Mark, J.A. (1983) ‘The service-producing sector: Some common perceptions reviewed’, Monthly Labor Review, April: 21–24. Lancaster, K.J. (1966) ‘A new approach to consumer theory’, Journal of Political Economy, 74: 132–157. Leamer, E. and Storper, M. (2001) ‘The economic geography of the internet age’, Journal of International Business Studies, 32: 641–665. Lorenzen, M. and Mudambi, R. (2013) ‘Clusters, connectivity and catch-up: Bollywood and Bangalore in the global economy’, Journal of Economic Geography, 13: 501–534.

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Maroto-Sánchez, A. and Cuadrado-Roura, J.-R. (2013) ‘Do services play a role in regional productivity growth across Europe?’, in J.-R. Cuadrado (ed.) Service Industries and Regions: Growth, Location and Regional Effects, Berlin, Heidelberg: Springer, 203–226. Mas-Verdu, F., Ribeiro Soriano, D. and Roig Dobon, S. (2010) ‘Regional development and innovation: The role of services’, Service Industries Journal, 30: 633–641. Michel, S., Brown, S.W. and Gallan, A.S. (2008) ‘Service-logic innovations: How to innovate customers, not products’, California Management Review, 50: 49–65. Miles, I. (2010) ‘Service innovation’, in P.P. Maglio, C.A. Kieliszewski and J.C. Spohrer (eds) Handbook of Service Science, New York: Springer, 511–533. Mina, A., Bascavusoglu-Moreau, E. and Hughes, A. (2014) ‘Open service innovation and the firm’s search for external knowledge’, Research Policy, 43: 853–866. OECD (2000) The Service Economy, Business and Industry Policy Forum Series, Paris: OECD. OECD (2005) Oslo Manual. Guidelines for Collecting and Interpreting Innovation Data, Paris: OECD. OECD (2007) Globalisation and Structural Adjustment. Summary Report of the Study on Globalisation and Innnovaion in the Business Services Sector, Brussels: OECD. Oliveira, P. and von Hippel, E. (2011) ‘Users as service innovators: The case of banking services’, Research Policy, 40: 806–818. Ordanini, A. and Parasuraman, A. (2011) ‘Service innovation viewed through a service-dominant logic lens: A conceptual framework and empirical analysis’, Journal of Service Research, 14: 3–23. Pavitt, K. (1984) ‘Sectoral patterns of technical change: Towards a taxonomy and a theory’, Research Policy, 13: 343–373. Probert, J., Connell, D. and Mina, A. (2013) ‘R&D service firms: The hidden engine of the high-tech economy?’, Research Policy, 42: 1274–1285. Roland Berger (2014) Evolution of Service, think:act content. Hamburg: Roland Berger Strategy Consultants. Online. Available HTTP: http://www.rolandberger.de/media/pdf/Roland_Berger_taC_Evolution_of_Service_ 20140107.pdf (20 February 2016). Roos, G. (2015) ‘Servitization as innovation in manufacturing: A review of the literature’, in R. Agarwal, W. Selen, G. Roos and R. Green (eds) The Handbook of Service Innovation, London: Springer, 403–435. Rubalcaba, L. (2006) ‘Which policy for innovation in services?’, Science and Public Policy (SPP), 33: 745–756. Rubalcaba, L., Michel, S., Sundbo, J., Brown, S.W. and Reynoso, J. (2012) ‘Shaping, organizing, and rethinking service innovation: A multidimensional framework’, Journal of Service Management, 23: 696–715. Salunke, S., Weerawardena, J. and McColl-Kennedy, J.R. (2013) ‘Competing through service innovation: The role of bricolage and entrepreneurship in project-oriented firms’, Journal of Business Research, 66: 1085–1097. Sayer, A. and Walker, R. (1992) The New Social Economy: Reworking the Division of Labor, Cambridge, MA: Blackwell. Schettkat, R. and Yocarini, L. (2006) ‘The shift to services employment: A review of the literature’, Structural Change and Economic Dynamics, 17: 127–147. Schuh, G., Gudergan, G. and Kampker, A. (eds) (2016) Management industrieller Dienstleistungen: Handbuch Produktion und Management 8, Berlin: Springer. Schulz, C., Dörrenbacher, H.P. and Liefooghe, C. (2004) ‘Far away, so close? Regional clustering of mail order firms and related business services in the Lille Metropolitan Area’, in J.W. Harrington and P.W. Daniels (eds) Knowledge-Based Services, Internationalization and Regional Development, Aldershot: Ashgate, 289–306. Snyder, H., Witell, L., Gustafsson, A., Fombelle, P. and Kristensson, P. (2016) ‘Identifying categories of service innovation: A review and synthesis of the literature’, Journal of Business Research, 69: 2401–2408. Sundbo, J., Sundbo, D. and Henten, A. (2015) ‘Service encounters as bases for innovation’, Service Industries Journal, 35: 255–274. Toivonen, M. and Tuominen, T. (2009) ‘Emergence of innovations in services’, Service Industries Journal, 29: 887–902. United Nations (2011) Promoting Innovation in the Services Sector, Geneva: United Nations Economic Commission for Europe. Valminen, K. and Toivonen, M. (2012) ‘Seeking efficiency through productisation: A case study of small KIBS participating in a productisation project’, Service Industries Journal, 32: 273–289. Vandermerwe, S. and Rada, J. (1988) ‘Servitization of business: Adding value by adding services’, European Management Journal, 6: 314–324. Vargo, S.L. and Lusch, R.F. (2008) ‘Service-dominant logic: Continuing the evolution’, Journal of the Academy of Marketing Science, 36: 1–10. Vergori, A.S. (2014) ‘Measuring innovation in services: The role of surveys’, Service Industries Journal, 34: 145–161. Witell, L., Snyder, H., Gustafsson, A., Fombelle, P. and Kristensson, P. (2016) ‘Defining service innovation: A review and synthesis’, Journal of Business Research, 69: 2863–2872.

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Wölfl, A. (2005) ‘The service economy in OECD countries: Statistical analysis of science, technology and industry’, STI Working Paper 2005/3, Brussels: OECD, Directorate for Science, Technology and Industry. Wood, P. (2002) ‘Knowledge-intensive services and urban innovativeness’, Urban Studies, 39: 993–1002. Wood, P. (2005) ‘A service-informed approach to regional innovation – or adaptation?’, Service Industries Journal, 25: 429–445. Wood, P. and Wójcik, D. (2010) ‘A dominant node of service innovation: London’s financial, professional and consultancy services’, in F.Z. Gallouj and F. Djellal (eds) The Handbook of Innovation and Services: A Multidisciplinary Perspective, Cheltenham and Northampton, MA: Edward Elgar Publishing, 589–619. Wyszkowska-Kuna, J. (2011) ‘Innovation in services: Theoretical approach’, Comparative Economic Research, 14: 25–46.

18. Design theories, creativity and innovation Pascal Le Masson, Armand Hatchuel and Benoit Weil

INTRODUCTION In this chapter, we analyze the relationship between creativity issues and design theory from a historical perspective. Although these two notions seemingly correspond to different academic fields (psychology, cognitive science and management for creativity; engineering science and logic for design theory), they appear to be deeply related when it comes to design methods and design management. This relationship is quite complex. For instance, in his presidential address of the Design Research Society in 2006, Cross (2006) underlined the coincidence between the renewal of design methods, based on problemsolving, and creativity issues related to creative problem-solving in the 1950s and 1960s in the US (Gordon 1961; Osborn 1953; Alexander 1964; Archer 1965; Simon 1969). But he also noted that design methods were strongly criticized in the 1970s, even by some of their former supporters (Alexander 1971), because they could not address “wicked” problems (Rittel and Webber 1972). This raises the question of whether design methods and theory address creativity issues or whether creativity issues find fault with design methods. The design professions answer this question in different ways. Ulrich and Eppinger (2008) define design through its two main professions (“design . . . includes engineering design (mechanical, electrical, software, etc.) and industrial design (aesthetics, ergonomics, user interfaces)” (p. 3)). Strangely enough, the two professions address creativity issues in different manners. Engineering design, as defined in the reference manuals for teaching design to engineers all over the world (Roth 1982; Rodenacker 1970; Pahl and Beitz 1977, 2006; Ulrich and Eppinger 2008; Pugh 1991; French 1999), aims to propose convergent thinking methods for developing new products, not relying on chance but based on scientific knowledge and design rules. It faces creativity issues in complex problem-solving, through expertise and knowledge acquisition, through well-planned design processes (e.g. stage-gate, new product development (NPD)) and sophisticated organizations (engineering departments, marketing departments, research labs, etc.). Recent critics have also underlined that some innovation issues require engineering design practices to evolve (Eppinger 2011). Industrial design insists on the risk of fixation due to established skills and representations of the objects; it favours out-of-the-box thinking, new visions, brainstorming and the acquisition of knowledge from users. It strives to address contemporary creativity issues such as the creation of meaning (Verganti 2008). These examples show that there are a variety of design methods and that they address creativity issues in different ways. In this chapter, we study this relationship between design methods and creativity issues to see whether it is a coincidence or whether there is a specific logic behind it. Clarifying this issue might provide a better understanding of contemporary issues in creativity and how they are related to recent results in research on design theory and methods. Confronted with the variety of design methods and creativity issues, we made two methodological choices, as described below. 275

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First, we focus on design theories. Recent literature reviews on product design (see for instance the special issue of the Journal of Product Innovation Management in May 2011 (Swan and Luchs 2011)) have underlined the difficulty in finding a consensual definition. We therefore focus more specifically on design theories, that is, models of design reasoning, to help address the variety of definitions and still have a rigorous means of controlling the consistency of the methods. In this perspective, recent advances in the academic community of engineering design (e.g. the Design Society), and more specifically in research on design theory, have shown interesting results regarding the relationship between design theory and creativity issues. For instance, it has been shown that past design theories (in particular Simon’s design theory based on problem-solving) could not tackle some creativity issues (Dorst 2006; Hatchuel 2002); and some authors have proposed new design theories which explicitly address specific creativity issues (see for instance Shai et al. 2009). There may therefore be a deep link between design theories, considered as models of design reasoning, and creativity issues. We will look for the models of design reasoning that underlie the methods of engineering design (or the methods of industrial design). This will enable us to analyze how these methods help designers to address some creativity issues and fail to address others. We identify three main notions: ● ●

●

Design theory, by which we mean a formal model of design reasoning. This model of design reasoning inspires forms of organizing collective design activities. We will characterize these forms through three features: the role of knowledge in design, the design process and the design organization. These forms of collective design help achieve a certain level of performance in terms of addressing creativity issues.

Second, we take a historical perspective. In this case, too, research carried out by the engineering design community is inspiring. For instance, Hatchuel et al. (2011a) have shown that recent design theories form a consistent body of knowledge that tends to increase the creativity of design. This result seems to confirm our belief that there are historical dynamics linking creativity issues and the development of new models of design reasoning. Hence our research question: we investigate the assumption that new models of design reasoning emerged to address new creativity issues; that the models that led to widespread methods also helped to better address these creativity issues; and that these models and related methods were finally criticized for not addressing new, emerging creativity issues. Creativity is a relatively recent academic notion (a large number of studies on creativity were launched in the field of psychology in the 1950s following the presidential address by a famous American psychologist, Joy Paul Guilford, who defined creativity as a form of intelligence to be distinguished from that measured by IQ (Guilford 1950)). But based on recent results in this academic field, one can identify creativity issues as the issues that limit creativity. Recent studies have shown that they can be analyzed as different forms of fixations (see in particular the synthesis in Hatchuel et al. 2011b). A study of these fixations helps to recognize creativity issues that were faced in the last two centuries. We give a schematic summary of these notions in Figure 18.1. There is therefore neither intrinsic opposition nor natural convergence between design theory and creativity issues. Our intuition is that of a “dialogue” between them. At certain historical moments, this dialogue enlightens the limits of collective designers relying on

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The set of creativity issues that can be reached by designers relying on a given design theory

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Border between creativity issues within reach (inside) and out of reach (outside)

New design theories tend to address new creativity issues

Figure 18.1 A schematic summary of the main notions for analyzing the interplay between creativity issues and design theory a design theory and confronted with new, emerging creativity issues. This can lead to the emergence of new design theories and new forms of collective design activities. Hence design theory and creativity issues may be two ways – one normative, the other critical – of dealing with collective design activities. Their interplay may lead to the invention of specific forms of collective design. To investigate the issue of the relationship between creativity issues and design theory, we revisit three historical moments in the building of design theory. First, the ratio method, that is, the design theory used for industrial upgrading in Germany in the 19th century; second, systematic design, that is, the theory used for organizing research and development (R&D) departments throughout the world from 1950 onwards; and third, the Bauhaus methods and theory of the 1920s, which were used in a large number of design schools around the world. For each period, we study the creativity issues addressed, the formal model of design reasoning underlying it, the types of design capabilities inspired by the design theory and the type of outcome expected. Finally, we point to the interplay linking creativity issues and design theory, structured around the notion of “fixation effect”: creativity identifies fixation effects, which become the targets of new design theories; design theories invent models of thought to overcome them; and, in turn, these new design theories can also create new fixation effects that will then be designated by creativity studies. This dialectical interplay leads to regular inventions of new ways of managing design, that is, new ways of managing knowledge, processes and organizations for addressing specific design issues.

AN ANALYTICAL FRAMEWORK FOR LEARNING FROM THE HISTORY OF CREATIVITY ISSUES AND DESIGN THEORIES Three Types of Tensions between Design Methods and Creativity Issues The literature distinguishes between three facets of the complex relationship between design methods and creativity issues, as discussed below.

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How to deal with knowledge in design? Engineering design methods underline the importance of relying on knowledge and competences. Acquiring and managing knowledge is one of the key issues in these methods. This has led to forms of evaluation of R&D. For instance, the notion of absorptive capacity characterizes the contribution of research to the innovation process as the capacity to absorb relevant external knowledge (Cohen and Levinthal 1990; Lane et al. 2006). Conversely, studies in creativity have shown how knowledge can create “fixation” (Jansson and Smith 1991; Smith et al. 1993) and how it can become a core rigidity instead of a core capability (Leonard-Barton 1992). Hence, knowledge can support but it can also limit design capabilities, and it is not always easy to devise compromises (Weisberg 1999; Basadur and Gelade 2006). Should the design process be divergent or convergent? Creativity studies insist on the necessity to diverge, although some authors do admit that convergence is also important, often advocating initial divergence followed by unavoidable convergence (Eris 2004; Dym et al. 2005; Cropley 2006). Conversely, literature on product development processes favours convergent thinking, even if divergence can also be required from time to time (e.g. diverge at the fuzzy front end (Koen et al. 2001; Reid and De Brentani 2004); or diverge during the processes, in flexible product development (Kelley 2009; MacCormack et al. 2001)). Is the design organization based on strong leadership and well-administered projects or more on autonomous, creative teams? What is the form of design work division? Since Osborn invented brainstorming at the advertising agency BBDO (Osborn 1957), creativity studies tend to analyze teams’ creativity in organizations (Hargadon and Sutton 1997; Paulus and Brown 2007; Paulus and Yang 2000). Working on how creativity is organized, Amabile showed how project structures and administration were poorly adapted to creative teams (Amabile et al. 1996; Amabile 1998). Conversely, engineering design methods tend to focus on how engineering design departments and marketing departments are organized and on their relationship to research labs. They insist on the structures, methods and administration of engineering design. Even in cases of radical innovation, requiring creativity from the teams, authors have shown that rigorous management is required, for instance for managing the unknown with well-balanced, sequential and parallel learning (Loch et al. 2006), managing concept shifts based on memorization and modularization (Seidel 2007) or managing major innovation with a “systems approach” (O’Connor 2008). Some authors have called for a combination of creative and non-creative teams in ambidextrous organizations (Tushman and O’Reilly III 1996), but empirical studies have stressed the limits of such simplifying compromises (Brown and Eisenhardt 1997). Beyond Compromises: The Dialectical Interplay between Creative Issues and Design Theories The relationship between design methods and creativity issues appears to be made of compromises: in knowledge, to balance fixation and non-fixation; between convergence and divergence in design processes; and between control and autonomy in design organization. Compromises can find tradeoffs between the extremes, but two clues suggest that some design theories and methods apparently invented compromises that helped to keep the advantages of the two extremes, overcoming the dilemmas by inventing combinations such as knowledge for unfixing, divergence for convergence, and design control for increased creative autonomy:

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One might think that creativity has no place in engineering design but this is far from true. Creativity was a historical issue for the theorists of systematic design, as underlined by Wolfgang König (1999). For instance, in the 1850s, the great ancestor of German systematics, Ferdinand Redtenbacher, proposed a protoversion intended to make designers (the technicians of that time) “more innovative” (Redtenbacher 1852a). The first teacher of elaborate “systematics”, the Russian professor Peter Klimentitsch von Engelmeyer, called his method a “theory of creative work” (Engelmeyer 1895). As analyzed by Mathias Heymann (2005), in the 1970s there were many debates in the German systematics community to clarify how far systematic design was already addressing the creativity issue. More recently, Udo Lindemann, former president of the Design Society, has shown how classical systematic design took into account the creativity required from design engineers (Lindemann 2010). This means that past design theories undoubtedly “invented” ways to manage knowledge, processes and organization for dealing with creativity issues. They were able to use knowledge and still be unfixed, to converge and diverge, and to control while preserving creativity in teams. This also underlines the need for a more precise analysis of the theoretical roots of design methods. In certain fields such as industrial design, the design methods and creativity issues are not in tension but, on the contrary, industrial design methods are said to match creativity issues. Could a design theory for industrial design propose ways of addressing opposites, that is, using knowledge without being fixed, diverging and converging, and organizing controlled autonomy in design?

We reinterpret the above-mentioned tensions in a more “historical” perspective, based on the theoretical roots of design methods. At certain moments in time, the incumbent design methods are considered too limited with regard to societal issues, new collective imagination and so on. Creativity issues then address the limits of past design theories and methods. As a result of this critique, new design theories are proposed to “stretch” design capacities to overcome fixations. They propose new frameworks with new ways of dealing with knowledge, processes and organizations, with a view to addressing the newly identified creativity issues. Finally, they enable new types of innovation output. This is our main research hypothesis: there may be “dialectic” interplay between creativity issues and design theories, which leads to the regular invention of new forms of design and new types of innovation output. Over time, this dialectical process has generated different ways of dealing with knowledge, different forms of design processes and design organizations, to address different types of creativity issues. Method: Analytical Framework to Study Historical Cases To study this hypothesis, we investigated three historical moments in the creation of design theory to analyze whether and in what manner they dealt with creativity issues and what the formal proposals tell us about knowledge in design, design process and organization. Recent works have shown the interest of a historical approach to management science, particularly in the realm of strategic management (Zan 2005). Authors have underlined the pertinence of the approach for studying the interaction between management theories and historical forms of collective action (Hatchuel and Glise 2003). We decided that it was

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a relevant method for our particular study because it enabled us to analyze the dynamic interplay between creativity issues, the emergence of design theories and their effects in terms of design methods and design outputs. Methodologically speaking, we focused on specific “tipping moments” when new design theories emerged rather than covering very long periods of time. Case selection The method of selecting cases was as follows: 1.

We selected three theories that were widely diffused: the ratio method was taught in a large majority of German Technische Hochschule from the 1850s to the early 20th century; systematic design still serves as the basis for the main courses in engineering design; and the Bauhaus theories have inspired industrial design teaching since their creation in the 1920s. We chose theories that are related to two contrasting professions in design, two from engineering and one from industrial design. We selected theories on which we had sufficient material to address theoretical aspects (books, papers, etc.), as well as the methods, the industrial context of the time and the innovation outputs related to these methods (handbooks, testimonies, historical monographs, work by historians, etc.).

2. 3.

It is interesting to note that the main historical sources were not translated into English, which explains why several elements of this history are hardly known in the English literature. In each case, we follow the same analytical framework: ●

●

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We characterize the creativity issues that the theory intended to address and the kind of “fixations” to be overcome. We analyze the principles of the theory (with a brief presentation of some illustrations) and how it helps to address the creativity issues and to overcome the fixation effects. In particular, we underline how it leads to new ways of dealing with knowledge in design, design processes and organization, that is, how it leads to the proposal of new design capabilities. Finally, we analyze the types of innovation expected from the theory and the type of fixation that it might cause.

HISTORICAL CASES OF INVENTIONS OF DESIGN THEORIES: GERMAN ENGINEERING DESIGN AND BAUHAUS INDUSTRIAL DESIGN The Method of Ratios Fixed by existing objects The first theory (or method) of engineering design is attributed to Redtenbacher (König 1999; Redtenbacher 1852a). In the 1840s, this Swiss engineer and professor, who taught

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machine construction at the newly created Technische Hochschule in Karlsruhe, developed an original course based on a new method called the “method of ratios”. In very close relationship to the industry and the machines of his time, he noted that techniciansdesigners rebuilt the same machine, whatever the context (environment, use of the machine, budget, available material, etc.). He was neither the first nor the only one to make this observation. Since Diderot, several “technologs”, in particular in the French engineering and science education system, had also seen the limits of technicians who were unable to innovate using the available technical knowledge. Two types of causes were identified. The French scientists and professors believed that the rules themselves had to be improved, through science, experiments and the diffusion of more accurate knowledge. In the German professor’s view, the quality of the knowledge was indeed necessary, but not enough. He wrote in his preface to Resultate für den Maschinenbau (Redtenbacher 1852b): With the principles of mechanics, machines cannot be invented, because to do so, one also requires precise knowledge of the mechanical process for which the machine is to be used. With the principles of mechanics, sketches of machines cannot be made, because a sense of composition, arrangement and forming is also required. With the principles of mechanics, no machines can be made as this requires practical knowledge of the materials to be worked and experience in handling tools and auxiliary equipment. With the principles of mechanics, one cannot manage an industrial business, as this requires a strong personality and knowledge of commercial affairs.

For Redtenbacher, the constant replication of a limited number of known objects was also due to the limited capacities of the technician-designer to make use of knowledge for creating new objects. The ratio method aimed precisely to address these two fixations: 1) it proposed synthetic models of existing objects, so-called “object models” (in a relatively classical mode, it created knowledge on existing objects (cf. laws of mechanics)) and 2) (and this is the most original part) it proposed a method to make use of these synthetic models to design partially unknown objects. It is interesting to note how careful Redtenbacher was, in his classes and his manuals, to separate the part where he built “complete theories” on existing objects from the part where he proposed an approach for gradually determining unknown objects. The classical teaching in mechanics inferred that the model of existing things was sufficient for designing, as if the model for designing a new object could be easily deduced from the models of existing ones. After modeling objects, Redtenbacher added a second part based on a “generative model”, which is the conceptual “twin” of the object model. The surprise was that this “twin” had a very different structure from that of the object model. The object model established relationships between the object’s attributes, whereas the method of ratios clarified the order in which the attributes that determine the object should be added. Beyond the method, Redtenbacher claimed to propose “principles for machine design” (Prinzipien für den Maschinenbau). He explained that he was not only providing a theory of existing objects but also a theory for constructing still partially unknown objects using knowns. The principles at the root of the method of ratios constitute a parametric design theory: in Redenbacher’s terms, machine design consists in instantiating a parametric model of the object taking into account contextspecific data. It was one of the first theories to propose rules for organizing the exploration of the unknown in relation to the known.

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These principles sought to avoid overlooking solutions and the too systematic use of knowns, when known solutions were reused although they were in fact ill-adapted. They also obliged designers to stay a little longer in the domain of the unknown, at higher levels of abstraction than those used to make physical models, to imagine alternatives to what was suggested by intuition and past experience. An illustration We can illustrate how the method works by looking at a simple case: designing waterwheels (Redtenbacher 1858). In the first part of the book Theorie und Bau der WasserRäder (Chapters 1 to 3), Redtenbacher made a state of the art review of wheels and existing theories, gradually formulating a series of “equations of effects” relating to the performance and dimensions of waterwheels. He based his arguments on work by Poncelet (1827), Belidor et al. (1819) and Morin (1836), but also by Smeaton (although his experiments dated back to 1759, Figure 18.2) (Smeaton 1810), and also gave the results of his own experiments. However, these studies did not look at any particular features of the wheel or its immediate environment. For example, there were no equations for the size of the wheel, its diameter and width, nothing about choosing blades or buckets, about the number of buckets or their shape, about the depth at which the wheel should plunge into the water, about care to be taken in assembly and in controlling leakage. All these limitations meant that designers could not use the scientific results that had been obtained by then. This is why, still in the first part of his works, Redtenbacher completed the state of the art review with comprehensive models of existing machinery, grouped by main types. Once he had built up these major descriptive models, Redtenbacher went on to the second, most original part of the book: the method of ratios. Chapter 4 described the series of rules to be used to assess “the specific forms and dimensions on which the effect of the wheel preferentially depends, in the conditions of perfect constructions”. The method began by following the main stages of a fictive dialogue between a designerentrepreneur and a client. According to Redtenbacher, the first question concerned the budget that the client was prepared to devote to the structure as, depending on the answer, the designer could choose between a wooden and a metal wheel, the performance and size equations being very different for the two options. Once the material had been chosen, two other questions had to be answered: the height of fall of the water flow and the usable flow (or the expected power generated on the shaft, which comes to the same thing). The designer then used a chart (see Figure 18.3) to help choose the best type of wheel depending on the height and the flow. At this stage, the method enabled the designer to choose a class of wheels by evaluating the expected performance, but without having to specify all the dimensions. At that time, this was the most critical part of the reasoning for Redtenbacher, as he had observed that most wheels were poorly adapted to their environment. The second phase in selecting the dimensions consisted in specifying step by step, in a specific order given by the method, all the parts of the construction, following methods of calculation or plans (proposed in the book) which were rather like patterns. The plans were dimensionless and also showed the ratios between the parts depending on a fixed known entity. He then specified the linkages and the level of precision for the entire construction. The last part concerned what could be called “finalization”: Redtenbacher

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Figure 18.2

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Smeaton’s experimental device (1759)

recalled the formulas for theoretical performance and the measurement technique for real performance, inviting designers to compare the performance measured on the construction with the theoretical performance and indicating how to improve the real performance of nearly completed wheels. Success of the method – types of innovation Redtenbacher’s theory was one of the first design theories for the world of machines. The method of ratios was not new; Redtenbacher himself recognized that it came from architecture. Wolfgang König pointed out that before Redtenbacher a similar method had

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Figure 18.3

Chart for selecting types of waterwheel depending on conditions of use

been used by English and German mechanics (König 1999, p. 24). But König also noted that Redtenbacher deserves the credit for introducing the method on such a wide scale, in polytechnic schools and in industrial practices. There were several successive editions of Redtenbacher’s works and they were also translated into French. Up to the 1880s, all the manuals and technical handbooks were based on the method of ratios. Moreover, despite the criticism it received at the end of the century, it was still widely used during the following century. There was wide recognition of Redtenbacher’s contribution among German engineers in the 19th century, as demonstrated by the many tributes paid to him by professors and students. What was the impact of the method in terms of innovation? It is striking that Redtenbacher made very few claims in this respect: the method served to treat problems in which the designer was already very knowledgeable, as the machine’s arrangement was already known in terms of its objectives and its order. Many of the machines covered in his books were not the high-technology machines of the time. In 1843, when Watt’s steam engine was already over 60 years old, Redtenbacher was still writing about waterwheels! But we know how misleading the term “innovation” can be. As far as Redtenbacher was concerned, the challenge was industrial upgrading. The idea was to provide, as quickly as possible, a cheap, efficient source of energy suited to the needs of the rapidly expanding industries of the time (particularly the textile industry). It was not even a question of making a “perfect” waterwheel (contrary to Poncelet, whose aim was to find a wheel that

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transmitted the entire momentum of the water flow to the shaft); Redtenbacher sought to provide tools for making a variety of different wheels that were well suited to their environments. Redtenbacher’s design principles were at the root of new forms of collective design. Although it is not an organizational model, the ratio method tells us a lot about knowledge for design, design processes and organization (for the 19th century). Regarding knowledge To avoid the fixation effect of existing objects, the ratio method provided models of existing objects (object models) (as Diderot’s encyclopedia did) and knowledge on how to use that knowledge at the right moment, depending on the context, that is, a kind of “context-sensitive” algorithm. This has a clear “unfixing” effect in the sense that technical designers were able to design very different objects (based on the same object model) and objects that were adapted to the context. The ratio method also structured a specific “convergent and divergent” process. Selfevidently, the method ensured convergence towards one acceptable solution. But it also prevented the designer from converging too fast. The method identified precisely, for each type of object, the moment in the design process when it was possible and fruitful to diverge and the type of investigation that was relevant: divergence on material, guided by the customer’s budget, divergence on the type of wheel, based on the chart, and divergence in finalization, based on the theoretical performance target. One can note that the ratio method corresponds to specific ways of dividing work in design. It leads to the distinction between two roles in the design organizations: a “rulemaker” (like Redtenbacher himself) designs rules (ratios etc.) for a technician-designer who is a “rule-user”. The rule-maker exerts leadership, choosing the product families and defining the areas of freedom to be delegated to the rule-user, whereas the latter exercises creativity, within these areas of divergent thinking. This very simple example of design theory illustrates how a design theory was developed to counterbalance some forms of fixation and supported ways of dealing with knowledge, process and organization to invent a new form of innovation management. Systematic Design Fixed by existing rules and machine elements. We shall now analyze a more sophisticated design theory, called systematic design. This method is very well known, is taught in several reference handbooks (Pahl and Beitz 2006; Ulrich and Eppinger 2008; French 1999; Pugh 1991), and is used today as a general framework for engineering projects. It is often summarized as a sequence of design steps: an initial step to clarify the task, a second phase of conceptual design, a third of so-called “embodiment” and a last step of detailed design (Figure 18.4). What are the origins and the formal model of systematic design? Systematic design was born step by step between 1900 and the 1960s, following a number of criticisms of the ratio method (Heymann 2005). The theory tried to address two main criticisms: ●

The ratio method, still in use at the beginning of the 20th century, was unable to take into account the regular progress in science and, more generally, the increased

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Figure 18.4

Systematic design according to Pahl and Beitz (Pahl and Beitz 1977)

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capacity for creating knowledge. This gave rise to a critical fixation: designers tended to reuse outdated, obsolete design rules. Moreover, designers tended (and were even taught) to use existing machine elements to design complex assemblies. Design could be seen as a combination of existing elements that determined the layout, the architecture, the organization and even the technical principles to be used for an object. The “attraction” (i.e. the fixation) exercised by machine elements tended to reduce the exploration of new technical principles and new architectures.

In this context, systematic design can be seen as a method that reopens spaces for creativity, pushing the designers NOT to reuse existing knowledge but to explore new knowledge on technical principles and architectures, in a rigorous, efficient way. Principles of systematic design reasoning Systematic design reasoning consists in refining the description of future, still unknown objects, following clear, rigorous steps to make use of and produce relevant knowledge (Hansen 1955; see Figure 18.5). ●

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In a given “design exercise” (Aufgabe), preliminary thinking (Vorüberlegung) helps determine the fundamental principle formulated in a few clear sentences. This fundamental principle is the “design core” (Wesenskern) that contains “all the possible solutions”. The “principles of work” (Arbeitsprinzipien) are then elaborated by combining elements of solutions including characteristic criteria (Konstruktionsgesichtpunkte or value criteria). These principles of work have three main characteristics: 1) they comprise elements of solutions, that is, physical systems or particular partial functions, especially those required for any solution; 2) the elements of solutions are completed by characteristic features (Merkmale, value attributes) that serve to determine, to the greatest possible extent, the characteristics such as materials, processes, forms, energy sources and so on, and 3) the principles of work must also specify the forms of matching (Abhängigketisverhältnis, the relation of dependency) that link the functional elements to one another. For each element of the solution there is a “residue” or “error”, that is, a distance remaining between what is “known” about the final solution and what has to be known to solve the design exercise. By analyzing errors, the designers identify improved principles of work (verbesserte Arbeitsprinzipien). They then define all the residual parameters, leading to a production project (Herstellunsunterlagen).

This process tends to overcome precisely the above-mentioned fixations: ●

The phase in which the Grundprinzip, or fundamental principle, is determined is original and the authors particularly stressed its importance: “Although such and such a solution has already emerged, it is important to clarify a fundamental principle. This step towards the abstract is needed to help find possibilities for new

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Figure 18.5

Basic diagram and process for systematic design (Hansen 1955)

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outputs, despite a lack of experience”, (Hansen 1955, p. 10, emphasis added). It is a way of overcoming the (precipitated) reuse of existing solutions. The design process is divided into phases, each including work to acquire specific knowledge on existing things. The first stage encourages designers to learn about the specifications. Which ones are indispensible? Which can to be taken into account on an optional basis, possibly with extra costs, and which can be met as part of overall development, but not necessarily during the design exercise underway. The authors insisted on the fact that state of the art reviews should only be done at the second stage. If they are carried out too early, they can encourage designers to follow paths that, although they seem promising, may prevent them from exploring potentially even better solutions and put an end to opportunities for constant progress. Identifying a variety of alternatives improves the ability to find any “gaps” in the reporting of state of the art reviews and known solutions. The third stage implies an ability to evaluate the solutions’ robustness, with a view to determining the possible variations (or in some cases anomalies) in the expected nominal behaviour. It also involves the ability to make calculations comparing the different principles of work that have been improved. The authors underlined that the designers should avoid adding properties too quickly to the unknown object at each stage. Hence, the aim of the fundamental principle is to prevent designers from running to the drawing board as soon as the design exercise is launched. The principles of work (Arbeitsprinzip) (end of stage 2) can be defined using rough hand sketches only and do not require detailed technical drawings, although certain geometric interdependencies may require a scale drawing. The second stage, which is essentially physico-mathematic, should not be restricted either by considerations relating to materials.

Systematic design and design management This gives several insights about knowledge on design, design processes and design organization: ●

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Regarding knowledge on design, systematic design aims to fight the fixation caused by existing design rules: it recommends the moment when design rules should be used and it supports the creation of new knowledge for expansion at the right time. Regarding the compromise between convergence and divergence, systematic design organizes convergence by predefining the order in which the unknown object should be described. At each level, a specific language and a specific type of knowledge and knowledge production should be used: functional, conceptual, embodiment and detailed design. This hierarchy is also the way to maintain divergence in the process, since exploration is required at each language level. Regarding design work division, systematic design enabled the complex division of labour found in contemporary engineering projects. The authors showed that complex machines can be designed using a process involving the systematic design of sub-units and by ensuring the latter’s integration by recursive loops. This type of reasoning enables project planning and basic methods in system engineering such as V-cycles (Pahl et al. 2007).

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The consequences of systematic design for innovation The method, although apparently complex and abstract, was a great success. First at Zeiss, in the former German Democratic Republic (GDR), where it was initially developed: “For small constructions, the method allows for savings of around 25%; for more complex systems, adjustment times could be reduced from 3 months to 2 weeks.” The method was then rapidly disseminated in the GDR, by the school in Ilmenau and by publications. The method was used both for company organization (defining the relations between research and development) (Hansen 1961) and for education (Hansen 1960). It was also a success abroad Whereas it is generally accepted that flows of knowledge have tended to move, overall, from the west to the east, many German historians believe that systematic construction was one of the few competencies that went from east to west (Heymann 2005). A small number of West German researchers were invited to the seminars in the GDR. In the Federal Republic of Germany, a similar rationalization movement did not take place until the 1960s. When the labour crisis became a public crisis, two major seminars were organized on the theme of “the design bottleneck” (EngpassKonstruktion) in 1963–64, where the notions of Hansen et al. were explained and greeted with much interest. They were further transformed before the reference works on systematic design such as Pahl and Beitz’s manual were published, but the latter contains many traces of the earlier works. Contrary to the method of ratios, which required knowledge of the specific ratios for each class of object, systematic design is largely independent of the objects. This explains why the method was adopted in a range of very different fields, such as the automobile, IT, pharmaceuticals, building and microelectronics industries. In the decades following its development by Rodenacker, Roth, Koller, Pahl and Beitz, and later by Hubka and Eder, the theory became widely used in the manuals, particularly in the Anglo-Saxon world once Pahl and Beitz’s work had been translated by Ken Wallace. It gave rise to a certain number of debates. Albert Leyer led one of the most violent. In the 1960s and 1970s and up to the 1983 International Conference on Engineering Design, Leyer, who was considered as a design genius, criticized the logic of the “scientization” of the construction methods to the detriment of creativity. The debate does not seem to have been really clarified during this period: the systematic design manuals soon integrated “creativity techniques” (see the successive editions of Pahl and Beitz’s works) and many authors like Pahl or Ehrlenspiel considered that it was sufficient to cater for Leyer’s concern that creativity should be taken into account. In the 1980s, empirical studies often revealed that the designers only rarely used formal frameworks explicitly. The famous author of a product development manual, Ehrlenspiel (Ehrlenspiel 1995), claimed that design reasoning is to a great extent unconscious. It appears that the theory is so deeply rooted in the organizations, particularly the product development organizations described by Ehrlenspiel, that it does not even require consciousness. Today, the formal framework of systematic design is so deeply embedded that the designers are mere cogs in the organization, who no longer even have an overall view or understanding of it, and in fact no longer need to.

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Bauhaus Fixed by clichés and limited perception A third historical moment in the creation of design theory took place in Weimar with the emergence of the Bauhaus. Bauhaus obviously does not appear as a direct legacy of engineering design, and the first stages of what will later be called industrial design introduce problems and goals that were less considered in traditional engineering design, like simplification of uses, emotional values, semantic and symbolic value, and so on. However these differences shouldn’t hide the fact that there were strong theoretical principles in the Bauhaus approach. We will now analyze these principles and the way they help to deal with creativity issues. Created by Walter Gropius, this school for artists and industrial designers “aimed to serve the modern development of housing, from the simplest domestic appliance to the whole dwelling” (Gropius 1925). It had a clear program: A resolute acceptance of the living environment of machines and vehicles; the organic creation of objects following their own present-day laws, without embellishments or romantic adornment; a limitation to typical, basic forms and colours that are accessible to everybody; simplicity in quantity, with a sparing use of space, material, time and money. (Gropius 1925)

It led to the invention of an original teaching method, and Itten, Klee and Kandinsky, who were in charge of the preliminary courses and the courses on form, developed a design theory for industrial designers. They were motivated by the idea of making students more creative. They did not consider creativity as a given talent; as Itten said: “imagination and creative ability must first of all be liberated and strengthened.” They identified several impediments or obstacles to creativity. Designers are fixed by common associations of attributes. Forms, materials, textures and meaning are too strongly, too deterministically, linked together. The “cliché” (a warm wood, a cold metal, etc.) is the main risk for designers. Itten proposed a theory of colours to fight against that fixation, to “liberate the study of colours’ harmony from associations with forms”. Klee developed new understandings of forms (form as movement, form as rhythm, form as music, form as a living body, etc.) to counterbalance the usual association between composition and the assembly of geometrical forms. As he explained, a circle is NOT the limit of a round shape, it is the result of the circular movement of a point; a round shape is the result of the circular movement of a segment (p. 176). Designing the structure of a painting is actually designing the movement of the eye of the “viewer” (Klee 1922, p. 127). Designers are also limited by their own perception and sensitivity. In Itten’s view, the first reason for studying old masters was to improve perception. Oskar Schlemmer reported what happened during a study of Mathias Grünewald’s Issenheim altarpiece (cited by Droste 2002, p. 28): “Itten looked at his students’ sketches then boomed: ‘If you had even the slightest artistic sense you wouldn’t draw in front of this sublime representation of tears, the tears of the world, you would sit down and burst out crying’. Having said that, he slammed the door.” In 1921, Itten wrote the following about his students’ studies: Don’t be discouraged if your copy doesn’t look like the original. The more the picture really comes to life within you, the more perfect your reproduction will be, as it is an exact measure of the strength of what you have experienced. You live the work of art, it is reborn within you.

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A theory of contrast aims to open new creative worlds to students, not only in the sense of providing new means of expression but also of “improving perceptions” (Itten 1961). Theories to disentangle and enable generative superimpositions Professors such as Itten, Klee and Kandinsky had a theoretical approach that enabled them to teach design, that is, to teach this capacity to overcome fixations. As underlined by Whitford (1984, p. 91), the need to teach led to the development of theories and not the contrary. As Itten wrote: “A theory of colour will help the students discover the expressive quality of colours and colour constrast.” He added: “The objective law of form and colour helps to strengthen a person’s powers and to expand his creative talents” (Itten 1975). To illustrate the method, we can analyze the series of exercises proposed by Itten to learn about textures (Itten 1975). In a first phase, students were told to draw a lemon. Beginning with the representation of an object, Itten wanted the students to go from “the geometrical problems of form” to the “essence of the lemon in the drawing”. It was an “unfixing” exercise, helping the students to avoid assimilating the object with a geometrical form. In a second phase, the students were asked to touch several types of textures, to “improve their tactile assessment, their sense of touch”. This was a learning phase in which students “sharpened observation and enhanced perception”. In a third phase, students built “texture montages in contrasting materials” (see Figure 18.6). During this exercise, students began to use textures as a means of design. The constraint (design only by contrasting textures) helped them to learn about textures (to explore the contrasting dimensions of different textures and improve their ability to distinguish between them). It also meant that they were able to explore the intrinsic generative power of textures, that is, the superimposition of textures that should create something new: “roughly smooth”, “gaseous fibrous”, “dull shiny”, “transparent opaque” and so on. The fourth phase could be qualified as “research”. As the students were by then more sensitive to the variety of attributes of a texture, they could “go out” to find “rare textures in plants”. It is interesting to underline that Itten did NOT begin with this phase, as he was conscious of the need to begin by strengthening their capacity to recognize new things, just as a botanical researcher has first to learn the plant classification system and discriminating features before being able to identify a new specimen. In particular, students were told to find new textures for a given material (see Figure 18.7 in which all the textures are made from the same wood). Once again, this was an exercise to disentangle texture from other fixing facets, that is, materials, in the case in point. The fifth phase consisted in representing textures. Itten stipulated that students had to represent “by heart”, “from their personal sensation”, to go from “imitation” to “interpretation”. Instead of being an objective “representation”, this exercise was intended as a design one, as students had to combine textures with their own personality. Just as phase 4 aimed at creating something new from the superimposition of contrasting textures, the idea in this phase was that the new should emerge from the superimposition of texture and the individual “heart”. It was also designed to help improve sensitivity. The sixth and last phase consisted in characterizing environmental phenomena as textures. For instance, the Figure 18.8 shows a marketplace painted as a patchwork

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Figure 18.6

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Texture montage exercise (Itten 1975)

blanket. Itten urged students to use texture as an autonomous means of expression and not just a “constrained” ornament. By combining their enriched algebra of textures and the algebra of scenes, they could create new “textured scenes” that were more than the scenes and more than the textures. As Itten explained: “It stimulates the students to detach themselves from the natural subject, and search for and reproduce new formal relations.” It should be underlined that this process was more than a “descriptive” theory of textures, just as Redtenbacher’s waterwheel design method was more than a theory of (existing) waterwheels. It was also a method for designing new textures and for using textures for expansion. It counterbalanced fixations due to “clichés” and limitations in perceptions by increasing the capacity to discriminate between textures (perception, descriptors of textures) and by increasing the generative power of textures. Bauhaus and innovation Although Bauhaus only lasted a short period of time (1919–1933) it had a great impact on industrial design. The school contributed to regenerating the grammar of objects in several fields (typography, consumer goods, building, furniture, etc.) (Figure 18.9). As

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Figure 18.7

Several textures with the same material

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Characterize environmental phenomena as textures

Whitford pointed out (p. 115): “theoretical aspects of preliminary courses have had, curiously, an effect on what was produced in the workshops.” In fact, the methods and theories developed in these courses were widely recognized and spread well beyond Bauhaus itself. The theories of design developed at Bauhaus are also a great source of inspiration for knowledge management, processes and organization for innovation: ●

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Regarding knowledge in design, the Bauhaus design theories are based on the notion that improved knowledge of textures, materials, forms, colours, constrasts and so on helps to overcome “clichés”: it disentangles the mechanical, unconscious associations between forms, colours, materials and so on. When the mechanical relationship is broken, then superimpositions of attributes support creative expansions. Regarding the compromise between convergence and divergence, the theories favour synthesis and the creation of an “organism” (Klee 1922). In that sense, there is convergence towards a final product. Divergence comes from the multiple explorations and, above all, from the effect of superimposition: each new “layer” (a

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Figure 18.9 Some examples of the new grammar of forms generated at Bauhaus – some famous products (Bauhaus poster, Marian Brand Tea Pot, Bauhaus building, Wassily Chair)

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texture on a form, a material with a texture, etc.) creates potential divergence and yet the added layer can still be considered as a convergence towards a final “organism”. The aggregation of “layers” is therefore both a divergent and convergent process. Regarding organization, the theories and the Bauhaus organization itself provide interesting indications, with two striking features. First, the future designers were taught to work together “to compare their work and their creative power”. This reinforced a form of collective control of creativity (by assessments, comparisons) inside the creative team. Second, Bauhaus directors (in particular Walter Gropius) insisted on the “program”: combine “art and technique”, work on “industrial products” (instead of combining art and craft, as indicated in the initial 1919 program). This second feature exemplifies strong leadership, not based on prescribed “projects” and “vision” but rather on the designation of a new area for imagination and expansion. Somewhere between the two extremes of the autonomous creator and of the administrated innovation, mutually controlled, creative teams and the inspiring, stimulating leader appeared. We should note that fixations caused by the Bauhaus design theory have also recently been identified. For instance, in the Bauhaus framework it is difficult to deal with new objects such as perfumes, services or web interfaces. More generally, the proposed theories have a fixation effect (in terms of colour, texture, material, etc.).

RESEARCH PROPOSALS, DISCUSSION ON THE RECENT DESIGN THEORIES AND FURTHER RESEARCH Main Results The analysis of the historical emergence of past design theories reveals an interesting interplay between creativity issues and design theory. Two main propositions emerge from this history: P1: Creativity issues are symptoms of the limits of existing design theories. They evolve over time. In the 1850s, the creativity issue concerned fixation by existing, already designed objects; in the first half of the 20th century, the creativity issue concerned fixation by existing design rules and machine elements, leading to the non-relevant reuse of existing knowledge; in the 1920s at the Bauhaus, the creativity issue concerned “clichés” and the limited capacity for perception. P2: Design theories emerge to overcome contemporary fixations and extend generative capacities. In the 1850s, the ratio method helped to use relevant rules for designing context-sensitive products; in the 1950s, systematic design proposed a design method based on pre-ordered languages (functional, conceptual, embodiment, detailed design) to enable divergence and

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the production of knowledge at the right moment and hence propose constantly improved products. In the 1920s, the Bauhaus theorists renewed the theories of forms, colours and materials to enable generative superimpositions. These design theories also provide interesting ways to deal with design capability management. P3: Design theories invent new ways to use knowledge for design. Each of these design theories provides sophisticated and original ways to make use of knowledge while overcoming knowledge fixation. Redtenbacher’s ratio method counterbalanced the tendency to use the knowledge on existing objects by creating a “context-sensitive” algorithm based on stabilized models of the object, enabling designers to use the right knowledge at the right moment. German systematic design manages knowledge creation in such a way as to prevent designers from continuing to reuse obsolete design rules. It is based on wide-ranging knowledge maps, which help identify the “gaps” and thus focus creativity where it is relevant. The Bauhaus theories built enriched models of materials, forms, colours and contrasts, to help disentangle them and support generative superimpositions. P4: Design theories invent new ways of combining divergent thinking and convergent thinking in design processes. Although Redtenbacher’s ratio method was highly convergent, it remained divergent at well-identified stages. In German systematic design, convergence is created by the progressive instantiation of pre-ordered languages of the objects, each new language also being a step involving temporary divergence. In the Bauhaus theories, the emergence of the “organism” resulted from superimpositions of dimensions (forms, material, colour, etc.) which were also opportunities for divergence. Finally design theories could have helped to invent new ways of combining autonomous creative teams and control. Redtenbacher’s ratio method led to a distinction between the rule-maker and the rule-user (initially the professor and the technician). In German systematic design, a distinction emerged between the project team with a clear target and a clear framework and the engineering department, in charge of controlling the reuse and production of knowledge. Bauhaus invented a form of “mutually assessed” collective creativity, in interaction with inspiring leadership, based on certain constraints (“use industrial processes and design rules”) and the designation of expansion areas (“modern housing”). We summarize these results in Table 18.1. It should be underlined that even if engineering design and industrial design obviously deal with different types of goals, we find a common pattern: in each case design theory helps to deal with creativity issues. Design Theory and Creativity Today? Testing Our Framework These propositions can be tested by looking at recent advances in creativity studies and design theories, two fields of research that have grown very fast in the last few decades. As

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Ratio method (Redtenbacher, 1850s) Parametric design: instantiate a parametric model, based on contextsensitive data Systematic Design Fixed by the (Hansen et al. reuse of non1950s, Pahl & Beitz relevant design 1970s); reduce rules the unknown to a minimum (residue) by using the known as much as possible (approximation of the unknown through the known) Fixed by “cliché” Bauhaus school (Itten, Klee, etc. 1920s) Generative superimposition of different perspectives on the object

Fixed by existing products

Creativity issues

Table 18.1 Summary of the main results

Variety, continuous innovation, continuous knowledge production Fixation 5 limited language of the object, no capacity to regenerate the languages New grammar of objects Fixation 5 limited to theories of colours, shape and texture

Project leader framed by a clear, specified target; engineering department heads control the relevant use and creation of rules

Mutual assessment of a group of creators; inspiring leader designating areas of expansions

Abstract and practical knowledge (on form, material, texture, colour, etc.; theory, value, variety, transformation procedures, etc.) to disentangle clichés

Convergence and divergence by superimposition

Adapted, varied products Fixation 5 fixed by existing models of objects

Dividing work between rule-maker and rule-user

Context sensitive algorithm ensuring convergence towards a satisfying solution and divergence at critical moments Convergence and divergence by preordered languages to create the object

A series of design rules, based on a stabilized, synthetic model of the object

Libraries and catalogs of product modules and design principles Knowledge creation at well-identified steps; identify “holes” (residue) to focus creativity where it is relevant

Type of innovation output & type of new fixation

Design organization (creative team vs control)

Design process (convergence vs divergence)

Method to deal with knowledge in design (creativity enabler/ fixation)

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a comprehensive study of the advances is out of the scope of this chapter we would simply like to underline what our proposals lead us to examine in the literature. Following proposition P1, our question is: what new forms of fixations have been identified in the literature? Extending the seminal works and experiments of Smith et al. (1993) and (Jansson and Smith 1991) on fixation by recently activated knowledge, recent studies have identified several types of fixations: fixation by the representations of things (Ward 1994), fixation by knowledge that is too “contaminated by the specific goal and task” (Finke 1990), fixation by the limited capacity to use knowledge that is a long way from the task (difficulty in using metaphors, in connecting with different types of knowledge) (Burkhardt and Lubart 2010), fixation by emotions (Zenasni and Lubart 2009), fixation by images and metaphors (Chrysikou and Weisberg 2005), and fixation by organizational and social relationship in firms that are not “creativity-experts” (Stewart and Stasser 1995; Sutton and Hargadon 1996). These newly identified forms could well be the new challenges for design theories. Proposition P2 invites us to analyze how recent design theories propose to overcome these new fixation effects and extend generative capacity. We can take a brief look at three theories or methods: TRIZ, C-K theory and “infused design” (for a broader perspective see also the recently published book (Le Masson et al. 2017)). TRIZ (or ASIT) aims to help users overcome fixation caused by relying on usual solutions to a problem; it proposes wide databases (wider than the classic libraries of systematic design) and a smart “browser”, the matrix of contradictions, to find “creative” solution principles to problems (Altshuller 1984; Rasovska et al. 2009; Reich et al. 2012). C-K theory (Hatchuel and Weil 2003, 2009) helps to overcome fixation by the representation of things. It supports the revision of object identities by the dual expansion of knowledge and concepts. It has also been proved relevant in counterbalancing several of the fixation effects listed above (Hatchuel et al. 2011b; see also Agogué et al. 2014). Infused design (Shai and Reich 2004a, b) supports rigorous relationships between different scientific objects (trusses, mechanics, cinematics, etc.) to increase designers’ capacity to make use of very heterogeneous disciplines (Shai et al. 2009), hence overcoming fixation by usual competences and skills. It has been shown that it helps to identify “gaps” in certain disciplines (e.g. relative velocity in cinematics has no equivalent in mechanics) and has led to the creation of new scientific objects (the face force) (Shai et al. 2009). It has also been shown that C-K theory and infused design increase generative capacities (Hatchuel et al. 2011a). Hence, these design theories can address some of the fixations listed above. Do these theories suggest new ways of dealing with knowledge for design (P3)? TRIZ proposes new ways of “browsing” for technologies; C-K theory supports rule-breaking in the knowledge base, the expansion of knowledge driven by the imagination, the creation of new definitions of things, as well as “knowledge re-ordering” required for the “preservation of meaning” in the new world and new forms of absorptive capacity based on structures of the unknown (Hatchuel and Weil 2007; Le Masson et al. 2012). More recently it was shown that certain knowledge structures, such as a so-called splitting knowledge structure, could support generativity, and this was illustrated and tested in several cases (Le Masson et al. 2016; Lenfle et al. 2016; Brun et al. 2016). Infused design aims to identify “gaps” in knowledge bases and to “fill” these gaps by using “complementary” knowledge for design (Shai et al. 2009). Do these theories suggest new ways of dealing with convergence and divergence

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in design processes (P4)? Methods inspired by TRIZ, such as ASIT, maintain strong convergence, in particular by making a “closed world assumption” that avoids too many explorations and tends to focus on the minimal “break” out of the “closed world” (Moehrle 2005; Reich et al. 2012). Processes derived from C-K theory are characterized by interdependent exploratory design paths. Each new design step can provoke unexpected expansions and these expansions can open new, unexpected paths for convergence in a growing tree of paths (Elmquist and Segrestin 2007; Elmquist and Le Masson 2009). Infused design suggests a distinction between fast convergence, using rigorous relations between disciplinary models, and divergence, to explore the “gaps” revealed by this conformity. These theories and methods can also inspire or support new forms of design organization for innovation (P5), balancing creation and control. The TRIZ method supports the intervention of “creative commandos” called on by the traditional project organizations to solve “extraordinary” problems that unexpectedly emerge during the project process (Engwall and Svensson 2001). C-K theory has helped to characterize new forms of organizations, when firms shift from R&D to RID (Research Innovation Development), organizing departments dedicated to innovative design (Le Masson et al. 2010). Two levels can be clearly distinguished in these design-oriented organizations (DO2): design spaces, where focused explorations and knowledge acquisition take place, and value management, which designates and launches design spaces, coordinates explorations, manages interdependency and repetitions, and gradually elaborates a design strategy that simultaneously and synergistically accelerates innovation outputs (convergence) and enables more and more disruptive explorations (Hatchuel et al. 2005). Infused design leads to new forms of interdisciplinarity, in which rigorous relations between disciplines encourage designers to be more creative and creative explorations enrich the different scientific discplines. Further Research Our study on the historical interplay between creativity and design theory is still exploratory. It shows 1) that there is a direct relationship between design theory and creativity and 2) that, as means of overcoming fixations, design theories open new paths for reflecting on innovation management. This requires further research on at least three topics: ●

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We identified fixation effects as one reason for changing from one theory to another. However, the new possibilities offered by formal theories (advances in logic, in mathematics, etc.) can also play a role, as is the case in the more recent theories. More generally, what factors drive the change to a new design theory? Is there a specific trend in the evolution of design theories? In our historical study, we see clear progress in the level of abstraction: from Redtenbacher’s method of ratios to systematic design, and then to contemporary design theories, the theory has become more and more independent of the objects; it overcomes more and more fixations and has gained in generativity. These trends have also been analyzed with respect to the more recent, formal, design theory, showing that increases in generativity and robustness might be two specific features of the advances of design theories (Hatchuel et al. 2011a). These trends call for further research.

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The Elgar companion to innovation and knowledge creation We have only briefly described the relations between fixation, design theories, design methods and new fixations. More detailed analyses are required: what are the processes that lead from creativity studies to design theories? What are the processes that help establish new design practices based on new design theories? What is the relationship between these new practices and the identification of new fixations?

This leads to a new framework to analyze different forms of design capabilities. For each form, our framework consists in: ● ●

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identifying creativity issues, that is, types of fixation, which have to be addressed; analyzing design theories addressing these fixations and the related design capabilities, that is, the way to deal with knowledge, processes and organization; clarifying the types of performance (and measures) to be reached by the different forms and the type of fixation that they might cause.

This work also paves the way to new forms of research on innovation. The use of design theories could help to propose: 1.

2.

3.

4.

New frameworks for comparative studies: for example, a study of different types of fixation and different types of “innovation” over time. The identification of new fixations might call for new design theories, whereas new design theories might cause new fixations that will be identified by creativity studies. What are the future fixations of the newly emerging design theories? New frameworks for analyzing data: recent studies have precisely used design theories to analyze absorptive capacity in radical innovation situations (Le Masson et al. 2012), front-end management in drug design (Elmquist and Segrestin 2007), project failure or success (Elmquist and Le Masson 2009) and exploration and exploitation in innovation (Gobbo and Olsson 2010). New frameworks for generating data: through experimentations (Agogué et al. 2011; Savanovic and Zeiler 2009) and in research–industry partnerships (Gillier et al. 2010) and so on. New frameworks for reinterpreting historical data about famous inventors or famous engineering companies.

Finally, by encouraging the interplay between creativity and design theory, by focusing creativity studies on the limits of existing design theories, by supporting the development of new design theories to overcome fixation effects, research on creativity and design theory can make a precious contribution to the invention of new forms of innovation management. Acknowledgement This chapter was initially published in 2011 in Creativity and Innovation Management Journal, 20 (4), pp. 217–237. We warmly thank the editors of the Journal and the Wiley Journals Development Editor for their permission.

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REFERENCES Agogué M, Cassotti M, Kazakçi A (2011) The Impact of Examples on Creative Design: Explaining Fixation and Stimulation Effects. Paper presented at the International Conference on Engineering Design, ICED’11, Technical University of Denmark. Agogué M, Kazakçi A, Hatchuel A, Le Masson P, Weil B, Poirel N, Cassotti M (2014) The Impact of Type of Examples on Originality: Explaining Fixation and Stimulation Effects. Journal of Creative Behavior 48 (1):1–12. Alexander C (1964) Notes on the Synthesis of Form, 15th printing, 1999 edn. Harvard University Press, Cambridge, MA. Alexander C (1971) The State of the Art in Design Methods. DMG Newsletter 5 (3):3–7. Altshuller GS (1984) Creativity as an Exact Science: The Theory of the Solution of Inventive Problems (trans: Williams A). Studies in Cybernetics: 5. Gordon and Breach Science Publishers. Amabile TM (1998) How to Kill Creativity. Harvard Business Review September–October:77–87. Amabile TM, Conti R, Coon H, Lazenby J, Herron M (1996) Assessing the Work Environment for Creativity. Academy of Management Journal 39:1154–1184. Archer LB (1965) Systematic Method for Designers. Council of Industrial Design, London. Basadur M, Gelade GA (2006) The Role of Knowledge Management in the Innovation Process. Creativity and Innovation Management 15 (1):45–62. Belidor BFd, Navier H (1819) Architecture hydraulique – avec des notes et addiations par M. Navier, ingénieur du corps royal des Ponts et Chaussées, tome 1: Cellot, tome 2: Didot, Paris. Brown SL, Eisenhardt KM (1997) The Art of Continuous Change: Linking Complexity Theory and TimePaced Evolution in Relentlessly Shifting Organizations. Administrative Science Quarterly 42 (1):1–34. Brun J, Le Masson P, Weil B (2016) Designing with Sketches: The Generative Effects of Knowledge Preordering. Design Science 2, DOI: https://doi.org/10.1017/dsj.2016.13. Burkhardt J-M, Lubart T (2010) Creativity in the Age of Emerging Technology: Some Issues and Perspectives in 2010. Creativity and Innovation Management 19 (2):160–166. Chrysikou EG, Weisberg RW (2005) Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task. Journal of Experimental Psychology: Learning, Memory and Cognition 31:1134–1148. Cohen WM, Levinthal DA (1990) Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly 35 (1):128–152. Cropley A (2006) In Praise of Convergent Thinking. Creativity Research Journal 18 (3):391–404. Cross N (2006) Forty Years of Design Research (Presidential Address). Paper presented at the 2006 Design Research Society International Conference, Lisbon, 1 November 2006. Dorst K (2006) Design Problems and Design Paradoxes. Design Issues 22 (3):4–17. Droste M (2002) Bauhaus 1919–1933. Taschen, Köln. Dym CL, Agogino AM, Eris O, Frey D, Leifer LJ (2005) Engineering Design Thinking, Teaching, and Learning. Journal of Engineering Education 94 (1):103–120. Ehrlenspiel K (1995) Intégrierte Produktentwicklung. Methoden für Prozessorganisation, Produkterstellung und Konstruktion. Carl Hanser Verlag, München, Wien. Elmquist M, Le Masson P (2009) The Value of a “Failed” R&D Project: An Emerging Evaluation Framework for Building Innovative Capabilities. R&D Management 39 (2):136–152. Elmquist M, Segrestin B (2007) Towards a New Logic for Front End Management: From Drug Discovery to Drug Design in Pharmaceutical R&D. Journal of Creativity and Innovation Management 16 (2):106–120. Engelmeyer PKv (1895) Was ist eine Erfindung? Civilingenieur 41:282–300. Engwall M, Svensson C (2001) Cheetah Teams. Harvard Business Review January:20–21. Eppinger S (2011) The Fundamental Challenge of Product Design. Journal of Product Innovation Management 28 (3):399–400. Eris O (2004) Effective Inquiry for Innovative Engineering Design. Kluwer Academic Publisher, Boston, MA. Finke RA (1990) Creative Imagery: Discoveries and Inventions in Visualization. Erlbaum, Hillsdale, NJ. French M (1999) Conceptual Design for Engineers, 3rd edn. Springer, London. Gillier T, Piat G, Roussel B, Truchot P (2010) Managing Innovation Fields in a Cross-Industry Exploratory Partnership with C–K Design Theory. Journal of Product Innovation Management 27 (6):883–896. Gobbo JAJ, Olsson A (2010) The Transformation between Exploration and Exploitation Applied to Inventors of Packaging Innovations. Technovation 30:322–331. Gordon WJ (1961) Synectics: The Development of Creative Capacity. Harper & Row, New York. Gropius W (1925) Neue Bauhauswerkstätten (new edition: 1981, Florian Kepfenberg, Mainz/Berlin). Guilford JP (1950) Creativity. American Psychologist 3:444–454. Hansen F (1955) Konstruktionssytematik, eine Arbeitsweise für fortschrittliche Konstrukteure. VEB Verlag Technik, Berlin.

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The Elgar companion to innovation and knowledge creation

Hansen F (1960) Konstruktionssystematik, ein neuer Weg zur Ingenieurerziehung. Feingerätetechnik 9 (2):44–51. Hansen F (1961) Grenzen und Wirkungsbereich der Konstruktionssytematik bei Forschungs- und Entwicklungsaufgabe. Feingerätetechnik 10 (10):452–460. Hargadon A, Sutton RI (1997) Technology Brokering and Innovation in a Product Design Firm. Administrative Science Quarterly 42 (4):716–749. Hatchuel A (2002) Towards Design Theory and Expandable Rationality: The Unfinished Program of Herbert Simon. Journal of Management and Governance 5 (3–4):260–273. Hatchuel A, Glise H (2003) Rebuilding Management: A Historial Perspective. In: Adler N, Shani ABR, Styhre A (eds) Collaborative Research in Organizations: Foundations for Learning, Change, and Theoretical Development. Sage, Thousand Oak, CA, pp. 5–22. Hatchuel A, Le Masson P, Reich Y, Weil B (2011a) A Systematic Approach to Design Theories Using Generativeness and Robustness. In: International Conference on Engineering Design, ICED’11, Technical University of Denmark, p. 12. Hatchuel A, Le Masson P, Weil B (2005) The Development of Science-Based Products: Managing by Design Spaces. Creativity and Innovation Management 14 (4):345–354. Hatchuel A, Le Masson P, Weil B (2011b) Teaching Innovative Design Reasoning: How C-K Theory Can Help to Overcome Fixation Effect. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25 (1):77–92. Hatchuel A, Weil B (2003) A New Approach to Innovative Design: An Introduction to C-K Theory. In: ICED’03, August 2003, Stockholm, Sweden, 2003, p 14. Hatchuel A, Weil B (2007) Design as Forcing: Deepening the Foundations of C-K Theory. In: International Conference on Engineering Design, Paris, 2007, p 12. Hatchuel A, Weil B (2009) C-K Design Theory: An Advanced Formulation. Research in Engineering Design 19:181–192. Heymann M (2005) “Kunst” und Wissenchsaft in der Technik des 20. Jahrhunderts. Zur Geschichte der Konstruktionswissenschaft. Chronos Verlag, Zürich. Itten J (1961) The Art of Color. John Wiley & Sons, New York. Itten J (1975) Design and Form, the Basic Course at the Bauhaus and Later, revised edn (1st edn 1963). John Wiley & Sons, London. Jansson DG, Smith SM (1991) Design Fixation. Design Studies 12 (1):3–11. Kelley D (2009) Adaptation and Organizational Connectedness in Corporate Radical Innovation Programs. Journal of Product Innovation Management 26 (5):487–501. Klee P (1922) Beiträge zur bildnerischen Formlehre (‘contribution to a pictorial theory of form’, part of Klee 1921–2 lectures at the Bauhaus). Weimar. Koen P, Ajamian G, Burkart R, Clamen A, Davidson J, D’Amore R, Elkins C, Herald K, Incorvia M, Johnson A, Karol R, Seibert R, Slavejkov A, Wagner K (2001) Providing Clarity and a Common Language to the “Fuzzy Front End”. Research/Technology Management 44 (2):46–56. König W (1999) Künstler und Strichezieher. Konstruktions- und Technikkulturen im deutschen, britischen, amerikanischen und französischen Maschinenbau zwischen 1850 und 1930, vol. 1287. Suhrkamp Taschenbuch Wissenschaft. Suhrkamp Verlag, Frankfurt am Main. Lane PJ, Koka BR, Pathak S (2006) The Reification of Absorptive Capacity: A Critical Review and Rejuvenation of the Construct. Academy of Management Review 31 (4):833–863. Le Masson P, Cogez P, Felk Y, Weil B (2012) Revisiting Absorptive Capacity with a Design Perspective. International Journal of Knowledge Management Studies 5(1/2):10–44. Le Masson P, Hatchuel A, Weil B (2016) Design Theory at Bauhaus: Teaching “Splitting” Knowledge. Research in Engineering Design 27 (April):91–115. Le Masson P, Weil B, Hatchuel A (2010) Strategic Management of Design and Innovation. Cambridge University Press, Cambridge. Le Masson P, Weil B, Hatchuel A (2017) Design Theory: Methods and Organization for Innovation. Springer Nature, Cham. Lenfle S, Le Masson P, Weil B (2016) When Project Management Meets Design Theory: Revisiting the Manhattan and Polaris Projects to Characterize “Radical Innovation” and Its Managerial Implications. Creativity and Innovation Management 25 (3):378–395. Leonard-Barton D (1992) Core Capabilities and Core Rigidities: A Paradox in Managing New Product Development. Strategic Management Journal 13:111–125. Lindemann U (2010) Systematic Procedures Supporting Creativity – A Contradiction? In Taura T, Nagai Y (eds) Design Creativity. Springer, London, pp. 23–28. Loch CL, De Meyer A, Pich MT (2006) Managing the Unknown. John Wiley & Sons, Hoboken, NJ. MacCormack A, Verganti R, Iansiti M (2001) Developing Products on “Internet Time”: The Anatomy of Flexible Development Process. Management Science 47 (1):133–150.

Design theories, creativity and innovation

305

Moehrle MG (2005) What is TRIZ? From Conceptual Basics to a Framework for Research. Creativity and Innovation Management 14 (1):3–13. Morin A (1836) Expériences sur les roues hydrauliques à aubes planes et sur les roues hydrauliques à augets. Madame Thiel, Metz. O’Connor GC (2008) Major Innovation as a Dynamic Capability: A Systems Approach. Journal of Product Innovation Management 25:313–330. Osborn AF (1953) Applied Imagination: Principles and Procedures of Creative Problem Solving, 1st edn. Charles Scribner’s sons, New York. Osborn AF (1957) Applied Imagination, 1st edn. Charles Scribner, New York. Pahl G, Beitz W (1977) Konstruktionslehre (English title: Engineering Design) (trans: Arnold Pomerans KW). Springer-Verlag (English edn: The Design Council), Heidelberg (English edn: London). Pahl G, Beitz W (2006) Engineering Design: A Systematic Approach (trans: Wallace K, Blessing L, Bauert F). Springer, Berlin. Pahl G, Beitz W, Feldhusen J, Grote K-H (2007) Engineering Design: A Systematic Approach (trans: Wallace K, Blessing L, Bauert F). Springer-Verlag (Third English edn), Heidelberg (English edn: London). Paulus PB, Brown VR (2007) Toward More Creative and Innovative Group Idea Generation: A CognitiveSocial-Motivational Perspective of Brainstorming. Social Personality Psychology Compass 1 (1):248–265. Paulus PB, Yang H-C (2000) Idea Generation in Groups: A Basis for Creativity in Organizations. Organizational Behavior and Human Decision Processes 82 (1):76–87. Poncelet J-V (1827) Mémoire sur les roues hydrauliques à aubes courbes mues par -dessous, suivi d’expériences sur les effets mécaniques de ces roues, nouvelle édition revue et corrigée et augmentée d'un second mémoire sur des expériences en grand relatices à la nouvelle roue contenant une instruction pratique sur la manière de procéder à son établissement, 2nd edn. Thiel, Metz. Pugh S (1991) Total Design: Integrated Methods for Successful Product Engineering. Prentice-Hall, Harlow, UK. Rasovska I, Dubois S, De Guio R (2009) Mechanisms of Model Change in Optimization and Inventive Problem Solving Methods. In: International Conference on Engineering Design, ICED’09, 24–27 August 2009, Stanford CA, 2009. Redtenbacher F (1852a) Prinzipien der Mechanik und des Maschinenbaus. Bassermann, Mannheim. Redtenbacher F (1852b) Resultate für den Maschinenbau, 2nd edn. Bassermannn, Mannheim. Redtenbacher F (1858) Theorie und Bau der Wasser-Räder, 2nd edn (1st edn 1846). Bassermann, Mannheim. Reich Y, Hatchuel A, Shai O, Subrahmanian E (2012) A Theoretical Analysis of Creativity Methods in Engineering Design: Casting and Improving ASIT within C-K Theory. Journal of Engineering Design 23 (2):137–158. Reid SE, De Brentani U (2004) The Fuzzy Front End of New Product Development for Discontinuous Innovations: A Theoretical Model. Journal of Product Innovation Management 21 (3):170–184. Rittel HWJ, Webber MM (1972) Dilemmas in a General Theory of Planning. University of California at Berkeley, Berkeley, CA. Rodenacker WG (1970) Methodisches Konstruieren. Konstruktionsbücher. Springer-Verlag, Berlin. Roth K (1982) Konstruieren mit Konstruktionskatalogen. Springer-Verlag, Berlin. Savanovic P, Zeiler W (2009) Integral Design Method for Conceptual Building Design. In: International Conference on Engineering Design, 24–27 August 2009, Stanford CA, 2009. Seidel VP (2007) Concept Shifting and the Radical Product Development Process. Journal of Product Innovation Management 24 (6):522–533. Shai O, Reich Y (2004a) Infused Design: I Theory. Research in Engineering Design 15 (2):93–107. Shai O, Reich Y (2004b) Infused Design: II Practice. Research in Engineering Design 15 (2):108–121. Shai O, Reich Y, Hatchuel A, Subrahmanian E (2009) Creativity Theories and Scientific Discovery: A Study of C-K Theory and Infused Design. In: International Conference on Engineering Design, ICED’09, 24–27 August 2009, Stanford CA, 2009. Simon HA (1969) The Sciences of the Artificial. MIT Press, Cambridge, MA. Smeaton J (1810) Recherches expérimentales sur l’eau et le vent, considérés comme forces motrices, applicables aux moulins et autres machines à mouvement circulaire, suivies d’expériences sur la transmission du mouvement et la collision des corps (trans: Girard PS), French edn. Courcier, Paris. Smith SM, Ward TB, Schumacher JS (1993) Constraining Effects of Examples in a Creative Generation Task. Memory and Cognition 21:837–845. Stewart DD, Stasser G (1995) Expert Role Assignment and Information Sampling during Collective Recall and and Decision-Making. Journal of Personality and Social Psychology 69:619–628. Sutton RI, Hargadon A (1996) Brainstorming Groups in Context: Effectiveness in a Product Design Firm. Administrative Science Quarterly 41 (4):685–718. Swan KS, Luchs M (2011) From the Special Issue Editors: Product Design Research and Practice: Past, Present and Future. Journal of Product Innovation Management 28 (3):321–326. Tushman ML, O’Reilly III CA (1996) Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change. California Management Review 38 (4):8–30.

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Ulrich KT, Eppinger SD (2008) Product Design and Development, 4th edn. McGraw-Hill. Verganti R (2008) Design, Meanings, and Radical Innovation: A Metamodel and a Research Agenda. Journal of Product Innovation Management 25 (5):436–456. Ward TB (1994) Structured Imagination: The Role of Category Structure in Exemplar Generation. Cognitive Psychology 27:1–40. Weisberg RW (1999) Creativity and Knowledge: A Challenge to Theories. In: Sternberg RJ (ed.) Handbook of Creativity. Cambridge University Press, Cambridge, pp. 226–250. Whitford F (1984) Bauhaus. World of Art. Thames & Hudson, London. Zan L (2005) Future Directions from the Past: Management and Accounting Discourse in Historical Perspective. Advances in Strategic Management 22:457–489. Zenasni F, Lubart T (2009) Emotion-Related Traits Moderate the Impact of Emotional State on Creative Performances. Journal of Individual Differences 29 (3):157–167.

19. The dark side of creativity David H. Cropley

KEY FINDINGS IN MALEVOLENT CREATIVITY RESEARCH This chapter will begin by tracing the development of the concept of malevolent creativity by the author, and colleagues. The focus will then shift to examine other work – both theoretical and empirical – that has built on the concept. Lastly, the chapter will explore some new avenues of research in malevolent creativity. The Core Concept of Malevolent Creativity The concept of malevolent creativity, as distinct from creativity that is merely negative in outcome, was first explored in Cropley (2005) in relation to terrorism and the events of September 11, 2001. That paper proposed a set of 11 principles that explore the role that creativity plays in both the production of novel, but deliberately harmful “products” (which can include artefacts, ideas, services or processes), and also the production of novel and effective products designed to counteract the production of malevolent creativity. The principles can be generalised as follows: ● ● ● ● ●

● ●

Creativity is not exclusively benevolent; Creativity, in any context, is a competitive lever; The characteristics of a creative product – novelty, for example – are universal; The more creative a product is, the more effective it becomes; In a competitive environment, the more creative a product is, the less effective its competing products become; The novelty, and therefore the effectiveness, of products decays over time; The decay of novelty is accelerated by competition.

The first three of these set the scene for subsequent research on malevolent creativity – that is, creativity that is intended to deliberately harm others. They did so by (1) turning attention not only to the fact that creativity can be bad (i.e. negative creativity), but that it can be intentionally bad; (2) recognising that, for some people (e.g. criminals, terrorists), there is value to be extracted from creativity in this malevolent context – creativity can help you to be a better and more successful criminal or terrorist, or even simply better at lying and; (3) acknowledging that the qualities that make something creative are not inherently good. Effectiveness, for example, is not defined by the achievement of a good outcome, but by the achievement of the intended outcome. Novelty, similarly, characterises the ability of the product to surprise, regardless of whether that surprise is nice or nasty. Creativity, traditionally, has found a home in the discipline of psychology. The 4Ps framework (Person, Product, Process and Press), whether treated individually, or as a complex system of interacting elements (Figure 19.1), ties much of creativity to human 307

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Figure 19.1

PERSON

PRODUCT

PROCESS

PRESS

The 4Ps of creativity as a system of interacting elements

behaviour (Rhodes 1961; Barron 1969). Creativity researchers, from the earliest days of the modern era, have been interested in questions of cognitive processes and creativity – divergent thinking in particular (e.g. Guilford 1950; Torrance 1963). They have also explored the range of personal factors that influence creativity – motivation, personal properties and feelings (e.g. Feist 2010). Researchers have also examined the influence of the surrounding social and organisational environments on creativity (e.g. Puccio and Cabra 2010; Amabile 1983). Creativity researchers have also been at the forefront of understanding what properties make artefacts and ideas novel and effective (Cattell and Butcher 1968; Sternberg 1999; D. H. Cropley and Cropley 2008). Framing creativity in a malevolent context, not surprisingly, turns attention immediately to additional questions based on the 4Ps. If personal qualities impact on creativity in a normal, benevolent, context, then what impact, if any, do malevolent (or even negative) personal qualities have on creativity? If openness to experience, for example, is strongly linked to creativity (see, for example, Kaufman 2009), then traditional creativity researchers might say that people high in openness are more likely to produce creative outputs. Therefore, is a person who is both high in openness, and high in a negative trait like psychopathy or aggression, more likely to produce outputs that are creative and deliberately harmful, that is, malevolent creativity? The latter four principles identified by Cropley (2005), though not specifically aspects of malevolent creativity, also revealed a great deal about how creativity works in practice, and the value that it adds in any context. These lead to interesting questions in application domains like business or engineering. The Development of the Malevolent Creativity Concept The initial exploration of malevolent creativity was expanded in Cropley et al. (2008). That article explored some of the key underpinning concepts in more detail. It offered, for example, suggestions for the tendency to view creativity only in a positive and beneficial light. The paper also noted that the concept of a Dark Side to creativity was not new, tracing its origins directly to the work of McLaren (1993), Clark and James (1999) and James et al. (1999), and perhaps indirectly to related questions of creativity and morality

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(e.g. Runco and Nemiro 2003). The paper also clearly articulated the distinction between malevolent creativity – creativity that is deliberately planned to damage others – and earlier conceptions of the dark side of creativity that consider only negative concepts of creativity – that is, unintentionally bad outcomes. Cropley et al. (2008) also began to turn attention specifically to the question of the practical implications of malevolent creativity. In other words, while setting the scene for subsequent psychological research on, for example, the influence of personality on malevolent creativity, the paper also began to explore the question of how this knowledge might be used. Whereas the former is representative of a descriptive approach to research, the latter represents a more experimental mindset that drives questions of application in areas such as policing, fraud prevention and counter-terrorism. Broadening the Focus of Malevolent Creativity Interest in malevolent creativity began to establish a foothold in creativity research more generally with the publication of Cropley et al. (2010). That edited volume explored all facets of the dark side of creativity – both intentional and unintentional – across a range of domains. The purpose of the book was to “increase both awareness of the dark side and . . . begin to develop the necessary conceptual framework” (p. 1) in a generally descriptive sense. In addition, the book also restated the importance of discussing “how to deal with it [malevolent creativity] in practical settings” (p. 1). As a result, the book covered a wide range of topics across different fields of study (e.g. psychology, criminal justice and education) and different areas of focus (e.g. personality and behaviour) as well as application areas like policing and counter-terrorism. From Malevolent Creativity to Malevolent Innovation In the same volume, D. H. Cropley (2010a) took the discussion of malevolent creativity further along an exploratory path as a means of developing the theoretical framework necessary for subsequent descriptive and experimental studies. The chapter in question proposed that, in the same way that creativity (the generation of effective novelty) is a driver of innovation (the exploitation of effective novelty), malevolent creativity is a driver of a malevolent innovation. In particular, this conceptual framework draws on a more highly differentiated view of the relationship between the dimensions of creativity – the 4Ps – and the stages of innovation (A. J. Cropley and Cropley 2008; D. H. Cropley and Cropley 2008). The essence of this framework was also used to explain how innovation in an organisational context is facilitated or blocked (Cropley and Cropley 2012; Cropley et al. 2013), in that successful malevolent innovation – that is, the successful exploitation of intentionally harmful creativity – depends for its success on an alignment in the individual and/or organisation with the characteristics and conditions that favour innovation at each stage of the innovation process. Whereas the interest in benevolent innovation is on maximising this favourable alignment (see, for example, Cropley 2016), for applications such as policing and counter-terrorism in a malevolent context, the interest is on preventing the malevolent innovator from achieving the necessary alignment. D. H. Cropley (2010a) set out the conceptual framework necessary to understand how to block and disrupt malevolent innovation in terms of the 4Ps.

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Table 19.1 The 4Ps and types of creativity Product

Person (Intent)

Process/Press

Type

Bad Good Bad Good Bad Good Bad Good

Malevolent Malevolent Benevolent Benevolent Malevolent Malevolent Benevolent Benevolent

Supportive Supportive Supportive Supportive Obstructive Obstructive Obstructive Obstructive

Conscious Malevolence Failed Malevolence Failed Benevolence Conscious Benevolence Resilient Malevolence Frustrated Malevolence Frustrated Benevolence Resilient Benevolence

A Differentiated Concept of Malevolent Creativity Also in the same volume (Cropley et al. 2010), D. H. Cropley (2010b) contributed further to more descriptive work by exploring more deeply the differences between benevolent creativity, negative creativity and malevolent creativity. This examined basic differences across the 4Ps (Person, Product, Process and Press), noting that the characterisation of creativity as benevolent, negative or malevolent is dictated by differences in the 4Ps. For example, where the primary consideration is the nature of the Product – is it good or bad – the creativity can only be characterised as either positive or negative. When this is supplemented by the intent, or motivation, of the Person (an intent to cause harm, for example), then, coupled with a bad Product, we can speak of malevolent creativity. Pursuing the same line of reasoning, the role of the Press (e.g. supportive or obstructive) can also be factored in, leading to a more highly differentiated understanding of creativity. Table 19.1 shows a total of eight forms of creativity that encompass different combinations of outcome (Product), motivation/intent (Person) and environment (Press), meaning that creativity may range from intentionally good to intentionally bad. This lays the groundwork for systems approaches to the study of malevolent creativity to match similar approaches that are emerging in creativity research more generally (e.g. Kozbelt et al. 2010; Gruber and Wallace 1999; Csikszentmihalyi 1999). Application Domains of Malevolent Creativity The opening up of discussion, and the establishment of a basic theoretical framework for malevolent creativity across disciplines and application domains (A. J. Cropley 2010b), was followed by a more specific focus on the intersection of creativity and law-breaking (Cropley and Cropley 2011). In particular, that paper placed both creativity and criminal activity on an equal footing, examining both as forms of deviance – that is, as forms of norm-violation. Among other things, this view places a greater focus on the reaction of the Press – the external environment – to acts of norm-violation, whether manifest as the production of novelty or as the breaking of laws. In addition, by placing law-breaking at the forefront of thinking, and creativity as a means of supporting law-breaking, the article advanced the underpinning framework for discussions of applications of the concept of malevolent creativity – in other words, how to counteract malevolent creativity in crime

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or terrorism. One important consequence of this was to recognise that creativity in a criminal context can only be associated with resourceful crime (Ekblom and Tilley 2000). Impulsive criminal acts – vandalism, for example – lack the intent that makes creativity a value-adding component of the crime, unlike, for example, a carefully planned act of financial fraud that utilises novelty to improve the chances of success. Cropley and Cropley (2013) then turned attention more fully to the application of the basic, underpinning concept of malevolent creativity to the broad domain of crime. Drawing on the previously established frameworks of the 4Ps and the notion of resourceful law-breaking, the book sought to drive thinking firmly towards practical applications of the underpinning psychological framework. This application focus sought to achieve four outcomes: ●

●

●

●

Supplement criminological methods to assist in identifying those people most likely to generate deliberately harmful novelty (i.e. malevolent creativity); Identify the environmental factors that promote the generation of malevolent creativity; Set out ways of understanding specific situations in terms of their vulnerability to malevolent creativity; Provide a conceptual framework to support the development of counter-measures for malevolent creativity.

Cropley and Cropley (2013) also attempted to draw together previously unconnected fields of research – namely Smith’s (2009) concept of criminal entrepreneurship, and mainstream psychological creativity research – to provide new insights and new approaches that address the absence of theoretical underpinnings supporting crime research noted by Wolfe and Piquero (2011). The book in question also augmented the development of theoretical underpinnings for research in the application of concepts of malevolent creativity by developing case studies – one addressing fraud, and the other addressing terrorism – that specifically discuss the role of the 4Ps (Person, Product, Process and Press) in each example of malevolent creativity. The purpose of these case studies was twofold: first, to illustrate how creativity is manifest in the things that resourceful criminals do, in how they think and behave, and in the environment in which they operate; and, second, to then show more clearly how this knowledge of creativity and crime can be used to disrupt or prevent malevolent creativity. The authors also reiterated an earlier concept (Cropley 2005), namely that, in a competitive creative environment, counter-measures should not be merely a reaction to criminal creativity, but should be driven as much by a proactive, creative approach of problem-solving. Practical examples of this, informed by the theoretical framework of malevolent creativity, include “thinking thief ” Ekblom (1997) and “red-teaming” – in other words, placing oneself in the position of the creative criminal to anticipate, and then counter, potential malevolent creativity. Methodologies of Malevolent Creativity Research One of the challenges inherent to research in malevolent creativity is the ethical dilemma that may be associated with certain research designs. While ex-post-facto (literally,

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after-the-fact) approaches can tap into unequivocally harmful outcomes, there are clear constraints on what can be learned from cases such as 9/11 because of a lack of meaningful data to support descriptive or experimental research questions and hypothesis testing. For example, it is difficult to draw firm conclusions about the relationship between aspects of personality among the 9/11 terrorists, and their creativity, except by inference. Equally, questions of causal relationships between environmental or personality factors and creativity can only be hypothesised from these cases. The resulting theoretical framework of malevolent creativity therefore cannot easily be tested except through the use of hypothetical scenarios combined with normal (i.e. non-criminal) participants. Cropley et al. (2014) was the first attempt (the study was conducted in 2010) to use hypothetical scenarios exploring deliberately harmful creativity, to examine relationships between creativity and personality. That study presented participants with four hypothetical scenarios – for example, “A student has a final exam tomorrow and has not had time to adequately study. She intends to. . .” – and then a series of possible courses of action. Participants rated each possible course of action for its creativity and for its malevolence on Likert-type scales. The purpose of the study was to explore one of the underpinning questions of malevolent creativity: is creativity only associated with benevolence, or do people see the possibility of creativity in all outcomes, regardless of how good or evil those outcomes are? The study provided several important findings. First, malevolence was characterised not as a distinction between good and evil, but more subtly, as a distinction between degrees of legality and illegality. Second, creativity was not linked exclusively with good (or evil) courses of action, but instead emerged most strongly in association with legally ambiguous courses of action. Third, where personality impacted on the relationship between creativity and malevolence, it was to shift the basic pattern of the relationship up or down, that is, to moderate it. In other words, the more creative the individual, the less creative they judged the courses of action to be – however, ambiguity was still seen as permitting the highest levels of creativity in the courses of action, regardless of individual creativity.

THEORETICAL AND EMPIRICAL FINDINGS IN MALEVOLENT CREATIVITY RESEARCH Categorising Malevolent Creativity Research As the original concept of malevolent creativity became established after Cropley et al. (2008), a body of work began to develop that can, with hindsight, be characterised in two ways. First, subsequent research has contributed to the development of the theoretical framework of malevolent creativity. This research can be categorised as having the purpose of better comprehending the nature of creativity, and of developing a deeper understanding of malevolent creativity, as well as serving as a basis for induction (theory building). In this sense, the first category of subsequent research in malevolent creativity is exploratory in nature (see, for example, Sekaran 2006). The second characterisation of subsequent research in malevolent creativity is the body of empirical work that has been undertaken. This, in turn, consists of two sub-categories:

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Descriptive studies of malevolent creativity – those that are typically quantitative in nature, and are undertaken to ascertain and describe the characteristics of the variables of interest in a given situation; Experimental studies – those that are typically quantitative in nature, and are undertaken for the purpose of hypothesis testing to explain the nature of certain relationships, or, to establish the differences among groups, or, to establish the independence of two or more factors in a situation.

These two categories of research in malevolent creativity – theoretical and empirical – are now examined in more detail. Theoretical Research in Malevolent Creativity In fact, the earliest research that directly impacts on the concept of malevolent creativity was that of McLaren (1993), who first raised the prospect of a dark side of creativity, in contrast to the almost quasi-religious and positive esteem in which creativity is often held. The discussion of creativity’s dark side was framed in more specific terms first by James et al. (1999), who explored examples of negative creativity, their paper acting as the precursor to Cropley et al.’s (2008) specific distinction of creativity involving intentional harm. The characterisation of malevolent, as opposed to merely negative, creativity was not met with universal accord, with James and Drown (2008), for example, arguing that the distinction between malevolent and negative – the intent of the creator – was adequately covered by the latter term. Nevertheless, the general notion of a form of creativity involving deliberately harmful intent, and relevant in particular to crime and terrorism, received general support in other articles (Eisenman 2008; Spooner 2008). Evidence that the concept of malevolent creativity had a useful explanatory power began to emerge with Runco (2009). Interest in the topic developed further, with theoretical discussions exploring malevolent creativity in the context of application domains such as education (A. J. Cropley 2010a), government-sponsored scientific research (Zaitseva 2010), correctional systems (Singer 2010), art and design (Gamman and Raein 2010), and the prevention of malevolent creativity (Sternberg 2010). At the same time exploratory research also examined the contributing factors – personality, for example (Gascon and Kaufman 2010). In parallel, it must be said, debate continued over the fundamental concept of malevolent creativity. Both Gaut (2010) and Kampylis and Valtanen (2010) incorporated the concept of malevolence into discussions of the general framework of creativity; however, Runco (2010) countered with a discussion arguing that creativity, in essence, is blind, and that the dark side, such as it is, should be seen as “ancillary to actual creative work.” (p. 15). Notwithstanding these definitional debates, the idea that creativity can have a distinct dark side, manifest in deliberate acts of intentional harm, has been taken up by other researchers. Gill et al. (2011) first made reference to malevolent creativity in discussions of terrorism, while Gino and Ariely (2012) tied creativity to intentional dishonesty. Researchers have continued to draw on the construct of malevolent creativity, both developing it and incorporating it into application domains, in areas a diverse as counter-terrorism (Asal et al. 2015; Gill, Horgan, Hunter, and Cushenbery 2013) and education (Beghetto and Kaufman 2014), while others continue to explore the theoretical

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underpinnings of antecedents to malevolent creativity, for example, social motivation (Forgeard and Mecklenburg 2013), implicit aggression (Harris 2013) and problem construction and ethicality (Harris et al. 2014). Empirical Research in Malevolent Creativity There is a small, but growing, corpus of empirical studies that can be said to relate directly to malevolent creativity. Not surprisingly, many of these take a descriptive, non-experimental approach, focusing primarily on the nature and characteristics of the variables of interest to situations involving malevolent creativity. Some adopt a broadly experimental approach, proposing and testing hypotheses and exploring the nature of certain relationships, or the differences among groups. The focus on the intentional production of harmful novelty – malevolent creativity – means that the empirical studies most directly associated with this concept are those that combine variables relating to the Person, with variables relating to the Product (i.e. the outcome). Descriptive/non-experimental research in malevolent creativity In keeping with a general trend in scientific research that sees qualitative, inductive theory-building followed by quantitative, deductive theory-testing, research in malevolent creativity has started to mature into empirical studies that explore both the relations between variables of interest – for example, personality and creativity – and between different groups – for example, individuals low in creativity and individuals high in creativity. Most obviously, given the strong psychological foundation of creativity, such studies have explored the relationship between aspects of the Person – for example, feelings, personal properties and motivation – and the outcomes of malevolent creativity. In fact, the first directly relevant empirical studies may be said to have begun with Clark and James (1999), who examined the production of positive and negative ideation in response to perceptions of just or unjust treatment. In parallel with the development of the distinct construct of malevolent creativity, Walczyk et al. (2008) then turned attention to creative ideation in the context of dishonesty – the concept of malevolent lying. By placing participants in hypothetical situations in which lying creatively provided a means for resolving a dilemma, the researchers were able to explore relationships between the effectiveness and novelty of the lies – in other words, a study of the Product of malevolent creativity. Coupled with this, the study also examined the relationship between the Product and the Process used to generate it – that is, divergent thinking – as well as the qualities of the Person undertaking the creative lying. Apart from a comprehensive examination of three key facets of (malevolent) creativity (Person, Process and Product), this study also established the utility of an important methodological approach for malevolent creativity research. The use of hypothetical scenarios overcomes a fundamental ethical hurdle in malevolent creativity research. Other studies have taken a range of approaches to explore personality and malevolent creativity in greater detail. Lee and Dow (2011), for example, examined personality and ideation, focusing on antagonism, sympathy, and aggressiveness and ideation that might be coded as expressing an intent to do harm. Riley and Gabora (2012), in contrast, sought to explore a hypothesis that threatening situations enhance creativity. In a manner similar

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to the scenarios concept described earlier, participants viewed photographs of situations classified as either threatening or non-threatening, and then produced outputs (short stories) that were rated for creativity. Beaussart et al. (2013) returned to the theme of dishonesty, extending this to examine the role of integrity and creativity. Their findings suggested that integrity, whether selfperceived or objectively measured, was negatively related to creativity. This might suggest that individuals who feel less bound by rules are able to behave more creatively. In other words, a willingness to break the rules favours creativity, whether directed to benevolent pursuits, such as art, or malevolent pursuits, such as fraud. Other studies have continued to explore relationships between aspects of personality, creative cognition and malevolent production. Crump (2013), for example, added a measure of criminogenic thinking to the scenarios reported by Walczyk et al. (2008), while Harris et al. (2013) examined the effect of emotional intelligence and task type on malevolent creativity. Most recently, descriptive studies of malevolent creativity have grown more sophisticated, for example combining scenario-type methods with benevolent and malevolent priming (Harris and Reiter-Palmon 2015) to explore personality and creative ideation more deeply, in ways that are more controlled and could be said to be moving closer to true experimental approaches. Kapoor (2015), in contrast, used a variety of scenarios to examine sub-clinical dark triad measures (narcissism, psychopathy, Machiavellianism) and negative creativity. Drawing on the related and developing work on the dark triad (Jones and Figueredo 2013; Jones and Paulhus 2014), McBain (2015) extended this path further still, exploring the priming effect of real-world scenarios on the selection of creative and malevolent outcomes. Finally, it should be acknowledged that malevolent creativity does not lay sole claim to studies of personality, creativity, and negative behaviours or outcomes. Around the periphery of malevolent creativity research may be added other descriptive studies that continue to expand knowledge of these relationships. Furnham (2015), for example, explores more general issues of the bright and dark side of creativity, creative ideation and personality, Dow (2015) examines cheating, expertise and creativity in the context of inadvertent plagiarism, and Beaussart et al. (2012) explore creativity, mental illness and personality in the context of mating success. Experimental research in malevolent creativity While it remains extremely important to establish the nature and characteristics of the relationships between the many variables of interest in malevolent creativity, and the full spectrum of Person, Product, Process and Press has by no means been adequately covered yet, a logical step forward is to examine cause-and-effect relationships. In malevolent creativity research, one obvious barrier to such experimental research, except perhaps in an ex-post-facto sense, is the ethical dilemma that would be posed by studies seeking to manipulate different groups to produce deliberately harmful creativity under different conditions. In fact, some of the studies already described might be seen not so much as descriptive (non-experimental) studies, but as pre- or quasi-experimental designs that are constrained in one or more respects. A goal, therefore, is to move towards studies of malevolent creativity that satisfy all of the common conditions of true experimental research

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including the manipulation of independent variables, the use of control groups, random assignment, pre-testing and so on. Several studies have approached this goal more than others, one (De Dreu and Nijstad 2008) only indirectly associated with malevolent creativity, and two others (Gino and Wiltermuth 2014; Mayer and Mussweiler 2011) more specifically associated with the core concept. All three have continued to explore personality and creative ideation in at least a negative context, and through the more truly experimental approaches, have shed greater light on causal relationships of interest to researchers in malevolent creativity, as well as factors that might mediate or moderate the core independent/dependent variable relationships.

NEW AVENUES OF MALEVOLENT CREATIVITY RESEARCH As the study of malevolent creativity continues to grow, there are interesting new lines of research being pursued. These continue to develop theoretical lines of inquiry – seeking to better understand the nature of malevolent creativity – as well as practical applications that seek to use evidence-based concepts to assist in areas such as law enforcement and counter-terrorism. Video Games and Malevolent Creativity Cropley (2015) examines the intersection of video games and creativity, asking if such games are capable of fostering malevolent creativity. In that chapter the author speculates that for such an outcome to occur, at least three conditions need to be met: (1) that video games have the capacity to foster learning; (2) that video games have the capacity to foster negative behaviours such as violence or aggression; and (3) that video games have the capacity to foster creativity. The chapter then proposes a set of conditions that together would have to be met for such a game to succeed in fostering malevolent creativity. These include: ●

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Game Mode – interactive, multiplayer games modes will favour the development of malevolent creativity; Design Requirements – a low threshold (ease of use), a high ceiling (support for expert use) and wide walls (diverse, extensive game worlds) will support the development of malevolent creativity; Design Criteria – games that implement Sternberg’s (2007) “keys for developing the creativity habit” (p. 8) will maximise the opportunity for malevolent creativity development; Playing Conditions – games that encourage frequent and extended game play will support the development of malevolent creativity; Game Content – games that present opportunities to engage in resourceful, criminal behaviour will foster malevolent creativity; Game Context – games that support legally and/or morally ambiguous themes will favour the development of malevolent creativity.

Cropley (2015) suggests that the value of this framework is to direct future research along promising lines. Instead of asking the more general question: do video games foster

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malevolent creativity, the set of design criteria proposed in the chapter allows researchers to contemplate selecting an existing game most likely to foster malevolent creativity, and then explore the ways in which this behaviour might be mitigated or avoided. Practical Tools for Counter-Terrorism At a practical, applied level, the underpinning concepts of malevolent creativity give rise to the opportunity to develop tools to support counter-terrorism, or law enforcement, activities. The task of the intelligence analyst working in counter-terrorism is similar to that of the research scientist working in organisational psychology. The former may ask, for example, “is Group X preparing to commit an act of terrorism?”, while the latter may ask “is Group Y functioning as a coherent team?” The crucial difference between the two, of course, is that while the organisational psychologist can simply “ask” the group – he or she has direct control over what is measured and how it is measured – the intelligence analyst generally cannot. The analyst must rely, therefore, on indirect means of measurement. Malevolent creativity has been identified as a construct that may shed important light on terrorist activity, and which can also be tapped indirectly from available data sources. When fused with existing data, measures of malevolent creativity will make it possible to answer a range of new questions, including, for example, “is Group X likely to carry out a conventional attack?” in contrast to “is Group X likely to carry out a novel form of attack?” Such a question has important implications for the allocation of counterterrorism resources and the focus of counter-terrorism activity. If a group can be shown to have a strong predisposition and aptitude for employing highly novel forms of attack – in other words, an aptitude for malevolent creativity – then counter-terrorism resources may choose to focus on unconventional technologies or methods. The ability to tap into the capacity for creativity and innovation among terrorist groups and individuals also presents another important opportunity for disrupting terrorist activities. The process of (malevolent) creativity and innovation follows a well-understood sequence of stages, each of which is characterised by a certain constellation of favourable psychological traits, at both the individual and group levels (A. J. Cropley and Cropley 2008; Cropley and Cropley 2012, 2013, 2015). The ability to measure the capacity for creativity and innovation among terrorist groups and individuals opens up the possibility of tailoring counter-measures to the strengths and weaknesses of the group or individual. If a terrorist group, for example, is assessed as adept at generating new ideas, this then enables the counter-terrorism organisation to tailor its counter-measures to focus on the group’s weaknesses in the process of innovation. The key to this approach, however, remains the problem of indirect measurement – in other words, how to measure psychological constructs without direct control of, or access to, the subjects of interest. Unless the capacity for (malevolent) creativity and innovation can be measured by tapping into the available data, from any source, the intelligence analyst will be unable to use these constructs to strengthen inferences. The key to solving the indirect measurement problem for malevolent creativity and innovation lies in biodata. First postulated in the 1940s (Guthrie 1944), biographical data (or biodata) is an approach to the assessment of various aspects of human behaviour that has an established track record in the assessment of creativity and innovation (Furnham et

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al. 2008; Hunter et al. 2012; Whiting 1973). Most importantly, for the present discussion, biodata offers an approach to assessment that can be freed from the constraints imposed by direct access to the subjects of interest. In other words, biodata offers a passive, indirect approach to the assessment of malevolent creativity and innovation – reliable, valid judgements of creativity and innovation can be made from extant data sources, guided by a theoretical framework describing the variables of interest. More importantly, reliable predictions can be made, drawing on the fundamental concept that past experience plays a strong role in determining future behaviour. By tapping into available, objective variables, biodata offers intelligence analysts a powerful opportunity to identify enhanced capacities for malevolent creativity and innovation, and to make sound predictions of future behaviour. Indeed, biodata offers additional opportunities for counter-terrorism, for example in relation to organisational identity and social identity theory. Biodata has been shown to facilitate reliable predictions of membership attrition in organisations (Mael and Ashforth 1995), resulting from mismatches between an individual’s background and the ideal “profile” for a given organisation. While most organisations use these data to better match recruits to the organisation, in order to mitigate attrition, the opportunity exists for security agencies to use this knowledge in the opposite manner – to accelerate attrition. Biodata gives us the means by which we can identify those individuals who are more, and less, likely to form strong group associations, for example with a terrorist organisation, and to use this knowledge to both monitor and disrupt recruitment processes. Representative Samples in Malevolent Creativity – Creative Criminals A relatively untapped area of research in malevolent creativity is the use of representative samples. For reasons that have already been outlined, malevolent creativity presents certain ethical and practical challenges to the researcher. While a great deal can be learned by studying normal samples of the wider population, particular interest in malevolent creativity naturally attaches to abnormal groups such as criminals and terrorists. A key premise of malevolent creativity, first discussed in Cropley (2005), is that it serves as a competitive lever. In other words, malevolent creativity is a mechanism by which individuals can increase their effectiveness as law breakers. Cropley and Cropley (2013) suggested that this would be manifest most clearly among resourceful criminals. How then can these questions be explored? It is axiomatic that the best source of data for questions about resourceful, creative, criminal entrepreneurs is the criminals themselves. A key research question that needs to be addressed is: what role does creativity play in the commission of crime? In order to tackle this problem, the research should focus on a sample group of potential criminal entrepreneurs consisting of convicted criminals serving custodial sentences. However, it is necessary first to characterise each individual in such a sample group across four descriptive dimensions: ● ● ● ●

How creative is each individual in the sample group? What is the criminological profile of each individual in the sample group? What is the personality profile of each individual in the sample group? What is the demographic profile of each individual in the sample group?

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With this basic information established for the individuals in the sample group, it is then possible to address further sub-problems of an explanatory nature: ●

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How does the creativity profile of the sample group compare to general population norms? For example, are convicted criminals, as a group, more or less creative than the general population? What is the relationship between individual creativity and demographic variables in the sample group? Are better-educated criminals, for example, also more creative? What is the relationship between individual creativity and personality in the sample group? Is it possible, for instance, to distinguish creative criminals from their less creative counterparts on the basis of aggression? What is the relationship between individual creativity and criminal behaviour in the sample group? Are creative criminals more successful than their less creative peers – that is, are they arrested fewer times, do they spend less time in prison and do they stay out of trouble when they do go to jail? What is the relationship between personality and criminal behaviour? Do certain personality constructs (e.g. sensation seeking) have a strong relationship to the type of crime committed? What variables (Creativity, Personality, and Demography) can be used to predict criminal behaviour?

Such research addresses a fundamental question stemming from the concept of malevolent creativity – is there such a thing as the criminal entrepreneur? Are resourceful criminals characterised, fundamentally, by higher creativity, a stable (albeit undesirable) personality profile, higher levels of criminal success and unique types/patterns of crime? What are the implications of these relationships as risk and protective factors, and how might they contribute to identifying, preventing and correcting criminal behaviour? There are a number of potential benefits to be derived from research into the role of creativity in the commission of crime. These include: ● ●

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Assisting in the planning and allocation of criminal justice resources; The potential to benefit the development of new policies or procedures that seek to address this malevolent creativity; The development of specific crime reduction strategies that target entrepreneurial (creative) crime and criminals; The development of policing policies or practices specific to the crime entrepreneur; The potential to inform the training of personnel, specifically in the correctional services systems; The development of offender support, for example through better targeted rehabilitation and intervention strategies specific to creative criminals.

This latter example highlights an important driving force for future research in malevolent creativity. In the same way that benevolent creativity is an enabler of improved educational outcomes (e.g. Beghetto and Kaufman 2014), malevolent creativity’s most interesting questions and applications are found in cross-disciplinary contexts. Thus, it is the intersection of creativity and criminology, for example, where the new avenues of malevolent

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creativity research will find their greatest application. The systems nature of creativity (e.g. Csikszentmihalyi 1999) adds weight to this argument – it is the interaction of the creative criminal person, the creative criminal product, the creative criminal process and the creative criminal press that generates questions which must be explored and answered.

CONCLUDING THOUGHTS Malevolent creativity has become established as a distinct area of interest in the wider field of creativity research over the last decade. The construct builds on earlier concepts of negative creativity that sought to acknowledge the possibility of harmful outcomes in the production of novelty. With a particular focus on the intentional production of harmful, novel outputs, malevolent creativity has particular relevance to fields such as criminal justice, policing and counter-terrorism. There is a growing theoretical foundation for malevolent creativity, and an expanding body of empirical work that continues to develop an understanding of the relevant variables and the relationships between them. Most recently, empirical work is beginning to shift towards cause-and-effect, and practical work is focusing more and more on the application of the concept to practical policing and security applications.

REFERENCES Amabile, T. M. (1983). The social psychology of creativity. New York, NY: Springer. Asal, V., Gill, P., Rethemeyer, R. K., and Horgan, J. (2015). Killing range: Explaining lethality variance within a terrorist organization. Journal of Conflict Resolution, 59(3), 401–427. Barron, F. X. (1969). Creative person and creative process. New York, NY: Holt, Rinehart and Winston. Beaussart, M. L., Andrews, C. J., and Kaufman, J. C. (2013). Creative liars: The relationship between creativity and integrity. Thinking Skills and Creativity, 9, 129–134. Beaussart, M. L., Kaufman, S. B., and Kaufman, J. C. (2012). Creative activity, personality, mental illness, and short-term mating success. Journal of Creative Behavior, 46(3), 151–167. Beghetto, R. A., and Kaufman, J. C. (2014). Classroom contexts for creativity. High Ability Studies, 25(1), 53–69. Cattell, R. B., and Butcher, H. J. (1968). The prediction of achievement and creativity. New York, NY: Bobbs-Merrill. Clark, K., and James, K. (1999). Justice and positive and negative creativity. Creativity Research Journal, 12(4), 311–320. Cropley, A. J. (2010a). Creativity in the classroom: The dark side. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 297–315). New York, NY: Cambridge University Press. Cropley, A. J. (2010b). The dark side of creativity. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 1–14). New York, NY: Cambridge University Press. Cropley, A. J., and Cropley, D. H. (2008). Resolving the paradoxes of creativity: An extended phase model. Cambridge Journal of Education, 38(3), 355–373. Cropley, A. J., and Cropley, D. H. (2011). Creativity and lawbreaking. Creativity Research Journal, 23(4), 313–320. Cropley, D. H. (2005). Eleven principles of creativity and terrorism. Paper presented at the Fourth Homeland Security Summit and Exposition, Canberra. Cropley, D. H. (2010a). Malevolent innovation: Opposing the dark side of creativity. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity. New York, NY: Cambridge University Press. Cropley, D. H. (2010b). Summary – The dark side of creativity: A differentiated model. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity. New York, NY: Cambridge University Press.

The dark side of creativity

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Cropley, D. H. (2015). Video games and malevolent creativity: Does one thing lead to another? In J. Kaufman and G. Green (Eds), Video games and creativity (pp. 61–81). San Diego, CA: Academic Press. Cropley, D. H. (2016). Measuring capacity for innovation in local government organisations. Journal of Creativity and Business Innovation, 2(1), 31–45. Cropley, D. H., and Cropley, A. J. (2008). Elements of a universal aesthetic of creativity. Psychology of Aesthetics, Creativity, and the Arts, 2(3), 155–161. Cropley, D. H., and Cropley, A. J. (2012). A psychological taxonomy of organizational innovation: Resolving the paradoxes. Creativity Research Journal, 24(1), 29–40. Cropley, D. H., and Cropley, A. J. (2013). Creativity and crime: A psychological approach. Cambridge, UK: Cambridge University Press. Cropley, D. H., and Cropley, A. J. (2015). The psychology of innovation in organizations. New York, NY: Cambridge University Press. Cropley, D. H., Cropley, A. J., Chiera, B. A., and Kaufman, J. C. (2013). Diagnosing organizational innovation: Measuring the capacity for innovation. Creativity Research Journal, 25(4), 388–396. Cropley, D. H., Cropley, A. J., Kaufman, J. C., and Runco, M. A. (Eds) (2010). The dark side of creativity. New York, NY: Cambridge University Press. Cropley, D. H., Kaufman, J. C., and Cropley, A. J. (2008). Malevolent creativity: A functional model of creativity in terrorism and crime. Creativity Research Journal, 20(2), 105–115. Cropley, D. H., Kaufman, J. C., White, A. E., and Chiera, B. A. (2014). Layperson perceptions of malevolent creativity: The good, the bad, and the ambiguous. Psychology of Aesthetics, Creativity, and the Arts, 8(4), 1–20. Crump, A. L. (2013). Malevolent creativity: Are innovative criminals the best liars? (Bachelor of Psychology (Honours)), University of South Australia. Csikszentmihalyi, M. (1999). Implications of a systems perspective for the study of creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 313–335). Cambridge: Cambridge University Press. De Dreu, C. K., and Nijstad, B. A. (2008). Mental set and creative thought in social conflict: Threat rigidity versus motivated focus. Journal of Personality and Social Psychology, 95(3), 648–661. Dow, G. T. (2015). Do cheaters never prosper? The impact of examples, expertise, and cognitive load on cryptomnesia and inadvertent self-plagiarism of creative tasks. Creativity Research Journal, 27(1), 47–57. Eisenman, R. (2008). Malevolent creativity in criminals. Creativity Research Journal, 20(2), 116–119. Ekblom, P. (1997). Gearing up against crime. International Journal of Risk, Security and Crime Prevention, 2, 249–265. Ekblom, P., and Tilley, N. (2000). Going equipped. British Journal of Criminology, 40(3), 376–398. Feist, G. J. (2010). The function of personality in creativity: The nature and nurture of the creative personality. In J. C. Kaufman and R. J. Sternberg (Eds), The Cambridge handbook of creativity (pp. 113–130). Cambridge: Cambridge University Press. Forgeard, M. J. C., and Mecklenburg, A. C. (2013). The two dimensions of motivation and a reciprocal model of the creative process. Review of General Psychology, 17(3), 255–266. Furnham, A. (2015). The bright and dark side correlates of creativity: Demographic, ability, personality traits and personality disorders associated with divergent thinking. Creativity Research Journal, 27(1), 39–46. Furnham, A., Batey, M., Anand, K., and Manfield, J. (2008). Personality, hypomania, intelligence and creativity. Personality and Individual Differences, 44(5), 1060–1069. Gamman, L., and Raein, M. (2010). Reviewing the art of crime: What, if anything, do criminals and artists/ designers have in common? In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 155–176). Cambridge, UK: Cambridge University Press. Gascon, L. D., and Kaufman, J. C. (2010). Both sides of the coin? Personality, deviance, and creative behavior. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 235–254). New York, NY: Cambridge University Press. Gaut, B. (2010). The philosophy of creativity. Philosophy Compass, 5(12), 1034–1046. Gill, P., Horgan, J., Hunter, S. T., and Cushenbery, L. (2013). Malevolent creativity in terrorist organizations. Journal of Creative Behavior, 47, 125–151. Gill, P., Horgan, J., and Lovelace, J. (2011). Improvised explosive device: The problem of definition. Studies in Conflict and Terrorism, 34, 732–748. Gino, F., and Ariely, D. (2012). The dark side of creativity: Original thinkers can be more dishonest. Journal of Personality and Social Psychology, 102(3), 445–459. Gino, F., and Wiltermuth, S. S. (2014). Evil genius? How dishonesty can lead to greater creativity. Psychological Science, 25(4), 973–981. Gruber, H. E., and Wallace, D. B. (1999). The case study method and evolving systems approach for understanding unique creative people at work. In R. Sternberg (Ed.), Handbook of creativity (pp. 93–115). New York, NY: Cambridge University Press. Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444–454. Guthrie, E. R. (1944). Personality in terms of associative learning. Personality and the Behavior Disorders, 1, 49–68.

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Harris, D. J. (2013). The influence of problem construction, implicit aggression, and task valence on malevolent creativity. Omaha, NE: University of Nebraska at Omaha. Harris, D. J., and Reiter-Palmon, R. (2015). Fast and furious: The influence of implicit aggression, premeditation, and provoking situations on malevolent creativity. Psychology of Aesthetics, Creativity, and the Arts, 9(1), 54–64. Harris, D. J., Reiter-Palmon, R., and Kaufman, J. C. (2013). The effect of emotional intelligence and task type on malevolent creativity. Psychology of Aesthetics, Creativity, and the Arts, 7, 237–244. Harris, D. J., Reiter-Palmon, R., and Ligon, G. S. (2014). Construction or demolition: Does problem construction influence the ethicality of creativity? In S. Moran, D. H. Cropley, and J. C. Kaufman (Eds), The ethics of creativity (pp. 170–186). Basingstoke: Palgrave MacMillan. Hunter, S. T., Cushenbery, L., and Friedrich, T. (2012). Hiring an innovative workforce: A necessary yet uniquely challenging endeavor. Human Resource Management Review 22(4), 303–322. James, K., Clark, K., and Cropanzano, R. (1999). Positive and negative creativity in groups, institutions, and organizations: A model and theoretical extension. Creativity Research Journal 12(3), 211–226. James, K., and Drown, D. (2008). Whether “malevolent” or “negative,” creativity is relevant to terrorism prevention: Lessons from 9/11 and hazardous material trucking. Creativity Research Journal, 20(2), 120–127. Jones, D. N., and Figueredo, A. J. (2013). The core of darkness: Uncovering the heart of the Dark Triad. European Journal of Personality, 27(6), 521–531. Jones, D. N., and Paulhus, D. L. (2014). Introducing the short dark triad (SD3). A brief measure of dark personality traits. Assessment, 21(1), 28–41. Kampylis, P. G., and Valtanen, J. (2010). Redefining creativity – Analyzing definitions, collocations, and consequences. Journal of Creative Behavior, 44, 191–214. Kapoor, H. (2015). The creative side of the dark triad. Creativity Research Journal, 27(1), 58–67. Kaufman, J. C. (2009). Creativity 101. New York, NY: Springer Publishing Company. Kozbelt, A., Beghetto, R. A., and Runco, M. A. (2010). Theories of creativity. In J. C. Kaufman and R.  J.  Sternberg (Eds), The Cambridge handbook of creativity (pp. 20–47). New York, NY: Cambridge University Press. Lee, S., and Dow, G. T. (2011). Malevolent creativity: Does personality influence malicious divergent thinking? Creativity Research Journal, 23, 73–82. Mael, F. A., and Ashforth, B. E. (1995). Loyal from day one: Biodata, organizational identification, and turnover among newcomers. Personnel Psychology, 48(2), 309–333. Mayer, J., and Mussweiler, T. (2011). Suspicious spirits, flexible minds: When distrust enhances creativity. Journal of Personality and Social Psychology, 101(6), 1262–1277. McBain, R. L. (2015). The Devil Made Me Do It: Influences of Personality and the Social Environment on Malevolent Creativity. (Bachelor of Psychology (Honours)), University of South Australia. McLaren, R. B. (1993). The dark side of creativity. Creativity Research Journal, 6, 137–144. Puccio, G. J., and Cabra, J. F. (2010). Organizational creativity: A systems approach. In J. C. Kaufman and R. J. Sternberg (Eds), The Cambridge handbook of creativity (pp. 145–173). Cambridge, UK: Cambridge University Press. Rhodes, M. (1961). An analysis of creativity. The Phi Delta Kappan, 42(7), 305–310. Riley, S., and Gabora, L. (2012). Evidence that threatening situations enhance creativity. Paper presented at the Proceedings of the 34th Annual Meeting of the Cognitive Science Society. Runco, M. A. (2009). The continuous nature of moral creativity. In D. Ambrose and T. Cross (Eds), Morality, ethics, and gifted minds (pp. 105–115). New York, NY: Springer. Runco, M. A. (2010). Creativity has no dark side. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 15–32). New York, NY: Cambridge University Press. Runco, M. A., and Nemiro, J. (2003). Creativity in the moral domain: Integration and implications. Creativity Research Journal, 15, 91–105. Sekaran, U. (2006). Research methods for business: A skill building approach. New York, NY: John Wiley & Sons. Singer, J. K. (2010). Creativity in confinement. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 177–203). New York, NY: Cambridge University Press. Smith, R. (2009). Understanding entrepreneurial behaviour in organized criminals. Journal of Enterprising Communities: People and Places in the Global Economy, 3(3), 256–268. Spooner, M. T. (2008). Commentary on malevolent creativity. Creativity Research Journal, 20(2), 128–129. Sternberg, R. J. (1999). A propulsion model of types of creative contributions. Review of General Psychology, 3(2), 83–100. Sternberg, R. J. (2007). Creativity as a habit. In A.-G. Tan (Ed.), Creativity: A handbook for teachers (pp. 3–25). Singapore: World Scientific. Sternberg, R. J. (2010). The dark side of creativity and how to combat it. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 316–328). New York, NY: Cambridge University Press.

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Torrance, E. P. (1963). Education and the creative potential. Minneapolis, MN: University of Minnesota Press. Walczyk, J. J., Runco, M. A., Tripp, S. M., and Smith, C. E. (2008). The creativity of lying: Divergent thinking and ideational correlates of the resolution of social dilemmas. Creativity Research Journal, 20, 328–342. Whiting, B. G. (1973). How to predict creativity from biographical data. Journal of Creative Behavior, 7(3), 201–207. Wolfe, S. E., and Piquero, A. R. (2011). Organizational justice and police misconduct. Criminal Justice and Behavior, 38(4), 332–353. Zaitseva, M. N. (2010). Subjugating the creative mind: The Soviet biological weapons program and the role of the state. In D. H. Cropley, A. J. Cropley, J. C. Kaufman, and M. A. Runco (Eds), The dark side of creativity (pp. 57–71). New York, NY: Cambridge University Press.

PART IV INNOVATION, NETWORKING AND COMMUNITIES

20. Social networks and innovation Michel Ferrary and Mark Granovetter

INTRODUCTION Several studies have pointed out that innovation-based competitiveness does not result from a single economic agent but from a complex process in which several geographically localized agents interact. Concepts like ‘industrial district’ (Becattini 2002), ‘cluster’ (Porter 1998), ‘habitat’ (Lee et al. 2000), ‘business ecosystem’ (Iansiti and Levien 2004) and ‘networks of innovation’ (Saxenian 1994) have been used to analyse geographically localized innovative environments (see also Rallet and Torre, Chapter 26, this volume; Bathelt and Henn, Chapter 28, this volume; Lundvall, Chapter 29, this volume). Silicon Valley is a privileged object of research in the effort to understand industrial clusters and innovation. Numerous innovative high-tech enterprises have been founded in this region and have created thousands of jobs. Hewlett-Packard, Intel, AMD, Oracle, Apple, Cisco Systems, Yahoo! and Google, just to mention the best-known companies, were founded and are based in Silicon Valley. In 2015, there were 1.481 million jobs and more than 22,000 companies in Silicon Valley (Joint Venture 2015). This study analyses Silicon Valley as an innovative cluster, not as an industrial one. An industrial cluster is characterized by its capacity to generate and develop incremental innovations that reinforce its excellence and its competitiveness in a specific industrial domain. For example, the finance industry in Wall Street, the film industry in Hollywood or the wine industries in Napa Valley qualify as industrial clusters. By contrast, an innovative cluster is characterized by its capability to generate and develop breakthrough innovations that create new industrial domains and to redesign radically its industrial value chain. Regarding Silicon Valley as a durable innovative cluster instead of an industrial cluster raises the question of the durability of its innovative capability. What explains its durable innovativeness? How did Silicon Valley cope with economic crises and new competitors in the last decades? This study uses complex network theory – CNT – (Newman 2003; Barabasi et al. 2006; Jen 2006) to analyse the innovative capability of Silicon Valley. CNT is useful to explain a phenomenon (biological, technological, sociological etc.) that does not result from simple interactions between a reduced number of agents in a linear relation but results from multiple interactions between numerous and diverse agents characterized by the non-linearity of their interactions. We consider the innovativeness of Silicon Valley as an economic phenomenon supported by a complex network. An innovative cluster is viewed as a complex network, whose nodes are companies and whose links represent the various economic and financial ties connecting them. First, the complexity is due to the numerous decentralized interactions between a large diversity of economic agents. Further, these economic agents foster multiplex 327

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ties by holding different social roles (employee, citizen, neighbour, friends, member of associations etc.), and the economic interactions that generate innovations are embedded in the non-economic interactions. Agents interact on different social levels and this influences the economic level. For example, two agents linked by friendship (social tie) can become business partners to create a company (economic tie). This has been the case for companies like Hewlett-Packard, Apple, Cisco, Yahoo! Google or Twitter. Second, CNT emphasizes the robustness (or resilience) of systems more than their stability to explain how a system can or cannot cope with external radical changes and competitive shocks. It also allows exploration of the robustness of the Silicon Valley complex network of innovation that has withstood several external competitive shocks in the last decades. As a complex system, Silicon Valley is made up of networks of heterogeneous, complementary and interdependent agents. A systemic understanding of innovative clusters emphasizes that the efficiency of each particular agent depends on the presence of other agents. Due to this interdependence, the absence of one agent weakens the efficiency of others and, ultimately, the efficiency and the robustness of the entire system. However, some agents contribute more than others to the robustness of a complex network of innovation. As mentioned by Thompson (2004), there is a tendency for networks to create hubs that provide more stability and robustness. We argue that venture capitalists are a major (and underestimated) source of robustness of the innovative complex network of Silicon Valley. Two dimensions justify exploring the contribution of venture capitalists to Silicon Valley. First, a minority of high-tech start-ups are funded by venture capitalists at the seed stage. On the other hand, almost all the large high-tech firms in Silicon Valley have been backed by venture capital (VC). Thus, it seems that VC firms back the seed stage of the most successful start-ups. Second, international studies of high-tech clusters point out that the main difference between Silicon Valley and other high-tech clusters around the world is not the size of universities, the presence of large companies or the quality of research laboratories but the huge presence of VC firms (Lee et al. 2000). In 2015, the National Venture Capital Association counted more than 200 VC firms in Silicon Valley (and 800 in the US). In 2014, $28.1 billion VC investments have been done in California, representing 57 per cent of all the VC investments in the US. The presence of VC firms in an innovative cluster creates potential specific interactions with other agents in the network (universities, large companies, laboratories) that determine a particular dynamic of innovation. In this perspective, what is distinctive about Silicon Valley is its complete and robust complex system of innovation supported by social networks of interdependent economic agents in which the VC firms have a specific function. We examine five different contributions of VC firms to the social networks of the Silicon Valley innovative cluster: financing, selection, collective learning, embedding and signalling.

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COMPLEX NETWORK THEORY: A LEARNING AND ROBUST SYSTEM OF HETEROGENEOUS ACTORS A network is complex if it is made up of numerous interacting agents (Barabasi 2002; 2005) who may be heterogeneous, that is, with different competences and different functions in the network. Agents of a complex network are also multiplex, that is, the same agent can fulfil different functions and optimize different kinds of interest. Networks are made up of agents that interact without formal hierarchy. Jen (2006) describes networks as interconnected, overlapping, often hierarchical networks with individual components simultaneously belonging to and acting in multiple networks, and with the overall dynamics of the system both emerging from and governing the interactions of these networks. A group of agents becomes a system when these agents interact. Interaction between heterogeneous actors is the second feature of complex networks. The probability of interactions between agents is higher when their interdependency is high. Watts and Strogatz (1998) point out that the structure of a network (diversity of agents and degree of connectedness) influences its dynamics. For example, the spread of epidemics depends on the connectedness of populations (Kretschmar and Morris 1996). In social systems, the degree of agents’ embeddedness impacts on the circulation of information (Granovetter 2005). CNT emphasizes the systemic dimension of the agent’s efficiency. Results of agents depend on their intrinsic competencies but also on their interactions with their environment. There is a systemic interdependence between the agents of the network. The viability of the entire system and the viability of each agent depend on the diversity of agents and the degree of their connectedness. The significance of complex networks lies more in their robustness than in their stability. Jen (2006) defines robustness as the ability of a system to maintain certain features when subject to internal or external perturbations. The persistence of a network is the result of its robustness. Resilience is another term used to describe the ability of a system to experience disturbance and still functionally persist (Newman 2003). Conversely, the weakness of a network is its inability to face large perturbations. The organizational architecture of the network plays a role in its robustness (Dodds et al. 2003). Outcomes of networks depend on resources owned by agents and on the way these agents transform and exchange their resources. Robustness is a by-product of the completeness of the network and of the quality of the interactions between its agents. Interactions of heterogeneous agents favour mutations that ensure the survival of the system. Interactions enhance robustness by producing and maintaining the persistence of vital functions. Robustness supposes a complete set of heterogeneous and complementary agents and a dense network (Hartman et al. 2001). The absence of one agent can weaken the entire network. A system is robust when it is able to reconfigure itself to face external shocks (Callaway et al. 2000). The robustness depends on the capability to evolve towards new functionalities, to integrate learning capabilities, to redesign its problem-solving processes and to promote creativity. The robustness is due to the capabilities of the complex network to collectively anticipate, learn and innovate in order to react to major internal or external changes. The learning process is a set of dynamic interactions with feedback across multiple scales and in multiple dimensions on

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multiple networks (Jen 2006). A change entails interpretation and action by agents that induces feedback from the environment. In reaction, networks may develop new functionalities unanticipated in their original design. Complex networks can generate innovative solutions to maintain and to reinforce themselves. Their robustness may depend on the capacity of the system to generate new agency or to connect itself to another system. Many significant revolutionary innovations occur in discrete bursts which fundamentally reorganize pre-existing ecological relationships. Complex networks are rarely in a stable situation because of a permanent adaptation to perturbations. The dynamics of interactions entails non-linear and sometimes chaotic changes (Barabasi et al. 2006).

SILICON VALLEY: AN INNOVATIVE CLUSTER UNDERLINED BY COMPLEX SOCIAL NETWORKS According to CNT, Silicon Valley can be qualified as ‘complex’ because of the heterogeneity and the multiplexity of its agents. It can be qualified as a ‘network’ due to the decentralization of decision making and to the importance of social ties to coordinate economic agents (Castilla et al. 2000; Ferrary 2003a). The complexity of these networks is due to the heterogeneity of agents and to the interplay of their organizational and human dimensions. Complex networks of organizations interact with complex networks of individuals in a continuous process of embedding and of decoupling (White 1992). Social ties and organizational ties are intertwined. Social ties create and coordinate organizations, and then organizations decouple from social ties and create new social ties that help found new organizations. Following CNT terminology, as nodes, two agents may have two kinds of complementary professional competences (one is an IT person, the other a business person), and they may be tied by multidimensional links, being friends and business partners, if they create a start-up together. The density of social ties matters in an innovative milieu because important elements of the knowledge used for innovation are tacit (Nonaka 1994). Social interactions underlie the circulation of knowledge among individuals and organizations (Granovetter 1985). Dense social ties determine the creation of knowledge. Uncertainty and tacit knowledge entail the incompleteness of contracts and imply handshake transactions and regular face-to-face contact to make economic exchanges possible. Regular face-to-face contacts justify the geographical clustering of agents. The clustering density is an important property of complex networks and Silicon Valley is characterized by high clustering density in which ethnic ties, university ties, friendship ties, past professional ties and current professional ties are intertwined to sustain innovation and entrepreneurship (Saxenian 1994). Silicon Valley is a complex network of innovation made up of heterogeneous and multiplex agents that interact at different levels. Beside universities, large companies and laboratories, there are also law firms, VC firms, consulting groups, recruiting groups and other service firms that contribute to the creation and the development of innovative start-ups. At least 12 different agents are involved in the creation and the development of successful start-ups: universities, large firms, research laboratories, VC firms, law firms, investment banks, commercial banks, certified public accountants (CPAs), consulting groups, recruitment

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agencies, public relation agencies and media. Each of these 12 agents contributes in different ways to the life-cycle of start-ups that create innovation. For example, the creation of Google involved Stanford University, where the founders were PhD students (and also friends before becoming business partners; this is at least a three dimensional link); later, the university provided the company with employees and continues to test new services developed by Google. Two major VC firms in the region, Sequoia Capital and Kleiner Perkins (short for Kleiner, Perkins, Caulfied and Byers; sometimes shortened to KPCB), funded the start-up. The law firm Wilson, Sonsini, Goodrich and Rosati, located in Palo Alto, was in charge of the legal dimension of the venture. Yahoo! (funded by Sequoia Capital) and AOL (funded by Kleiner Perkins) were the two first clients of Google. Local newspapers like the San Jose Mercury News, the San Francisco Chronicle and the Red Herring publicized the company. Hambrecht & Quist and CSFB, two San Francisco investment banks, organized Google’s IPO. By 2006 Google had become one of the largest firms of Silicon Valley and contributed to the complex system by acquiring start-ups in the region, for example YouTube (funded by Sequoia Capital). Based only on economic interactions, in the simplest design that we can imagine, the 12 agents can potentially have 66 types of dyadic interactions ((12*11)/2) in the course of the creation and the development of a single start-up. Due to the number of potential ties, Figure 20.1 offers a suggestive diagram that shows the complexity of networks that support the innovativeness of Silicon Valley. It illustrates the complex nature of networks

Commercial banks

Research laboratories

Law firms CPA

Universities

Large high-tech firms

VC firms

PR agencies

Medias

Investment banks

Figure 20.1

Consulting groups

Recruitment agencies

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in Silicon Valley in a form regularly used by network theorists to visualize their analysis. Based on Baran’s typology (Barabasi 2002), it aims to point out that the structure of networks of Silicon Valley is distributed more than centralized or decentralized. Some agents interact more frequently than others. This induces stronger ties between some agents (Granovetter 1973). Universities, large firms, research laboratories, VC firms and law firms have more interactions among themselves than with others. There is a virtuous self-reinforcing dynamic of creation of high-tech start-ups. Several large firms that currently contribute to the complex network of Silicon Valley have previously been high-tech start-ups founded in the region (Oracle, Apple, Cisco Systems, Yahoo!, Google, Facebook) and have been developed with the support of other agents of the system. Another positive effect of the regional innovative dynamic is that large foreign high-tech firms (Nokia, Siemens, Alcatel, Samsung) open branch offices in the region and reinforce the system (Ferrary 2011). Identifying the complete set of agents that interact and underlie the virtuous dynamics of the complex network of innovation is a critical issue. The robustness of Silicon Valley (i.e. its sustainable innovativeness) lies in the completeness of its networks. The entire system is weakened if only one of its members is missing. All members are not equally important but all of them contribute to the system. According to CNT, these agents may play different roles and contribute in several ways to the innovative cluster. They can contribute directly or indirectly to the creation and to the development of high-tech start-ups. For example, a direct contribution is when a law firm helps a start-up to secure its intellectual property, when a consulting group provides its expertise or when an investment bank underwrites the start-up’s IPO. An indirect contribution is when universities nurture entrepreneurs or when future entrepreneurs accumulate social ties as employees of large firms. Some agents contribute involuntarily to the creation of start-ups. For example, some entrepreneurs nurture their projects as employees in research laboratories or large firms and they leave their employers to create start-ups. In this way large organizations involuntarily nurture start-ups. Also, when start-ups recruit from large firms, these firms contribute involuntarily to the development of start-ups. Lastly, some agents, such as public relations (PR) agencies, consulting groups or recruitment agencies contribute to the network in innovative clusters by connecting agents. They organize social events or meetings where people create social ties. For these reasons, innovation in Silicon Valley results from a complex network. Numerous heterogeneous agents (nodes) are involved with multiplex functions and these agents have multidimensional ties (professional, friendship, familial). The coordination between these agents is completely decentralized. There is no central agent (or central node) that coordinates the others. The economic success of a start-up does not result only from the quality of the entrepreneur and his or her innovation, but also from the entrepreneur’s embeddedness in complex social networks. The more connected an entrepreneur is, the better is his or her access to financial resources, to advice, to partners and experts. Conversely, an isolated entrepreneur would have more difficulty mobilizing the resources needed. According to CNT, the quality of interactions between agents determines the success of each agent and, finally, the achievement of the entire system. In the case of Silicon Valley, a startup can interact with a complementary agent only if the latter belongs to the cluster. The dynamics of innovation depends on the completeness of the environment. Accordingly,

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the innovative capability of Silicon Valley is a product of the completeness of its set of interdependent and heterogeneous agents. Silicon Valley can be characterized as a robust system because of its capacity to generate radical innovations in the long run, to support new industries and to face major industrial disruptions. CNT is particularly useful to qualify and to understand the specific robustness of Silicon Valley. Jen (2006) insists on the difference between stability and robustness. Stability describes the system’s capacity to survive by returning to the same position after a shock. Robustness describes the system’s capacity to survive a shock by radically reorganizing itself and restabilizing in a new configuration. Silicon Valley was originally based on the semiconductor industry, with companies like Fairchild Semiconductor, National Semiconductor, Intel, AMD and Cypress. This industry was shaken in the early 1980s by strong Japanese and Taiwanese competition. According to CNT, Silicon Valley would be a stable system if the region were to stay the leader of a semiconductor industry configured in the same way. Actually, Silicon Valley has shown its robustness for two reasons. First, the Californian semiconductor industry radically redesigned the value chain of the sector by focusing on the design of semiconductors and outsourcing the production to Asia. Second, the region got involved in new industrial sectors such as personal computers (Apple) and software (Oracle, Sun Microsystems, Symantec, Electronic Arts, Intuit). Later, Silicon Valley gave rise to telecommunication equipment start-ups (Cisco System, Juniper Networks, 3Com) and finally to the internet industry (Netscape, Excite, eBay, Yahoo!, Google, Facebook, Twitter). Each new industry was supported by the previous industries. The semiconductor industry enabled the computer industry and the software industry. These industries supported the telecommunication equipment industry. Finally, all these industries enabled the internet sector. Each new mature industry reinforced the innovative capacity of the cluster and improved the robustness of the complex network. The complex network of innovation generates agents with new competences that interact with the former agents and reinforce its innovative capability. More recently, Silicon Valley has radically redesigned its role in the software industry. The competition from India and China based on cheap software engineers is a major challenge for the Californian innovative cluster. In reaction, Silicon Valley radically changed its role by becoming a coordinator of the international software industry (Saxenian 2006). This move, which made sure that Silicon Valley retained its central position in the industry, highlights the robustness of the region’s innovative capability. This evolution illustrates the general growth model of complex networks described by Newman (2003) in which networks are resilient because they are able to add new links and new nodes in order to survive. The completeness and the embeddedness of heterogeneous and interdependent agents are sources of the innovativeness of Silicon Valley. Different studies have emphasized the contributions to innovation by universities, research laboratories and large high-tech firms. The next section highlights the contribution of another agent: VC firms. It does not argue that VC firms by themselves explain the innovative capability of the Silicon Valley, but rather that their presence in the complex network enables specific interactions between agents and contributes to its completeness. These specific interactions sustain the robustness of the complex network of innovation.

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VC FIRMS AS A SOURCE OF ROBUSTNESS IN SILICON VALLEY’S COMPLEX INNOVATION NETWORK A longitudinal and historical analysis of the innovative complex network of Silicon Valley points out that it was not built all at once, but over decades by a progressive aggregation of heterogeneous and complementary agents. This gradual aggregation initiated a virtuous dynamic of endogenous growth that led to the entry of new agents who reinforced the complex network of innovation and nurtured the creation of new high-tech start-ups. Founded in 1891, Stanford University was the first agent in the system of innovation. It has incubated a number of groundbreaking technologies and notable entrepreneurs and has trained many workers for the region. Byers et al. (2000) estimate that more than 2000 Silicon Valley companies have been created by Stanford alumni or faculty. Historically, a network of agents aggregated around Stanford has improved the system of innovation and made it more complex. In the 1930s, numerous non-Californian firms established branches in Palo Alto: General Electric, Eastman Kodak, Lockheed, and IBM. Private research laboratories were established, such as the Stanford Research Institute (1946) and the Xerox/Parc (1970). At the same time, other agents of the complex network aggregated in the region. In 1968, an investment bank, Hambrecht and Quist, was established in San Francisco to underwrite initial public offerings. Later, investment banks from Wall Street, such as Goldman Sachs, JP Morgan and Citicorp implanted offices in the region. The 1980s were characterized by the development of law firms working with the high-tech industry: Wilson, Sonsini, Goodrich and Rosati; Ware and Friedenrich; and Fenwick, Davis and West. If one considers the number of new start-ups as revealing the innovativeness of a cluster, then, in the 1940s, Silicon Valley was not very innovative. The region did not create many start-ups or high-tech jobs despite the presence of universities, large firms and the support of the state. We argue that the incompleteness of the network at that time explains the weakness of innovation. Adams (2005) points out that in 1939, when Hewlett-Packard was created, electrical and radio firms employed only 464 people in the San Francisco Bay Area (by 1963 there would be 17,000) and only 243 high-tech companies were created in this region between 1960 and 1969. The acceleration of the endogenous growth of Silicon Valley came with the development of the semiconductor industry in the late 1950s and the early 1960s. This was also when the Californian VC industry began to develop. In 1958, Draper, Gaither and Anderson created the first Californian VC firm. In 1961, in San Francisco, Arthur Rock and Tommy Davis established Venrock Associates, the first VC firm adopting limited partnership as its legal structure. Later, this legal structure became common in the VC industry. The most prominent VC firms were created in the 1970s. In 1969, the Mayfield Fund was founded; Sequoia Capital and Kleiner Perkins followed in 1972. By 1975, more than thirty VC firms were located in the Bay Area. The first Silicon Valley high-tech start-ups were funded by individuals or large firms. Shockley Semiconductor was backed by a large firm in 1955 (Beckman Instruments), and Fairchild Semiconductor was funded by Fairchild Corporation in 1957, but the later semiconductor start-ups were supported by venture capitalists. In 1968, Intel was backed by Venrock Associates. Cypress Semiconductor, Teledyne and AMD got funding from Sequoia Capital. By 2006, 28 of the 30 largest high-tech employers of Silicon Valley

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(including Intel, Sun Microsystems, Apple, Oracle, Cisco Systems, eBay, Yahoo! and Google) have been backed by VC firms in the first years of their inception. The fact that, in the mid-1960s, the high-tech endogenous growth in Silicon Valley and the development of the VC industry in this region coincided in time leads one to inquire about the contribution of VC firms to the innovative cluster.

THE CONTRIBUTION OF VC FIRMS TO THE SILICON VALLEY INNOVATIVE CLUSTER The complex network of Silicon Valley is made up of heterogeneous agents that contribute in different ways to innovation and the creation of start-ups. VC firms do not have a value by themselves but because they interact with others. The systemic dimension of Silicon Valley is such that the VC firms do not sustain by themselves the robust innovativeness of the region but, on the other hand, without them the system would be less innovative. VC firms are complex agents that handle multiplex functions through multiplex interactions with the other agents of the networks. We define five different functions of VC in the Silicon Valley innovative cluster. Financing Function The best-known economic function of VC is funding the creation and the development of start-ups (Gompers and Lerner 2004). There is a stage in the life-cycle of high-tech startups when they need external funding because they do not generate sufficient revenues. VC funding is crucial at this stage. VC firms get equity shares in the start-ups in return for their funding. The financial risk of VC investment is very high. Lacking assets or a proven cash flow, start-ups are unable to raise capital from conventional sources, such as commercial banks or the public market. VC investments sustain and accelerate the growth of start-ups (Hellmann and Puri 2002). VC firms fund start-ups directly and other agents of an innovative cluster indirectly. A start-up partly uses its funding to pay for the services of law firms, consulting groups, PR agencies and recruiting agencies. Through the funding of start-ups, VC investments sustain different service providers. Start-ups also use their funding to recruit employees trained in local universities. Thus, indirectly, VC funds the labour market of the cluster. The creation of start-ups is thus a business activity that involves different agents that are indirectly paid by VC money. For this reason, VC investment is more than just the funding of start-ups; it is, more broadly, a source of funding for the entire innovative cluster. To make a parallel with the power network (Watts and Strogatz 1998), and by considering money as the energy of the network economy, a VC firm empowers the network by creating a financial tie with a start-up. The financial flux coming from the VC firm enables the start-up to create business ties by paying other agents of the network (lawyers, consultants, experts etc.).

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Selection Function A traditional economics perspective attributes the function of selecting enterprises to the market. Evolutionist theory (Nelson and Winter 1982) argues that natural selection of the best products is due to the customers’ choice. In this perspective, the rise of a new technology that becomes a ‘dominant design’ results from market competition. A company disappears if it does not have enough customers. In Silicon Valley, the life-cycle of start-ups is different. VC firms select companies before the market has a chance to do so. At the seed stage, when a VC firm considers its business plan, a start-up typically has no or very few clients. Some start-ups do not face the market for several years after they get VC funding. Thus, they are highly dependent on VC investments. The survival probability of a start-up is very low if it does not receive VC money to fund its development. Venture capitalists fund three or four start-ups out of more than 500 business plans received per year (Perez 1986). They try to pick the most promising projects because their earnings depend on the performance of their investments. Twenty per cent of the returns on their investment come from carried interest (Gomper and Lerner 2004). A venture capitalist typically evaluates three kinds of risk before any investment: the risk related to the technology, the risk related to the market and the risk related to the entrepreneur. In Silicon Valley, VC firms specialize in certain sectors (telecommunication equipment, software, biotechnology, the internet). They receive business plans in their area of specialization because their reputation is well established in the cluster. The investor can evaluate and compare all the start-ups before picking the best one with the right technology and the best people. VC firms implicitly decide the survival and the death of start-ups by choosing which of them to fund. Venture capitalists are well connected to each other. Thus, if a prominent one refuses to invest in a start-up, the information is quickly spread in networks and it becomes very difficult for the start-up to raise funding from other VC firms. Such pre-market selection saves resources in the innovative cluster. The specific venture capitalist’s competence is to evaluate the business opportunity of a start-up. Venture capitalists can often judge the potential of an innovation better than entrepreneurs. VC firms eliminate start-ups by refusing to invest in some of them at the seed stage. VC funding determines which start-ups will be connected, or not, to the complex network of innovation. By selecting start-ups, the VC firms implicitly prevent the other agents in the complex network of innovation from collaborating with start-ups that do not get VC funding. Signalling Function Collaborating with a start-up is a risky choice for any business partner. Service providers such as law firms, recruitment agencies and consulting groups are not necessarily able to evaluate the risk that the start-up that uses their services will be solvent. The level of risk can prevent some service providers from working with start-ups. Workers face the same issue when they consider working for a start-up. An engineer who resigns from a large high-tech company to work for a start-up risks losing his or her job if the company goes bankrupt. This risk is even higher if his or her compensation is largely based on stock options. Finally, large firms face the same risk when they consider possible contracts with start-ups.

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This uncertainty is a crucial issue in an innovative cluster. The risk of dealing with startups is so high that some economic agents may refuse to do business with them. There is a potential vicious circle where high-tech start-ups fail because other agents refuse to deal with them. Ultimately, the entire system can collapse because of the reluctance of some interdependent agents to interact with each other. Funding by a VC firm, especially a prominent one like Sequoia Capital, Kleiner Perkins or Menlo Ventures, gives a positive signal to other agents of Silicon Valley, where it is common knowledge that the main competence of venture capitalists is to evaluate startups. When a top-tier VC firm invests in a start-up, it does not guarantee success, yet it gives a positive signal. Podolny (1994) points out that economic agents tend to collaborate with agents having the same status when they face uncertainty. In an uncertain environment, high-status agents tend to aggregate and to exclude low-status agents. Newman (2003) generalizes this finding by mentioning that it is a common phenomenon in many social networks that we tend to associate preferentially with people who are similar to ourselves in some way. The propensity to homophily is exacerbated in an uncertain environment. Therefore, agents of the complex network of innovation, especially high-status ones, are more likely to create ties with start-ups that have previously been able to connect with high-status VC firms. A connection with a high-status VC firm signals the high status of the start-up and encourages other agents to link to it. Conversely, a negative signal is sent if a start-up raises funding from an unknown VC firm or, even worse, does not raise any VC. Many of the agents of Silicon Valley want to know the VC investors in a start-up before deciding on collaboration. Funding by VC firms signals the quality of start-ups to other agents. By investing or refusing to do so they signal the level of risk for each start-up and indirectly modify the risk evaluation and the behavior of the other agents of the system. They encourage other agents to collaborate with the most promising start-ups and to avoid involvement with more fragile companies. By funding promising start-ups, VC firms contribute heavily to the capabilities of anticipation and of innovation that characterize a robust complex network. Collective Learning Function The persistence of innovative capability underlies the robustness of the complex system of Silicon Valley. In spite of competition from new high-tech clusters in the US and abroad, Silicon Valley keeps creating high-tech start-ups. Many high-tech companies have been created over the last fifty years, but many of them have disappeared. Migration of workers from the US and from abroad has fluctuated. Each new high-tech growth attracts a wave of immigrants and each crisis entails emigration. The VC industry is a source of stability in the midst of these changes. The prominent VC firms of the 2000s were created in the 1970s and the 1980s (Sequoia Capital, Kleiner Perkins, Menlo Ventures, Mayfield Fund). This durability of venture capitalists ensures that over the years they accumulate tremendous knowledge on the creation and the development of high-tech companies. Senior venture capitalists have evaluated thousands of projects and funded and accompanied dozens of start-ups. They have a deep understanding of industrial, technological, legal and managerial issues. Venture capitalists are, moreover, strongly involved in the management of the start-ups they have backed. They meet the entrepreneurs at least monthly at board meetings and sometimes daily. In some cases, the venture capitalist becomes a temporary

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worker for the start-up or the entrepreneur is housed in the VC offices. The structure of social networks affects the spread of information; therefore connections to VC firms give access to entrepreneurial knowledge. An entrepreneur gets access to the venture capitalist’s knowledge by being funded by a VC firm. This contribution is reinforced by two facts. First, venture capitalists are often former entrepreneurs and have personal entrepreneurship experience to share. Second, general partners of the same VC firm share their knowledge. Partners can always ask their associates for advice on an issue they face in one of their start-ups. By financing start-ups, venture capitalists accumulate entrepreneurial knowledge. They are the memory of the complex network of Silicon Valley. They share with the entrepreneurs the best and the worst entrepreneurial practices they know. CNT emphasizes the learning capability of robust complex networks. In the perpetually changing environment of Silicon Valley’s networks, the VC firms are perennial agents that accumulate and diffuse entrepreneurial knowledge through different life-cycles of technological industries. By accumulating this knowledge during the maturation of an industry (e.g. the semi-conductor industry) and by transferring it to an emerging one (e.g. the software industry), the VC firms sustain the reconfiguration that ensures the survival of the complex network when an industry declines. Embedding Function The embeddedness of entrepreneurs in the complex networks of Silicon Valley is a major factor determining the success of start-ups. Several studies point out that in Silicon Valley social networks matter in the circulation of knowledge and the business coordination of agents (Saxenian 1994; Castilla et al. 2000; Ferrary 2003a). The social ties between economic agents, or the ease of creating them, strongly affect the start-ups. An entrepreneur who is poorly embedded in the complex networks gets few resources from the agents of the cluster and may compromise his or her success. This raises a specific issue for the network theory about the embedding process of agents. A large set of researchers points out the influence of the social network structures on agents. Conversely, few researchers analyse how agents become embedded in a specific network. The case of Silicon Valley points out that embeddedness can result from the strategic behaviors of agents. Agents are active nodes that influence the structure of networks. It is well established by CNT that networks are not randomly structured (Newman 2003; Barabasi et al. 2006); rather, the structure of the network results from the behavior of the nodes because agents are ‘networkers’ (Granovetter 2003). Silicon Valley highlights the latter situation. Some entrepreneurs of Silicon Valley become embedded before they find a company. They may have worked for a long time in a large local company. Some of them grew up in the region or graduated from a university in the Bay Area. Yet, the majority of entrepreneurs are less embedded and some of them could be isolated from business networks. This applies especially to new immigrants. More than 35 per cent of the population of Silicon Valley is foreign (Joint Venture 2015). The embedding of isolated potential entrepreneurs is a major issue for the dynamics of innovation. Entrepreneurs behave strategically to embed themselves. The VC firms also embed the start-ups they fund. For an entrepreneur, it is more strategic to be connected with a VC firm than with,

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for example, a CPA. A VC firm can more easily create a tie between a start-up and a CPA than the other way round. Venture capitalists are deeply embedded in the social networks of Silicon Valley. They have resided in the region for several years. They have worked in different large hightech firms of the region. They belong to several boards of directors of start-ups or even of large firms. They frequently interact with universities as speakers or advisers. They graduated from local universities. They are partly recruited for their social network and to be embedding agents. The deeply embedded venture capitalists are embedding agents for the isolated entrepreneurs they back. VC firms are the main hubs between entrepreneurs and the complex networks of Silicon Valley. They enable interactions between interdependent economic agents. They do this because the profitability of their investments depends on these interactions. All agents of the innovative cluster want to be connected with VC firms because venture capitalists nurture strong ties with their entrepreneurs and get inside information. A close relationship with a VC firm is a way to get private information on start-ups it has invested in. Some large firms invest in VC funds as limited partners in order to get access to inside information. The relationship between Cisco Systems and Sequoia Capital is a good illustration. Cisco Systems is famous for its acquisitive strategy to get new products and new technologies. Cisco Systems has bought ten start-ups funded by Sequoia Capital. The close relationship between the two companies (Sequoia Capital funded Cisco at its inception, D. Valentine, a Sequoia partner has been the vice-chairman of Cisco for a long time and Cisco invests in Sequoia’s funds) underlies these acquisitions (Ferrary 2003b). Venture capitalists integrate the innovative cluster by creating ties between interdependent agents. By connecting people, they contribute to a better coordination inside the complex network. CNT points out the multiplexity of interactions in complex networks. By embedding entrepreneurs, the VC firms build the multiplexity that sustains Silicon Valley’s complex innovation network. As one of the main hubs between start-ups and complex networks of innovation, the VC firms are one of the agents (or nodes) that supports the robustness of the complex network. CNT points out that some nodes are more important for the resilience of the network because of the non-randomness of complex networks (Newman 2003). Out of the 200 VC firms of Silicon Valley, the destruction of the ten most prominent of them might strongly affect the connectivity of the cluster and, then, its innovativeness, because these prominent firms are network ‘hubs’ with far more ties than other nodes.

CONCLUSION CNT is useful for understanding the innovativeness of Silicon Valley because this region is a complex network of innovation. With respect to the definition of a complex network, 1) Silicon Valley is a network of heterogeneous and multiplex agents; 2) interactions between agents are multiplex and self-organized; 3) Silicon Valley is a robust system that has evolved to resist different industrial and technological shocks to maintain its innovativeness; and 4) this robustness is due to the anticipating and learning capabilities of the system, mainly supported by VC firms. The use of CNT to analyse innovative clusters emphasizes the systemic dynamics of innovation. The exploration of the complex networks of Silicon Valley points out the

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specific functions of VC firms and their contribution to the robustness of the system. Beyond the funding of start-ups, the VC firms select the most promising projects of the region, signal the best start-ups to the business community, accumulate and spread entrepreneurial knowledge in the cluster and embed the interdependent agents of the network. VC firms depend on other agents of the complex network of innovation and vice versa. Due to this systemic interdependence, the absence or the presence of only one type of agent can weaken or reinforce the entire system. By emphasizing the heterogeneity and the interdependence of agents (the nodes), the analysis of Silicon Valley has implications for CNT. The importance of a node depends on the number of ties (links) it gets and not on its intrinsic nature. In the case of the complex network of Silicon Valley, the removal of VC firms would also weaken the entire system because of the specificity of their competencies. When the complexity of a network is due to the heterogeneity of its nodes as much as the structure of its ties, then the consequences of the removal of one node depend on its intrinsic nature as much as its connections. The analysis of Silicon Valley based on CNT can be used to understand public policies that try to reproduce the same kind of innovative clusters elsewhere, and offers new research perspectives. It thus appears that understanding Silicon Valley’s complexity and the hidden functions of VC firms can help policy-makers who try to create innovative clusters. Future comparative research would do well to consider how the success of such new clusters is related to the extent and significance of VC in their organization and functioning. Another theoretical perspective is to define the complexity of an innovative network by the large diversity of functions or competences needed to generate innovation, then to identify which agents (nodes) fulfil these functions and how they interact with each other (the network structure). Then complex networks of innovation may differ depending on which agent carries out which function. For example, the selecting, signalling, learning and embedding functions handled by VC firms in Silicon Valley could be carried out by other agents, depending on the particular history and institutional arrangements in a particular setting. Acknowledgement This chapter is a revised version of the article by Michael Ferrary and Mark Granovetter (2009), ‘The role of venture capital firms in Silicon Valley’s complex innovation network’, Economy and Society, 38(2), 326–359. Reprinted by permission of the publisher Taylor & Francis Ltd, www.tandfonline.com.

REFERENCES Adams, S. (2005). Stanford and Silicon Valley: Lessons on becoming a high-tech region. California Management Review, 48(1), 29–51. Barabasi, A.-L. (2002). Linked. New York: Plume Books. Barabasi, A.-L. (2005). Taming complexity. Nature Physics, 1, 68–70. Barabasi, A.-B., Newman, M. and Watts, D. (2006). The structure and dynamics of networks. Princeton, NJ: Princeton University Press. Bathelt, H. and Henn, S. (2017). National and regional innovation systems. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar companion to innovation and knowledge creation (pp. 457–471). Cheltenham, Northampton, MA: Edward Elgar Publishing.

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Becattini, G. (2002). Industrial sectors and industrial districts: Tools for industrial analysis. European Planning Studies, 10(4), 483–93. Byers, T., Keeley, R., Leone, A. and Parker, G. (2000). The impact of a research university in Silicon Valley: Entrepreneurship of alumni and faculty. Journal of Private Equity, 4(1), 7–15. Callaway, D., Newman, M., Strogatz, S. and Watts, D. (2000). Network robustness and fragility: Percolation on random graphs. Physical Review Letter, 85, 5468–71. Castilla, E., Hwang, H., Granovetter, E. and Granovetter, M. (2000). Social networks in Silicon Valley. In C. Lee, W. Miller, M. Hancock and H. Rowen (eds), The Silicon Valley edge (pp. 218–47). Stanford, CA: Stanford Business Books. Dodds, P., Watts, D. and Sabel, C. (2003). Information exchange and the robustness of organizational networks. Proceedings of the National Academy of Sciences, 100, 12516–21. Ferrary, M. (2003a). The gift exchange in the social networks of Silicon Valley. California Management Review, 45(4), 120–38. Ferrary, M. (2003b). Managing disruptive technologies life cycle by externalizing the research: Social network and corporate venturing in the Silicon Valley. International Journal of Technology Management, 2(1–2), 165–80. Ferrary, M. (2011). Specialized organizations and ambidextrous clusters in the open innovation paradigm, European Management Journal, 29, 181–92 Gompers, P. and Lerner, J. (2004). The venture capital cycle. Cambridge, MA: MIT Press. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–80. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 9(1–3), 481–510. Granovetter, M. (2003). Ignorance, knowledge, and outcomes in a small world. Science, 301, 773–4. Granovetter, M. (2005). The impact of social structure on economic outcomes. Journal of Economic Perspectives, 19(1), 33–50. Hartman, J.L., Garvik, B. and Hartwell, L. (2001). Principles for the buffering of genetic variation. Science, 291, 1001–4. Hellmann, T. and Puri, M. (2002).Venture capital and the professionalization of start-up firms: Empirical evidence. Journal of Finance, 58(1), 169–97. Iansiti, M. and Levien, R. (2004). The keystone advantage: What the new dynamics of business ecosystems mean for strategy, innovation, and sustainability. Cambridge, MA: Harvard Business School Press. Jen, E. (2006). Robust design: A repertoire of biological, ecological, and engineering case studies. Oxford: Oxford University Press. Joint Venture (2015). Index of Silicon Valley. San Jose, CA: Joint Venture Editor. Kretschmar, M. and Morris, M. (1996). Measures of concurrency in networks and the spread of infectious disease. Mathematical Biosciences, 133, 165–95. Lee, C., Miller, W., Hancock, M. and Rowen, H. (2000). The Silicon Valley edge: A habitat for innovation and entrepreneurship. Stanford, CA: Stanford Business Books. Lundvall, B.-Å. (2017) National innovation systems and globalization. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar companion to innovation and knowledge creation (pp. 472–489). Cheltenham, Northampton, MA: Edward Elgar Publishing. Nelson, R. and Winter, S. (1982). An evolutionary theory of economic change. London: Belknap Press. Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–38. Perez, R. (1986). Inside venture capital. New York: Praeger. Podolny, J. (1994). Market uncertainty and the social character of economic exchange. Administrative Science Quarterly, 39(3), 458–83. Porter, M. (1998). Clusters and the new economics of competition. Harvard Business Review, November– December, 77–90. Rallet, A. and Torre, A. (2017) Geography of innovation, proximity and beyond. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar companion to innovation and knowledge creation (pp. 421–439). Cheltenham, Northampton, MA: Edward Elgar Publishing. Saxenian, A. (1994). Regional advantage. Cambridge, MA: Harvard University Press. Saxenian, A.L. (2006). The new Argonauts. Regional advantage in a global economy. Cambridge, MA: Harvard Business Press. Thompson, G. (2004). Is all the world a complex network? Economy and Society, 33(3), 411–24. Watts, D. and Strogatz, S. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–42. White, H. (1992). Identity and control. A structural theory of action. Princeton, NJ: Princeton University Press.

21. Community, creativity and innovation Joanne Roberts

INTRODUCTION Community is a ubiquitous term in today’s world. We all live in a local community, yet our lives are influenced by the activities of those who are active in the international community. We talk about social communities, cultural communities and communities of interest. In the business context, we can identify producer communities, user communities, design communities, research and development (R&D) communities, and innovation communities among others. In the past, such communities had a specific spatial context, which allowed for rich social interaction. In recent decades, technological developments in the form of the Internet and social networking platforms have dislocated communities from particular locations. Consequently, communities can now be global yet maintain high levels of social interaction through online exchanges. Communities of all sorts have emerged online from hobby based (Hall and Graham, 2004) to those that purposely produce knowledge (Roberts, 2014) and still others that are facilitated and nurtured within multinational organizations like those established by Shell Oil Company and international institutions such as the World Bank (Wenger et al., 2002). Community then has become a descriptor of many types of spatially varied groups, organizations, and institutions. Moreover, since the 1990s business organizations have increasingly turned to communities to support their knowledge-based activities and to enhance their capacities for creativity and innovation (Amin and Roberts, 2008a). This turn towards community reflects the shift towards knowledge-based production in the advanced countries (Drucker, 1993; OECD, 1996). In such economies, creativity and innovation are essential for sustained success in highly competitive markets. The speed of innovation and the capacity for continual creativity determine the survival of companies and the prosperity of nations. As a result, creativity and innovation have become a focus of scholarly research, policy interest and popular debate as evidenced by the publication of numerous books and reports on the creative economy and its various components in the first decade of the twenty-first century. Examples include John Howkins’ (2007) The Creative Economy, Richard Florida’s (2002) The Rise of the Creative Class and the United Nations’ series of reports on the creative economy (2008, 2010, 2013). In the contemporary context, then, communities are being nurtured and adopted by business organizations with the purpose of promoting and appropriating the creative and innovative potential that they offer. This chapter aims to critically examine the relationship between community, creativity and innovation to reveal how community can facilitate creative and innovative activity. The chapter begins by considering the concepts of community, creativity and innovation. To explore the creative potential of community, the communities of practice approach to understanding and facilitating learning and knowledge generation is reviewed. This 342

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is followed by an examination of different types of community as sites of creativity and innovation. The potential of communities to inhibit creativity and innovation is then considered before brief conclusions are drawn.

COMMUNITY AND CREATIVITY AND INNOVATION Community The idea of community has attracted much research attention among social scientists (Tonnies, 1963; Etzioni, 1996; Putnam, 2000; Bowles and Gintis, 2002; Delanty, 2010; inter alia). The term community is open to multiple interpretations and it has therefore been variously defined. For example, Bowles and Gintis (2002, p. 420) define a community as ‘a group of people who interact directly, frequently and in multi-faceted ways’. Such a definition is broad ranging and it includes people who work together, some neighbourhoods, groups of friends, professional and business networks, sports teams and gangs of various types. Definitions of this sort underline the ubiquitous nature of community since it can be found anywhere that people interact and develop relationships. Communities rely on various mechanisms that people have traditionally used to regulate their common activity. These include trust, solidarity, reciprocity, reputation, personal pride, and respect, as well as mechanisms to punish nonreciprocal behaviour such as vengeance and retribution (Bowles and Gintis, 2002, p. 424). The presence of such regulatory mechanisms gives communities their cohesion. Hence, for the purposes of this chapter community is taken to be characterized by relationships among a group of people, which are governed by an accepted set of norms and values. The governance of communities depends on social capital, which Putnam (2000, p. 19) defines as ‘connections among individuals – social networks and the norms of reciprocity and trustworthiness that arise from them’. Putman makes a distinction between two types of social capital: ‘[b]onding social capital constitutes a kind of sociological superglue, whereas bridging social capital provides a sociological WD-40’. In a sense, bridging social capital underpins the strength of weak ties (Granovetter, 1973) and allows community members to access knowledge from a diverse range of other communities. Importantly, bonding social capital, which underpins reciprocity, loyalty and solidarity within communities, can encourage an inward-looking stance and resistance to change. Nevertheless, the presence of social capital within communities can support the efficient organization of activities by facilitating the coordination of actions among individuals. Although overshadowed by markets and hierarchies in the economic context it is important to recognize that community is not a new phenomenon in this context (Storper, 2008). Social norms and practices underpin trust and reciprocity, thereby providing the foundation for legal contracts upon which economic activity depends. In recent years, there has been a growing interest in community as an alternative or complementary organizational form (Wenger and Snyder, 2000; Adler, 2001; Amin and Roberts, 2008a). For instance, Adler (2001) forwards community as a third organizational form relative to markets and hierarchy, arguing that with its reliance on trust as its key coordination mechanism, rather than price in the market and authority in the hierarchy,

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community has a stronger capacity to effectively manage knowledge assets, which are often embedded in a knowledge-intensive and highly creative workforce. Monitoring high-skilled knowledge workers is difficult because they often require a high level of autonomy to work effectively and, due to their specialist knowledge, the quality of their work can be difficult for managers to assess on an ongoing basis. Such factors undermine any recourse to contractual and hierarchical management arrangements. Hence, rather than aligning workers’ interests with the organization through the use of incentives and the exertion of authority, promoting a community-based organization allows alignment to occur through the adoption and promotion of certain social norms. Indeed, Mintzberg (2009, p. 141) calls for companies to rebuild as communities, noting that highly successful and innovative companies like Toyota, Semco (Brazil), Mondragon (a Basque federation of cooperatives) and Pixar have a strong sense of community. Although communities and communities of practice are largely synonymous, a specific practice is central to the latter. Communities engage in various practices, but communities of practice form around a specific practice. Before examining communities of practice to elaborate on the creative potential of community, it is necessary to turn in the next subsection to the notions of creativity and innovation. Creativity and Innovation Discussions of the nature of creativity often focus on identifying the characteristics of creative individuals (see, for example, Csikszentmihalyi; 1996; Amabile, 1997). In her theory of creativity, Amabile (1997) identifies three key components of individual creativity: expertise, creative-thinking skills and intrinsic task motivation. She argues that creativity is most likely to occur when an individual’s skills overlap with their strongest intrinsic interests; and the higher the level of each of these three elements, the greater the propensity for creativity. This focus on the individual as a source of creativity is evident in popular debates, which emphasize the role of creative individuals and their need for freedom to express their talent or vision (Bilton, 2007). This conception is reflected in reports on how companies noted for their creativity, like Google, Lego and Pixar, provide opportunities for individual creativity and play in the workplace in the form of recreational areas that include brightly coloured beanbags, games and other forms of entertainment (Stewart, 2013). The link between individualism and creativity is rooted in the Western philosophical tradition. Yet as Bilton (2007) argues, conflating creativity with individualism disconnects creative thinking and creative people from the socio-cultural and economic contexts that give meaning and value to innovations and individual talents. Recognizing the importance of context, Csikszentmihalyi (1996, p. 6) notes that: creativity results from the interaction of a system composed of three elements: a culture that contains symbolic rules, a person who brings novelty into the symbolic domain, and a field of experts who recognize and validate the innovation. All three are necessary for a creative idea, product, or discovery to take place.

Moreover, as Cohendet et al. (2014) show in relation to the epistemic community formed around the Cubism artistic movement, which emerged in Paris during the early years of the twentieth century, there is a dynamic process of knowledge creation based on clashes between different frames of reference, which enables bits of knowledge to be progressively

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revealed, enhanced, interpreted and enacted collectively. Creativity does not then occur in isolation; rather it is situated in a context and occurs through interactions between people and their existing norms and practices. Creativity does not merely add to existing systems but also challenges and changes existing frames of reference. For creativity to have value in the business context, it must result in something that is new to the world – in the sense that it should deviate in some way from established business norms and conventions – rather than be merely new to the individual (Bilton, 2007). Moreover, Amabile notes that in the field of business, creativity goes beyond originality: ‘To be creative, an idea must also be appropriate – useful and actionable. It must somehow influence the way business gets done – by improving a product, for instance, or by opening up a new way to approach a process’ (1998, p. 78). This view of creativity in the business field requires innovation. Although the terms innovation and creativity have become increasing synonymous in contemporary debates, it is important to note that while creativity is a necessary component in innovation, alone it does not guarantee the development of a new product or process. Innovation involves the production of some new knowledge or creation/invention that can result in novel intermediate and/or final products and/or processes or services available in commercial markets. Innovation, then, necessitates the development of value from creativity. Nevertheless, to take an invention through to the market may require a degree of creative thinking at every stage in the process, from the generation of a new idea through to the manufacturing of the corresponding object or service, its delivery system, its marketing and its sales, and even the consumption process. Additionally the wider socio-economic institutional environment influences innovation. Hence, territorial innovation models (Moulaert and Sekia, 2003) suggest that the development and diffusion of innovation is supported by macro contexts that can be characterized as, for instance, national and regional systems of innovation (Freeman, 1987; Lundvall, 1992; Howells 1999; Bathelt and Henn, Chapter 28, this volume; Lundvall, Chapter 29, this volume). While such territorial innovation models focus on the institutional and policy structures within which creativity and innovation occur and diffuse, a focus on community offers the potential to explore the micro-level social interactions that underpin knowledge creation and innovation (Amin and Cohendet, 2004; Gertler, 2008; Cohendet and Simon, 2008; Cohendet et al., Chapter 13, this volume). Indeed, communities, whether members are co-located or distributed, can function as spaces of innovation (Roberts, 2013). To elaborate on the creative potential of communities, attention now turns to the idea of communities of practice, which has been adopted in academic and practitioner fields as a means to both analyse and stimulate learning and knowledge generation.

COMMUNITIES OF PRACTICE AS SITES OF CREATIVITY AND INNOVATION Communities of Practice Since the term ‘communities of practice’ was coined by Jean Lave and Etienne Wenger (1991) in their seminal work, Situated Learning: Legitimate Peripheral Participation,

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it has gained recognition beyond the field of education studies and has entered into academic and practitioner discourse in disciplines as varied as management, economic geography and medicine. Lave and Wenger (1991) investigated learning in the context of five apprenticeships: Yucatec midwives, Vai and Gola tailors, naval quartermasters, meat cutters, and non-drinking alcoholics. They found that newcomers to these communities gain knowledge initially as legitimate peripheral participants engaging in socially situated practice alongside old-timers. As time passes, newcomers develop their skills and become full participants. As the title of their book suggests, Lave and Wenger’s (1991) idea of communities of practice does not take centre stage in this work. Rather the focus is the concept of legitimate peripheral participation with communities of practice being the context within which this idea is explored. Nevertheless, by introducing the idea of communities of practice as ‘a set of relations among persons, activity, and world, over time and in relation with other tangential and overlapping communities of practice’, Lave and Wenger (1991, p. 98) initiated the development of a vast amount of academic and practitioner reflection on knowledge and learning through socially situated practice (see, for example, Amin and Roberts, 2008b; Murillo, 2011). Brown and Duguid (1991, 1998) took up Lave and Wenger’s (1991) idea of communities of practice and, drawing on Orr’s (1996) study of Xerox repair technicians, developed its relevance to organizing knowledge in a management context. Importantly, Wenger (1998), in his book Communities of Practice: Learning, Meaning, and Identity, presented a detailed account of the dynamic operation of communities of practice drawn from an ethnographic study of an insurance claims processing office. Wenger (1998, pp. 72–84) identified three dimensions of the relation by which practice is the source of coherence of a community. Firstly, members interact with one another, establishing norms and relationships through mutual engagement. Secondly, members are bound together by an understanding of a sense of joint enterprise. Finally, members produce over time a shared repertoire of communal resources, including, for example, language, routines, artefacts and stories. The existence of a community of practice may not be evident to its members because, as Wenger (1998, p. 125) notes, ‘a community of practice need not be reified as such in the discourse of its participants’. Nevertheless, he argues that a community of practice does display a number of characteristics including those listed in Table 21.1. For Wenger (1998), communities of practice are important places of negotiation, learning, meaning and identity. Within communities of practice, meaning is negotiated through a process of participation and reification – defined as the process of giving form to experience by producing objects (Wenger, 1998). Such forms take on a life of their own outside their original context where their meaning can evolve or even disappear. From Learning to Creativity and Innovation The idea of communities of practice emerged in the 1990s during a period in which the central significance of knowledge to the economic success of nations and firms became apparent (OECD, 1996; Drucker, 1993; Grant, 1996). Hence, knowledge management, which may be defined as ‘any process or practice of creating, acquiring, capturing, sharing and using knowledge, wherever it resides, to enhance learning and performance in organizations’ (Scarbrough et al., 1999, p. 1), was taken up by firms as a means of

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Table 21.1 The characteristics of communities of practice Key characteristics of a community of practice ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Sustained mutual relationships – harmonious or conflictual Shared ways of engaging in doing things together The rapid flow of information and propagation of innovation Absence of introductory preambles, as if conversations and interactions were merely the continuation of an ongoing process Very quick setup of a problem to be discussed Substantial overlap in participants’ descriptions of who belongs Knowing what others know, what they can do, and how they can contribute to an enterprise Mutually defining identities The ability to assess the appropriateness of actions and products Specific tools, representations and other artefacts Local lore, shared stories, inside jokes, knowing laughter Jargon and shortcuts to communication as well as the ease of producing new ones Certain styles recognized as displaying membership A shared discourse reflecting a certain perspective on the world

Source: Compiled from Wenger (1998, pp. 125–126).

securing and developing competitive advantage (Davenport and Prusak, 1998; Nonaka and Takeuchi, 1995; Leonard-Barton, 1995; inter alia). Initially, knowledge management practices focused on the use of information technology to capture and codify knowledge. However, such approaches, which emphasized knowledge as an object rather than knowing as a process, neglected the tacit and personal dimensions of knowledge (Davenport et al. 1998; Roberts, 2001). Recognition of these limitations encouraged the search for a deeper understanding of knowledge drawing on philosophical considerations of the nature of knowledge such as those of Polanyi (1966, 1958). Hence, attention turned to the tacit and personal dimensions of knowledge as well as to knowing as a socially situated dynamic practice. For as Orlikowski (2002, p. 249) notes, ‘knowing is not a static embedded capability or stable disposition of actors, but rather an ongoing social accomplishment, constituted and reconstituted as actors engage the world in practice’. With this change in emphasis, practice-based approaches focused on the socially interactive dimensions of learning and knowledge creation attracted attention from a variety of organizational researchers (Blackler, 1995; Boland and Tenkasi, 1995; Gherardi et al., 1998; inter alia). Against this backdrop, communities of practice gained traction as a mechanism to manage tacit and socially embedded knowledge within organizations, as well as a method of analysing knowledge within a variety of contexts. But how do communities of practice, initially developed as an approach to understanding and promoting situated learning, facilitate knowledge creation and innovation? It is firstly important to recognize that learning itself involves creativity at the individual level and learning can also stimulate creativity at the level of the organization. Moreover, as organizations adapt to their changing environment, learning occurs. In their seminal contribution on Organizational Learning, Chris Argyris and Donald Schön (1978) distinguish between single-looped and double-looped learning. Single-looped learning occurs when organizational errors are

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detected and corrected, allowing the organization to proceed with its present policies to achieve its current objectives. Double-loop learning takes place when the correction of an organizational error requires the modification of the organization’s underlying norms, policies and objectives. Thus, learning involves more than merely passing on information and ensuring that it is correctly applied. Learning implies evolution and organizational change. Learning involves creativity, which can lead to significant innovations. As Lave and Wenger (1991) elaborated, learning occurs in everyday practice and therefore communities of practice offer a framework for understanding this learning process within groups that are situated in, and beyond, the boundaries of organizations. Importantly, communities of practice are not stable or static entities, rather they evolve over time as new members join and others leave (Borzillo et al., 2011). However, it is important to note that newcomers bring with them knowledge that can be brought to bear on the activities of the community. Learning is not a one-way process, but rather interactive. As Wenger (1998) notes, meaning is negotiated within the community; oldtimers and newcomers participate in these negotiations and it is through this negotiation of meaning that new knowledge is created and applied. Such knowledge can travel beyond the boundaries of the community as members participate in external activities and other related communities. Communities of practice also gain creative stimulus from their interactions with other related communities. As Wenger (1998) noted, communities of practice may be a part of a number of constellations of communities of practice sharing a variety of characteristics. According to Wenger (1998), when a social configuration is viewed as a constellation rather than a community of practice, the sustaining of the constellation must be maintained in terms of interactions among practices involving boundary processes. Wenger (1998, 2000) identifies a number of boundary processes through which knowledge can be transferred, including brokering, boundary objects (Star and Griesemer, 1989), boundary interactions and cross-disciplinary projects. Elements of styles and discourses can travel across boundaries (Wenger, 1998, p. 129) and as they diffuse through a constellation they can be shared by multiple practices. This sharing of resources can stimulate creativity as ideas travel between communities and evolve when adapted to new contexts. Moreover, it is at the boundaries of communities that friction arising from interactions between different bodies of knowledge and alternative frames of reference can generate the spark required for creative insights that can lead to major innovative breakthroughs. Importantly, though, as Nooteboom (2008) notes, for creative interaction between communities there is a need for sufficient cognitive proximity to allow productive communication. Such cognitive proximity may imply a degree of shared social capital. Too little cognitive proximity will undermine the ability of members from different communities to engage with one another – there will be insufficient common knowledge from which to develop mutual understandings. However, where cognitive proximity is too high there will be little to be gained from interaction since the knowledge sets of both parties will be too similar to stimulate cognitive dissonance and the opportunities for creativity that such dissonance may inspire. Hence, a balance between cognitive distance and cognitive proximity is required to allow productive communication between the communities yet simultaneously provide sufficient cognitive dissonance to spark creativity. So while communities may offer a means of facilitating innovation, intercommunity innovation may be limited by the extent to which the interaction is

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characterized by cognitive dissonance rather than cognitive resonance. Nooteboom’s (2008) analysis underlined the need for bridging social capital to facilitate links between different communities as well as the significance of weak ties for the creative potential of inter-community activity. Communities of practice would then appear to hold value in terms of understanding learning and knowledge generation beyond the boundaries of commercial organizations at various spatial scales. However, it is questionable as to whether what are conceptualized as communities of practice in many empirical studies and practical applications are communities of practice in the sense of Lave and Wenger’s (1991) original elaboration. The original conceptualization was very much one of learning and knowledge creation embedded and situated in social practices. Yet the idea has evolved over the past 20 years to incorporate a wide range of community activities (Duguid, 2008; Lave, 2008) Many of the activities referred to as communities of practice by management practitioners and academics do not involve situated social practice, but rather dislocated practice, with members being separated in time and/or space. For instance, many academic studies used the communities of practice approach to analyse knowledge transfer in online or virtual organizations (Pan and Leidner, 2003; Hara et al., 2009; Roberts, 2014; inter alia). Additionally, although much of the interaction incorporated in studies of communities of practice may be situated, in the sense that it occurs face to face, it may not be directly linked to the process of learning and knowledge creation in practice. Social interactions in professional associations, for instance, may influence the knowledge or learning of individual members, but that learning does not necessarily occur through the socially situated practice of a number of members working together. For instance, the recommendation of a particular text given in a meeting of a professional association does not result in learning during the social interaction of the meeting. Rather the learning occurs as individuals separately engage with the text. Social interaction can occur, then, without their being a direct impact on practice or learning. As noted above, communities of practice have been identified at various spatial scales from the local to the global. For instance, in studies of urban economic development they have been identified in relation to Silicon Alley in Newcastle upon Tyne (Conway et al., 2005) and as facilitators of creativity in the city of Montréal (Cohendet et al., 2010). At a global level, Saxenian’s (2006) study of Silicon Valley’s immigrant high-technology entrepreneurs illustrates how community ties can facilitate learning and the construction of across-borders communities that reach from, for example, the US’s Silicon Valley to Israel’s Tel Aviv and Taiwan’s Hsinchu regions and to China’s Beijing and Shanghai regions. Such spatially distributed communities of practice provide a mechanism through which globally distributed knowledge can contribute to locally specific innovation projects. Studies employing a communities of practice framework cover a wide range of activities from those focused on professions, such as law and architecture (Faulconbridge, 2007, 2010), creative sectors, including advertising and videogames development (McLeod et al., 2011; Vallance, 2014), and healthcare (Bate and Robert, 2002) to education (Orsmond et al., 2013), interdisciplinary research activities (Siedlok et al., 2015), university–industry interactions (Gertner et al., 2011) and environmental regulation (Madsen and Noe, 2012). As Murillio (2011) demonstrates, the number of academic studies applying the

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communities of practice approach to understanding knowledge creation and sharing have increased rapidly since the publication of Lave and Wenger’s (1991) study. Furthermore, communities of practice have been adopted and practically applied in a wide variety of organizational contexts (Amin and Roberts, 2008a), including the Shell Oil Company, Daimler Chrysler, the Hewlett-Packard Company, McKinsey and Company, and the World Bank (Wenger et al., 2002). There are then many different types of knowledgegenerating communities. Based on an extensive review of the communities of practice literature, Amin and Roberts (2008b) present a typology to differentiate between various sorts of knowing in action associated with communities (Table 21.2). Four types of knowing associated with four separate, though often overlapping, activities are identified: Craft/task-based, Professional, Epistemic/creative and Virtual. Although these groups are not intended to be exhaustive or mutually exclusive, they do illustrate how variety matters. As Amin and Roberts (2008b) note, the development of knowledge in communities forming around these four activities require distinctive patterns of social interaction and spatial characteristics and they give rise to different sorts of creativity and innovation and are characterized by different organizational dynamics. To examine the relationship between community and creativity and innovation it is worth examining the innovation potential of these various activities further. In Craft-/task-based activities, knowing is acquired through the development of kinaesthetic and aesthetic senses achieved through the repeated practice of certain tasks under close supervision from core members of the community. The aim of members is to master a given set of tacit knowledge in the form of embodied skills, which requires close proximity and face-to-face interaction with skilled workers. Craft-/task-based communities, such as the flute makers studied by Cook and Yanow (1993) and insurance claims administrators observed by Wenger (1998), are concerned with the mastery and preservation of certain skills. In craft activities where the perpetuation of specific methods of production are central to the value of the activity, as in the case of the production of bespoke hand-made shoes, the introduction of innovations into the making process can undermine the nature and value of the activity. Consequently, the creative and innovation capacities of communities that form around such activities tend to be incremental and arise from the honing of processes and customized or bespoke production. In contrast, the knowledge dynamic in professional activity is quite different in that knowledge is often acquired through lengthy periods of training involving the absorption of a given, largely codified, cannon of knowledge through the application of intellectual capacities. Tacit knowledge in the form of embodied skills may also be important in some professions, such as medicine where areas like surgery require the ability to draw on tacit knowledge. In addition, professions tend to be highly regulated in terms of membership requirements and aspects of practice; factors that may create barriers to radical innovation. Resistance to change is likely to be particularly acute when it has potential to undermine the interests of the profession’s members. In their study of the healthcare sector, Currie and Suhomlinova (2006) found that innovations with potential to undermine professional status are likely to be resisted. Consequently, innovation in communities forming around professional activities is likely to be taken up slowly or to be incremental in nature. Moreover, as Ferlie et al. (2005) discovered in a qualitative study tracing a number of innovations in the UK healthcare sector, when innovation involves interactions

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Aesthetic, kinaesthetic and embodied knowledge

Specialized expert knowledge acquired through prolonged periods of education and training Declarative knowledge Mind-matter and technologically embodied (aesthetic and kinaesthetic dimensions) Specialized and expert knowledge, including standards and codes, (including meta-codes) Exist to extend knowledge base Temporary creative coalitions; knowledge changing rapidly Codified and tacit from codified Exploratory and exploitative

Craft/ task-based

Professional

Source: Amin and Roberts (2008b, p. 357).

Virtual

Epistemic/ creative

Type of knowledge

Activity

Long lived and apprenticeship based Developing sociocultural institutional structures Long lived and slow to change. Developing formal regulatory institutions

Temporal aspects

Social interaction

Social interaction Long and short lived mediated through Developing through technology – face to fast and screen. Distantiated asynchronous communication interaction Rich web-based anthropology

Co-location required in the development of professional status for communication through demonstration. Not as important thereafter Spatial and/or Short lived, drawing relational proximity. on institutional Communication resources from facilitated through a variety of a combination of epistemic/creative face-to-face and fields distantiated contact

Knowledge transfer requires co-location – face-to-face communication, importance of demonstration

Proximity/nature of communication

Table 21.2 Varieties of knowing in action

Hierarchically managed Open to new members

Organizational dynamic

Incremental Large hierarchically or radical but managed strongly bound organizations or by institutional/ small peer-managed professional rules organizations Radical innovation Institutional stimulated by restrictions on contact with other the entry of new communities members Trust based on High energy, radical Group/project reputation and innovation managed expertise; weak Open to those with a social ties reputation in the field Management through intermediaries and boundary objects Weak social ties; Incremental and Carefully managed by reputational radical community trust; object moderators or orientation technological sequences Open, but self-regulating

Institutional trust based on professional standards of conduct

Interpersonal Customized, trust – incremental mutuality through the performance of shared tasks

Nature of social ties

Innovation

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between professional communities it is more likely to be promoted than when it involves interactions between professional and non-professional communities. Communities that form around epistemic/creative activities, like the communities of physicists and molecular biologists studied by Knorr Cetina (1999), have as their central purpose the advancement of knowledge. Hence, by bringing experts together explicitly to develop new knowledge, they display a high propensity for creativity and the production of radical innovation. Even though they may be short-lived, like those formed around the production of a film, with the individuals coming together temporarily and dispersing once their objective has been achieved (DeFillippi and Arthur, 1998), the creative potential is not undermined. Once a project is completed, members of such temporary creative projects remain loosely connected through relationships of trust, reputations and expertise. Many communities engage in virtual activities, often as a complement to face-to-face interaction (Grabher and Ibert, Chapter 33, this volume). Amin and Roberts (2008b) identify virtual as a fourth type of knowing in action, but, in so doing, they recognize the overlaps with the other three types of knowing activity that they delineate. Indeed, virtual communities engage in a wide range of heterogeneous activity, much of which is not centrally concerned with creativity and innovation. However, Amin and Roberts (2008b) identify two types of online interaction that hold creative potential. Firstly, those engaged in innovation-seeking projects that can involve a large number of participants such as Open Source Software (OSS) communities, and, secondly, relatively closed interest groups facing specific problems and consciously organized as knowledge communities. The latter includes self-help groups, such as the Swedish patient online community studied by Josefsson (2005), which have become important mechanisms for information exchange and new knowledge formation. Virtual communities can produce incremental or radical innovation. For example, in OSS communities, incremental innovation may take the form of fixing a bug in existing software, and radical innovation may involve the development of a completely new software products. Virtual communities demonstrate that it is possible to identify forms of distantiated knowledge activity that promote the social dynamics required to support creativity and innovation. By bridging across distance, virtual communities can link spatially distributed creative capacities, thereby allowing opportunities for the development of new knowledge beyond those available in locationally bound communities. Consequently, the mediated sociality occurring in virtual communities can be no less powerful than the face-to-face sociality of co-located communities (Amin and Roberts, 2008b). Different types of knowing activity give rise to different types of creativity and innovation. Furthermore, it is important to note the varieties of contexts within which knowledge activities are embedded. This is clear in relation to the professional activities – the structures that ensure cohesion in a profession, such as associations, regulatory bodies and educational requirements, can lead to rigid membership criteria and resistance to change. Hence, where a practice is highly regulated, creativity and innovation may be dampened by the requirements of such regulation. In contrast, where a practice is free from regulatory control and membership is open to newcomers, learning, creativity and innovation will be promoted. Furthermore, communities, including their regulatory characteristics, do not exist in isolation, and the extent to which creativity and innovation may occur through situated

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practice will also be influenced by the broader institutional structures and socio-cultural contexts within which a community is embedded. There may be cultural differences, for instance in the levels of trust, that influence engagement in community activities, and such differences may impact on the level of creative activity or the level of openness to innovation and change (Roberts, 2006). Cultural differences may be embedded in national institutional structures. For instance, in a study of the introduction of new practices in the subsidiaries of two multinational enterprises in two contrasting national institutional systems, Hotho et al. (2014) found that the interplay between the national institutional context and organizational structure does matter. Even so, they found that actors engaged in situated learning can overcome a lack of institutional alignment (Hotho et al., 2014).

COMMUNITY: FACILITATOR OR INHIBITOR OF CREATIVITY AND INNOVATION? To secure competitiveness, based on a steady flow of creative products, processes and services, companies of all sizes are seeking to harness the innovative resources emerging in social environments and through local and distributed communities of all sorts. Although communities may be seen as supplementary to formal organizations, whether they are situated within, between or beyond the boundaries of firms, they are not synonymous with them. Communities that reach across organizations give firms opportunities to access knowledge from the wider environment. Such communities include the social and professional communities in which employees participate. By maintaining links to external communities, such as professional bodies and trade associations, business organizations maintain open channels through which external knowledge can be accessed. Interaction with such communities adds to the porosity of the organization’s boundaries and thereby to its flexibility and ability to respond to changing market opportunities and challenges. Social interaction is a powerful source of creative stimulus; free from the imperatives of ownership and control, ideas can flow freely among community participants (Roberts, 2010). As a vehicle or container of sociality, community is therefore fertile ground for creativity that can be harnessed for commercial innovative activity (Leadbeater, 2008). Communities of practice that exist independently of business organizations may take on an increasingly important role in the creation and transfer of commercial knowledge. Workers increasingly operate in an individualistic world of weak ties where resources are frequently obtained through personal networks and individual relationships rather than through organization-based communities (Kimble and Hildreth, 2004). Individuals belong to a variety of communities of practice, some internal to their work organization while others arise from their personal and professional networks. For business organizations, employee engagement in external communities offers a source of additional knowledge. However, such engagement raises the risk of knowledge leaking to competitors. Nevertheless, in fast-changing markets there is an increasing trend towards open innovation (Chesbrough, 2003) as well as a recognition that innovation does not always occur within the firm (Gabriel, 2005). For instance, lead-user communities have been identified as important contributors to product development in a variety of sectors (von Hippel, 2005), and companies are increasingly seeking to harness their input through, for instance, specific social software (Burger-Helmchen and Cohendet, 2011).

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However, as the analysis detailed above illustrates, the creative potential of communities may vary depending upon their purpose and the context within which they are situated. Communities, even those formed around learning and knowledge activity, are not necessarily conducive to innovation. In some areas, community can stifle change and therefore hinder creativity and innovation. The issue of power has been marginalized in discussions of situated learning in communities (Contu, 2014; Contu and Willmott, 2003; Fox, 2000). This may be because the idea of community carries with it the positive connotations of a warm, comfortable, cosy place, characterized by a common understanding and consensus. Yet, it is important to recognize that relationships of power and points of conflict do exist within communities (Bauman, 2000; Roberts, 2006). Power and its distribution within and between communities can influence the extent to which creative activities lead to new practices. As noted above in relation to professional communities, vested interests may marginalize creative activity in favour of the status quo. In addition, as Mutch (2003) notes, members of communities have pre-existing characteristics and preferences, which result from their habitus and the social codes with which they are familiar, developed independently of the community. Such characteristics may influence the capacity of members to engage in creative and innovative behaviour. More broadly, the balance within communities between bonding and bridging social capital will influence the extent to which communities take up opportunities to develop new knowledge through interaction with other communities. Hence, communities with a high proportion of bonding relative to bridging social capital may encourage an inwardlooking stance rather than an open and outward-looking attitude. Given that innovation often begins at the intersections between communities, a higher proportion of bonding social capital relative to bridging social capital will negatively affect the community’s propensity to innovate. The value of communities of practice in the promotion of learning and knowledge generation will vary according to the broad socio-cultural context (Roberts, 2006). For instance, national competitiveness deriving from knowledge creating and sharing capabilities may vary according to nation-specific socio-cultural characteristics, such as levels of trust or the relative position of the individual versus the community. The community of practice as a tool of learning and knowledge creation and innovation may well be more successful in those regions and nations that have a strong community spirit, so long as they remain open to outside influences, compared to those nations that have a weak community spirit. Consequently, the broad national system of innovation will influence the success of communities of practice as a mechanism for knowledge transfer and generation. It is also important to recognize that a nation’s innovative capacities may also be influenced by the presence of immigrant communities that are linked to a widely dispersed diaspora or from returnee entrepreneurs like those highlighted by Saxenian (2006) in the high-tech sector. These groups bring with them fresh socio-cultural perspectives that have the potential to influence a regional or national innovation system. In the highly globalized world of the twenty-first century, those involved in innovation have a wide variety of socio-cultural knowledge from which to draw. Even so, the degree to which communities are open to new ideas and the changes that these may bring about varies. A range of differences, including norms, values, cultures, ethics and so on, will mediate openness to outside perspectives. For knowledge to be

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successfully mobilized, transferred or shared to facilitate creativity and innovation, a degree of alignment between insiders and outsiders is crucial. Individuals who act as brokers and boundary spanners are vital as facilitators of creative interchanges between communities, especially when the communities display a low level of alignment (Wenger, 2000).

CONCLUSION What this chapter shows is that community can certainly facilitate creativity and innovation. As many studies demonstrate, the communities of practice approach can be usefully employed to examine the learning and knowledge creation that occurs both within and between communities. However, it is important to note that communities are not homogenous in their knowledge dynamics and consequently some are more likely to facilitate incremental changes rather than engage in radical creativity and innovation. It is also the case that some communities that are established to maintain certain practices will inhibit creativity and innovation when it is likely to undermine the purpose of the community or the standing of its members. Furthermore, the context from which members are drawn can influence the capacity of a community to innovation. Where members are drawn from different backgrounds, there may be opportunities for creativity arising from conflicting frames of reference, compared to when members have similar backgrounds. However, the habitus and social codes that are inculcated into individual members can also hinder creative and innovative activity if they result in resistance to change. Additionally, the capacities of communities to facilitate creativity and innovation are influenced by the context within which they are situated. This context is relevant at various levels, so, for instance, the sectoral context may be important in terms of the degree to which the community’s activities are regulated. Context is also relevant at a macro level in terms of national institutional structures and socio-cultural conditions. Although communities, and especially communities of practice, have received much attention in the past 20 years as sources of situated learning and knowledge generation, it is necessary to recognize the challenges that communities may present to creative activity and the adoption of radical innovation. Many studies have highlighted the positive aspects of communities as facilitators of creativity and innovation; further efforts are required to develop a deeper appreciation of how inward-looking communities and those in with powerful members with strong vested interests suppress the creative drive of individual members.

REFERENCES Adler, P. S. (2001) ‘Market, hierarchy, and trust: the knowledge economy and the future of capitalism’, Organization Science 12(2): 215–234. Amabile, T. M. (1997) ‘Motivating creativity in organizations: on doing what you love and loving what you do’, California Management Review, 40(1): 39–58. Amabile, T. M. (1998) ‘How to kill creativity’, Harvard Business Review, 76(5): 77–87. Amin, A. and Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities, and Communities, Oxford: Oxford University Press.

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Amin, A. and Roberts, J. (2008a) ‘The resurgence of community in economic thought and practice’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 11–34. Amin, A. and Roberts, J. (2008b) ‘Knowing in action: beyond communities of practice’, Research Policy, 37(2): 353–369. Argyris, C. and Schön, D. (1978) Organizational Learning: A Theory of Action Perspective, Reading, MA: Addison Wesley Longman Publishing Co. Bate, S. P. and Robert, G. (2002) ‘Knowledge management and communities of practice in the private sector: lessons for modernising the National Health Service in England and Wales’, Public Administration, 80(4): 643–663. Bathelt, H. and Henn, S. (2017) ‘National and regional innovation systems’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 457–471. Bauman, Z. (2000) Liquid Modernity, Cambridge: Polity Press. Bilton, C. (2007), Management and Creativity: From Creative Industries to Creative Management, Oxford: Blackwell Publishing. Blackler, F. (1995) ‘Knowledge, knowledge work and organizations: an overview and interpretation’, Organization Studies, 16(6): 1021–1046. Boland, R. J. and Tenkasi, R. V. (1995) ‘Perspective making and perspective taking in communities of knowing’, Organization Science, 6(4): 350–372. Borzillo, S., Aznar, S. and Schmitt, A. (2011) ‘A journey through communities of practice: how and why members move from the periphery to the core’, European Management Journal, 29: 25–42. Bowles, S. and Gintis, H. (2002) ‘Social capital and community governance’, Economic Journal, 112: F419–F436. Brown, J. S. and Duguid, P. (1991) ‘Organizational learning and communities of practice: towards a unified view of working, learning and innovation’, Organization Science, 2: 40–57. Brown, J. S. and Duguid, P. (1998) ‘Organizing knowledge’, California Management Review, 40(3): 90–111. Burger-Helmchen, T. and Cohendet, P. (2011) ‘User communities and social software in the video game industry’, Long Range Planning, 44(5–6): 317–343. Chesbrough, H. W. (2003) Open Innovation: The New Imperative for Creativity and Profiting from Technology, Boston, MA: Harvard Business School Press. Cohendet, P., Grandadam, D. and Simon, L. (2010) ‘The anatomy of the creative city’, Industry and Innovation, 17(1): 91–111. Cohendet, P., Grandadam, D., Simon, L. and Capdevila, I. (2014) ‘Epistemic communities, localization and the dynamics of knowledge creation’, Journal of Economic Geography, 14(5): 929–954. Cohendet, P., Parmentier, G. and Simon, L. (2017) ‘Managing knowledge, creativity and innovation’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 197–214. Cohendet, P. and Simon, L. (2008) ‘Knowledge-intensive firms, Communities and creative cities’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity, and Organization, Oxford: Oxford University Press, 227–253. Contu, A. (2014) ‘On boundaries and difference: communities of practice and power relations in creative work’, Management Learning, 45(3): 289–316. Contu, A. and Willmott, H. (2003) ‘Re-embedding situatedness: the importance of power relations in learning theory’, Organization Science, 14(3): 283–296. Conway, C., Dawley, S. and Charles, D. (2005) ‘An investigation of the learning dynamics involved within the creative industries sector in the Newcastle City region’, Working Paper, June, Centre for Urban and Regional Development Studies, Newcastle University. Cook, S. and Yanow, D. (1993) ‘Culture and organizational learning’, Journal of Management Inquiry, 2(4): 373–390. Csikszentmihalyi, M. (1996) Creativity: The Psychology of Discovery and Invention, New York: Harper Collins Publisher. Currie, G. and Suhomlinova, O., (2006) ‘The impact of institutional forces upon knowledge sharing in the UK NHS: the triumph of professional power and the inconsistency of policy’, Public Administration, 84(1): 1–30. Davenport, T. H., De Long, D. W. and Beers, M. C. (1998) ‘Successful knowledge management projects’, Sloan Management Review, 40 (Winter): 43–57. Davenport, T. and Prusak, L. (1998) Working Knowledge: How Organizations Manage What They Know, Boston, MA: Harvard Business School Press. Delanty, G. (2010) Community, 2nd edition, London: Routledge. DeFillippi, R. J. and Arthur, M. B. (1998) ‘Paradox in project-based enterprise: the case of film making’, California Management Review, 40(2): 125–139.

Community, creativity and innovation

357

Drucker, P. F. (1993) Post-Capitalist Society, New York: Harper Business. Duguid, P. (2008) ‘Prologue: Community of practice then and now’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 1–10. Etzioni, A. (1996) The New Golden Rule: Community and Morality in a Democratic Society, New York: Basic Books. Faulconbridge, J. R. (2007) ‘Exploring the role of professional associations in collective learning in London and New York’s advertising and law professional service firm clusters’, Environment and Planning A, 39(4): 965–984. Faulconbridge, J. R. (2010) ‘Global architects: learning and innovation through communities and constellations of practice’, Environment and Planning A, 42(12): 2842–2858. Ferlie, E., Fitzgerald, L., Wood, M. and Hawkins, C. (2005) ‘The nonspread of innovations: the mediating role of professionals’, Academy of Management Journal, 48(1): 117–134. Florida, R. (2002) The Rise of the Creative Class, New York: Basic Books. Fox, S. (2000) ‘Communities of practice, Foucault and actor-network theory’, Journal of Management Studies, 37(6): 853–868. Freeman, C. (1987) Technology Policy and Economic Performance: Lessons from Japan, London: Pinter. Gabriel, R. P. (2005) Innovation Happens Elsewhere: Open Source As Business Strategy, San Francisco: Morgan Kaufmann. Gertler, M. S. (2008) ‘Buzz without being there? Communities of practice in context’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 203–226. Gertner, D., Roberts, J. and Charles, D. (2011) ‘University–industry collaboration: a CoPs approach to KTPs’, Journal of Knowledge Management, 15(4): 625–647. Gherardi, S., Nicolini, D. and Odella, F. (1998) ‘Towards a social understanding of how people learn in organizations’, Management Learning, 29(3): 273–298. Grabher, G. and Ibert, O. (2017) ‘Knowledge collaboration in virtual communities’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 537–555. Granovetter, M. S. (1973) ‘The strength of weak ties’, American Journal of Sociology, 78(6): 1360–1380. Grant, R. M. (1996) ‘Toward a knowledge-based theory of the firm’, Strategic Management Journal, 17, Winter Special Issue: 109–122. Hall, H. and D. Graham (2004) ‘Creation and recreation: motivating collaboration to generate knowledge capital in online communities’, International Journal of Information Management, 24(3): 235–246. Hara, N., Shachaf, P. and Stoerger, S. (2009) ‘Online communities of practice typology revisited’, Journal of Information Science, 2009(35): 740–757. Hotho, J. J., Saka-Helmhout, A. and Becker-Ritterspach, F. (2014) ‘Bringing context and structure back into situated learning’, Management Learning, 45(1): 57–80. Howells, J. (1999) ‘Regional systems of innovation?’, in Archibugi, D., Howells, J. and Michie, J. (eds), Innovation Systems in a Global Economy, Cambridge: Cambridge University Press, 67–93. Howkins, J. (2007) The Creative Economy, London: Penguin Books. Josefsson, U. (2005) ‘Coping with Illness online: the case of patients’ online communities’, The Information Society, 21: 143–153. Kimble, C. and Hildreth, P. (2004) ‘Communities of practice: going one step too far?’, Proceedings 9e colloque de L’AIM, Evry, France, May. Knorr Cetina, K. (1999) Epistemic Cultures: How the Sciences Make Knowledge, Cambridge, MA: Harvard University Press. Lave, J. (2008) ‘Epilogue: situated learning and changing practice. Prologue: community of practice then and now’ in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 283–296. Lave, J. and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation, Cambridge: Cambridge University Press. Leadbeater, C. (2008) We-Think: Mass Innovation, Not Mass Production, London: Profile Books. Leonard-Barton, D. (1995) Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Boston, MA: Harvard University Press. Lundvall, B.-Å. (1992) ‘Introduction’, in Lundvall, B. (ed.), National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter, 1–19. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in Bathelt, H., Cohendet, P.,  Henn, S.  and Simon, L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 472–489. Madsen, M. L. and Noe, E. (2012) ‘Communities of practice in participatory approaches to environmental regulation. Prerequisites for implementation of environmental knowledge in agricultural context’, Environmental Science and Policy, 18, April: 25–33.

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McLeod, C., O’Donohoe, S. and Townley, B. (2011) ‘Pot Noodles, placements and peer regard: creative career trajectories and communities of practice in the British advertising industry’, British Journal of Management, 22(1): 114–131. Mintzberg, H. (2009) ‘Rebuilding companies as communities’, Harvard Business Review, July–August: 140–143. Moulaert, F. and Sekia, F. (2003) ‘Territorial innovation models: a critical survey’, Regional Studies, 37(3): 289–302. Murillo, E. (2011) ‘Communities of practice in the business and organization studies literature’, Information Research, 16(1), available at: http://informationr.net/ir/16-1/paper464.html (accessed 12 February 2012). Mutch, A. (2003) ‘Communities of practice and habitus: a critique’, Organization Studies, 24(3): 383–401. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford: Oxford University Press. Nooteboom, B. (2008) ‘Cognitive distance in and between communities of practice and firms: where do exploitation and exploration take place, and how are they connected?’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 123–147. OECD (1996) The Knowledge-Based Economy, OCDE/GD(96)102, Paris: Organisation for Economic Co-operation and Development. Orlikowski, W. J. (2002) ‘Knowing in practice: enacting a collective capability in distributed organizing’, Organization Science, 13(3): 249–273. Orr, J. E. (1996) Talking about Machines: An Ethnography of a Modern Job, Ithaca, NY and London: IRL Press. Orsmond, P., Merry, S. and Callaghan, A. (2013) ‘Communities of practice and ways to learning: charting the progress of biology undergraduates’, Studies in Higher Education, 38(6): 890–906. Pan, S. L. and Leidner, D. E. (2003) ‘Bridging communities of practice with information technology in pursuit of global knowledge sharing’, Journal of Strategic Information Systems, 12: 71–88. Polanyi, M. (1958) Personal Knowledge: Towards a Post Critical Philosophy, London: Routledge and Kegan Paul. Polanyi, M. (1966) The Tacit Dimension, London: Routledge. Putnam, R. D. (2000) Bowling Alone: The Collapse and Revival of American Community, New York: Simon & Schuster. Roberts, J. (2001) ‘The drive to codify: implications for the knowledge-based economy’, Prometheus: The Journal of Issues in Technology Change, Innovation, Information Economics, Communications and Science Policy, 19(2): 99–116. Roberts, J. (2006) ‘Limits to communities of practice’, Journal of Management Studies, 43(3): 623–639. Roberts, J. (2010) ‘Community and international business futures: insights from software production’, Futures, 42(9): 926–936. Roberts, J. (2013) ‘Communities as spaces of innovation?’, in Adam, F. and Westlund, H. (eds), Innovation in Socio-Cultural Contexts, New York and London: Routledge, 80–100. Roberts, J. (2014) ‘Community and the dynamics of spatially distributed knowledge production: the case of Wikipedia’, in Rutten, R., Benneworth, P., Dessy, I. and Boekema, F. (eds), The Social Dynamics of Innovation Networks: From Learning Region to Learning in Socio-Spatial Context, New York and London: Routledge, 179–200. Saxenian, A. L. (2006) The New Argonauts: Regional Advantage in a Global Economy, Cambridge, MA: Harvard University Press. Scarbrough, H., Swan, J. and Preston, J. (1999) Knowledge Management: A Literature Review, London: Chartered Institute of Personnel and Development (CIPD). Siedlok, F., Hibbert, P. and Sillince, J. (2015) ‘From practice to collaborative community in interdisciplinary research contexts’, Research Policy, 44(1): 96–107. Star, S. L. and Griesemer, J. R. (1989) ‘Institutional ecology, “translations” and boundary objects: amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39’, Social Studies of Science, 19(3): 387–420. Stewart, J. B. (2013) ‘Looking for a lesson in Google’s perks’, The New York Times, March 15, http://www. nytimes.com/2013/03/16/business/at-google-a-place-to-work-and-play.html?pagewanted5all&_r50 (accessed 2 February 2015). Storper, M. (2008) ‘Community and economics’, in Amin, A. and Roberts, J. (eds), Community, Economic Creativity and Organization, Oxford: Oxford University Press, 37–68. Tonnies, F. (1963) Community and Association. Trans. Charles P. Loomis, New York: Harper and Row. United Nations (2008) Creative Economy Report 2008: The Challenge of Assessing the Creative Economy: Towards Informed Policy-Making, New York: United Nations. United Nations (2010) Creative Economy Report 2010: Creative Economy: A Feasible Development Option, New York: United Nations. United Nations (2013) Creative Economy Report 2013: Widening and Local Development Pathways, New York: United Nations.

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Vallance, P. (2014) ‘Creative knowing, organisational learning, and socio-spatial expansion in UK videogame development studios’, Geoforum, 51, January: 15–26. von Hippel, E. (2005) Democratizing Innovation, Cambridge, MA: MIT Press. Wenger, E. (1998) Communities of Practice: Learning, Meaning, and Identity, Cambridge: Cambridge University Press. Wenger, E. (2000) ‘Communities of practice and social learning systems’, Organization, 7(2): 225–246. Wenger, E., McDermott, R. and Snyder, W. M. (2002) Cultivating Communities of Practice: A Guide to Managing Knowledge, Boston, MA: Harvard Business School Press. Wenger, E. C. and Snyder, W. M. (2000) ‘Communities of practice: the organizational frontier’, Harvard Business Review, January–February: 139–145.

22. Industrial clusters in global networks Elisa Giuliani

INDUSTRIAL CLUSTERS BEYOND LOCAL NETWORKS Industrial clusters, a form of industrial organization where firms are spatially co-located and operate in the same or interconnected industries, are known globally for the power of their local connections. Firms’ local embeddedness has long been considered a key ingredient of their success, since it is expected to enhance access to and spur the generation of knowledge (Maskell and Malmberg 1999; Capello and Faggian 2005; Rallet and Torre, Chapter 26, this volume). In particular, the presence of local business networks is often associated with the capability of a cluster to promote localized learning (Keeble et al., 1999) both vertically, between clients and suppliers, and horizontally, among rival firms. Yet, as different generations of scholars have emphasized the virtues of local connectivity, many also pointed at the importance of an industrial cluster’s external openness and connections to global knowledge and innovation networks. The relevance of global connections became evident at the end of the 1980s, when the process of globalization (Cook and Kirkpatrick 1997) and the new international division of labor (Frobel et al. 1980; Dicken 1994) emphasized the importance of local clusters being interconnected with distant markets, both in terms of demand and supply (Amin and Thrift 1992). This perspective emphasized the significance of extra-cluster networking and the acquisition of extra-cluster knowledge to avoid phenomena of entropic death and negative lock-in (Grabher 1993) and to allow local competencies to be nurtured by knowledge transferred from non-local sources (Bathelt et al. 2004). Thus, the importance, for innovation, of extra-cluster networking was increasingly highlighted by the cluster literature in both highly developed and developing economic contexts (Becattini and Rullani 1993; Bell and Albu 1999; Humphrey and Schmitz 2000) and various contributions have explored the processes by which the integration of extracluster and intra-cluster knowledge occurs. At the outset, studies showed that the inflow of knowledge (and other valuable assets) into a cluster could be driven both by actors from outside that are attracted into the cluster by the availability of natural or knowledge resources and by local actors who try to tap into outside knowledge (Cantwell and Iammarino 2003). Among the former, key actors of the local–global nexus are those multinational corporations (Belberbos et al. 2001; Castellani and Zanfei 2002) that establish production plants in a local cluster or operate as global buyers or manufacturers that connect local clusters to global value chains (GVCs) (Gereffi and Korzeniewicz 1994; Humphrey and Schmitz 2000). Among those local actors that tap into outside knowledge, the literature emphasizes the impact of leading firms (e.g. Lazerson and Lorenzoni 1999), which are typically large, technologically advanced firms (Albino et al. 1999), sometimes referred to in the literature as technological or knowledge gatekeepers (Giuliani and Bell 2005; Morrison 2008). Compared to local firms, the role of actors from outside appears to be particularly 360

Industrial clusters in global networks 361 relevant in clusters in developing countries, where extra-cluster knowledge is of utmost importance to feeding internal learning processes (Bell and Albu 1999; Humphrey and Schmitz 2002). As Schmitz (2004, p. 4) remarks, “the clusters in developed countries are often global leaders and play a decisive role in innovation and product design. In contrast, developing country clusters tend to work to specifications that come from outside.” This chapter aims to provide an overview of three strands of scholarly research that have contributed to understanding different dimensions of a cluster’s openness to global networks and its impacts on innovation. Particular emphasis will be placed on the creation of extra-cluster linkages formed by cluster firms in order to learn, upgrade and innovate. In the following sections I will discuss international development studies on GVCs, international business (and connected international and development economics) research on technological spillovers of multinational enterprises (MNEs) and, finally, geography of innovation perspectives and work in economic geography on leading firms and technological gatekeepers in industrial clusters. At the end, I will conclude by integrating these three strands of literature and discuss some of the limitations.

INTERNATIONAL DEVELOPMENT PERSPECTIVES: GLOBAL VALUE CHAINS AND UPGRADING OF INDUSTRIAL CLUSTERS At the end of the 1990s, development scholars put emphasis on the capacity of industrial cluster firms to learn through their global linkages. Focus here was on accessing crucial knowledge to enable economic growth and development and possibly stimulate internal innovation capabilities through so-called upgrading processes. To achieve this aim, scholars adopted the GVC approach (Gereffi 1999) to the analysis of clusters (relatedly, see research on global production networks (GPNs) (e.g. Coe et al. 2008)). A GVC is defined as the full range of activities including all production stages and complementary services and supplies that are required to bring a product from its conception to the final consumer. This approach is grounded on evidence that, over the past decades, production activities have disintegrated into different phases, taking place in different parts of the globe. The literature on GVCs has stressed the role played by GVC leaders, and particularly by global buyers and manufacturers, in both orchestrating and coordinating this globally fragmented production, as well as in transferring knowledge along the chain (Van Assche, Chapter 45, this volume). A prominent example is the textile industry, where mega-brand firms like Gap or Primark act as global buyers and source their finished products from small suppliers clustered in different parts of Asia. These localized clusters have, in turn, often developed out of family and friend networks (Li 2013). For smaller firms in industrial clusters, participation in GVCs is a way to get access to new markets and, more importantly, gain the opportunity to learn about more demanding quality standards in both production processes and products – an aspect that is considered to stimulate learning and innovation. GVC scholars have referred to this process of learning using the concept of “upgrading” (Humphrey and Schmitz 2000), and have identified four types of upgrading that occur when firms connect to GVCs:

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The Elgar companion to innovation and knowledge creation process upgrading, which refers to transforming inputs into outputs more efficiently by re-organizing the production system or introducing superior technology; product upgrading, which implies moving into more sophisticated product lines in terms of increased unit values (Gereffi 1999); functional upgrading, that is, acquiring new, superior functions in the chain, such as design or marketing, or abandoning existing low-value-added functions to focus on higher-value-added activities (Bair and Gereffi 2001); intersectoral upgrading, which consists in applying the competence acquired in a particular function to move into a new sector (Guerrieri and Pietrobelli 2004; Humphrey and Schmitz 2002).

Each of these forms of upgrading is associated with specific learning processes and the implementation of new forms of knowledge. Learning and knowledge accumulation deriving from upgrading may spur firms to develop distinctive innovative capabilities. For instance, in their study on coffee producers in Brazil, Cafaggi et al. (2012) show that local companies engaged in joint product development programs with the lead firm of their value chain – Illycaffé – a collaboration that resulted in the development of new-to-theworld coffee bean varieties. In China, Nike has contributed to process upgrading in the football industry, where it has promoted the adoption of lean manufacturing principles by their football manufacturing suppliers (Nadvi 2011). These examples reflect the interest of GVC scholars in investigating the role played by the leaders of the GVCs in fostering and supporting the cluster firms’ upgrading and innovative processes (Giuliani et al. 2005; Pietrobelli and Rabellotti 2011, among many others). The general expectation was that cluster firms would benefit from being inserted into GVCs, and would experience different forms of upgrading thanks to knowledge transferred along the chain by leading firms in GVCs (Gereffi 1999; Gereffi et al. 2005; Sturgeon et al. 2008). However, in spite of a few successful cases, the empirical evidence of such processes is not clear. A recent review of studies on GVCs’ impacts on local innovation processes in developing countries suggests that these expectations are not fulfilled (De Marchi et al. 2015). They show that, first, local suppliers, in spite of being part of one or more GVCs, do not always use their connections to different GVC firms, among them leaders, as a privileged source of knowledge and technologies. Rather, local suppliers exploit the GVC only as a complementary source to access other channels of knowledge (collective learning at the local level, imitation and learning from other non-GVC actors, etc.). Second, they find that hardly any of the chains analyzed in their review is innovative or portrays significant evidence of upgrading – suggesting that global connections matter only to the extent to which local firms are able to absorb and leverage external knowledge resources. These findings are coherent with other disciplinary perspectives, which are discussed below.

INTERNATIONAL BUSINESS AND ECONOMICS PERSPECTIVES: MULTINATIONAL ENTERPRISES AND TECHNOLOGICAL SPILLOVERS The perspective in international business revolves around the behavior and impact of MNEs. While international business scholars have mainly been studying MNEs’ strate-

Industrial clusters in global networks 363 gies, motivations and organization, economists have mostly been interested in assessing their impacts on host countries. There is a long-standing tradition of research in this latter area: since the original contribution of Caves (1974), the economic literature has been quite prolific in empirically assessing the impact of MNEs on host countries and economists have identified different ways through which the presence of MNEs can have advantages or disadvantages for local firms. One impact that is particularly relevant in the context of knowledge creation is the generation of technological or productivity spillovers through MNEs, which are often measured as productivity improvements of domestic firms operating in the MNEs’ industry or in connected industries. Based on the assumption that MNEs possess technological capabilities that are superior to those of domestic firms (especially, but not only, in developing countries), scholars have in conventional studies associated these productivity improvements to MNEs’ processes of technology transfers. Thus MNEs are seen as key conduits of technological knowledge for local firms. The accumulation of new technological capabilities by domestic firms via MNEs can occur in various ways, spanning from imitation, labor mobility and collaboration to the creation of backward/forward production linkages (see Crespo and Fontoura 2007, Smeets 2008 and Giuliani and Macchi 2014 for recent reviews). However, evidence regarding the generation of technological spillovers by MNEs in host countries is widely considered inconclusive. Broader econometric studies have found evidence of positive spillovers for domestic firms (see, among many others, Kokko 1994; Sjöholm 1999; Javorcik 2004; Javorcik and Spatareanu 2011). Case studies also indicated positive contributions of MNE subsidiaries to the upgrading processes of their suppliers, through direct and purposeful transfers of knowledge or training of the labor force (see, among others, Albornoz and Yoguel 2004; Boehe 2007; Giroud 2007; McDermott et al. 2009). But, at the same time, other studies found evidence of insignificant or negative spillovers, pointing at a crowding out effect of domestic firms and, possibly, at the incapacity of MNE subsidiaries to generate a positive impact on innovation in the target countries (among others, see Aitken and Harrison 1999; Djankov and Hoekman 2000; Hu and Jefferson 2002; Lenger and Taymaz 2006). In a bid to understand what factors influence the generation of technological spillovers by MNEs, scholars have considered two aspects to be highly relevant: first, host country effects and their firms’ absorptive capacity (Cohen and Levinthal 1989; 1990) and, second, MNEs’ subsidiary characteristics. With respect to the former, scholars have shown that, in order to be able to absorb, adapt and master foreign technologies and skills, local people, firms and institutions need to have already accumulated a certain level of capabilities (see, among many others, Kokko 1994; Kinoshita 2001; Konings 2001; Lall and Narula 2004; Meyer and Sinani 2009). This reduces the technological gap that potentially exists between the local context and global knowledge and, in so doing, facilitates the transfer and successful exploitation of globally generated knowledge. With respect to the latter, scholars investigating the economic impacts of MNEs found that, to be able to generate positive spillovers, local subsidiaries need to be innovative and technologically active in their own right (Marin and Bell 2006; Miozzo and Grimshaw 2008; Marin and Sasidharan 2010; Marin and Giuliani 2011). This argument rests on the idea that “it is not merely the existence of MNEs that yields spillover benefits for the economy, but what subsidiaries actually do once they have been established or acquired” (Marin and Bell 2010, p. 928). On these grounds, technological spillovers are unlikely to

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occur if subsidiaries do not undertake any internal innovative or entrepreneurial activity that is valuable for the local economy. Another MNE subsidiary characteristic that is likely to influence its capacity to act as a conduit of international knowledge is its level of autonomy vis-à-vis its headquarters. There is significant consensus that subsidiaries that are more autonomous or are minority-owned by the headquarters are more likely to embed themselves more fully in the local economy and generate significant spillover effects (Javorcik 2004; Albornoz et al. 2005; Gentile-Lüdecke and Giroud 2012; Giroud et al. 2012; Iršová and Havránek 2012). Theoretically, this is explained through knowledge appropriation: MNEs headquarters have a direct interest in minimizing the leakage of corporate knowledge to other firms and, hence, tighter control over the subsidiary may prevent such leakage, reducing spillover effects in the host country. By contrast, as Jindra et al. (2009, p. 176) suggest, “subsidiaries with high autonomy and initiative, as well as own technological capability generate more potential for technology diffusion” locally. While this research is in principle relevant to research on cluster learning and innovation, analyses of MNEs’ local technological spillovers have, originally, neither paid particular attention to the processes through which technology transfers to domestic firms occur, nor have they focused on clusters or regions as recipients of MNEs’ technology. Instead, their focus has rather been on host countries or, at best, industries, as their final unit of analysis. More recently, international business scholars have displayed a growing interest in connecting this research to economic geography, setting a clear research agenda on how this intersection contributes to better understand local and global innovative processes (Mudambi and Swift 2011; Beugelsdijk and Mudambi 2013; Dau 2013; Iammarino and McCann 2013). At the same time, economic geographers have intensified their interest in international business research (Lorenzen and Mudambi 2012; Bathelt and Li 2014; Bathelt and Cohendet 2014). Since these are relatively recent tendencies, it is difficult to provide an overarching review of the key achievements of this new research agenda here. However, this is a promising area that is likely to provide a richer understanding of MNEs’ contribution to the innovativeness of cluster firms (and vice versa), and it could become an important complement to geography of innovation and economic geography research discussed in the section that follows.

GEOGRAPHY OF INNOVATION PERSPECTIVES: TECHNOLOGICAL GATEKEEPERS IN INDUSTRIAL CLUSTERS So far, I have discussed research that has examined the relevance of external actors like global buyers or MNEs for firm learning and innovation. A parallel strand of research has instead investigated how local firms in clusters tap into extra-cluster knowledge. Giuliani and Bell (2005) define the actors that are critical for clusters’ global–local connectivity as “technological gatekeepers” – a concept originally developed in the arena of intraorganizational studies (Allen 1977, p. 145) – and conceive them as firms that channel extra-cluster knowledge into the local, intra-cluster knowledge system (see also Bell and Albu 1999 for an earlier introduction of the concept). Thereafter, innovation scholars, economic geographers and industrial district scholars have analyzed the characteristics

Industrial clusters in global networks 365 and behaviors of technological gatekeepers (among others, see Morrison 2008; Graf 2010; Giuliani 2011; Munari et al. 2012; Hervas-Oliver and Albors-Garrigos 2014). For two reasons, extant studies consider technological gatekeepers to be very important firms for the cluster. First, they are capable of searching and selecting extra-cluster knowledge and, hence, are able to identify new techniques, products or ideas that could be introduced into their own or other local firms. Second, in addition to being open to external (and often distant) knowledge sources, technological gatekeepers contribute also to the diffusion of knowledge at the local level, hence they potentially help firms with poor connections outside the cluster to access new knowledge. Gatekeeping behaviors have often been associated with leading cluster firms (Albino et al. 1999; Lazerson and Lorenzoni 1999; Morrison 2008; Munari et al. 2012); that is, with firms that orchestrate the local value chain, establishing relationships with a large body of local suppliers. Through their vertical networks, leading firms are seen as promoting “an intense knowledge exchange with . . . supplier firms to achieve higher performance in terms of innovation, efficiency, quality, time, and therefore competitiveness” (Albino et al. 1999, p. 58). Munari et al. (2012) argue that, under certain conditions, technological gatekeepers may enhance the international competitive capabilities of all firms in the cluster, thanks to their ability to drive the processes of new knowledge creation and diffusion, and their introduction of external technological novelties in the cluster. According to these studies, the incentive of leading firms to behave as technological gatekeepers in local vertical networks is explained by the fact that their performance depends on the performance of upstream activities (e.g. the quality of production of components or raw materials), hence they have an interest in transferring knowledge and capabilities to their suppliers (Mesquita and Lazzarini 2008). More interestingly, studies show that leading firms may be concerned about maintaining their gatekeeping role over time, since stable and trustful local linkages with their suppliers reduce transaction costs and improve bi-directional learning opportunities (Lorenzoni and Lipparini 1999). While there is little controversy about the motivations for firms to behave as gatekeepers in local vertical networks, the motivations for technological gatekeepers to transfer knowledge horizontally, that is, to other firms – potentially rivals – operating at the same stage in the value chain, are somewhat less understood (Giuliani 2011). Research on horizontal knowledge transfers suggest that firms exchange technical knowledge through the social connections built by their knowledge workers, who are members of the local communities of practice (Giuliani and Bell 2005; Giuliani 2007). The behavior of these workers is noticeably similar to that of the steel engineers described by von Hippel (1988, p. 76), who, “when required know-how is not available in-house . . . learn what they need to know by talking to other specialists. Since in-house development can be time-consuming and expensive, there can be a high incentive to seek the needed information from professional colleagues”. Similar advice-seeking behavior is found in the context of wine clusters by Giuliani (2011), who shows that technical advice among agronomists and enologists is a very important channel for the transfer of technological knowledge, providing significant input into the production and innovation process. Sharing knowledge seems to be part of agronomists’ and enologists’ day-to-day practice and they seem not to be concerned with the possibility of proprietary knowledge being revealed among the community of practice. Overall, research on technological gatekeepers has shown that these actors possess advanced technological capabilities (e.g. Giuliani and Bell 2005; Graf 2010; Giuliani 2011;

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Munari et al. 2012), which allow them to be more open externally and to be able to transfer knowledge to the local context, in line with the research on absorptive capacity (Cohen and Levinthal 1990; Giuliani and Bell 2005). This has been an important advancement in cluster research, emphasizing how the heterogeneity in cluster firms’ knowledge bases conditions their learning trajectories, and their openness to external sources of knowledge. The role of technological gatekeepers in industrial clusters has also been investigated in a longitudinal perspective, with reference to cluster innovation and development processes. For instance, in the context of a wine cluster in Chile, analyzed through repeated fieldwork research, Giuliani (2011) finds that technological gatekeepers do not perform this role on a temporary basis, but persist in playing this role as the cluster evolves. Persistence of knowledge exchanges at the local level is considered to be due to the formation of stable relationships among cluster firms. In particular, reciprocal ties become progressively more important over time – a result that is coherent with the embeddedness literature and shows the importance of stable and reciprocal relationships for fostering trust and increasing the quality and value of the knowledge originating from technological gatekeepers (Uzzi 1997; Lorenzoni and Lipparini 1999). Meanwhile, in the context of a ceramic cluster in Spain, Hervas-Oliver and Albors-Garrigos (2014) find that, while it is true that some gatekeepers are stable over time, their stability can hamper cluster renewal. According to these authors, stable technological gatekeepers may exert tight control on their local networks and prevent radical innovations from occurring in the cluster. This shows that studies on gatekeepers and cluster development form an important area of research, which requires more efforts in the future, as elaborated in the conclusion of this chapter.

CONCLUSION Scholarly research on the external openness of clusters to international sources of knowledge and innovation has developed along at least three scholarly research traditions: first, international development agendas have revolved around the understanding of the upgrading processes of industrial cluster firms that enter GVCs; second, the international economics and business communities originally looked at MNEs technological spillovers in host countries and has, more recently, started to focus on cluster or regional impacts of MNEs, drawing on economic geography perspectives; third, scholars interested in the geography of innovation and economic geography have focused on analyzing the role of indigenous leading firms and on technological gatekeepers in fostering innovation in industrial clusters. While coming from different scholarly communities, these studies have a lot in common. First, although sometimes focusing on different actors, these literatures eventually point out the critical role of local firms’ absorptive capacities, both for their ability to absorb knowledge from within the GVC or from MNE subsidiaries operating in their own territory, and for their capacity to behave as technological gatekeepers. Hence, openness requires an effort by local firms, which have to invest in strengthening their knowledge bases and make a steady commitment to learn from whatever actor has to offer valuable knowledge – for example a MNE subsidiary or another local firm. Second, neither MNEs nor GVCs are panacea for clusters’ innovativeness. Their presence can be irrelevant to a cluster if they have little valuable knowledge to offer. It is often taken for granted

Industrial clusters in global networks 367 that MNEs or global buyers possess advanced capabilities that are relevant to cluster firms. However, this may not always be the case. Extant research has shown that there are instances when MNEs subsidiaries are technologically inactive, thus inhibiting any valuable flow of knowledge to the local context (Marin and Bell 2006; Marin and Giuliani 2011). In addition, learning from within the GVC should not to be taken for granted, as many studies show that local firms sometimes resort to other knowledge sources and do not consider GVC actors as valid sources for learning (De Marchi et al. 2015). While research in international development, international business, international economics and economic geography have proceeded in parallel, their interactions have been limited (albeit to a growing extent) – as reflected by the significant interactions between cluster researchers and GVC scholars at the end of the 1990s and in subsequent decades, as well as by recent exchanges between international business research and economic geography. Connections between these strands of scholarly research are all the more important now, not only because, as discussed in this chapter, these research streams reach similar conclusions about the drivers and outcomes of clusters’ global connectivity to international sources of knowledge and innovation, but, most importantly, because each of them can contribute something specific to this common agenda. International development perspectives on GVCs offer an enhanced understanding about how power imbalances and governance models within GVCs condition the learning process of small producers in downstream production phases. International business literature can offer valuable insights into how MNEs function internally, and in explaining how subsidiaries work and differ from one another in their potential to be valuable sources of international knowledge for clusters. The strength of geography of innovation perspectives is to offer an understanding on how knowledge diffuses at the local level and about the local institutional, social and cultural factors affecting such diffusion. Hence, a recommendation to those who study the external openness of industrial clusters may be to build on the weaknesses and strengths of these respective strands of research to promote an interdisciplinary research agenda with respect to local clusters, global networks and innovation. To conclude with some caveats, it should be stressed that this chapter reviews extant research about the local–global nexus in industrial clusters, while drawing on the firm as the unit of analysis. Given this, it is worth mentioning that there are important areas of inquiry, focusing on other actors as well, which may be relevant for understanding industrial places’ connectivity to global networks. First, research on the role of individuals is particularly valuable, as individuals’ connections and mobilities are important channels that generate international openness of clusters. Extant research has pointed at the importance of migration and reverse-brain-drain effects in spreading technologies and knowledge across space (Zucker and Darby 2007; Mayr and Peri 2008). In this context, for instance, Saxenian (2006) points to the role of the “New Argonauts” – that is, highly skilled migrants from developing countries – in the creation of new technological businesses in Silicon Valley, and their returns to their home countries. In a similar vein, other researchers have looked at inventors’ mobilities (e.g. Cheyre et al. 2015) and at MNE managers’ mobilities (Schotter and Beamish 2013). Second, this chapter has not duly considered the role played by other organizations – such as universities, public research organizations, private–public organizations, among others – in connecting local clusters to global knowledge networks (see, among others, McDermott et al. 2009; Giuliani et al. 2010). Finally, the focus in this chapter is on industrial clusters, while cities (and possibly

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other region types) may be other relevant hotspots of local–global connectivity (e.g. Goerzen et al. 2013; Breschi and Lenzi 2014).

REFERENCES Aitken, B. and Harrison, A. E. (1999) Do domestic firms benefit from direct foreign investment? Evidence from Venezuela. American Economic Review, 89(3): 605–618. Albino, V., Garavelli, C. and Schiuma, G. (1999) Knowledge transfer and inter-firm relationships in industrial districts: the role of the leader firm. Technovation 19: 53–63. Albornoz, F., Milesi, D. and Yoguel, G. (2005) Knowledge circulation in vertically integrated production networks. Innovation, 7(2–3): 200–221. Albornoz, F. and Yoguel, G. (2004) Competitiveness and production networks: the case of the Argentine automotive sector, Industrial and Corporate Change, 13(4): 619–642. Allen, T. J. (1977) Managing the flow of technology: Technology transfer and the dissemination of technological information within the R&D organization. Cambridge, MA: MIT Press. Amin, A. and Thrift, N. (1992) Neo-Marshallian nodes in global networks. International Journal of Urban and Regional Research 16: 571–587. Bair, J. and Gereffi, G. (2001) Local clusters in global chains: the causes and consequences of export dynamism in Torreon’s blue jeans industry. World Development, 29(11): 1885–1903. Bathelt, H. and Cohendet, P. (2014) The creation of knowledge: local building, global accessing and economic development – toward an agenda. Journal of Economic Geography, 14(5): 869–882. Bathelt, H. and Li, P.-F. (2014) Global cluster networks – foreign direct investment flows from Canada to China. Journal of Economic Geography, 14(1): 45–71. Bathelt, H., Malmberg, A. and Maskell, P. (2004) Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28: 31–56. Becattini, G. and Rullani, E. (1993) Sistema locale e mercato globale. Economia e Politica Industriale, 80: 25–49. Belderbos, R., Capannelli, G. and Fukao, K. (2001) Backward vertical linkages of foreign manufacturing affiliates: evidence from Japanese multinationals. World Development 29: 189–208. Bell, M. and Albu, M. (1999) Knowledge systems and technological dynamism in industrial clusters in developing countries. World Development, 27: 1715–1734. Beugelsdijk, S. and Mudambi, R. (2013) MNEs as border-crossing multi-location enterprises: the role of discontinuities in geographic space. Journal of International Business Studies, 44(5): 413–426. Boehe, D. M. (2007) Product development in MNC subsidiaries: local linkages and global interdependencies. Journal of International Management, 13: 488–512. Breschi, S. and Lenzi, C. (2014) The role of external linkages and gatekeepers for the renewal and expansion of US cities’ knowledge base, 1990–2004. Regional Studies, 49(5): 782–797. Cafaggi, F., Macedo, R., Swensson, L., Andreotti, T., Piterman, C. and de Almeida, L. (2012) Accessing the global value chain in a changing institutional environment: Comparing aeronautics and coffee. Inter-American Development Bank Working Paper 370. Online. Available HTTP: (2 November 2015). Cantwell, J. and Iammarino, S. (2003) Multinational corporations and European regional systems of innovation. London, New York: Routledge. Capello, R. and Faggian, A. (2005) Collective learning and relational capital in local innovation processes. Regional Studies, 39 (1): 75–87. Castellani, D. and Zanfei, A. (2002) Multinational experience and the creation of linkages with local firms: evidence from the electronics industry. Cambridge Journal of Economics 26: 1–25. Caves, R. (1974) Multinational firms, competition and productivity in host-country markets. Economica, 41(162): 176–193. Cheyre, C., Klepper, S. and Veloso, F. (2015) Spinoffs and the mobility of U.S. merchant semiconductor inventors. Management Science, 61(3): 487–506. Coe, N. M., Dicken, P. and Hess, M. (2008) Introduction: global production networks – debates and challenges. Journal of Economic Geography, 8(3): 267–269. Cohen, W. and Levinthal, D. (1989) Innovation and learning: the two faces of R&D. Economic Journal, 99: 569–596. Cohen, W. and Levinthal, D. (1990) Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35: 128–152. Cook, P. and Kirkpatrick, C. (1997) Globalization, regionalization and Third World development. Regional Studies, 31: 55–66.

Industrial clusters in global networks 369 Crespo, N. and Fontoura, M. P. (2007) Determinants of FDI spillovers – what do we really know? World Development, 35(3): 410–425. Dau, L. A. (2013) Learning across geographic space: pro-market reforms, multinationalization strategy, and profitability. Journal of International Business Studies, 44(3): 235–262. De Marchi V., Giuliani, E. and Rabellotti, R. (2015) Local innovation and global value chains in developing countries. UNIDO/UNU-MERIT Working Paper Series 022. Online. Available HTTP: (2 November 2015). Dicken, P. (1994) The Roepke lecture in economic geography. Global–local tensions: firms and states in the global space economy. Economic Geography, 70: 101–128. Djankov, S. and Hoekman, B. (2000) Foreign investment and productivity growth in Czech enterprises. The World Bank Review, 14(1): 49–64. Frobel, F. J., Heinrichs, J. and Kreye, O. (1980) The new international division of labour. Cambridge: Cambridge University Press. Gentile-Lüdecke, S. and Giroud, A. (2012) Knowledge transfer from TNCs and upgrading of domestic firms: the Polish automotive sector. World Development, 40(4): 796–807. Gereffi, G. (1999) International trade and industrial upgrading in the apparel commodity chain. Journal of International Economics, 48: 37–70. Gereffi, G., Humphrey, J. and Sturgeon, T. (2005) The governance of global value chains. Review of International Political Economy, 12(1): 78–104. Gereffi, G. and Korzeniewicz, M. (1994) Commodity chains and global capitalism. Westport, CT: Praeger. Giroud, A. (2007) MNEs vertical linkages: the experience of Vietnam after Malaysia. International Business Review, 16(2): 159–176. Giroud, A., Jindra, B. and Marek, P. (2012) Heterogeneous FDI in transition economies – a novel approach to assess the development impact of backward linkages. World Development, 40(11): 2206–2220. Giuliani, E. (2007) The selective nature of knowledge networks in clusters: evidence from the wine industry. Journal of Economic Geography, 7: 139–168. Giuliani, E. (2011) The role of technological gatekeepers in the growth of industrial clusters: evidence from Chile. Regional Studies, 45(10): 1329–1348. Giuliani, E. and Bell, M. (2005) The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research Policy, 34(1): 47–68. Giuliani, E. and Macchi, C. (2014) Multinational corporations’ economic and human rights impacts on developing countries: a review and research agenda. Cambridge Journal of Economics, 38(2): 479–517. Giuliani, E., Morrison, A., Rabellotti, R. and Pietrobelli, C. (2010) Who are the researchers that are collaborating with industry? An analysis of the wine sectors in Chile, South Africa and Italy. Research Policy, 39(6): 748–761. Giuliani, E., Pietrobelli, C. and Rabellotti, R. (2005) Upgrading in global value chains: lessons from Latin America clusters. World Development, 33(4): 549–573. Goerzen, A., Asmussen, C. G. and Nielsen, B. B. (2013) Global cities and multinational enterprise location strategy. Journal of International Business Studies, 44(5): 427–450. Grabher, G. (1993) The embedded firm: On the socioeconomics of industrial networks. London, New York: Routledge. Graf, H. (2010) Gatekeepers in regional networks of innovators. Cambridge Journal of Economics, 35(1): 173–198. Guerrieri, P. and Pietrobelli, C. (2004) Industrial districts’ evolution and technological regimes: Italy and Taiwan. Technovation, 24(11): 899–914. Hervas-Oliver, J. L. and Albors-Garrigos, J. (2014) Are technology gatekeepers renewing clusters? Understanding gatekeepers and their dynamics across cluster life cycles. Entrepreneurship and Regional Development: An International Journal, 26(5/6): 431–452. Hu, A. G. Z. and Jefferson, G. H. (2002) FDI impact and spillover: evidence from China’s electronic and textile industries. The World Economy, 25(8): 1063–1076. Humphrey, J. and Schmitz, H. (2000) Governance and upgrading linking industrial cluster and global value chain research. Brighton: Institute of Development Studies. Humphrey, J. and Schmitz, H. (2002) How does insertion in global value chains affect upgrading in industrial clusters? Regional Studies, 36: 1017–1027. Iammarino, S. and McCann, P. (2013) Multinationals and economic geography: Location and technology, innovation. Cheltenham and Northampton, MA: Edward Elgar Publishing. Iršová, Z. and Havránek, T. (2012) Determinants of horizontal spillovers from FDI: evidence from a large metaanalysis. World Development, 42: 1–15. Javorcik, B. S. (2004) Does foreign direct investment increase the productivity of domestic firms? In Search of spillovers through backward linkages. American Economic Review, 94(3): 605–627. Javorcik, B. S. and Spatareanu, M. (2011) Does it matter where you come from? Vertical spillovers from foreign direct investment and the origin of investors. Journal of Development Economics, 96(1): 126–138.

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Jindra, B., Giroud, A. and Scott-Kennel, J. (2009) Subsidiary roles, vertical linkages and economic development: lessons from transition economies. Journal of World Business, 44: 167–179. Keeble D., Lawson, C., Moore, B. and Wilkinson, F. (1999) Collective learning processes, networking and “institutional thickness” in the Cambridge region. Regional Studies, 33: 319–332. Kinoshita, Y. (2001) R&D and technology spillovers via FDI: innovation and absorptive capacity. CEPR Discussion Paper No. 2775. Kokko, A. (1994) Technology, markets characteristics, and spillovers. Journal of Development Economics, 43: 279–293. Konings, J. (2001) The effects of foreign direct investment on domestic firms: evidence from firm level panel data in emerging economies. Economics of Transition, 9: 619–633. Lall, S. and Narula, R. (2004) Foreign direct investment and its role in economic development: do we need a new agenda? European Journal of Development Research, 16: 447–464. Lazerson M. H. and Lorenzoni, G. (1999) The firms that feed industrial districts: a return to the Italian source. Industrial and Corporate Change, 8: 235–266. Lenger, A. and Taymaz, E. (2006) To innovate or to transfer? A study on spillovers and foreign firms in Turkey. Journal of Evolutionary Economy, 16: 137–153. Li, P. F. (2013) How industrial clusters helped light manufacturing. In: World Bank (ed.): Tales from the development frontier: How China and other countries harness light manufacturing to create jobs and prosperity. Washington, DC: World Bank, 122–145. Online. Available HTTP: . Lorenzoni, G. and Lipparini, A. (1999) The leveraging of interfirm relationships as a distinctive organizational capability: a longitudinal study. Strategic Management Journal, 20(4): 317–338. Lorenzen, M. and Mudambi, R. (2012) Clusters, connectivity and catch-up: Bollywood and Bangalore in the global economy. Journal of Economic Geography, 13(3): 501–534. Marin, A. and Bell, M. (2006) Technology spillovers from foreign direct investment (FDI): an exploration of the active role of MNC subsidiaries in the case of Argentina in the 1990s. Journal of Development Studies, 42(4): 678–697. Marin, A. and Bell, M. (2010) The local/global integration of MNC subsidiaries and their technological behaviour: Argentina in the late 1990s. Research Policy, 39(7): 919–931. Marin, A. and Giuliani, E. (2011) MNC subsidiaries’ position in global knowledge networks and local spillovers: evidence from Argentina. Innovation and Development, 1(1): 91–114. Marin, A. and Sasidharan, S. (2010) Heterogeneous MNC subsidiaries and technological spillovers: explaining positive and negative effects in India. Research Policy, 39: 1227–1241. Maskell, P. and Malmberg, A. (1999) Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23(2): 167–185. Mayr, K. and Peri, G. (2008) Return migration as channel of brain gain. NBER Working Paper, 14039, National Bureau of Economic Research. Online. Available HTTP: (2 November 2015). McDermott, G., Corredoira, R. and Kruse, G. (2009) Public–private institutions as catalysts of upgrading in emerging market societies. Academy of Management Journal, 52(6): 1270–1296. Mesquita, L. F. and Lazzarini, S. G. (2008) Horizontal and vertical relationships in developing economies: implications for SMEs’ access to global markets. Academy of Management Journal, 51(2): 359–380. Meyer, K. E. and Sinani, E. (2009) When and where does foreign direct investment generate positive spillovers? A meta-analysis. Journal of International Business Studies, 40: 1075–1094. Miozzo, M. and Grimshaw, D. (2008) Service multinationals and forward linkages with client firms: the case of IT outsourcing in Argentina and Brazil. International Business Review, 17: 8–27. Morrison, A. (2008) Gatekeepers of knowledge within industrial districts: who they are, how do they interact. Regional Studies, 42(6): 817–835. Mudambi, R. and Swift, T. (2011) Leveraging knowledge and competencies across space: the next frontier in international business. Journal of International Management, 17(3): 186–189. Munari, F., Sobrero, M. and Malipiero A. (2012) Focal firms as technological gatekeepers within industrial districts: knowledge creation and dissemination in the Italian packaging machinery industry. Industrial and Corporate Change, 21: 429–462. Nadvi, K. (2011) Labour standards and technological upgrading: competitive challenges in the global football industry. International Journal of Technological Learning, Innovation and Development, 4(1/2/3): 235–257. Pietrobelli, C. and Rabellotti, R. (2011) Global value chains meet innovation systems: are there learning opportunities for developing countries? World Development, 39(7): 1261–1269. Rallet, A. and Torre, A. (2017) ‘Geography of innovation, proximity and beyond’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 421–439. Saxenian, A. L. (2006) The New Argonauts: Regional advantage in a global economy. Cambridge, MA: Harvard University Press.

Industrial clusters in global networks 371 Schmitz, H. (ed.) (2004) Local enterprises in the global economy: Issues of governance and upgrading. Cheltenham, and Northampton, MA: Edward Elgar Publishing. Schotter, A. and Beamish, P. W. (2013) The hassle factor: an explanation for managerial location shunning. Journal of International Business Studies, 44(5): 521–544. Sjöholm, F. (1999) Technology gap, competition and spillovers from direct foreign investment: evidence from establishment data. Journal of Development Studies, 36: 53–73. Smeets, R. (2008) Collecting the pieces of the FDI knowledge spillovers puzzle. The World Bank Research Observer, 23(2): 107–138. Sturgeon, T., Van Biesebroeck, J. and Gereffi, G. (2008) Value chains, networks and clusters: reframing the global automotive industry. Journal of Economic Geography, 8(3): 297–321. Uzzi, B. (1997) Social structure and competition in interfirm networks: the paradox of embeddedness. Administrative Science Quarterly, 42: 35–67. Van Assche, A. (2017) ‘Global value chains and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 739–751. von Hippel, E. (1988) The sources of innovation. New York: Oxford University Press. Zucker, L. G. and Darby, M. R. (2007) Star scientists, innovation and regional and national immigration. NBER Working Papers, 13547, National Bureau of Economic Research. Online. Available HTTP: (2 November 2015).

23. The user innovation phenomenon Cyrielle Vellera, Eric Vernette and Susumu Ogawa

INTRODUCTION Establishing a true innovation strategy is a major challenge for firms today. Confronted with the current economic landscape and its substantial evolution, firms try to discover unoccupied territory and constantly seek new points of differentiation in order to become sustainably efficient and in line with market expectations. In this context, innovation constitutes an undeniable asset (Balachandra and Friar 1997), a true “leitmotiv” (Isckia and Lescop 2011) or a “vital activity” for companies (Chesbrough 2003). Indeed, value creation appears to be largely dependent on innovation and managers often face enormous pressure to instigate this (Prahalad and Ramaswamy 2003). However, innovation is a particularly sensitive activity for firms for the risks of failure and wasted efforts are legion (Griffin 1997; Barczak et al. 2009). Furthermore, firms no longer appear satisfied with capitalizing on their own efforts in terms of research and development (R&D): the time has come for them to open up their innovation processes. Companies now resort to involving external actors to strengthen their capabilities of competitiveness and innovation capabilities (Chesbrough 2003; Lettl et al. 2006). An external source recognized as fertile for discovering new ideas has been uncovered in users (von Hippel 1988; Lettl and Herstatt 2006; Enkel et al. 2005). In fact, many studies covering several sectors of activity have shown that a large proportion of highly successful products are instigated by users (Enos 1962; Freeman 1968; von Hippel 1988; Shah 2000; Baldwin et al. 2006). Indeed, certain firms have users participate in co-creation activities through collaborative platforms, suggestion forums, openly collaborative practices or crowdsourcing and innovation communities (Cohendet and Simon, Chapter 3, this volume; Roberts, Chapter 21, this volume). We are thus witnessing the emergence of users as actors and creators of value.

OPEN INNOVATION PARADIGM: POWER TO THE USERS Every product is the result of an idea. Traditionally, in order to get new ideas, firms would resort only to their R&D department and to the creative potential of their own internal developers (Chesbrough 2003). These developers were used to being “lone wolves”, cloistered inside the R&D department, generating ideas for new products; it was an era that symbolized the “Golden Age of in-house R&D” (Isckia and Lescop 2011). However, in the face of new economic realities, this “closed off ” innovation has gradually given way to a more open position (Chesbrough 2003), and for several years innovation has no longer been considered as the “exclusive reserve of R&D departments” (Hamdi et al. 2011). Indeed, according to the study by Eliashberg et al. (1997) carried out on 154 American company directors, 58 percent of them consider R&D as an ineffective source for generating new products and 46 percent question the real importance of R&D in developing innovations. 372

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Open innovation, inspired by the principles of open source, has now become widespread. It consists of the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation. Open innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology. (Chesbrough 2003)

This mode of innovation postulates that R&D should now be seen as an open system and that ideas that are effective and valuable may just as well come from the firm’s outside environment as from inside its walls. Following this idea, firms collaborate with partners outside their conventional boundaries. This gives them a wider base from which to generate new ideas for new products and increase their capabilities for rapid innovation. Nowadays, innovation has become “everybody’s business”; von Hippel (2005) even mentions “democratizing innovation”. Opening up the innovation process to clients and users thus constitutes a major element of open innovation (Gassmann 2006) that is welcomed by numerous firms. For Füller et al. (2007), “in the age of ‘open innovation’, researchers as well as consultants, proclaim active integration of consumers in the process of development of new products and services” for the objective is to reduce the risks of failure and increase the likelihood of success (Gruner and Homburg 2000; Lilien et al. 2002; Chesbrough 2003; Gassmann and Enkel 2004) and consequently to increase the efficiency of innovation processes (Rigby and Zook 2002). From the literature and concrete experiences, it appears that consumers, rather than companies, are at the origin of new products in various domains (Enos 1962; Freeman 1968; von Hippel 1988; Pavitt 1984; Shah 2000; Baldwin et al. 2006; Lilien et al. 2002). Thus for the past few years we seem to have witnessed a change in paradigm – the “manufacturer active paradigm” has made way for the “consumer active paradigm” (von Hippel 1977). Collaborative innovation sparks off increasing enthusiasm and interest among practitioners; as a result, users are more and more in demand for co-creation with companies.

INNOVATION AND USERS: TANGIBLE RESULTS The mountain bike, paper handkerchief, three-wheel baby buggy, Apache and Linux, mayonnaise in a tube, disposable razors, the energy drink Gatorade or even TipEx are all examples of inventions initiated by creative, talented users that became famous and commercially attractive innovations. This phenomenon of user-led innovation has been noticeable over the last three decades and is the subject of an extensive literature; it is very common in business-to-business (B2B) and increasingly so also in business-toconsumer (B2C) interaction. Studies agree that users are considered an important source of innovation (Voss 1985; von Hippel 1986; Urban and von Hippel 1988; Herstatt and von Hippel 1992; Morrison et al. 2000; Shah 2000; Olson and Bakke 2001; Franke and Shah 2003; Lüthje 2004; Bonner and Walker 2004; Franke et al. 2006). Overall, the literature makes four main observations (Shah and Tripsas 2012; Shah et al. 2012):

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Table 23.1 Proportion of products developed or modified by users Field of innovation

Author(s)

Percentage of products developed or modified by users

Medical Printed-circuit CAD software Construction (pipes and air vents) Library information systems Medical surgery equipment Apache OS server software security features Equipment for outdoor activities Equipment for extreme sports Mountain biking equipment Kite surfing equipment Kayak

Shaw (1985) Urban and von Hippel (1988) Herstatt and von Hippel (1992) Morrison et al. (2000) Lüthje (2003) Franke and von Hippel (2003)

53 24 36 26 22 19

Lüthje (2004) Franke and Shah (2003) Lüthje et al. (2002) Tietz et al. (2002) Hiernerth et al.(2013)

10 38 19 26 87

Source: Adapted from Lüthje and Herstatt (2004) and Shah and Tripsas (2007).

Many significant innovations that are commercially attractive come from users. In fact, users (professional-user innovators and/or end-user innovators) (Shah et al. 2012) are the source of many innovations that are now established and highly profitable products/services (see Table 23.1). For example, 76 percent of significant innovations in the domain of scientific instruments (von Hippel 1976), 67 percent in the semiconductor industry (von Hippel 1977) and over 60 percent in sports equipment (Shah 2003) (37 percent of which are in outdoor activities and 32 percent in extreme sports (Lüthje 2004; Franke and Shah 2003)) come from the market, specifically from users. Indeed, in certain domains, more innovations come from users than from manufacturers (see Table 23.2). A significant proportion of users innovate. For example, 26 percent of users of information systems for libraries (Morrison et al. 2000), 19 percent of users of Apache software (Franke and von Hippel 2003) and 38 percent of users in sports (Franke and Shah 2003; Lüthje et al. 2005), including 87 percent of users in the domain of kayaking (Hiernerth et al. 2013), are the source of innovations. Users innovate on very varied markets and in many different industries and disciplines. We can mention scientific apparatus (Riggs and von Hippel 1994), software for computer assisted design (Urban and von Hippel 1988), surgical and medical equipment (Lettl et al. 2006), refining/petroleum processing (Enos 1962), software solutions for libraries (Morrison et al. 2004), equipment for extreme sports and outdoor activities (Franke and Shah 2003; Franke et al. 2006; Luethje et al. 2005), mobile telephony (Ozer 2009) and so on. The content of user-led innovations is different from that of manufacturer-led innovations. First, in the service sector of mobile telecommunications, users produce more innovative ideas than do manufacturers’ internal designers (Kristensson et al. 2002), and second, user-led innovation in the domain of scientific equipment,

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Table 23.2 Distribution of sources of innovation Sectors

Author(s)

Users

Firms

Others

Petroleum processing Computer innovations Chemical processes and process equipment Scientific instruments Plastic pultrusion technology Semiconductor and electronics subassembly manufacturing equipment Electronic assembly Sports equipment (windsurfing, skateboarding and snowboarding)

Enos (1962) Knight (1963) Freeman (1968)

43% 26% 70%

14% 74% 30%

43% 0% 0%

von Hippel (1976) Lionetta (1977) von Hippel (1977)

76% 85% 67%

24% 15% 21%

0% 0% 12%

Vanderwerf (1982) Shah (2003)

11% 60%

33% 25%

56% 15%

Source: Adapted from Shah and Tripsas (2007).

produces more new functionalities, while manufacturer-led innovation responds to already recognized needs (Riggs and von Hippel 1994). Moreover, more recently, Hiernerth et al. (2013) showed that kayaking users develop innovations that are three times less costly than those originating from manufacturers. User-led innovation has become a practice that is easy to observe in the current economic landscape. For example, a study carried out by the Canadian Survey of Innovation and Business Strategy in 2009 highlights that 54 percent of surveyed companies (with over 20 employees) develop innovation in cooperation with users. Two other studies carried out by NESTA (National Endowment for Science, Technology and the Arts) in the UK stipulate that out of 1004 firms (with between 10 and 250 employees), 15 percent innovate with users, and also that out of 2019 British consumers, 6.2 percent declare themselves to be active in innovation processes. In a similar perspective, von Hippel et al. (2011) and Vernette and Hamdi-Kidar (2013) showed that according to the country (UK, USA, Japan and France), between 2.1 percent and 4.6 percent of users have already created a product and between 2.5 percent and 6.1 percent have already modified one. Finally, it appears that users play an active and profitable role in generating innovations. Consequently, involving them in the early stages of innovation projects constitutes a promising long-term strategy for firms. This context gives rise to what is called the “co-creation” of the offer – a sort of appeal to clients’ creativity (and not merely to their reactivity) for developing new products.

STRATEGIES FOR CO-CREATION WITH CONSUMERS Taking a genealogical standpoint, and with an extensive literature review, Cova and Cova (2009) describe the significant changes in approaches to marketing and the face of the postmodern consumer over the last thirty years:

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The Elgar companion to innovation and knowledge creation The late 80s/early 90s saw the apogee of relational marketing and the advent of a new, individualistic consumer; The late 90s/early 2000 witnessed the development of experiential marketing and the rise of a new hedonistic consumer-seeking states of pleasure and self-satisfaction; The mid-2000s highlight the rise of collaborative marketing (or “co-constructed marketing” according to Filser and Vernette 2011) and the emergence of a new proactive or even creative consumer.

Each of these approaches acknowledges the gradual build-up of consumer competence. According to Cova and Cova (2009), this development is linked to the process of “consumer empowerment” (Wright 2006) in their relationships with brands and products linked to “the idea of power and its exercise through the control the consumer has over his/her consumption” (Cova and Cova 2009). In fact, users seek to exercise their power and influence in the business world (Prahalad and Ramaswamy 2004a) on two particular levels: (1) that of their own consumption and (2) that of marketing, especially during new product design and the improvement of company offerings (Fuchs and Schreier 2011). Users then wish to interact with firms and co-create value with them (Prahalad and Ramaswamy 2004a). As a result, firms encourage participation on the part of consumers who are now better informed, more connected, more involved and more active (Prahalad and Ramaswamy 2004a). The slippage of consumer posture mentioned by Cova (2008) goes beyond behaviors of diverting or getting round the market ((re)appropriation initiatives); it is rather partly a tactic of overturning the system of consumption. This new position resulted in several new expressions, such as “prosumers” (producers and consumers) according to Alvin Toffler in the 1980s, “underground innovators” according to Mollick or the “co-productor” (Kristensson et al. 2002). “The consumer considers him/herself more legitimate than the provider to know what is good or not in a particular situation, the situation that he/she experiences personally” (Carù and Cova 2008). The previous barriers between producers and consumers tend to break down (Firat and Venkatesh 1995), with consumers acquiring the status of being true actors in the commercial system: they are contributors and creators (Pitt et al. 2006). Consumers also become a new source of competences for the manufacturer, given their personal knowledge, their willingness to learn and experiment and their capacity to engage in active, explicit and continuous dialogue (Prahalad and Ramaswamy 2000). Downstream versus Upstream Co-creation To describe this trend, the literature since the 2000s proposes the notion of co-creation. This idea is rooted in the works of Prahalad and Ramaswamy (2000, 2002 and 2004a and b), who define it as being the “joint creation of value by the company and the customer” (Prahalad and Ramaswamy 2004a). Co-creation may vary in the nature, intensity (major or minor) and timing of the consumer contribution (Dabholkar 1990), as well as by its object (production – offer and mix – versus consumption – usage and experience) and the type of subjects involved (ordinary consumers versus creative consumers). Co-creation processes have long been used in B2B interaction, and the co-creation processes for innovation are also expanding (perhaps less easily) for the end user (B2C),

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since co-creation is seen as the competitive weapon for the 21st century (Ramaswamy and Gouillart 2010). The B2C domain is generally characterized by a marked distance between firms and consumers, the existence of intermediaries (retailers), and many potential consumers who are disloyal and have changing preferences (Spann et al. 2009). Given these characteristics, co-creation practices in B2C are often hard to establish. In fact, “today’s consumer wants to collaborate in one way or another to specify the offer and the experience” (Cova and Cova 2009). The literature exhibits highly varied terminology to designate the different forms of co-creation with consumers. Reniou (2009) proposes a hierarchy for this participative management technique, distinguishing between (1) co-production (consumer participation as a productive resource during the design and manufacture of the final product/offer), (2) co-conception (personalization, mass customization and co-design with users), (3) pre-conception (consumer participation upstream of the offer conception process (during the reflection stage)) and (4) participative operations by brands (advertising campaigns, contests). We propose a more synthetic approach to illustrate the various forms of consumer co-creation: consumers can co-create with the brand and/or the company, either before the product/service is taken to market (upstream) or during the consumption process (downstream). The Internet provides consumers with platforms and interactive tools (websites, toolkits) that facilitate co-creation. These tools are integrated into interactive platforms. Upstream co-creation. Consumer participation may occur at all the development phases of a new product, before the company takes the product to market: from the ideation (the idea) to the product design (making a model, plan, sketch; developing and building a prototype) and on through the stages of market testing until the advertising campaign. For Hoyer et al. (2010), it is a question of “collaborative product development by firms and consumers”. This participation takes the form of crowdsourcing, that is, an open call mobilizing a crowd of people to achieve the required task. Participation can also target particular consumers such as lead users or emergent nature consumers (see further down). Interactive platforms hosted on a dedicated website offer co-creators simplified software for drawing and designing products. These toolkits make it possible to invent new designs, or even to create truly new products (von Hippel and Katz, 2002). Brands and companies such as Nivea, Orange, Starbucks, SFR, Microsoft, and Procter & Gamble are involved in upstream co-creation. Specialized consulting companies such as InnoCentive, e-Yeka or Hyve organize and manage the calls for contributions to these companies. Downstream co-creation. This takes place during the use of the product or service. Over thirty years ago, services marketing showed the necessity of co-creating with clients (Eiglier et al. 1977). One characteristic of services as opposed to products is the more or less strong necessity for client participation if their consumption is to be a source of value. The company, via contact personnel, and the client, via the degree of participation, co-produce the service offer. Vargo and Lusch (2004; 2008) extend this logic to the consumption of products. S-D (service dominant) gives way to G-D (good dominant). The product is a “value proposition”. The client assesses whether its potential value satisfies his/her own needs, knowing that for this, he/she has to get involved in co-creation that requires specific

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knowledge and competences. Consumers are now co-creators of value (Vargo and Lusch 2004; Prahalad and Ramaswamy 2004b), no longer simple passive receivers (Wikström 1996). In this perspective, for O’Hern and Rindfleisch (2009) co-creation refers to “a collaborative new product development activity in which consumers actively contribute and select various elements of a new product offering”. Thus consuming a product (or a service) is an opportunity for privileged interaction in which the consumer co-creates a personalized experience with the brand throughout the whole life of the product. The result is “a joint creation of value by the company and the customer” (Prahalad and Ramaswamy 2004b). Downstream co-creation needs regular careful observation of clients in their common consumption, that is, their everyday usage, to understand what the product has to do to “get the job done” (Christensen et al. 2005). Managers can also focus on extraordinary experiences to discover unforeseen or extreme usage of the product. It is a matter of determining what makes sense in the consumption of the product or service (Prahalad and Ramaswamy 2000; 2004b; Ramaswamy and Gouillart, 2010). Platforms for discussion on the Internet and online communities offer the potential to discover new uses and future consumption trends. They provide a mine of possibilities to observe, test and develop new value propositions with clients (Prahalad and Ramaswamy 2000; 2004b; Füller 2010). For example, the Nike + platform allows clients to co-create services that make the most of Nike’s product propositions (Ramaswamy and Gouillart 2010). Consumer Competence, Motivation and Engagement For co-creation to be effective, consumers must be able to rely on their personal competences, that is a minimum of familiarity with the product category. On the other hand, they do not need to be experts in the domain. The competences of experts and ordinary consumers appear to be complementary, especially during the ideation phases of upstream co-creation (Magnusson 2009): the ideas of non-specialists encourage radical innovation, while those of experts tend more towards incremental innovations. However, the ideas of ordinary consumers are often unfeasible given the current state of technology, whereas those of experts may be more achievable by companies, leading the latter to explore new technological directions. Magnusson (2009) suggests using ordinary consumers to discover new unsatisfied needs as a source of radical innovations: “by learning more about the actual needs of users through an interpretation of their suggestions, companies can be inspired to seek radical innovations and, in some cases, perhaps even discover a radical redirection of the company itself.” Beyond competences, co-creation cannot take place if the consumer is not motivated, either intrinsically (e.g. self-fulfillment) or extrinsically (e.g. financial reward) (Füller 2010; Roser et al. 2009). More specifically, according to Hoyer et al. (2010), users are motivated to co-create for different reasons. These may be financial (monetary prizes, profit, intellectual property), social (social recognition, social esteem, social status), cognitive/ technological (acquiring knowledge about technologies, products and services) and/or psychological (self-expression, pride, positive affect, pleasure, altruism, dissatisfaction). Nambisan and Baron (2009) come to similar conclusions when they identify cognitive, social personal and hedonic benefits; as does Etgar (2008), for whom there are three types of motivation to co-create: economic, psychological and social.

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In general, firms have thus redefined their way of thinking about consumers by deciding to involve them in their innovation activities. Consumers are thus no longer seen as simply passive, isolated receivers. Instead, we witness a deliberate convergence of roles and a change in the relationship between consumer and producer. Consumers are perceived as creators of value, pushing managers to capture their creative energy in creative activities (ascending innovation), thereby modifying the dynamics of markets and the industrial system (Prahalad and Ramaswamy 2000). This phenomenon has grown in recent years with the take-off of the Internet and the mushrooming of virtual communities.

CO-CREATION SUPPORTS: VIRTUAL TOOLS AND ONLINE COMMUNITIES There are several ways of capturing consumer creativity. The traditional methods are based on estimating client and consumer needs (identifying unmet needs and unsolved problems). Several techniques are used for this: brainstorming and focus groups (Rossiter and Lilien 1994; Hargadon 1996), visits to clients’ homes (McQuarrie 1998), joint analysis and “the creation of channeled ideas” (Goldenberg et al. 1999). These traditional methods are based essentially on analyzing current needs (rather than anticipating future ones), identifying uses and detecting problems. They often turn out to be ineffective and are in fact little used for bringing to light opportunities for developing new products. Thus they do not enable respondents to formulate emerging needs; nor do they allow respondents to seek possible solutions to such needs (von Hippel 1988). Finally, with these traditional methods, it is “those who develop new products inside the firm” who “are supposed to find the ideas to create products and services that should respond to these needs” (Lilien et al. 2002). Using unconventional methods for seeking solutions is very different. These methods collect data from creative, innovative consumers on their needs for new products and, above all, on the solutions that they themselves imagine and their ideas for satisfying those needs. For Füller et al. (2007), such consumers are essentially to be found in online communities that constitute a preferred meeting place for creative and innovative consumers. Members of online communities modify existing products and generate ideas for entirely new developments. They share their ideas with other members of the community who then discuss and evaluate the proposed ideas and come up with suggestions for improvement. Thus, all members contribute to the advancement of the idea.

In this perspective, the authors identify three forms of virtual cooperation with online communities: obtaining information; virtual integration of consumers on single projects; and continuous dialogue with consumers (Grabher and Ibert, Chapter 33, this volume). These three types of cooperation use a combination of three methods: netnography; innovation based on communities; and innovation communities. Netnography: obtaining information (needs, ideas, trends) through observation. Netnography is a sort or ethnography on the Internet; it was introduced by Kozinets (2002). This method makes it possible to carry out surveys and interpret publicly

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accessible data, which is often narrative and asynchronous, from virtual communities (forum messages, chat room discussions etc.) (Bernard 2004). Netnography refers to natural observation and the analysis of discussions, social interactions, communication acts and the innovative know-how of virtual community members by an investigator who remains involved over a long period (5 to 6 months) and at a distance (passive cooperation). In terms of product and services development, netnography gives “first insights into emerging trends and allows deriving innovative problem solutions” and “it generates insights into the behavior of online community members, the meanings of symbols used, and prevalent consumption patterns” (Füller et al. 2007) (e.g. the case of Niketalk.com, community on trainers). The virtual integration of consumers via communities-based innovation. Virtual integration consists of calling on community members’ ingenuity regarding specific questions or single projects for which the firm is seeking solutions (active cooperation). Innovation based on communities takes place over four successive stages (Füller et al. 2007): (1)  determination of the participants’ profiles, (2) identification of appropriate online communities, (3) design of the virtual interaction (designing a platform that encourages virtual integration) and (4) real contact and integration of community members. This method can be used at all phases of the development process (e.g. modular and adaptable sports shoes). Continuous dialogue with consumers through innovation communities. Innovation communities refer to permanent platforms for continuous cooperation, integrating consumers who are engaged with the brand (active dialogue). The missions for the community range from modifying existing products to generating ideas, via evaluation and improvement of concepts and testing innovations. A company may either take the initiative to create an innovation community, or cooperate with existing communities. Many examples of successful applications of this method can be cited. For example, in 2006, the Lego company created the Lego Factory site (currently Lego Design by Me), a platform open to the creativity of confirmed Lego addicts as well as novices. The platform makes it possible to design a made-to-measure construction via the inventiveness of over 300 000 creators all over the world. In a similar way, other community sites have been created in innovative sectors or in retail such as My Starbucks Idea (Starbucks), Décathlon Création (Décathlon), Local Motor, Fiat Mio (Fiat), P&G Open Innovation Challenge and Vocalpoint (Procter & Gamble), Ideas4Unilever (Unilever), Dell Idea Storm (Dell), BMW Customer Innovation Lab (BMW), Betavine (Vodafone), Ideas Project (Nokia), Innovate with Kraft (Kraft), InnovationJam (IBM) and “Tech café” (Ducati). Platforms specialized in co-creation (strategy of intermediation or subcontracting co-creation) with Internet surfers have also been established such as eYeka.com, Hyves.com, Innocentive.com, 99designs.com,Yet2.com, OneBillionMinds. com and Ninesigma.com. These platforms are slightly different from the former type because they propose intermediation for companies that are out of ideas for questions of innovation. The literature stipulates that innovation tends to be concentrated around two categories of atypical users: lead users and, more recently, emerging nature consumers; these profiles are not widely distributed in the population, but they do have a high potential for innovation (von Hippel 1986; Hoffman et al. 2010).

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FAVORED TARGETS FOR CO-CREATION: LEAD USERS AND EMERGENT NATURE CONSUMERS Involving consumers in innovation processes encourages the success of new products (von Hippel 2001). Consumers are thus seen as co-producers and generators of new product ideas (Prahalad and Ramaswamy 2000), passing from being a “mere” source of information to having an active role in the conception of innovation. The pioneering work of von Hippel (1986) has shown that one group of atypical and savvy users stands out. He calls them lead users and shows their capacity for developing resolutely new concepts in response to emerging needs on the target market. In a similar vein, Hoffman  et  al. (2010) more recently introduced a status close to the lead user, that of the emergent nature consumer. Thus lead users and emergent nature consumers appear as two targets with a high potential for innovation that should be encouraged for co-creation. Moreover, these two groups have competences and are ready to participate in marketing co-creation (Vernette and Hamdi-Kidar 2013). The Concept of Lead User Lead users are users whose present strong needs will become general in a marketplace months or years in the future. Since lead users are familiar with conditions which lie in the future for most others, they can serve as a need-forecasting laboratory for marketing research. Moreover, since lead users often attempt to fill the need they experience, they can provide new product concept and design data as well. (von Hippel 1986)

Moreover, lead users combine two predominant characteristics (von Hippel 1986; Urban and von Hippel 1988; Lilien et al. 2002): (1) they express precursory needs not yet satisfied by the current offer (needs that prefigure those of the majority of potential users of the target market but expressed a few months or years earlier); and (2) to respond to such unsatisfied needs – initially personal needs for which no satisfactory solution exists on the market – lead users are actively and intrinsically motivated to innovate, because they expect to find and develop a solution for themselves through inventing or finding an emblematic solution to future market needs. As a result, lead users are experts and at the forefront of movement in a given domain (and not in an absolute sense). Nevertheless, although lead users are highly knowledgeable about their own needs and usage, this type of information is not easily transferable inside the firm and not easy to use in a context of information seeking, given its “information stickiness” (von Hippel 1994); in other words, it is strongly linked to the user’s context and therefore costly to transfer. Furthermore, the term for “lead user” suffers from fluctuation; translations and synonyms are also highly variable in the literature on the subject. Thus we find the term “leading edge status” (Morrison et al. (2004) and Schreier et al. 2007), “innovating user” (Lüthje and Herstatt (2004) or “pilot user” (von Hippel et al. 2003; Lilien et al. 2002). According to Hamdi et al. (2011), “these variations are not without effect. They introduce constitutive essences of the concept that differ from one author to another.” Drifts away from the concept of lead user as originally defined by von Hippel (1986) are legion. In fact, depending on the author in question, lead users are characterized by:

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The Elgar companion to innovation and knowledge creation expertise in a specific domain of competence (expertise combining knowledge and familiarity in the sense of Alba and Hutchinson 1987) – this meaning is taken up by Voss (1985), Morrison et al. (2004), Lüthje (2004), Schreier and Prügl (2008), Béji-Bécheur and Gollety (2007); specific know-how (Füller et al. 2008; Franke and Shah 2003).

In a similar perspective, Schreier and Prügl (2008) add knowledge, experience in usage, the locus of control and innovativeness as prerequisites of lead-usership. Finally, for Béji-Bécheur and Gollety (2007), a lead user is “at the forefront”, has “a propensity to innovate”, shows “strong motivation with respect to innovation” and knows “how to communicate his/her ideas” (p. 25). Relying on the works of Amabile (1982), Béji-Bécheur and Gollety (2007) underline the fact that beyond certain characteristics common to creative personalities (cognitive style and creative personality traits), the lead user can be distinguished by the fact of being a lead user only in a particular domain and not in an absolute sense. They thus insist rather on the “avant gardist” nature of lead users reflected in their capacity to express pioneering ideas. Furthermore, it appears that lead users may act from two different types of motivation: the motivation to innovate for oneself, and that to innovate for others (Béji-Bécheur and Gollety 2007). From a study carried out by Hienerth et al. (2013) on the motivation for innovation among kayak users (n521), we find these two types of motivation to be similar to the opposition between intrinsic and extrinsic motivation. In fact, 61 percent of subjects declare being motivated to innovate for their personal use, 1 percent for the profit that comes from the sale of their innovations, 17 percent for the pleasure of innovating, 8 percent for information gained through innovating, 10 percent to help others and 2 percent for other reasons. The concept of lead user has found favor in a number of companies (3M, Hilti, Johnson & Johnson Medical, Boeing), especially in North American industries. Indeed, given their specific characteristics, lead users constitute precious sources of innovation and can intervene at each stage of the process of new product development (Enkel et al. 2005). Their innovations range from improving existing products to creating completely new ones (radical innovation). (Béji-Bécheur and Gollety 2007; von Hippel 1986; von Hippel 2005; Lilien et al. 2002; Lettl and Herstatt 2006; Lettl et al. 2006; Morrison et al. 2000). Various studies have also revealed the role and operational reach of lead users as pools for new ideas in various domains. Their integration appears to promote the chances of success for new products (Gruner and Homburg 2000). In his early research, von Hippel (1988) revealed the inventiveness of lead users in the semiconductor industry. He showed that 76 percent of innovations in the domain of scientific instruments came from lead users. Furthermore, in another study Urban and von Hippel (1988) showed lead users’ capacity to develop a computer assisted process for modeling printed circuits (PC-CAD) that was more satisfactory and preferred to the systems existing on the market that had come from more traditional methods (as evaluated by 71 PC-CAD users). These authors showed that 87 percent of lead users had already innovated. Similarly, in line with other studies on the subject, Herstatt and von Hippel (1992) mention a study carried out in partnership with the company Hilti. They show that a new range of pipe fixings that came from a project headed by lead users was a real commercial success for the company, thus confirming the managerial interest of

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associating such targets to innovation projects. In the same vein, in a natural experiment carried out in the company 3M, Lilien et al. (2002) highlighted that annual sales of products originating from lead users (pilot client method) were estimated to be eight times higher than sales for ideas generated by so-called traditional working groups (146 million dollars after five years compared to 18 million dollars). Similarly, the results of this research indicate that projects from the lead-user sample are significantly newer, respond to more original needs, correspond to significantly higher forecasts of market share and are at the origin of a new product line (as opposed to traditional methods that are more likely to result in improvements to existing products or extensions of existing product lines). Consequently, ideas produced by lead users achieve better results, generating more attractive innovations than those based on more traditional working groups. Encouraged by the successful experiences of 3M, other studies were carried out in various domains such as medical equipment and protective surgical clothing (Lüthje 2003; Lüthje et al. 2003; Lettl et al. 2006), information systems (Morrison et al. 2004), extreme and outdoor sports (Lüthje et al. 2002; Franke and Shah 2003; Lüthje 2004; Franke et al. 2006; Schreier et al. 2007), telecommunications (Ozer 2009), audio software (Jeppesen and Laursen 2009) and banking services (Oliveira and von Hippel 2011). Finally, all these studies show two main results. First, co-creation tends to concentrate on one category of users, lead users, because they are a more effective source for discovering new ideas than “ordinary” or “typical” users. Second, many innovations from lead users are highly successful commercially speaking. However, even if seeking out and involving lead users enables firms to maintain, or even create, competitive advantage, lead users still have to be identified. The problem of identifying lead users constitutes a major challenge for firms involved in innovation. In fact, lead users are hard to find because they make up a minority of the population (Lüthje and Herstatt 2004). However, the marketing literature has found three main ways of identifying lead users. Designation by peers (or pyramid model). This way of identifying lead users consists of asking individuals (researchers, consultants etc.) to designate people in their entourage (i.e. among their peers) whom they perceive as lead users. This is a variation of “snowball” sampling (Lilien et al. 2002). Despite the effectiveness of this networking system, which may reduce the effort to find lead users by as much as 72 percent (von Hippel et al. 2009), the approach turns out to be vague and complicated to set up (Béji-Bécheur and Gollety 2007). In particular, it requires access to a closed community made up of highly involved members (Hamdi et al. 2011). The process takes place in four steps (Lilien et al. 2002): (1) defining the objectives and setting up a team to implement the project (selecting the priority target market and determining the level of innovation sought; this team is typically made up of three to five members); (2) identifying trends (collecting information on emerging trends in the targeted market and on technical trends); (3) identifying lead users via a pyramid network (constituting a group of lead users from other markets); and (4) holding workshops for developing ideas. Observing virtual communities. This consists of observing communities in social networks or online forums to identify the lead users; it is a branch of netnography (Füller et al. 2006). Self-evaluation questionnaires. Although not ideal, this is the most flexible method of evaluating whether an individual has the attributes of a lead user; it is also the simplest

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method to implement and the one that works best for firms (Béji-Bécheur and Gollety 2007). According to Hamdi et al. (2011), the self-evaluation measurement scales can be grouped into three categories: (1) scales that reflect the “original essences of lead users”, that is, dissatisfaction, expected benefits and anticipated future needs (Béji-Bécheur and Gollety 2007; Franke and Shah, 2003; Schreier et al. 2007); (2) scales that integrate “behavioral manifestations” such as technological expertise, the capacity to modify and build on existing products, and being part of communities (Franke et al. 2006; Shreier and Prügl 2008); (3) scales incorporating “characteristics of other concepts” such as the tendency for innovation, creativity or being an opinion leader (Jeppesen and Laursen 2009; Morrison et al. 2000; Ozer 2009; Spann et al. 2009). Nevertheless, few of these tools have been “formalized and validated with clients of retail products” (Béji-Bécheur and Gollety 2007). Moreover, leadership is not confined to a strict dichotomy (lead user versus non-lead user); it is rather a continuum that characterizes individuals as being more like or less like lead users (Morrison et al. 2004; Franke et al. 2006). Finally, despite the increasing number of methods and instruments for identifying lead users, finding and selecting them constitutes a particularly difficult task for firms given the small proportion of lead users in the population. According to the study and the domain of application, this proportion varies between 3 and 30 percent (Urban and von Hippel 1988; Lüthje 2004; Morrison et al. 2004). More recently a new target has been actively sought out for the co-creation process: emergent nature consumers. Emergent Nature Consumers: A Similar Profile to Lead Users Hoffman et al. (2010), working in the domain of marketing and outsourcing, introduced a new category of consumers named emergent nature consumers. They define these consumers as creative individuals who have “the unique capability to imagine or to envision how concepts might be developed so that they will be successful in the mainstream marketplace”. Notably, these authors show that in the retail sector, product concepts developed by emergent nature consumers lead to better results (such as in the case of home delivery) and, moreover, to significantly more commercially attractive products that are also more useful than those from “ordinary” consumers, creative consumers and especially from lead users. According to the authors, emergent nature consumers make up a small proportion of the population. They possess a particular combination of attributes that are specific to each individual, such as cognitive capabilities for processing information and particular personality traits. This concept therefore includes several characteristics (Hoffman et al. 2010): 1.

2. 3.

openness to new experiences and ideas (curiosity vis-à-vis the outside and inside worlds) – according to the authors, an individual with a high score for openness will be more imaginative and will appreciate experiences high in aesthetic, emotional and intellectual content; reflection; the ability to synergistically apply both an experiential (valuing intuition with a holistic sensitivity and emotional expression) and rational processing style (valuing analy-

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5. 6.

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sis, logic and a causal, systematic approach) – both these modes operate in synergy and act on the usefulness of the concept; the capacity to engage simultaneously in a verbal and visual mode of information processing (verbal: rational style that prefers linguistic information; visual: preferring visual information processing and experiential style) – this double capability encourages the visualization and rationalization of the concept; a high level of creativity; optimism.

Although the concepts of lead users and emergent nature consumers are similar, emergent nature consumers, unlike lead users, are not necessarily experts in a specific domain. Besides the pioneering work of Hoffman et al. (2010), emergent nature consumers have been little studied. In France, Hamdi and Vernette (2013) highlight two major results on the basis of an online sample of 995 people representative of the French population. First, they show that lead users and emergent nature consumers engage significantly more frequently in co-creation activities than ordinary individuals (sharing consumption experiences through posting comments on products/services, proposing ideas for new products or services on collaborative platforms, imagining future advertising campaigns, taking part in forum discussions devoted to a product/brand). Moreover, these authors point out that lead users and emergent nature consumers are more competent than ordinary consumers for co-creation: lead users and emergent nature consumers have significantly more expertise and are more inclined to file for patents, thus confirming the necessity of targeting these two types of consumer. Finally, they show that the competence for co-creation mediates the effects of the type of user (lead user and emergent nature consumer) on engagement in co-creation activities. Recently, Hamdi-Kidar (2013) attempted to study the potential motivations of lead users and emergent nature consumers for co-creating products/services with firms (in the domain of video games). The author asserts that the two targets are more highly motivated to engage in co-creation than “ordinary” consumers (scores for motivation in relation to “financial and social recognition”, the “pleasure of the creative activity” and “self-fulfillment” were significantly higher); the author also identifies divergent motivations for co-creation in each of the two targets. In fact, lead users seem more motivated to co-create for reasons of financial and social recognition than for the anticipated pleasure of taking part in a creative activity. The “self-fulfillment” dimension has no effect. On the contrary, emergent nature consumers are more motivated by selffulfillment than by hedonic pleasure linked to the experience. Financial gain and social recognition do have an effect on the motivation of emergent nature consumers, but to a lesser degree than for lead users. From the point of view of identifying these consumers, Hoffman et al. (2010) have created a measurement tool that conforms to psychometric requirements. This onedimensional tool comprises eight items and need not be adapted to product category. It has been formalized and validated with purchasers of retail services (home deliveries and dental care). Although co-creation is popular because both companies and practitioners are enthusiastic about its advantages, co-creation/co-innovation does have its limits.

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THE ADVANTAGES AND LIMITS OF CO-CREATION AND CO-INNOVATION WITH USERS Co-creation practices are attractive to many firms such as Apple, Nestlé, Lego, Orange and SAP (Ramaswamy and Gouillart 2010). These practices have many advantages. For Hoyer et al. (2010), co-creation gives companies involved in the practice advantages in terms of (1) reducing costs and (2) improving the efficiency of co-conceived products (products that are closer to final users’ expectations, thus encouraging market acceptance, intentions to re-purchase, positive word of mouth etc.). Similarly, according to Le Nagard-Assayag and Reniou (2013), for companies, co-innovation with users encourages closeness to clients with a view to increasing the commercial acceptability of the new products (offering a better response to consumer needs). It also leads to tightening links with clients (developing client relations) and stimulates the creativity of internal teams. Nevertheless, according to these same authors, co-creation is not without limits. These limits are relative first to a reduction of control (in terms of management and planning, intellectual property and confidentiality) and second to the increase of complexity in terms of managing the firm’s objectives and stakeholder interests (information overload, complexity of production). Furthermore, because co-creators have at their disposal tools similar to those used by firms, they could even constitute a new source of competition by developing versions that could compete with the firms’ offers. Other stumbling blocks, sources of wariness or perceived risks for firms are also mentioned: loss of control (the problem of yielding rights to intellectual property/attributing the authorship of an idea and confidentiality; risk of information leaks); the fear of client disappointment if the creative proposals are not followed up; the perceived cost of the process (using human and technical resources); and finally, the user’s incompetence in the product category. Regarding this last point, authors mention four aspects: (1) users’ weak engagement in the product category, (2) users’ lack of expertise given the complexity of the products, (3) users’ incapacity to project themselves into the future (poor capacity to see the future and imagine future trends) and (4) users’ lack of creativity. From another perspective firms wonder about the reward, whether financial or not, for the value created by the client. Lego, for example, pays royalties to consumers who have imagined new figurines that end up on the market (up to 1 percent of the turnover generated). However, these reward initiatives remain little used in practice and the status of the consumer tends to oscillate between “volunteer consultant” and “clandestine worker” (Filser and Vernette 2011). In fact, although co-creation is based on free participation, Vernette and Tissier-Desbordes (2012) observe that it may drift towards actors’ “semi professionalization” and, moreover, communities being organized into clans. Thus “co-creation” could paradoxically become a new form of job seeking or a source of complementary revenue for certain consumers . . . a new “profession”, that of “co-creator”. Finally, co-creation is related to a call for users’ creativity. This means that its performance is strictly linked to the creative qualities of the users involved, creativity being an individual characteristic sought after in the generation phase of ideas and concepts (Le Nagard-Assayag and Reniou 2013). Nowadays, it appears that innovations from socalled ordinary users, although sometimes outstanding, are more often incremental in

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nature, that is, only marginally and gradually “new” (von Hippel 2005); in other words, innovations from ordinary users are only rarely radical (Lettl et al. 2006; Lüthje and Herstatt 2004). Moreover, innovations from these users apply little to product categories characterized by rapid change and high and/or complex technology, such as “high tech” products (von Hippel 1988; Urban and von Hippel 1988; Moriarty and Kosnik 1989). Two main factors may explain the poor capacity of these “ordinary” users to generate disruptive ideas (Lettl and Herstatt 2006): (1) the barrier of not knowing and (2) the barrier of not wanting. Furthermore, “ordinary” users find it difficult to distance themselves from their own context of actual usage (Allen and Marquis 1964; von Hippel 1986; Lilien et al. 2002). This is known as the effect of functional fixedness or functional fixation. This results in individuals having little capability to use or conceive of a familiar object in a new and unusual way. From the point of view of creation, this effect refers to the fact that “users steeped in the present are thus unlikely to generate novel products concepts which conflict with the familiar” (von Hippel 1986). This effect is also found when assessing radically new concepts and prototypes because points of references are lacking (Lettl and Herstatt 2006); this also occurs when putting the innovation into practice (Allen and Marquis 1964; Lilien et al. 2002).

CONCLUSION This chapter aimed to examine the role of users in the service of innovation. More and more firms co-create and co-innovate with their current or potential users by having them participate in idea production phases, creativity sessions, concept tests, and so on, through collaborative platforms, forums for suggesting new ideas or improvements to existing products, inventive crowdsourcing practices and so on. However, the fruitfulness of ideas is strictly dependent on the “creativity quotient” of the users involved. Creativity thus appears as an essential factor in the emergence of new ideas and business opportunities; in other words, it constitutes a basis for innovation. Nevertheless, although users constitute a precious source of innovation, only a minority of them bring their own solutions to market (Lettl et al. 2006; Shah and Tripsas 2007; Shah and Tripsas 2012; Shah et al. 2012), thus capturing the benefits of their ideas beyond their own personal use (Shah and Tripsas 2007). This phenomenon, named user entrepreneurship, which has been rarely studied in the extant literature, refers to the “commercialization of a new product/service by an individual or group of individuals who are also users of this product and/or service” (Shah and Tripsas 2007). According to Shah and Tripsas (2012), there are many alternatives to commercialization: users may “freely share innovations with industrial partners, issue licenses for their innovations to manufacturers or attempt to commercialize their products independently . . . or not at all”. For this reason, the phenomenon of user entrepreneurship is found in various domains such as stereophony, (Langlois and Robertson 1992), extreme sports (skateboarding, snowboarding, windsurfing, mountain biking, kayaking) (Shah 2003; Lüthje et al. 2005; Baldwin et al. 2006), cars (Franz 2005), baby care (Shah and Tripsas 2007) and even the cinema (Haefliger et al. 2010). Users are more active than ever before!

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REFERENCES Alba J.W. and Hutchinson J.W. (1987) Dimensions of expertise, Journal of Consumer Research, 13, 411–454. Allen T.J. and Marquis D.G. (1964) Positive and negative biasing sets: The effects of prior experience on research performance, IEEE Transactions on Engineering Management, EM-11, 4, 158–161. Amabile T.M. (1982) Social psychology of creativity: A consensual assessment technique, Journal of Personality and Social Psychology, 43, 5, 997–1013. Balachandra R. and Friar J. (1997) Factors for success in R&D projects and new product innovation: A contextual framework, IEEE Transactions on Engineering Management, 44, 276–287. Baldwin, C.Y., Hienerth, C. and von Hippel, E. (2006) How user innovations become commercial products: A theoretical investigation and case study. Research Policy, 35, 9, 1291–1313. Barczak G., Griffin A. and Kahn K.B. (2009) Perspective: Trends and drivers of success in NPD practices: Results of the 2003 PDMA best practices study, Journal of Product Innovation Management, 25, 1, 3–23. Béji-Bécheur A. and Gollety M. (2007) Lead user et leader d’opinion: Deux cibles majeures au service de l’innovation, Décisions Marketing, 48, 21–34. Bernard Y. (2004) La netnographie: Une nouvelle méthode d’enquête qualitative basée sur les communautés virtuelles de consommation, Décisions Marketing, 36, 49–62. Bonner J. and Walker O. (2004) Selecting influential business-to-business customers in new product development: Relational embeddedness and knowledge heterogeneity considerations, Journal of Product Innovation Management, 21, 155–169. Carù A. and Cova B. (2008) Le consommateur interpassif: nouvel avatar postmoderne?, 7ème Congrès des Tendances du Marketing, Venise, 17–18–19 Janvier, http://www.escpeap.net/conferences/marketing/2008_cp/ Materiali/Paper/Fr/Caru_Cova.pdf. Chesbrough H. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Boston, MA: Harvard Business School Press. Christensen J.F., Olesen M.H. and Kjær J.S. (2005) The industrial dynamics of open innovation – evidence from the transformation of consumer electronics, Research Policy, 34, 10, 1533–1549. Cohendet P. and Simon L. (2017) Concepts and models of innovation, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 33–55. Cova B. (2008) Consumer made: Quand le consommateur devient producteur, Décisions Marketing, 50, 19–27. Cova B. and Cova V. (2009) Les figures du nouveau consommateur: Une genèse de la gouvernementalité du consommateur, Recherche et application en marketing, 24, 3, 81–100. Dabholkar P.A. (1990) How to improve perceived service quality by improving customer participation, in B.J. Dunlap (ed.), Developments in Marketing Science, Cullowhee, NC: Academy of Marketing Science, 483–487. Eiglier P., Langeard, E., Lovelock, C.H., Bateson, J.E.G. and Young, R.F (1977) Marketing consumer services: New insights from consumers and managers, Working Paper, Marketing Science Institute, Cambridge, MA. Eliashberg J., Lilien G.L. and Rao V. (1997) Minimizing technological oversights: A marketing research perspective, in R. Garud, P. Nayyar, P. Rattah and Z.B. Shapira (eds), Technological Innovation: Oversights and Foresights, New York: Cambridge University Press, 214–230. Enkel E., Perez-Freije J. and Gassmann O. (2005) Minimizing market risks through customer integration in new product development: Learning from bad practice, Creativity and Innovation Management, 14, 4, 425–437. Enos J.L. (1962) Petroleum Progress and Profits: A History of Process Innovation, Cambridge, MA: MIT Press. Etgar M. (2008) A descriptive model of the consumer co-production process, Journal of the Academy of Marketing Science, 36, 1, 97–108. Filser M. and Vernette E. (2011) Quels pouvoir pour le consommateur, Décisions Marketing, 61, 5–9. Firat A.F. and Venkatesh A. (1995) Liberatory postmodernism and the reenchantment of consumption, Journal of Consumer Research, 22, 3, 239–267. Franke N. and Shah S. (2003) How communities support innovative activities: An exploration of assistance and sharing among end-users, Research Policy, 32, 1, 157–178. Franke N. and von Hippel E. (2003) Satisfying heterogeneous user needs via innovation toolkits: The case of Apache security software, Research Policy, 32, 7, 1199–1215. Franke N., von Hippel E. and Schreier M. (2006) Finding commercially attractive user innovations: A test of lead-user theory, Journal of Product Innovation Management, 23, 4, 301–315. Franz K. (2005) Tinkering: Consumers Reinvent the Early Automobile, Philadelphia, PA: University of Pennsylvania Press. Freeman C. (1968) Chemical process plant: Innovation and the world market, National Institute Economic Review, 45, 29–57. Fuchs C. and Schreier M. (2011) Customer empowerment in new product development, Journal of Product Innovation Management, 28, 1, 17–32.

The user innovation phenomenon

389

Füller J. (2010) Refining virtual co-creation from a consumer perspective, California Management Review, 52, 2, 98–122. Füller J., Bartl M., Ernst H. and Mühlbacher H. (2006), Community based innovation: How to integrate members of virtual communities into new product development, Electronic Commerce Research, 6, 1, 57–73. Füller J., Jawecki G. and Mühlbacher H. (2007) Développement de produits et services en coopération avec des communautés en ligne, Décisions Marketing, 48, 47–58. Füller J., Matzler K. and Hoppe M. (2008) Brand community members as a source of innovation, Journal of Product Innovation Management, 25, 6, 608–619. Gassmann O. (2006) Opening up the innovation process: Towards an agenda, R&D Management, 36, 3, 223–228. Gassmann O. and Enkel E. (2004) Towards a theory of open innovation: Three core process archetypes, Proceedings of the R&D Management Conference, Lisbon, Portugal, 6 July. Goldenberg J., Mazursky D. and Solomon S. (1999) Toward identifying the inventive templates of new products: A channeled ideation approach, Journal of Marketing Research, 34, 2, 200–210. Grabher G. and Ibert O. (2017) Knowledge collaboration in virtual communities, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 537–555. Griffin A. (1997) PDMA research on new product development practices: Updating trends and benchmarking best practices, Journal of Product Innovation Management, 14, 6, 429–458. Gruner K. and Homburg C. (2000) Does customer interaction enhance new product success?, Journal of Business Research, 49, 1, 171–187. Haefliger S., Jager P. and von Kroght G. (2010) Under the radar: Industry entry by user entrepreneurs, Research Policy, 39, 9, 1198–1213. Hamdi L., Korotkova N. and Vernette E. (2011) Comparaison de deux échelles de mesure du caractère lead-user, 27ème Congrès de l’Association Française du Marketing (AFM), Brussels, 18–20 May. Hamdi L. and Vernette E. (2013) Co-creation with consumers: Who has the competence and wants to cooperate?, International Journal of Market Research, 55, 4, 539–561. Hamdi-Kidar L. (2013) Les Lead-Users et Emergent-Nature Consumers: Deux cibles majeures pour la cocréation aux motivations divergentes, 30ème Congrès de l’Association Française du Marketing (AFM), La Rochelle, 15–17 May. Hargadon A. (1996) Brainstorming groups in context: Effectiveness in a product design firm, Administrative Science Quarterly, 41, 685–718. Herstatt C. and von Hippel E. (1992) From experience: Developing new product concepts via the lead user method, Journal of Product Innovation Management, 9, 3, 213–222. Hienerth C., von Hippel E. and Berg Jensen M. (2013) User community vs. producer innovation development efficiency: A first empirical study, Working Paper, MIT Sloan School of Management, Cambridge, MA. Hoffman D.L., Kopalle P.K. and Novak T.P. (2010) The “right” consumers for better concepts: Identifying consumers high in emergent nature to develop new product concepts, Journal of Marketing Research, 47, 5, 854–865. Hoyer W.D., Chety R., Dorotic M., Krafft M. and Singh S.S. (2010) Consumer cocreation in new product development, Journal of Service Research, 13, 3, 283–296. Isckia T. and Lescop D. (2011) Une analyse critique des fondements de l’innovation ouverte, Revue Française de Gestion, 210, 87–98. Jeppesen L. and Laursen K. (2009) The role of lead users in knowledge sharing, Research Policy, 38, 10, 1582–1589. Knight K.E. (1963) A Study of Technological Innovation: The Evolution of Digital Computers, PhD Dissertation, Carnegie Institute of Technology. Kozinets R. (2002) The field behind the screen: Using netnography for marketing research in online communities, Journal of Marketing Research, 39, 1, 61–72. Kristensson P., Magnusson P.R. and Matthing J. (2002) Users as a hidden resource for creativity: Findings from an experimental study of user involvement, Creativity and Innovation Management, 11, 1, 55–61. Langlois R.N. and Robertson P.L. (1992) Networks and innovation in a modular system: Lessons from the microcomputer and stereo component industries, Research Policy, 21, 4, 297–313. Le Nagard-Assayag E. and Reniou F. (2013) Co-innover avec les clients: Entre intérêt et réticence pour les entreprises grand public, Décisions Marketing, 71, 59–75. Lettl C. and Herstatt C. (2006) Users as inventors and developers of radical innovation: An explorative case study analysis in the field of medical technology, Journal of Customer Behaviour, 4, 2, 277–297. Lettl L., Herstatt C. and Gemuenden H.G. (2006) Users’ contributions to radical innovation: Evidence from four cases in the field of medical equipment technology, R&D Management, 36, 3, 251–272. Lilien G., Morrison P.D., Searls K., Sonnack M. and von Hippel E. (2002) Performance assessment of the lead user idea-generation process for new product development, Management Science, 48, 8, 1042–1059. Lionetta W. (1977) Sources of Innovation within the Pultrusion Industry, S.M. thesis, MIT Sloan School of Management, Cambridge, MA.

390

The Elgar companion to innovation and knowledge creation

Lüthje C. (2003) Customers as co-inventors: An empirical analysis of the antecedents of customer-driven innovations in the field of medical equipment, Proceedings of the 32th EMAC Conference, Glasgow. Lüthje C. (2004) Characteristics of innovating users in a consumer goods field: An empirical study of sportrelated product consumers, Technovations, 24, 9, 683–695. Lüthje C. and Herstatt C. (2004) The lead user method: An outline of empirical findings and issues for future research, R&D Management, 34, 5, 553–568. Lüthje C., Herstatt C. and von Hippel E. (2002) The dominant role of local information in user innovation: The case of mountain biking, Working Paper, MIT Sloan School of Management, Cambridge, MA. Lüthje C., Herstatt C. and von Hippel E. (2005) User-innovators and “local” information: The case of mountain biking, Research Policy, 34, 6, 951–965. Lüthje C., Lettl C. and Herstatt C. (2003) Knowledge distribution among market experts: A closer look into the efficiency of information gathering for innovation projects, International Journal of Technology Management, 26, 5/6, 561–577. Magnusson P.R. (2009) Exploring the contribution of involving ordinary users in ideation of technology based services, Journal of Product Innovation Management, 26, 5, 578–593. McQuarrie E.F. (1998) Customer Visits: Building a Better Market Focus, Newbury Park, CA: Sage. Moriarty R.T. and Kosnik T.J. (1989) High-tech marketing: Concepts, continuity and change, Sloan Management Review, 30, 4, 7–17. Morrison P., Roberts I. and Midgley D. (2004) The nature of lead users and measurement of leading edge status, Research Policy, 33, 2, 351–363. Morrison P., Roberts I. and von Hippel E. (2000) Determinants for user innovation and innovation sharing in a local market, Management Science, 46, 12, 1513–1527. Nambisan S. and Baron R.A. (2009) Virtual customer environments: Testing a model of voluntary participation in value co-creation, Journal of Production Innovation and Management, 26, 4, 388–406. O’Hern M.S. and Rindfleisch, A. (2009) Customer co-creation: A typology and research agenda, in N.K. Malholtra (ed.), Marketing Research, 6, Armonk, NY: M.E. Sharpe, 84–106. Oliveira P. and von Hippel E. (2011) Users as service innovators: The case of banking services, Research Policy, 47, 2, 806–818. Olson E. and Bakke G. (2001) Implementing the lead user method in a high technology firm: A longitudinal study of intentions versus actions, Journal of Product Innovation Management, 18, 2, 388–395. Ozer M. (2009) The roles of product lead-users and product experts in new product evaluation, Research Policy, 38, 8, 1340–1349. Pavitt K. (1984) Sectoral patterns of technical change: Towards a taxonomy and a theory, Research Policy, 13, 6, 343–373. Pitt L.F., Watson R.T., Berthon P., Wynn D. and Zinkhan G. (2006) The penguin’s window: Corporate brands from an open-source perspective, Journal of the Academy of Marketing Science, 34, 2, 115–127. Prahalad C.K. and Ramaswamy V. (2000) Co-opting customer competence, Harvard Business Review, 78, 1, 79–87. Prahalad C.K. and Ramaswamy V. (2002) The co-creation connection, Strategy and Business, 27 (Second Quarter 2002), 50–61. Prahalad C.K. and Ramaswamy V. (2003) The new frontier of experience innovation, MIT Sloan Management Review, 44, 4, 12–18. Prahalad C.K. and Ramaswamy V. (2004a) Co-creation experiences: The next practice in value creation, Journal of Interactive Marketing, 18, 3, 5–14. Prahalad C.K. and Ramaswamy V. (2004b) The Future of Competition: Co-creating Unique Value with Customers, Boston, MA: Harvard Business School Press. Ramaswamy V. and Gouillart F. (2010) The Power of Co-creation, New York: Free Press. Reniou F. (2009) Opérations participatives des marques: Pourquoi et comment faire participer les consommateurs? De la compréhension des opérations participatives et des motivations des consommateurs à s’y engager à l’analyse de leurs effets sur la marque, Thèse de doctorat en sciences de gestion, Université de Paris-Dauphine, Paris. Rigby D. and Zook D. (2002) Open-market innovation, Harvard Business Review, 80, 10, 80–89. Riggs W. and von Hippel E. (1994) The impact of scientific and commercial values on the sources of scientific instrument innovation, Research Policy, 23, 4, 459–469. Roberts J. (2017) Community, creativity and innovation, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 342–359. Roser T., Humphreys P., Samson A. and Cruz-Valdivieso E. (2009) Co-Creation: New Pathways to Value. An Overview, http://personal.lse.ac.uk/samsona/CoCreation_Report.pdf. Rossiter J.R. and Lilien G.L. (1994) New “brainstorming” principles, Australian Journal of Management, 19, 1, 61–72.

The user innovation phenomenon

391

Schreier M., Oberhauser S. and Prügl R. (2007) Lead users and the adoption and diffusion of new products: Insights from two extreme sports communities, Marketing Letters, 18, 1–2, 15–30. Schreier M. and Prügl R. (2008) Extending lead user theory: Antecedents and consequences of consumer lead userness, Journal of Product Innovation Management, 25, 4, 331–346. Shah S.K. (2000) Sources and patterns of innovation in a consumer products field: Innovations in sporting equipment, Working Paper, MIT Sloan School of Management, Cambridge, MA. Shah S.K. (2003) Community-Based Innovation and Product Development: Findings from Open Source Software and Consumer Sporting Goods, Doctoral dissertation, MIT, Cambridge, MA. Shah S.K., Smith S. and Reedy E.J. (2012) Who are User Entrepreneurs? Findings on Innovation, Founder Characteristics and Firm Characteristics, Kauffman Foundation Report, Kauffman Foundation, Kansas City, MO. Shah S.K. and Tripsas M. (2007) The accidental entrepreneur: The emergent and collective process of user entrepreneurship, Strategic Entrepreneurship Journal, 1, 123–140. Shah S.K. and Tripsas M. (2012) When do user innovators start firms? A theory of user entrepreneurship, in D. Harhoff and K.R. Lakhani (eds), Revolutionizing Innovation: Users, Communities and Open Innovation, Cambridge, MA: MIT Press, 285–308. Shaw B. (1985) The role of the interaction between the user and the manufacturer in medical equipment innovation, R&D Management, 15, 4, 283–292. Spann M., Holger E., Skiera B. and Soll H. (2009) Identification of lead users for consumer products via virtual stock markets, Journal of Product Innovation Management, 26, 3, 322–355. Tietz R., Morrison P.D., Lüthje C. and Herstatt C. (2005) The process of user-innovation: A case study on user innovation in a consumer goods setting, International Journal of Product Development, 2, 321–338. Urban G.L. and von Hippel E. (1988) Lead users analyses for the development of new industrial products, Management Science, 34, 5, 569–582. Vanderwerf P. (1982), Parts suppliers and innovators in wire termination equipment, Working Paper WP 128982, MIT Sloan School of Management, Cambridge, MA. Vargo S.L. and Lusch R.F. (2004) Evolving to a new dominant logic for marketing, Journal of Marketing, 68, 1, 1–17. Vargo S.L. and Lusch R.F. (2008) Service-dominant logic: Continuing the evolution, Journal of the Academy of Marketing Science, 36, 1–10. Vernette E. and Hamdi-Kidar L. (2013) Co-creation with consumers: Who has the competence and wants to cooperate?, International Journal of Market Research, 55, 4, 539–561. Vernette E. and Tissier-Desbordes E. (2012) La participation du client, la co-production, la co-création, un nouvel eldorado pour le marketing?, Décisions Marketing, 65, 5–8. von Hippel E. (1976) The dominant role of users in the scientific instrument innovation process, Research Policy, 5, 3, 212–239. von Hippel E. (1977) Transferring process equipment innovations from user-innovators to equipment manufacturing firms, R&D Management, 8, 1, 13–22. von Hippel E. (1986) Lead users: A source of novel product concepts, Management Science, 32, 7, 791–805. von Hippel E. (1988) The Sources of Innovation, Oxford: Oxford University Press. von Hippel E. (1994) “Sticky information” and the locus of problem solving: Implications for innovation, Management Science, 40, 4, 429–439. von Hippel E. (2001) Perspective: User toolkits for innovation, Journal of Product Innovation Management, 18, 247–257. von Hippel E. (2005) Democratizing Innovation, Cambridge, MA: MIT Press. von Hippel E., Franke N. and Prügl R. (2009) Pyramiding: Efficient search for rare subjects, Research Policy, 38, 9, 1397–1406. von Hippel E. and Katz R. (2002) Shifting innovation to users via toolkits, Management Science, 48, 7, 821–833. von Hippel E., Ogawa S. and de Jong J.P.J. (2011) The age of the consumer-innovator, MIT Sloan Management Review, 53, 1, 27–35. von Hippel E., Thomke S.H. and Sonnack M. (2003) Creating breakthroughs at 3M, Harvard Business Review, 77, 5, 47–57. Voss C. (1985) The role of users in the development of applications software, Journal of Product Innovation Management, 2, 39, 113–121. Wikström S. (1996) Value creation by company–consumer interaction, Journal of Marketing Management, 12, 5, 359–374. Wright L.T. (2006) Guest editorial: special issue on consumer empowerment, European Journal of Marketing, 40, 9/10, 917–922.

24. Horizontal learning Pengfei Li

INTRODUCTION Learning does not take place in isolation. Both the acquisition of existing knowledge and the creation of new ideas require the intensive interaction of agents, either individuals or organizations – or it may be a consequence of former such interaction and the reflection on it. This suggests that learning, besides being a cognitive activity, is also a social process (Saxenian 1994; Nooteboom 2000; Gertler 2004; Bathelt and Glückler 2011; Storper 2013). In some contexts, knowledge can be exchanged more easily and effectively, while in other settings, interactive learning becomes difficult, if not impossible. In the knowledge economy, the exploration of how technologies and business practices are shared and created in different communities and societies gains momentum in the discussion of the competitive advantages of firms, industries, regions, and nations (Porter 1990; Brown and Duguid 1991; Nonaka and Takeuchi 1995; Maskell 2001; Amin and Cohendet 2004; Kogut 2008). Based on the relationship of interactive agents, the knowledge creation process in an economic context can be split into two categories: vertical and horizontal learning. On the one hand, vertical learning refers to the process of how knowledge is accumulated through input–output relations of firms. Collaboration between producers and users/ buyers or suppliers in value chains in the design of new products is in line with this type of learning. The opportunity of vertical learning increases exponentially with the deepening of the social division of labor in societies, as economic activities have been increasingly disintegrated in many sectors over the past decades. Not surprisingly, a large body of literature explores how learning can be organized vertically in various contexts, such as knowledge spillovers from foreign firms to local suppliers (UNCTAD 2001), upgrading of local producers in their transaction-based relations with global buyers (Gereffi 1999), and national and regional learning systems based on producer–user interactions (Porter 1990; Lundvall 1992 and Chapter 29, this volume; Bathelt and Henn, Chapter 28, this volume). On the other hand, horizontal learning – which is distinctly different from vertical learning – relates to a process in which knowledge is shared and created, directly or indirectly, among individuals and firms that have the same areas of expertise and are conducting similar activities. At first sight, it seems unreasonable to expect these firms to engage in learning with each other since knowledge sharing with competitors could jeopardize the competitive advantage of technological leadership. Even if communication between competitors occurs, conventional wisdom is that it would less likely lead to technological advances, but more often result in price fixing and restrictions of competition at the expense of others in the economy and society. Adam Smith ([1766] 2007) expressed a strong concern about this in suggesting that “people of the same trade seldom meet together, even for merriment and diversion, [because] the conversation ends in a conspiracy against the public, or in some diversion to raise prices” (p. 137). Following 392

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Adam Smith, a competitive market is modeled as one that is composed of atomistic producers, with the price mechanism as the main way competing firms communicate with each other. In a pure market economy, firms are expected to conduct their research and development activities either in-house or in collaboration with trade partners, not with their competitors. From a social welfare perspective, knowledge exchange among competitors in market economies has been regarded as unnecessary at best or illegal at worst. However, as will be argued in this chapter, there may be some misunderstanding of horizontal learning when it comes to the innovation process. To enrich our understanding of innovation, this chapter defines horizontal learning within the legal boundary of knowledge exchanges among peers and competing firms and does not intend to justify collusion or the dark side of horizontal interaction. Thinking about horizontal learning within such a framework, we still find that, in both the early stages of capitalism and the current knowledge economy, interactions among peers and competing firms play an important role in generating innovations. From British machinery districts in the 19th century, to Silicon Valley over the past decades, to open-source software projects on the Internet, and even scientific communities in academia, communication and collaboration among actors and colleagues who do similar things is crucial for the creation of new knowledge. Issues surrounding such horizontal learning lead to numerous questions. First, questions regarding the incentives for sharing information with competitors arise. If such learning goes against the self-interests of economic agents, it would be inconsistent with conventional conceptualizations of innovation. A rational explanation thus is required to understand such learning behavior. A second question concerns the different forms and mechanisms of knowledge sharing among competitors. A few of these channels of horizontal learning have been explored to some extent in the literature, such as labor mobility, while others have received little attention. This chapter does not intend to challenge existing innovation theories, but aims to broaden our interpretation of learning by discussing how knowledge can also be generated among competing firms and actors. In the following, I will first explain why horizontal learning can be a rational and efficient choice for firms and individuals under certain conditions and then summarize important ways of horizontal knowledge creation related to studies of different communities. The last section concludes.

GOVERNANCE Knowledge develops in a cumulative way. New technologies and innovations are built upon what is already known. For innovators, existing knowledge pools are indispensable for drawing reference points or inspirations. As “social animals”, individuals exchange information naturally. Talking, listening, reading, and writing take up a large part of everyday life for the majority of people. Individuals spend much of their time sharing information with others voluntarily, without considering the economic value of such exchanges (Popper 2013). As such, free knowledge sharing is a common, rather than an exceptional, practice in all societies. And, as a consequence, knowledge exchange between colleagues in the same field is not surprising. However, when turning to the economic world and the level of firms and organizations,

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useful knowledge becomes a crucial input in the production process and is viewed as a proprietary asset. Philosophically, without being attached to a specific context or meaning, knowledge can potentially be transferred indefinitely. Of course, this would erase the advantages enjoyed by technology leaders and discourage the efforts of knowledge creators. To avoid uncontrolled and almost automatic knowledge spillovers to competing firms, institutional arrangements, such as patent and copyright systems, are in place to grant exclusive rights to innovators for a temporary period of time. Within this legal context, the use of patented technologies by competitors is forbidden unless licenses are granted. The reason as to why horizontal learning is not a common practice in this and other economic settings is therefore not that there is no incentive to learn, but that it is prohibited. In sectors where patent and copyright systems do not apply, such as fashion or food industries, one could argue that more innovations occur because of the practice of imitation among competitors associated with subsequent differentiation (Raustiala and Sprigman 2012). The argument regarding horizontal learning in this chapter does not question the legitimacy of the patent or copyright system, but aims to deepen our understanding of how innovations are created and diffused within communities and societies. In the long run, knowledge diffusion to competitors may greatly improve social welfare as new technologies become freely and widely available for many to benefit. From this, a debate has developed on how the patent system can be designed to better promote innovations, rather than impede them (Jaffe and Lerner 2007; Bessen and Meurer 2008). In some fields such as telecommunications and software, where technologies have become highly connected and develop very quickly, the monopoly of a patentee of a certain technology for a time period of many years may unintentionally increase the cost of related innovations to a degree that commercialization processes become very difficult. Further, loose regulations of what is patentable and cursory examination processes of patent applications may also contribute to blocking knowledge sharing and creation among peers. In the knowledge economy, the question of how to balance knowledge diffusion and appropriation processes becomes more challenging than ever. It should be noted that horizontal learning, of course, is not just governed by patent law. Before the establishment of the patent system when technologies were less codified, organizations such as business associations and trade unions played a strong role in keeping industrial know-how within specific communities and preventing it from leaking out. In contrast, horizontal sharing of technologies and skills was practiced in the apprenticeship system, within which strong personal relations were developed over time in parallel with the learning process. Since much of the knowledge was tacit and embodied in master’s crafts, face-to-face communication and on-the-job-training turned out to be the most efficient methods to pass this knowledge along. It is not surprising that knowledge sharing through apprenticeship was slow and strongly localized. Only individuals with strong commitments and high levels of trust were included in this training process. In other words, this horizontal learning was highly selective. The institutional setting effectively blocked knowledge leakage even without the patent system. In contrast with the patent system, there was no predefined end to the knowledge monopoly under the influence of traditional guilds and trade unions and as a result these settings were more successful in restricting unintended knowledge spillovers and less helpful in promoting business and technical innovation. Still today, horizontal learning in the apprenticeship system may aim at protecting traditional skills, rather than creating new knowledge.

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Aside from being regulated by the patent system and business associations, the horizontal learning process is usually characterized by self-governance. In general, we can observe formal and informal sharing of industrial and technical knowledge among competitors in many communities. When a technology is uncertain and the exploration of new knowledge goes beyond a single firm’s capability or is very expensive and risky, inter-organizational collaboration through alliances and joint ventures becomes a common strategy, widely adopted in high-technology industries (Gnyawali and Park 2011). At other times, it may be natural to share certain tacit business know-how and organizational practices as a by-product of interaction and communication among professionals in collaborative firms, despite the fact that inputs and outputs of the associated joint research and development are clearly specified ex ante. Perhaps, informal knowledge exchange among competing firms is the most interesting but also the most controversial part of horizontal learning. According to its geographical reach, informal horizontal learning can be divided into two types which are governed in different ways. First, knowledge can be shared informally within local settings, which means that a boundary can be drawn for horizontal learning. As a consequence, the supportive institutional settings for such horizontal learning may enable knowledge exchange within, but not beyond, some communities. This can be related either to existing social relations or to some pre-existing consensus among community members. Within innovative industrial clusters, ethnic connections, localized professional associations, and alumni relations may support open knowledge sharing among peers even in competing firms and generate a unique regional culture, as discussed by Saxenian (1994) in the case of Silicon Valley. Under such circumstances, knowledge is being channeled through local social networks and creates what has been labeled “local buzz” (Storper and Venables 2004; Bathelt et al. 2004). In other communities, even without personal relations, technology can be revealed to competing firms based on information or knowledge trading agreements (von Hippel 1987; Schrader 1991). The rules governing informal information trading are based on a consensus of reciprocity, as knowledge providers expect to receive a return favor from beneficiaries in the future. This can lead to a situation where professionals from a firm agree to share some knowledge, even if they do not know the other party well, especially if this is not at the heart of a firm’s competitiveness but important for the other party. However, like learning processes in social networks, such self-organized knowledge exchange mechanisms are sensitive to opportunistic behavior. Second, with advances in information and communication technologies, informal horizontal learning can also occur on a global scale. The development of open-source software projects illustrates that knowledge sharing within certain epistemic communities can exist without economic incentives (Osterloh and Rota 2007). Cohendet et al. (2014) refer to certain individuals in these communities as “underground” creators. These knowledge creators generate innovations and share them publicly because they feel pleasure and satisfaction when their ideas and efforts are recognized and used by peers. Horizontal learning in this context can be characterized as selfless behavior without or with little monetary compensation. Open-source communities on the Internet object to innovation monopoly via copyrights. Compared to horizontal learning processes in social networks and through trading agreements, knowledge sharing in open-source communities is more solidified through unique institutional arrangements. Open-source

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software developers attach licenses to their innovations to rule out appropriation of these innovations by others, and users have to agree to these licenses before using and modifying them further. Ironically, although open-source software developers reject the copyright system, they use copyright-based licenses to protect new innovations as public goods and to guarantee the sustainability of free knowledge sharing. As a result, horizontal learning in open-source communities can exist without personal relations and be extended globally, albeit that opportunistic behavior and misuse of the respective innovations is always a possibility. As illustrated above, horizontal knowledge exchange among competing agents is quite common in many communities and societies. Although there is a danger that unregulated horizontal interaction and communication between competitors may result in negative outcomes, in most cases horizontal learning is an ongoing process, governed by formal and informal rules and by relations. With these forms of governance, horizontal learning becomes an important category of innovation processes in the knowledge economy.

MECHANISMS When analyzing horizontal learning processes systematically, different mechanisms can be observed in various settings. This section will discuss four important mechanisms of horizontal learning, two of which happen at the individual level and two at the firm level. Since a large part of important knowledge is tacit and embodied in individuals, knowledge can be more easily exchanged among competing firms when managers have social bonds with each other (socially embedded learning) or when they move frequently across firms (labor mobility). Horizontal learning through social networks and labor mobility sometimes happens more frequently in certain localized communities or clusters and is generally beyond the control of a single firm. In addition, knowledge can also flow to competitors as a direct consequence of inter-firm interactions, either unintentionally or intentionally. On the one hand, there are situations within which a firm reveals technologies for strategic reasons (collective invention). On the other hand, horizontal learning can also take place when competing firms pick up knowledge from each other, either illegally through industrial espionage or legally through a normal process of observing and monitoring (Glückler 2013). As suggested above, this chapter focuses on the legal dimension of horizontal learning – based on a situation where knowledge from competing firms, such as new product designs and strategic plans, is publicly observed and monitored as a common business practice (interaction and monitoring). Socially Embedded Learning In ideal-type conceptualizations of economic interaction in competitive markets, there is no room for social relations among competitors. By viewing individuals as social beings, we need to acknowledge the existence of social structures, such as friendship and family ties among entrepreneurs that operate in the same industry. By sharing similar industrial experience and a common knowledge structure, entrepreneurs in the same business environment may even be more likely than others to become friends, as they have a natural interest to communicate with each other and exchange their experience. From

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an economic view, even without considering the possibility of illegal collusion, there can be substantial benefits from friendship networks with competitors. Such social relations, for instance, enable competitors to visit each other’s factories, through which knowledge about technologies, products, management practices, and markets is circulated. Ingram and Roberts (2000) have made an interesting observation of friendship networks of hotel managers in Sydney. It is through these friendship networks that information about price and occupancy of hotels in the city are routinely shared on a daily basis. Such horizontal information sharing provides these hotels with an important advantage as they can easily catch up with market changes and respond quickly. As a result, friendship ties of hotel managers improve the performance of the respective hotels. Socially embedded learning among competitors can also be found in communities with kinship networks. In traditional industrial districts both in developed and developing economies, family ties are a common phenomenon (Piore and Sabel 1986; Nadvi 1999). Although transactions among family firms and internal control mechanisms of family members may do more harm than good to local firm communities, kinship networks can provide an important mechanism for localized horizontal learning in regions with a poor knowledge base (Li et al. 2012). For example, in Dali, a small town in Southern China which has specialized in aluminum extrusion activities, technical know-how used to be shared within the local firm community through kinship and close friendship ties. This was crucial in the early stages of cluster development during the 1990s. It illustrates that social learning processes can facilitate market information sharing and promote technology diffusion among competitors, especially in local contexts. Similar processes based on friend networks and technical communities have also been reported in Silicon Valley (Saxenian 1994). Being socially embedded, the horizontal learning process is structured by family and friend networks in local communities. Since social networks of competitors are selective, this implies that informal learning is not open to all firms in local communities – which differs from the idea of almost freely available local buzz (Storper and Venables 2004; Bathelt et al. 2004). Just “being there” is in this case not sufficient to participate in learning. It requires time and effort for individuals, especially for those from outside, to be accepted into social networks of learning. Generally, the more connected individuals are within social communities, the more learning opportunities can be derived from local competitors. However, being over-embedded into local communities can cause myopia and, as a result, firms may neglect to explore new knowledge at a distance. The risk of being locked into a local setting can be high if horizontal learning is based on family ties (Li 2017). Compared to friend networks, family ties are more stable, more hierarchical, and more cohesive, and new ties to generate new knowledge cannot easily be launched. Therefore, the role of kinship relations for horizontal learning is contingent on the specific context (Ingram and Lifschitz 2006). Family ties can be powerful to channel the diffusion of knowledge among competitors; but they are less helpful to support knowledge exchange and creation in dynamic technology contexts, such as in high-technology regions. In a field that requires ongoing learning processes, friend networks, which can be more easily extended and restructured than family ties, are more supportive for knowledge creation among peers. Friend-based horizontal learning becomes more common when communities of professionals are situated in different cultural contexts, as in the case of Chinese and Indian immigrants in Silicon Valley (Saxenian 2006). With increasing integration in the global economy, transnational communities that are connected by ethnic

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ties not only provide an opportunity to learn best practices and advanced technologies from developed countries, but, more importantly, they play an increasingly important role to leverage knowledge and business know-how across different cultural contexts for generating global innovations (Henn and Bathelt, Chapter 39, this volume). Labor Mobility For horizontal learning, immigrant entrepreneurs not only indicate a way of learning via social relations, but also indicate that knowledge can be shared among competitors through labor mobility. Labor mobility becomes an important mechanism of horizontal learning since much valuable economic and technological knowledge exists in a tacit form. For example, business practices, organizational routines, communication skills, and understandings of technologies are to a large extent embodied in the minds of professionals. Inter-firm mobility of labor and spin-off processes from organizations provide significant opportunities for transferring business and technological knowledge between competitors. Although the mobility of professionals across national borders has increased over the past decades, labor mobility is in many industries still mainly a local phenomenon, especially in areas where firms in the same or related activities agglomerate. As a result of the mobility of highly skilled labor, knowledge is exchanged and created consistently in some industry hotspots, generating regional competitive advantages. A key factor behind the success of Silicon Valley in its early development stages was the high degree of labor mobility among semiconductor firms (Saxenian 1994). Using survey data, Fallick et al. (2006) were able to test Saxenian’s observations and found that the rate of jobhopping in the computer industry in the second half of the 1990s was 40 percent higher in Silicon Valley compared to the US average. Similarly, high degrees of labor mobility in high-technology industries have also been documented in other contexts (Power and Lundmark 2004; Tambe and Hitt 2014). It is straightforward to assume that labor mobility goes along with knowledge flows among competing firms – but hard to capture this effect in empirical studies. Using patent citations to quantify knowledge flows, Almeida and Kogut (1999) found that inter-firm mobility of engineers in the semiconductor industry had a positive effect on knowledge transfers. Measuring knowledge flows in a similar way, Song et al. (2003) suggested that the way firms benefited from labor mobility depended on how the competences of mobile professionals matched the knowledge pools of new employers. This implies that, although labor mobility is mainly a local phenomenon, local firms do not benefit equally from this horizontal learning channel. “Being there” in a cluster area can be advantageous and generate learning opportunities, but localized learning from competing firms may not happen automatically. The higher a firm’s innovation capabilities are, the more the firm is able to benefit from labor flows in clusters and, conversely, the more the firm may also be willing to share knowledge with its competitors. This suggests that large innovative clusters can develop self-sustaining horizontal learning dynamics. However, it may not be easy for clusters to reach a mature stage of development where horizontal learning becomes a self-sustaining feature. Especially at the early stages of cluster development, lead firms may exist in which local professionals are trained who later establish their own firms or move on to join competing firms. These focal firms become to some extent “public schools” from which the entire local business community benefits.

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During the early stages of computer development, Fairchild Semiconductor in Silicon Valley and DEC in Boston played such roles (Saxenian 1994). These firms suffered from high rates of job-hopping, but contributed substantially to the local innovation dynamics. Generally, knowledge spillovers in local labor markets lead to the question being asked as to whether it is justifiable for employees to share knowledge with competing firms when they switch jobs. It is viewed to be illegal to transfer “sensitive knowledge” from the former employer to a competing firm, for instance patent and client information. However, in many settings, it is hard to predefine precisely what could be shared and what should not be shared when changing jobs, since complex and tacit knowledge is hard to specify. As a result, institutional arrangements are introduced to regulate labor mobility. In many industries, non-compete clauses are agreed upon as part of labor contracts and prohibit an employee from working with a competitor for several years after resignation. Recently a new legal practice of “garden leave” has been discussed to regulate labor mobility (Bishara and Westermann-Behaylo 2012). According to this practice, an employee is required to give notice to the current employer several months before leaving, during which the individual still receives a salary but is not allowed to work elsewhere. The way non-compete clauses are used, in any case, differs across industries, regions, and countries. In some regions, such as Massachusetts, non-compete covenants have been strictly enforced, while in others, such as California, they do not play a big role. Gilson (1999) used the differences of non-compete covenants enforced in different regions to provide an institutional explanation of the divergence of Silicon Valley and the Boston Route 128 region. This analysis suggests that different regulations have caused different rates of labor mobility in the two regions and, as a consequence, different performances of the respective regional economies. Altogether, it is a challenging task, both theoretically and practically, to find an appropriate and efficient solution to govern horizontal learning via labor mobility. Interaction and Monitoring Of course, peers can learn from each other when they are friends, but they can also learn mutually when they are enemies or competitors. In contrast to collaborative learning through social networks, rivalry brings about competitive interaction and monitoring, which also entails knowledge exchange. In a competitive market, it is the pressure from competitors that forces firms to pursue new or improved technologies or designs. It is this competition that drives firms to differentiate their products and become better than peers. Porter (1990) emphasized this point strongly in his diamond model of national competitive advantages, within which rivalry and competitive strategy are the basis of one of the four fundamental pillars. For firms, any information from competitors is relevant. Even if a firm does not know exactly how a competitor makes a specific new product, the very information that such an innovation is possible may be valuable to the firm’s own research and development activities. Further, new products entail substantial knowledge of the innovating firm, including its understanding of market trends, technology capabilities, strategies, and its research directions. This knowledge can be crucial for competitors. Reverse engineering is, for instance, a common business practice in both developed and developing contexts. Competing firms do not just learn from each other’s products, but also from their actions, interactions, and reactions in different contexts. For example, when bidding for a project,

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a firm is able to develop a better understanding of its own strengths and weaknesses when comparing itself to its competitors. In business meetings, even though managers of two competing firms may not know each other in person, a brief conversation about market changes can transfer important knowledge that helps to adjust existing strategies or develop new ones. Even in situations without direct interaction, firms can learn from competitors by simply watching, listening, and monitoring these firms’ activities. This is especially the case when competing firms are located nearby (Malmberg and Maskell 2006). Since it is hard to find an excuse for not being as successful as a local competitor, the pressure of peers can be motivating, and strong local rivalry and associated product differentiation may be the result (Baum and Mezias 1992; Maskell 2001). Learning by monitoring works because firms in the same field share a common knowledge base that allows them to make efficient comparisons. Small variations in products or processes that may be overlooked by outsiders can be noticed as significant information by peers nearby. Interaction with competitors also happens at temporary business events, such as trade fairs and conferences. Along with the transformation of the global knowledge economy, trade shows and exhibitions are becoming more and more knowledge intensive (Bathelt et al. 2014; Bathelt, Chapter 31, this volume; Golfetto and Rinallo, Chapter 32, this volume). In exhibitions that connect global and local business communities, firms are provided with opportunities to observe new products of competing firms from all over the world (Li 2014a). Not only can they learn from specific competitors, they can also receive a broad overview and develop an overall understanding of new trends in the industry when scanning through exhibits at such events. At the same time, while examining other exhibits, participating firms can directly feel the pressure of generating better products and solutions from assessing their competitors’ products and technologies (Li 2014b). Collective Invention A fourth mechanism of horizontal knowledge exchange exists just because of the fact that firms operate in the same business. This mechanism is called collective invention (Allen 1983). When examining the technological progress of blast furnaces in the iron industrial district in Cleveland, UK, in the late 1800s, Allen (1983) surprisingly found intensive technology sharing among plants in the same industry. He explained this horizontal learning in two ways. First, at that time when firms did not specifically invest in research and development, many technologies developed as a by-product of building plants. Therefore, there was no direct financial cost to block technology sharing among competitors. Second, and more importantly, to discover the fact that energy consumption was lower in taller furnaces required huge experimentation cost, which went far beyond a single firm’s capability. Being eager to know how to build a furnace that consumed less coal, engineers saw no problem in sharing detailed technological information of their plants with competing firms. Collaboration and knowledge sharing was necessary under these circumstances to generate technological innovations since new knowledge was often discovered in a collective way, which benefited the entire industrial community. Nuvolari (2004) made a similar observation about knowledge sharing among competitors in a UK mining district during the industrial revolution. Such collective invention seemed to be common in traditional industrial districts before the establishment of the patent regime.

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With the rise of modern corporations since the early 1900s, it has become less likely to create knowledge as a by-product of investments. With the patent system, large corporations invest heavily into research and development to create new technologies ahead of their competitors. Such knowledge is usually kept in-house, rather than shared. For this reason, Allen’s (1983) first reason for collective invention no longer applies. With respect to the second reason, the situation is less clear. Would peers and competing firms share information when facing technology uncertainty? Some observations indeed point in that direction. In the embryonic stages of the IT industry when computer design was unclear, pioneering engineers discussed and displayed their new ideas openly among each other in clubs and communities (Meyer 2003). Horizontal learning was intensive at this stage before a dominant design of computers made its way. Similarly, it has been emphasized that high-technology firms in Silicon Valley were willing to share some of their new discoveries with competitors, not only to demonstrate their innovative capabilities, but also with the expectation or hope that their solutions would be picked up by others and become the technological standard in unsettled fields (Sturgeon 2003). In the knowledge economy, with increasing difficulty to predict market and technological changes, such horizontal knowledge sharing may become more important for new knowledge generation (Powell and Giannella 2010). Based on these observations, it may be safe to say that knowledge sharing among peers and competitors is more likely to happen during the early stages of innovation processes when dominant technologies have not yet been established, rather than during mature stages later on. In the early stages, it is beyond the capability of a single firm or individual to know the technology roadmap of the future. Also, the market value of new ideas may not be fully clear and complementary innovations are still needed. At this point, firms may not yet compete with each other for market shares because the market for new products has yet to develop. Under these circumstances, it is a huge challenge for all firms in the field to create a new market for the new products. Firms are interested to learn more about the actions of others and are willing to share their ideas and understandings with each other to get crucial feedback that helps them evaluate whether they are “on the right track” or not. However, in turning from the exploration to the exploitation stage of technologies when a dominant design takes shape, firms start to compete in commercializing their innovations and try to increase their market shares. As a result, windows of collective invention shut down at this stage. Since all innovations begin in an uncertain stage, the role of sharing knowledge among peers may be substantially underestimated in the current literature (Henn and Bathelt 2015).

CONCLUSIONS It has been long noticed that new technologies and practices can diffuse quickly among competing firms – and may do so quite frequently (Mansfield 1985). However, the mechanisms of horizontal learning and their significance have received little attention in the innovation literature. Communication and collaboration between competitors is often interpreted in a negative way (Baker and Faulkner 1993), suggesting that competing firms tend to illegally strive for price arrangements, rather than developing technologies further. If we recognize that inspiration and interaction are crucial elements for

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knowledge generation, then information sharing among peers may, however, also have positive impacts and contribute significantly to innovation. Another reason to consider horizontal learning is related to the different nature of these linkages in relation to vertical knowledge flows between suppliers, producers, and users. From the perspective of vertical interaction, firms benefit from ongoing long-term relationships with suppliers and/or buyers. In contrast, the success of firms in a horizontal framework depends on whether they can learn faster than their competitors. A fundamental difference between the two dimensions of learning is that vertical learning presumes firms learn via transactions, while a horizontal perspective proposes that learning can also occur without traded linkages (von Hippel 2007). In fact, some vertical learning in which firms acquire information about their competitors through their common suppliers or clients can actually be viewed as indirect horizontal knowledge flows. Even without traded linkages, firms engaged in the same activities may be bound together by various subtle relationships, as indicated in this chapter. This may be one of the reasons why it is not easy to examine horizontal learning processes systematically. The actors engaged can be rivals, friends, and collaborators at the same time. While they may compete for market share, they might sit together and do some brainstorming at business and social events. While a firm may take its competitor to court for infringing patents, they may later come together to conduct some joint research and development. While competing firms may fight each other in a certain market segment, they may in other circumstances feel like they are in the same boat when facing common pressures. These multiple dimensions of horizontal relations bring about complex interactions of competitors. Since horizontal learning is often based on untraded linkages, it is not always easy to identify and understand the rationales and mechanisms of related knowledge sharing. Horizontal learning can occur in various settings for different reasons. The underlying rationales can be economic, social, psychological, or even strategic reasons. Whatever the rationales are, knowledge sharing among competitors is usually developed from preexisting norms, conventions, and relations. In many industrial communities, horizontal learning is self-regulated. Although some government intervention, such as non-compete clauses, affect knowledge flows via labor mobility, it is less clear how the public sector can play an active role in other mechanisms of horizontal learning, including socially embedded learning, competitive interaction and monitoring, and collective invention. This brings forth the question of whether and how public policies can help promote horizontal learning while preventing negative outcomes. If, as argued in this chapter, horizontal learning can be important for knowledge creation and innovation in the early stages of technology development, perhaps the horizontal dimension of innovation can be important to better understand why many government initiatives to create an innovative economy fail and how better innovation policies can be designed.

REFERENCES Allen, R.C. (1983) Collective invention, Journal of Economic Behavior and Organization, 4: 1–24. Almeida, P. and Kogut, B. (1999) Localization of knowledge and the mobility of engineers in regional networks, Management Science, 45: 905–917.

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Amin, A. and Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities and Communities, Oxford: Oxford University Press. Baker, W.E. and Faulkner, R.R. (1993) The social organization of conspiracy: illegal networks in the heavy electrical equipment industry, American Sociological Review, 58: 837–860. Bathelt, H. (2017) Trade fairs and innovation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 509–522. Bathelt, H. and Glückler, J. (2011) The Relational Economy: Geographies of Knowledge and Learning, Oxford: Oxford University Press. Bathelt, H., Golfetto, F. and Rinallo, D. (2014) Trade Shows in the Globalizing Knowledge Economy, Oxford: Oxford University Press. Bathelt, H. and Henn, S. (2017) National and regional innovation systems. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 457–471. Bathelt, H., Malmberg, A. and Maskell, P. (2004) Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation, Progress in Human Geography, 28: 31–56. Baum, J.A.C. and Mezias, S.J. (1992) Localized competition and organizational failure in the Manhattan hotel industry, 1898–1990, Administrative Science Quarterly, 37: 580–604. Bessen, J. and Meurer, M.J. (2008) Patent Failure: How Judges, Bureaucrats, and Lawyers Put Innovators at Risk, Princeton, NJ: Princeton University Press. Bishara, N.D. and Westermann-Behaylo, M. (2012) The law and ethics of restrictions on an employee’s postemployment mobility, American Business Law Journal, 49: 1–61. Brown, J.S. and Duguid, P. (1991) Organizational learning and communities of practice: toward a unified view of working, learning, and innovation, Organization Science, 2: 40–57. Cohendet, P., Grandadam, D., Simon, L. and Capdevila, I. (2014) Epistemic communities, localization and the dynamics of knowledge creation, Journal of Economic Geography, 14: 929–954. Fallick, B., Fleischman, C.A. and Rebitzer, J.B. (2006) Job-hopping in Silicon Valley: some evidence concerning the micro foundations of a high-technology cluster, Review of Economics and Statistics, 88: 472–481. Gereffi, G. (1999) International trade and industrial upgrading in the apparel commodity chain, Journal of International Economics, 48: 37–70. Gertler, M. (2004) Manufacturing Culture: The Institutional Geography of Industrial Practice, Oxford: Oxford University Press. Gilson, R.J. (1999) The legal infrastructure of high technology industrial districts: Silicon Valley, Route 128, and covenants not to compete, New York University Law Review, 74: 575–629. Glückler, J. (2013) Knowledge, networks and space: connectivity and the problem of non-interactive learning, Regional Studies, 47: 880–894. Gnyawali, D.R. and Park, B.-J. (2011) Co-opetition between giants: collaboration with competitors for technological innovation, Research Policy, 40: 650–663. Golfetto, F. and Rinallo, D. (2017) Innovation through trade show concertation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 523–536. Henn, S. and Bathelt, H. (2015) Knowledge generation and field reproduction in temporary clusters and the role of business conferences, Geoforum, 58: 104–113. Henn, S. and Bathelt, H. (2017) Transnational entrepreneurs and global knowledge transfer. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 638–651. Ingram, P. and Lifschitz, A. (2006) Kinship in the shadow of the corporation: the interbuilder network in Clyde River Shipbuilding, 1711–1990, American Sociology Review, 71: 334–352. Ingram, P. and Roberts, P.W. (2000) Friendship among competitors in the Sydney hotel industry, American Journal of Sociology, 106: 387–423. Jaffe, A.B. and Lerner, J. (2007) Innovation and Its Discontents: How Our Broken Patent System Is Endangering Innovation and Progress, and What to Do about It, Princeton, NJ: Princeton University Press. Kogut, B. (2008) Knowledge, Options, and Institutions, Oxford: Oxford University Press. Li, P.-F. (2014a) Global temporary networks of clusters: structures and dynamics of trade fairs in Asian economies, Journal of Economic Geography, 14: 995–1021. Li, P.-F. (2014b) Horizontal vs. vertical learning: divergence and diversification of leading firms in Hangji toothbrush cluster, China, Regional Studies, 48: 1227–1241. Li, P.-F. (2017) Family networks for learning and knowledge creation in developing regions. In J. Glückler, E. Lazega and I. Hammer (eds) Knowledge and Networks, Berlin: Springer, pp. 67–84. Li, P.-F., Bathelt, H. and Wang, J. (2012) Network dynamics and cluster evolution: different trajectories in the aluminum extrusion industry in Dali, China, Journal of Economic Geography, 12: 127–155.

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Lundvall, B.-Å. (1992) User–producer relationships, national systems of innovation and internationalization. In B.-Å. Lundvall (ed.) National Innovation Systems: Towards a Theory of Innovation and Interactive Learning, London: Pinter, pp. 47–70. Lundvall, B.-Å. (2017) National innovation systems and globalization. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 472–489. Malmberg, A. and Maskell, P. (2006) Localised learning revisited, Growth and Change, 37: 1–18. Mansfield, E. (1985) How rapidly does new industrial technology leak out? Journal of Industrial Economics, 34: 217–223. Maskell, P. (2001) Towards a knowledge-based theory of the geographical cluster, Industrial and Corporate Change, 10: 921–943. Meyer, P.B. (2003) Episodes of collective invention, U.S. Bureau of Labor Statistics, Working Paper 368, available at http://www.bls.gov/ore/abstract/ec/ec030050.htm, accessed on 6 June 2015. Nadvi, K. (1999) Shifting ties: social networks in the surgical instrument cluster of Sialkot, Pakistan, Development and Change, 30: 141–175. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford: Oxford University Press. Nooteboom, B. (2000) Learning and Innovation in Organizations and Economies, Oxford: Oxford University Press. Nuvolari, A. (2004) Collective invention during the British industrial revolution: the case of the Cornish pumping engine, Cambridge Journal of Economics, 28: 347–363. Osterloh, M. and Rota, S. (2007) Open source software development – just another case of collective invention? Research Policy, 36: 157–171. Piore, M.J. and Sabel, C.F. (1986) The Second Industrial Divide: Possibilities for Prosperity, New York: Free Press. Popper, K. (2013) The Open Society and Its Enemies, Princeton, NJ: Princeton University Press. Porter, M. (1990) The Competitive Advantage of Nations, New York: Free Press. Powell, W.W. and Giannella, E. (2010) Collective invention and inventor networks. In B.H. Hall and N. Rosenberg (eds) Handbook of the Economics of Innovation, Vol. 1, Amsterdam: Elsevier, pp. 575–605. Power, D. and Lundmark, M. (2004) Working through knowledge pools: labour market dynamics, the transference of knowledge and ideas, and industrial clusters, Urban Studies, 41: 1025–1044. Raustiala, K. and Sprigman, C. (2012) The Knockoff Economy: How Imitation Sparks Innovation, Oxford: Oxford University Press. Saxenian, A. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128, Cambridge, MA: Harvard University Press. Saxenian, A. (2006) The New Argonauts: Regional Advantage in a Global Economy, Cambridge, MA: Harvard University Press. Schrader, S. (1991) Informal technology transfer between firms: cooperation through information trading, Research Policy, 20: 153–170. Smith, A. [1776](2007) Wealth of Nations, New York: Cosimo. Song, J., Almeida, P. and Wu, G. (2003) Learning-by-hiring: when is mobility more likely to facilitate interfirm knowledge transfer? Management Science, 49: 351–365. Storper, M. (2013) Keys to the City: How Economics, Institutions, Social Interaction, and Politics Shape Development, Princeton, NJ: Princeton University Press. Storper, M. and Venables, A.J. (2004) Buzz: face-to-face contact and the urban economy, Journal of Economic Geography, 4: 351–370. Sturgeon, T.J. (2003) What really goes on in Silicon Valley? Spatial clustering and dispersal in modular production networks, Journal of Economic Geography, 3: 199–225. Tambe, P. and Hitt, L.M. (2014) Job hopping, information technology spillovers, and productivity growth, Management Science, 60: 338–355. United Nations Conference on Trade and Development (UNCTAD) (2001) World Investment Report 2001: Promoting Linkages, New York: United Nations. von Hippel, E. (1987) Cooperation between rivals: informal know-how trading, Research Policy, 16: 291–302. von Hippel, E. (2007) Horizontal innovation networks – by and for users, Industrial and Corporate Change, 16: 293–315.

25. Innovation versus technological achievement Dominique Foray

INTRODUCTION Historically we observe two logics or two routes that coexist to translate the knowledge that is produced and absorbed into the wealth of nations. In the first route, knowledge is a wealth factor by dint of its capacity to generate technological achievements in certain strategic domains, which allows the king and then the state to strengthen their security, energy independence and prestige. Military or energy inventions, space exploration, architectural masterpieces or the opening up of major shipping routes are all examples of this first approach. In the second route, knowledge is a wealth factor by dint of its capacity to allow commercial innovation. In order to simplify the analysis, I only consider commercial innovations here. However, my analysis could extend to social innovations according to the definition of Phills et al. (2008) and common innovations according to the works of Swann (2015). See my essay (Foray 2015) on the relationships between common innovation and commercial innovation. What I would like to do in this chapter is to exploit and develop this framework to explain what innovation is not in order to gain a better understanding of its intrinsic nature. In order to understand that two different routes are involved here, a distinction must be made between the notion of technological achievement and the notion of innovation. Our societies have realised extraordinary technological achievements throughout the ages: pyramids and fortifications, temples and cathedrals, Sputnik and Apollo. These are masterpieces that demonstrate the power of states or kings, but they are not innovations in the sense of the implementation of an idea and its entry into the economy, to be tested and possibly adopted, which is done by the entrepreneur, the innovator or the user, and not necessarily the inventor or the engineer. Unlike technological achievement, innovation – or at least our definition of innovation – is essentially economic. It is the result of an economic discovery procedure, and it is its adoption in the commercial sphere that qualifies it as innovation. I think this distinction is useful, particularly for understanding the difficulties experienced by certain countries in adapting to the new challenges posed by the world economy. I am therefore going to develop it in this chapter. In this analysis, I will quite often use the example of France, a country that is particularly gifted as far as the technological achievements route is concerned but that has difficulties in transforming itself (move onto the other route). It is therefore a convenient example to illustrate the theories presented here. However, this chapter must not be considered as a case study of a particular country. Indeed, I think that the conceptual framework elaborated here has a far more general significance.

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A COMMON BASE AND A SPECIFIC STAGE ON THE ROUTE TO INNOVATION There are thus two routes. These two routes emanate from the same base – that of technological knowledge – that in each country is composed of science and technology institutions as well as education and training (human capital) and networks that allow the circulation of knowledge, together with the importation or absorption of foreign know-how and talents. Figure 25.1 also shows that while the first route – technological achievement – goes directly from this base of scientific and technological knowledge towards the realisation of the ‘masterpiece’ that constitutes the final aim – the second route inevitably passes through the economic knowledge space (Phelps 2013), that is, the knowledge concerning what can be produced and what works, not scientifically or technically but economically, and thus what can be adopted by society. Economic knowledge also includes what does not work or is not accepted economically or socially. This essential stage evokes Hayek’s famous discovery procedure (further elaborated by Phelps). It involves the process that is going to determine whether the imagined product can be developed and, if so, whether it will be adopted. The commercial or social validation of a new idea (a product or process) is based on the fact that (i) this idea can be developed and given tangible form as a product or process in appropriate economic or social or domestic conditions and that (ii) once developed, this idea is adopted. An innovation that is not adopted is in fact not an innovation. The distinction between invention (or discovery) and innovation is clear. Invention or discovery leads to new technological and scientific knowledge. Some of it will give birth to new ideas, which must be tested and tried out in the economic world in order to possibly become innovations.

Common base: Science & technology Invention & discovery

Economic knowledge entrepreneurial discovery

Technological achievements in strategic domains Innovations 1

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Figure 25.1 A general process of translating technological knowledge into the wealth of nations

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Of course, the science and technology base is important for innovation (it is even no doubt increasingly important in certain domains) but the nature of innovation requires that this scientific knowledge be combined with the economic knowledge that will qualify or disqualify it as innovation. ‘What brought mass innovation to a nation was not scientific advances but economic dynamism: the desire and the space to innovate’ (Phelps 2013). Feedback is important, and serves to recharge the knowledge bases: feedback from technological achievements towards the scientific and technological knowledge base; feedback from the innovation (from the discovery of what works and what doesn’t economically) towards the economic knowledge space. The lessons learned from the commercial or social successes and failures are incorporated into the economic knowledge base. Innovation Capacities Our definition of innovation implies that innovation capacity includes those who develop and experiment with new ideas in the economic sphere, those who finance these activities and finally those who adopt, try, and improve, thanks to their feedback, the new products or services offered. Innovation capacity thus comprises all the companies and entrepreneurs who implement this economic discovery procedure, the financiers who provide the necessary financing and the consumers who represent the ultimate market test. And the performance of innovation capacity will thus be assessed as ‘the extent to which imaginative and creative business people and business students wanting to start a new company can expect to find financing and talented workers determines the degree to which innovation can come from the grassroots, not just from the top’ (Foray and Phelps 2011). This conception leaves a great many things out of the innovation capacity. Therefore I haven’t yet mentioned the terms of ‘science’, ‘technology’ and ‘engineering’ as major components of it (Héraud, Chapter 4, this volume). These are of course important dimensions as they feed the development process of new commercial ideas, but they are not central elements. Of prime importance are the socio-economic institutions that encourage and promote the dynamism of innovators – product market, labour market and knowledge market, intellectual property, financial institutions – and the economic governance of companies with a view to favouring innovation strategies (see also Glückler and Bathelt, Chapter 8, this volume; Dosi and Marengo, Chapter 43, this volume). This conception also allows a lot of ‘new’ things to be included in this capacity, particularly all the non-technological innovations, innovations without research and development (R&D), innovations in terms of use or business models. And yet in certain domains, it is these innovations without R&D (R&D being understood in the narrow sense as being that which is generally carried out in a technological and industrial laboratory) that make the most significant contribution to economic change: huge and successful innovators such as Walmart, FedEx, Amazon and Cisco have grown not so much by mastering the intricacies of physics, chemistry and molecular biology as by structuring human work and organisational processes in radically new ways. The same is true of companies based on more radical innovation, such as the network firms Google, YouTube, eBay and Yahoo. All of the aforementioned companies have added hundreds of billions to the annual gross

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domestic product of the United States, with only modest contributions from industrial research as it has been traditionally understood. (Kahin and Hill 2010)

What is certain is that a particular, quite common vision, according to which R&D programmes, technological platforms and science parks are at the heart of innovation capacity, is not necessarily the one that we will favour here. This conception of innovation, which places economic discovery and economic knowledge at the centre, offers new perspectives, as we shall see, for analysing and comparing different countries in terms of innovative performances, growth and employment. Thanks to this conception we can build national models according to which the best are not necessarily those that have the most powerful science and technology or those that have the greatest number of researchers and inventors.

EACH ROUTE IS A LABORATORY To gain a good grasp of what is presented here, it must be understood that route 1 is not that of public research and route 2 that of private innovation. In both routes there are companies; in both routes, there will also be start-ups! But the companies of route 1 are not fundamentally involved in economic discovery processes. The important thing is therefore to know in which laboratory this company is situated. Within the framework of route 1, it is the scientific and engineering laboratory that occupies the central place. Almost exclusively nourished by the science and technology base, it is a protected space for imagining the wildest of technological dreams. The economic tests will only come ex post. Here it is the technological challenge, the greatness of the endeavour, which has priority. Within the framework of route 2, it is an economic laboratory that is at work: ‘a vast imaginarium, a space for imagining new products and processes’ (Phelps 2013). In this laboratory, it’s the entrepreneur rather than the scientist or engineer who prevails. The economic experiment is what counts. There are thus experiments on both sides but not the same ones. Exaggerating a little, on one side is the captivating world of the projects of scientists and engineers, relatively disconnected from the economy; on the other, the modern economy according to Edmund Phelps, in other words the world of entrepreneurs who, unceasingly, conceive, develop and test new commercial ideas and discover what works and what doesn’t, what people will want to buy and consume and what they will refuse or ignore. The respective roles of the scientist and the company are reversed: whereas in the laboratory of route 1, the scientist and the engineer occupy the main role, in that of route 2, they are in a way at the service of the entrepreneur, of business activities. At the risk of caricaturing, we can symbolise route 1 with the famous Concorde and route 2 with the extraordinary innovation of low cost. Here are two ‘innovations’ belonging to the same sector but whose nature, status and socio-economic impact are radically different! Concorde – a magnificent and prestigious object – never went through the laboratory of economic knowledge, otherwise it would probably never have seen the light of day! The

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example of Concorde is obviously ‘easy’, but even the great economic historian Nathan Rosenberg (2004) liked to use this case to illustrate more or less the same problem! He wrote: How well will the new product perform, not only technologically, but in economic terms? Will a high performance be attained, but only at a prohibitively high cost? The Concorde airplane was a simply magnificent achievement in terms of engineering design and speed, but it was also an unqualified financial disaster.

The low cost phenomenon is a radical, extremely powerful innovation, whose creation mobilised very few sciences and technologies and which originated primarily from a process of economic discovery, the macroeconomic importance and impact of the latter having been extraordinarily greater than those of the aeronautic gem.

TEMPORALITIES In comparing the laboratory of Jules Verne to that of the entrepreneur dear to Schumpeter but also Baumol and Phelps, we see that the notions of temporality and long term assume different meanings. Things can move very fast in the laboratory of Jules Verne (remember the experience of the Manhattan Project) but the project’s dynamic, validity and pertinence are based on the long-term horizon. Overstepped and extended deadlines are not a big problem. In the case of the entrepreneur on the other hand, the short time span – that of the economic testing of the new idea, the ‘timing’ of the innovation to capture the market and overtake potential competition – is crucial; things must always be speeded up, even when the time necessary for the production of the innovation is long (as in the case of pharmacy).

DELVING DEEPER INTO THE BASIC DISTINCTION The decisive criterion underlying this distinction is that an innovation must be validated within the framework of the economic knowledge space while the technological achievement is governed only by scientific and technical constraints and laws. It is a decisive criterion but not one that is always easy to implement. There are grey areas where the distinction between technological achievement and innovation is less simple. For instance, projects of technological achievement can be very well managed, based on binding milestones, deadlines and costs objectives, in a time of stronger public accountability. Think for instance of the recent case of the Gotthard tunnel achievement in Switzerland. However, the distinction remains between a well-managed project of technological achievement and an innovation which is fundamentally characterised by an economic discovery. We must no doubt be wary of making too rapid a correlation between big technoindustrial projects and technological achievements. Certain large projects represent genuine innovations, undergoing the economic tests immediately. Airbus seems a good example. Airbus is not Concorde! It was conceived right from the start in a setting of

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duopolistic competition (with Boeing) and thus had to undergo the economic tests very quickly in order to extend its market shares. However, we can also think that the greater the scale of the programme, the more gigantic the fixed costs, the greater the probability of discovering that it will not work economically and the greater the temptation to avoid the economic discovery stage for the defenders of the project!

THE FEEDBACK FROM THE TWO ROUTES CONCERNING EDUCATION AND RESEARCH INSTITUTIONS There is obviously feedback from each of the two routes concerning the way in which education and research are institutionalised and organised. We generally observe two institutional forms for organising public research, corresponding to two solutions to the famous problem of the production of a public good at an optimal level: the solution of public production and the solution of public subsidising of private production. The first solution corresponds in knowledge economics to the public research organisation; the second corresponds to the research university (Dasgupta and David 1994; Foray 2004). It is fairly easy to understand that the public research organisation form is rather adapted to the first route whereas the research university form is rather adapted to the second. Zucker and Darby (1997) illustrate this well when they write: the idea of a public research organization sounds very attractive, particularly in a small country that sees them as a vehicle to achieve a critical mass by concentrating the nation’s best scientists in one place. In fact, we ourselves would like to have our research well funded until retirement and the opportunity to build a more permanent research group without the need to educate and train successive generations of graduate students and post-doctoral fellows. Despite the personal attractions, we can also see how that situation might cool the entrepreneurial spirit as well as our impact on the most important objective of any knowledge institution: the generation of high quality human capital.

We can also understand that the historical importance of each route in a particular country will contribute to shaping the institutions. In France, where the first route is historically dominant, the eminent share of the large research organisations and the weakness of research universities reflect this domination. Consequently we see that public research, its institutions and its financing are not neutral with regard to these two routes. There is certainly a common base but the public financing of research does not nurture the two routes indiscriminately; it is allocated to one or the other according to what exactly is being financed (public research organisation or university, state programme or national research agency), thus in accordance with the specific institutional architecture of the country in question. If we go even further upstream, it’s easy to understand that the feedback from the two routes is also going to affect training and education. Education is not conducted in the same way in a country strongly characterised by route 1 as in a country in which route 2 dominates. Training is not carried out in the same way in France and Finland!

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INSTITUTIONS AND VALUES IN THE TWO ROUTES Historically, the first route is always present throughout history, quite simply because the institutions and mental and psychological attitudes adapted to this route are fairly basic and were already in evidence in ancient societies, those that produced the pyramids and temples, the first arms systems and the first urban or rural infrastructures. But this capacity for technological achievement that traverses history generates few economic gains in terms of productivity and standard of living. The Renaissance, for example, an exceptional period for science and technology, is not notable for the productivity gains and gross domestic product (GDP) growth associated with it. The explanation is that there were not many innovations and this weakness results from the absence of institutions and attitudes that encourage the creation and testing of economic ideas. While route 1 is present everywhere and at all times, route 2 only appears late – during the 19th century – at the time of the transition from mercantile capitalism to modern capitalism, which rewards the activities of conceiving, concretising and launching new commercial ideas. It is the constitution of institutions and attitudes favourable not only to entrepreneurship but also to innovatorship that lies at the heart of the advent of the second route for converting technological knowledge into the wealth of nations. Baumol expresses this well: ‘capitalism [a certain type of capitalism that Phelps calls modern] is unique not in its capacity to invent but in its capacity to innovate’ (Baumol 2002). Institutions and Values, Specific to Each of the Two Routes The first route – as I said – can blossom almost anywhere, in any socio-economic system. Any centralised and disciplined organisational structure can be effective when it comes to producing a technological achievement, from the construction sites of Ancient Egypt to the Soviet military laboratory; from the American NASA to the French CEA (Commissariat à l’Énergie Atomique (Atomic Energy Agency)). In these cases there are no very sophisticated organisational logic or complex incentive problems to be resolved. The state is usually the prime contractor and main client. There is one sole agency in charge of the plan and its execution – in other words in charge of the management and monitoring of a scientific and technological elite executing the defined objective (The institutions and governance modes of the first route exhibit many similar features of the so-called mission-oriented research activities, see Foray et al. 2012). The second route is based on a far more complex institutional framework (see also Glückler and Bathelt, Chapter 8, this volume). It is the economic institutions that encourage economic dynamism and experimentation. There must be the right institutions and incentives, not only to stimulate entrepreneurship, but also to direct it towards innovation (Baumol 2002; Phelps 2013). It is the institutions that relate to the different markets and the rules concerning competition, access to credit and other forms of financing of risky or uncertain projects, intellectual property and company governance. The financial structures suitable for the financing of innovation are much more complex than what is required for the financing of a technological achievement. Indeed, the very nature of innovation is going to confer certain characteristics on the relationship between the credit applicant and the financier – uncertainty, information asymmetry,

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moral hazards, long-term process – that create great difficulties. Financial mechanisms such as venture capital are going to play an important role here by developing an array of tools allowing these potential problems to be overcome. But it is also the way in which the risk is assessed and expressed for a whole series of alternative activities that is crucial since an incorrect risk assessment can make the financing of far less productive and socially desirable activities than innovation appealing. In the final analysis, the relative simplicity of the institutions of the first route and the fact that any socio-economic system can adopt it stems from the fact that the possibility or aptitude to successfully complete technological achievement projects is relatively isolated, protected from economic incentives. What counts in this first route is the state decision-making system and the capacity to mobilise a technological elite towards the objective defined by the state. Innovation capacity on the other hand is essentially dependent on economic incentives and thus on the effective functioning of fairly sophisticated institutions. This does not mean that there is no market for the products of the technological achievement (I will come back to that), but they are first produced for the state, before possibly thinking about markets. Human Values, Mental Attitudes The values and attitudes differ between one route and the other. A range of values that focus on elitism, the respect of authority and centralisation correspond to the first route. Because such values do exist in any society, this route works well in any system. While on the other hand, we find what Phelps calls the modern values, such as working and thinking for oneself, self-expression, competition and acceptance of change, individual legal rights, the desire for economic experimentation (Rosenberg 1992).

DISCONNECTION OR SYMBIOSIS The two routes that I have distinguished call on institutions and values that are very different, and it is not easy to make them coexist harmoniously within the framework of a given country. Historically there are only a few examples of countries that have succeeded in bringing about this virtuous conciliation, and we shall see that the United States of the 20th century is quite a unique example in this respect. From Sputnik to the New Economy (of 1995–2000) The USSR is a fascinating model from the point of view of the relationship between knowledge and wealth, which illustrates well the possibility of an immense discrepancy between powerful and sophisticated science and technology capacities in certain strategic domains and an almost total absence of innovation in the economy! The USSR succeeded in developing first-class scientific capacities – the Russian Academy of Sciences and Sputnik being indisputable symbols of Soviet power and instruments of this power through the development of very advanced arms systems. On the other hand, the second route, that of innovation, remained non-existent; knowledge was never able to serve the wealth of the nation through innovation, for want of the appropriate economic institutions and incentives.

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The contrast is provided for us by the exceptional episode of the new economy in California in the late 1990s. This episode obviously illustrates the success of the second route thanks to institutions favourable to entrepreneurship and innovation (the Bayh–Dole Act, the extension of intellectual property to new domains of business opportunities, the relaxation of access rules to Nasdaq and the enforcement of anti-trust laws in domains such as telecommunications and information technologies (see Mowery and Simcoe 2002), are examples of fundamental institutional changes and adjustments towards ‘innovatorship’ involved at this time). However, the episode also illustrates the role of federal investments, particularly by the Department of Defense, in training and research in information technology and electronics that nourish both route 1 and route 2 (thanks to relatively open institutions and programmes) (National Academy of Sciences 1999). This is a model in which the two routes seem to coexist harmoniously, mutually strengthening each other. That is, at any rate, what can be learned from the works of historical economists concerning this specific case (Mowery and Simcoe 2002). But remember – what is shown here is no doubt only valid for that time period (from the 1960s to the mid-1990s) and for the sectors concerned (information technology, electronics and communication). The United States has not succeeded in developing such harmonious relations between the two routes, especially in the energy domain (Mowery 2006)! Another example of symbiotic relations between route 1 and route 2 is undoubtedly provided today by Israel, in the drone domain for example.

THE FIRST ROUTE: A TORTUOUS, RISKY AND INEFFECTIVE PATH TOWARDS INNOVATION, AS THE VALUES AND INSTITUTIONS ARE INADEQUATE There must be a good understanding of what the first route is, and it must not be taken for what it is not. This effort to understand is difficult, and even the greatest minds can fail! Thus Obama in his State of the Union address of 2011 constantly calls for innovation but his examples and metaphors illustrate this confusion (‘We will fund the Apollo projects of our time’ ). It is of course useful to be capable of mobilising a country by proposing to its elites large-scale projects that stir the imagination (‘Is there life on Mars?’), but this does not have much to do with entrepreneurial dynamics and innovation. So a good understanding of the first route is essential! It’s the way to rapidly attain the technological achievements needed by a state or society and that are not destined to be innovations. We need these achievements to fascinate and encourage us to dream, to defend ourselves and secure necessary resources, to rapidly produce a technological solution in the face of such and such a threat, open up new horizons of creativity and so on. On the other hand, this first route is not the high road to innovation. The paths that would lead to innovation via this route are tortuous and very indirect (Figure 25.2). Spillovers: A Magical Notion! There are of course the famous spillovers. The almost magical resort to this notion has often served to justify expenditure for technological achievement in terms of innovation policy, which is not very pertinent. Any major technological project can of course

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Economic knowledge entrepreneurial discovery

Common base: Science & technology Invention & discovery

Ex post economic test Technological achievements in strategic domains

Spillovers Innovations 1

Figure 25.2

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The tortuous path to innovation via route 1

generate these spillovers – this depends on the quality of the project organisation and on the absorption capacities of the external actors (see in particular the abundant empirical works done by the BETA team at the University of Strasbourg, e.g. Cohendet 1993; Cohendet and Simon, Chapter 3, this volume) – but the possible presence of spillovers is in no case an essential driver for innovation and cannot therefore economically justify the expenditure allocated to route 1. Difficult Transposition Another possibility is not merely being satisfied with spillovers but attempting to transpose technological achievements to the economic space – like the project for commercialising human spaceflights, for example. But generally speaking, this doesn’t work since in the logic of route 1 the natural tendency is to favour technological excellence and sophistication to the detriment of economic pertinence. In such a perspective, dual use (military–civilian) as a technology policy works rather well when the technology which is applied in both the military and the civilian domains is developed under one single logic which is that of technology achievement (route 1). It will not work so well when the goal is to transpose a military technology into the economic discovery space in order to make it an ‘innovation’. It is the engineer who overrides the entrepreneur. Economic tests are carried out ex post and will in most cases reveal the economic non-pertinence of the technological achievement. Something that is totally normal, anticipated and assumed when you send robots to Mars (the stuff that dreams are made of . . .) becomes more delicate when you decide you want to commercialise military planes, technological gems obviously designed and produced within the framework of the logic of route 1 and the prime impetus for which was an order from the state: A prime example being le Rafale, which was ‘too expensive, too ahead of its time’ (Bezat 2011). Le Rafale is the latest-generation military plane of the French

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company Dassault, which has tremendous difficulty in winning over clients apart from the French State. It is fascinating to see to what extent, in France, the same problem reoccurs. Thus the quote concerning le Rafale is oddly reminiscent of an older comment regarding the Plan Calcul: ‘At the start, a commercial error made by the company itself: the Gamma 60, a large computer considered at the time as being the most advanced – too large, in fact, and too advanced – is put onto the market’ (Salomon 1991: 53). And then more or less the same quote crops up again in the case of new-generation nuclear technologies: ‘the EPR is a too sophisticated technology and is therefore a commercial disaster’ (Bezat 2014)! Once again, our problem is not the technological achievement in itself! It’s very good to be ahead of your time when you’re aiming at a particular technological performance. The problem is the confusion between the two routes. These failed transpositions also reveal the problem of industrial companies which are too used to orders from the state in the framework of route 1 and which no longer know how to go through the economic stage (that of route 2), companies with large technological capacity but very low innovation capacity. Here we could draw a parallel, in the telecommunications domain, between Nokia, a company always present and active in economic discovery, and Alcatel, a company that has always carefully avoided the space of economic discovery (Escande 2015). There are of course exceptional cases that make us wonder. Companies typically belonging to route 1 – they have been founded in this framework and have long kept well away from economic discovery – enjoy a certain success on export markets (Dutheil 2014). These tend to be exceptions, and these successes should in any case be weighed against the enormous public expenditure and its opportunity costs that, for decades, have allowed these companies to be kept afloat. Situated in the other route, these companies would have disappeared long ago. It would therefore be crazy to trust only this tortuous path, in other words the agenda of a scientific elite, mobilised in the framework of a large-scale technological objective, to deliver the innovation rate necessary for the country’s economic growth. How Can a Sector Pass from One Route to the Other? Rather than trying to twist the arm of the technological achievement system to make it do things it does not know how to do, something that can get things moving and force the repositioning of assets and organisations from route 1 towards route 2 is without doubt the arrival of outsiders. Outsiders who radically transform the codes of a profession, by envisaging it essentially within the framework of the economic discovery space and no longer in that of technological exploit. It seems to me that this is what is happening with the Space X episode – an aerospace project in start-up mode! Space X, founded in a garage, radically changes the codes of a profession and creates terrible pressure on Ariane and its institutions. Space X is first an economic challenge, based on a technical definition and industrial organisation that right from the start were designed to minimise costs. Instead of being a booster rocket at the cutting edge of technology, Falcon 9 uses tried and tested technologies, easy to develop and inexpensive to industrialise. As CNES President J.Y. Le Gall rightly says, ‘it’s about reinventing Ariane; it’s the lesson taught us by the garages of California’ (Le Gall 2014).

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REMARKS REGARDING PRODUCTIVITY A recent Organisation for Economic Co-operation and Development (OECD) publication (2015) shows clearly that the productivity problem of developed countries is by definition less that of firms at the frontier than that of the diffusion of technologies, organisational practices and new business models from those who invent them towards all the other companies. Yet we can clearly see that this diffusion does not happen easily from route 1 (we’ve just demonstrated that) while it is in a way the very purpose of route 2. In other words, the economic discovery space acts as a very powerful diffusion machine. This is Baumol’s theory in his seminal work published in 2002. This is why countries in which route 1 dominates inflict an additional handicap on themselves. They can admittedly have their technological defenders but the latter are disconnected from other companies and the diffusion of sources of productivity functions poorly.

BY WAY OF CONCLUSION: WHAT’S AT STAKE NOW The Great Transformation and the Problem of Bogged Down Countries! The technological achievement route (route 1) was for a long time the main method of translating knowledge into the wealth of nations; innovation intervened in only a sporadic and limited way at some very precise moments in history (e.g. the Industrial Revolution). In other words, the wealth of nations was essentially based on the capacity of countries to achieve military domination, build empires and control commercial routes; all this is possible via route 1. Then certain institutional and cultural changes throughout the 19th century – linked to the transition from a mercantile capitalism to modern capitalism (Phelps 2013) – meant that innovation was gradually going to establish itself as the main route (route 2), without however causing the other one to disappear. But the innovation route became the favoured route for growth and employment. This shift of the foundations of the wealth of nations towards innovation is problematic for certain countries (we could name Russia or France among others). For example, France, which enjoyed a great deal of success thanks to route 1, has difficulty in transforming itself when the other route, that of innovation, becomes the principal route. France thus became somewhat bogged down in route 1, and has trouble in digging itself out; this places it in difficulty at a time when innovation has become the alpha and omega of economic power. The difficulty experienced by France in changing routes is quite unique among countries in the West. It would be too ambitious within the framework of this chapter to try to identify all the historical roots. But regarding these historical roots, we will find very pertinent observations and arguments in the recent work of Jean Peyrelevade (2014) on the warlike nation, the most warlike of all, which implies that ‘our vision of the economy is basically a wartime economy, at the service of the sovereign and supporting his dreams of power’; a vision therefore where the logic of technological achievement not only dominates the other logic but crushes it. We can recall here the writings of François Perroux (1979) – the famous post-war French economist – who wrongly describes ‘innovation’ as

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an almost exclusive result of scientific and technological capacities, deployed in domains like atomic energy or information technology and implemented in the large national research centres, great military research laboratories and new aerospace and aeronautic industries (p.  1543), thus ignoring a whole innovation economy, that of decentralised entrepreneurship, young technological enterprises and the multitude of entrepreneurial experiments, which will be slow to establish itself in France. The domination of one route over the other is therefore very clear in France. It has never, until recently, been really challenged by the political and administrative elite. The institutional structures and cultural values of route 1 block the transition towards a socio-economic and political system that would be more favourable to the innovation route. Public research, education and training as well as economic institutions and values are involved here. This difficulty is especially pronounced as a new wave of technological changes is on the way, creating extraordinary innovation opportunities for numerous countries. But only those whose innovation capacity works well will reap the benefits. Digitalisation, Biomedical, Nanoscience – Who Will Benefit? This new wave of technological change offers absolutely huge potential for structural change in the realms of digitalisation, biomedicine and nanoscience. But this potential will be realised especially in countries where innovation capacity is working well: a diversity of entrepreneurial projects that unceasingly test the new commercial ideas generated by these technological changes, appropriate financial structures, users and consumers eager to try out the new products and services. In this respect, the impression of exuberance and effervescence that seems to be developing pretty much everywhere is rather misleading. Start-ups are springing up all over the place to be sure, but not all of them are in the domain of economic discovery and innovation. It’s the President of BPI France who tells us that France is a Silicon Valley without knowing it! You only have to see the incredible number of start-ups that are appearing everywhere. But are we so sure of this? The OECD in its recent report on France notes that most of these start-ups have a satisfactory survival rate but a very low growth rate, and it comments (OECD 2014: 245): ‘scientific logic overrides economic logic. Many start-ups are simply not in the world of economic discovery’ (the case of spinoffs of large military R&D organisations – such as SOITEC in France – is a good illustration of start-ups which do not operate in the economic discovery laboratory, Jacquin 2015). So this exuberance might exist in certain countries, without their benefiting from its conversion into innovation, which as always reflects a sort of upstream routing error. Certain ecosystems remain nestled in the great technological laboratory, which is indeed buzzing with excitement, without ever taking the plunge and opening the door of the economic laboratory. Acknowledgements I wish to thank the participants of the seminars in the University of Strasbourg (November, 2013), Chambre de Commerce et d’Industrie de Bourgogne, Dijon (November 2014), Institut des Hautes Etudes pour la Science et la Technologie, Paris (October 2014) and Genopole d’Evry (2014) for their comments and reactions, which were sometimes sharp! My hearty thanks also go to Patrick Cohendet for his encouragement and patience!

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REFERENCES Baumol, W. (2002) The Free-Market Innovation Machine, Princeton, NJ: Princeton University Press. Bezat, J.M. (2011) ‘Rafale, TGV, nucléaire: quand le made in France peine à se vendre’, Le Monde, 12 May. Bezat, J.M. (2014) ‘EDF et Areva veulent rendre l’EPR enfin exportable’, Le Monde, 2 & 3 March. Cohendet, P. (1993) ‘Evaluating the industrial indirect effect of technological programmes: the case of the European Space Agency programmes’, Working Paper, Paris: DSTI, OECD. Cohendet, P. and Simon, L. (2017) ‘Concepts and models of innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 33–55. Dasgupta, P. and David, P.A. (1994) ‘Towards a new economics of science’, Research Policy, 23(5), 487–521. Dosi, G. and Marengo, L. (2017) ‘The dynamics of organizational structures and performances’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 702–722. Dutheil, G. (2014) ‘Thales veut “le plus vite possible” mettre le cap sur les activités civiles’, Le Monde, 12 April. Escande, P. (2015) ‘Colbert et le papetier Finlandais’, Le Monde, 4 December. Foray, D. (2004) The Economics of Knowledge, Cambridge, MA: MIT Press. Foray, D. (2015) ‘Common innovation in the light of reflections on mass flourishing’, Research Policy, 44(7), 1403–1405. Foray, D., Mowery, D.C. and Nelson R.R. (2012) ‘Public R&D and social challenges: what lessons from mission R&D programs?’, Research Policy, 41(10), 1697–1702. Foray, D. and Phelps, E. (2011) ‘The challenge of innovation in turbulent times’, MTEI working paper no. 2011-002, EPFL, Lausanne. Glückler, J. and Bathelt, H. (2017) ‘Institutional context and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 121–137. Héraud, J.-A. (2017) ‘Science and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 56–74. Jacquin, J.B. (2015) ‘N’est pas start up qui veut’, Le Monde, 21 January. Kahin, B. and Hill, C. (2010) ‘United States: the need for continuity’, Issues in Science and Technology, Innovation Policy around the World, XXVI(3), Spring. Le Gall, J.Y. (2014) ‘Réinventons le programme Ariane pour rivaliser avec les Américains’, Le Monde, 10 January. Mowery, D.C. (2006) Lessons from the History of Federal R&D Policy for an ‘Energy ARPA’, Committee on Science, US House of Representatives. Mowery, D.C. and Simcoe, T. (2002) ‘Is the Internet a U.S. invention? An economic and technological history of computer networking’, Research Policy, 31(8–9), 1369–1387. National Academy of Sciences (1999) Funding a Revolution, Washington, DC: National Academies. OECD (2014) Examens de l’OCDE des politiques d’innovation: France, Paris: OECD. OECD (2015) The Future of Productivity, Paris: OECD. Perroux, F. (1979) ‘Politique de la science: analyse de l’innovation et de sa propagation’, Economies et Sociétés, no. 23. Peyrelevade, J. (2014) Histoire d’une névrose: la France et son économie, Paris: Albin Michel. Phelps, E. (2013) Mass Flourishing: How Grassroots Innovation Create Jobs, Challenges and Changes, Princeton, NJ: Princeton University Press. Phills, J.A., Jr, Deiglmeier, K. and Miller, D.T. (2008) ‘Rediscovering social innovation’, Stanford Social Innovation Review, 6(4), Fall. Rosenberg N. (1992) ‘Economic experiments’, Industrial and Corporate Change, 1(1), 181–203. Rosenberg, N. (2004) ‘Innovation and economic growth’, Paris: OECD, https://www.oecd.org/cfe/ tourism/34267902.pdf, accessed Jun 13, 2017. Salomon, J.J. (1991) ‘La capacité d’innovation’, in M. Lévy-Leboyer and J.C.Casanova (eds), Entre l’Etat et le Marché, Paris: Gallimard. Swann, P. (2015) Common Innovation, Cheltenham and Northampton, MA: Edward Elgar Publishing. Zucker, L. and Darby, M. (1997) ‘The economists’ case for biomedical research’, in C.E. Barfield and B.L.R. Smith (eds), The Future of Biomedical Research, Washington, DC: The AEI Press.

PART V INNOVATION IN PERMANENT SPATIAL SETTINGS

26. Geography of innovation, proximity and beyond Alain Rallet and André Torre

INTRODUCTION The localized nature of innovation has, for the last 40 years, been the object of much debate in the literature. However, it has been approached from different angles according to different schools of thought and types of policies chosen by local or national governments. The 1970s and 1980s were marked by a renewed awareness of the importance of localized economic systems. The goal was initially to highlight the role of Marshallian externalities in the capacity of local networks of small and medium-sized enterprises (SMEs) to compete with large firms, the ‘Third Italy’ being a typical example of this productive configuration. In the Marshallian tradition, those productive systems rested on customer–supplier relations and, more generally, on externalities, that is to say, the ‘industrial climate’ that characterizes a territory (‘the secrets of the industry are in the air’, Marshall 1890). The literature of those years on industrial districts (Becattini 1979; 1987; Becattini and Rullani 1995; Becattini et al. 2009) and innovative milieus (Camagni 1991; Maillat 1995) brought to light the complexity of localized systems by taking into account not only inter-industrial linkages, but also other components such as the labor market and an ad hoc culture, in the sense of Becattini. This led to a shift, in the literature on districts, from a concept of localized economic systems centered on production (industrial district) to one centered on innovation (technological district), while the research on innovative milieus focused from the very beginning on territorial mechanisms conducive to producing innovation. In this way, innovation was considered the product of territorial systems responding to specific characteristics, conceptualized in different ways depending on the approach (district, milieu, localized system of innovation, etc.) and covered by the allencompassing concept of the cluster, which has been the theoretical reference point for local innovation policies for over 30 years. In the 1980s, another approach to the geographical dimension of innovation emerged as a critique of the prior conception according to which innovation was the product of a territory that behaves like a system, with innovation policy intending to fill the gaps in this system (research, universities, high-technology industries, etc.) to improve its performance. According to this approach, innovation no longer is the product of a local system but that of a complex geography of both local and non-local relations between innovation actors. The idea, in this case, is to identify the role of geographical proximity and assess its relative weight in economic linkages generating innovation. This perspective, grounded in coordination mechanisms, is quite different from the previous approach. It is the perspective adopted by the Proximity School of thought in the 1990s (RERU 1993) and later by economic geography research, which generated much empirical research on the various dimensions of proximity and the localization of innovation processes 421

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(Feldman 1994; Feldman and Massard 2002; Asheim and Gertler 2006; Boschma and Martin 2010; Torre and Wallet 2014; Shearmur et al. 2016). These studies suggest that geographical proximity plays a relative role and therefore that innovation policies should not only promote global connections but especially local synergies among local actors. These approaches have greatly expanded our understanding on the role of innovation in localized systems and led to the implementation of active public policies. This chapter argues that new approaches are necessary today, because the existing ones either suffer from analytical shortcomings or have failed to take into account changes in the conception of innovation and in the organization of contemporary societies. Accordingly, the next section is devoted to the cluster-oriented approach, which highlights the systemic nature of innovation processes – seen as less and less technology-based – thereby moving closer towards a definition of industrial ecosystems. Then, we discuss the coordination-based approach, highlighting shortcomings in the analysis of the concepts of proximity and their coordination-related dimension. Finally, we discuss the need for a broader conception of innovation, and the necessity to look beyond its technological dimension by considering new forms and new sources of innovation, linked to social and organizational issues as well as environmental questions and the relation with local populations’ desire to express themselves.

FROM CLUSTERS TO INDUSTRIAL ECOSYSTEMS Whatever name they are given (district, milieus, etc.), and whatever form they may assume as instruments of public policy (technopoles, science parks, industrial clusters, competitiveness poles, etc.), local production and innovation systems seemed for a long time to be the types of organizations that best represented the relation between space, industry and innovation. Related approaches also helped better understand the nature of local interactions and configurations. They had the merit of placing SMEs, sometimes very small enterprises, at the heart of the analysis, and of showing that the resilience of local systems was based largely on their being part of a network. Starting with a review of the ever-deepening analysis of the concept of the cluster, its synonyms and derivatives, we then proceed below to a review of the concepts of industrial ecosystems and business ecosystems, the latest embodiment of clusters. (a) The Cluster-Based Approach – Constantly Refined and Put into Perspective Local production systems are here referred to under the generic term of the ‘cluster’, which expresses the idea of businesses and organizations grouped together in a particular context, and which maintain close relations with one another without excluding distance relations with outside organizations. This concept and its multiple variations have undergone significant changes and developments over time. The first example is that of the concept of industrial districts, which emerged under the inspiration of Marshall’s work and was later developed anew by Becattini’s pioneering work (1987). The research on local production systems (Courlet and Pecqueur 1992) and innovative milieus (Aydalot 1986; Camagni 1991) brought new insights. Besides their theoretical objectives at the crossroad of industrial and spatial analysis, these works also aimed to address needs in terms of

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conceptual and operative instruments which national and local governments often fail to tackle. In this quest for conceptual and operative tools, an important shift has resulted from Michael Porter’s work on ‘clusters’. His work initially concentrated on the management side of clusters, but he soon broadened the scope of analysis, giving rise to a growing interest for other fields. This work aimed at gaining more insights into why some spatially concentrated clusters of firms were effective. On the one hand, an emphasis is placed on the interaction between actors, and, on the other, a managerial analysis helps identify the virtues of the winning configurations and, in so doing, provides more normative and action-oriented instruments. Porter (1998; 2003) argues that a cluster is ‘a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities’. Initially, the concept was applied to success stories, with the iconic example of Silicon Valley. The tendency has since been to extend the concept towards systems that are less centered on high-technology activities or whose level of performance is weaker, and can serve as instruments of local or national economic policy. A whole array of literature in both economic and management sciences developed along this line, but expressed different developments. The initial developments resulted in recognizing the relative and contingent nature of clusters, or, in other words, the diversity in the forms of productive groupings according to local characteristics or to the nature of firms’ relations to their environment. This was marked by rising criticism of the cluster concept, which though ‘trendy’, was believed to lack clarity and innovativeness (Martin and Sunley 2003). The need to bring some order into this theoretical mess, but also to clarify the various forms of local action, then led to the development of different, more or less interesting and explanatory typologies (Lagendijk, Chapter 30, this volume). We discuss the main implications of these approaches in terms of innovation and interaction between actors below, apart from knowledge-based cluster approaches, buzz-andpipelines and so on (Maskell 2001; Bathelt et al. 2004). Gordon and McCann (2000) produced an analytical typology of clusters built around three main categories according to the theoretical foundations on which local systems were built. The first two categories, economic in nature, and of neoclassical inspiration, are based on the idea that agglomeration economies and industrial complexes are the bedrock of the development of these groupings. Regarding agglomeration phenomena, the emphasis is placed upon economies of scale and of scope in the provision of services, as well as on the effectiveness of technology transfer, to explain why the process of spatial concentration does not necessitate interaction or strategies of cooperation between the local actors. With regard to the industrial systems approach, on the contrary, the savings on transaction costs generated through local interactions have contributed the most to the success of the process of clustering. Finally, the third category, based on social networks and more sociological in nature, focuses primarily on cooperative relations between local actors, as well as on the establishment of trust between producers or innovators. Another aspect is related to the quality of the institutional and governance mechanisms, which affects the competitiveness of many successful local systems. In a different perspective, many classifications have been inspired, more or less successfully, by Markusen’s work (1996), which identifies inductively the existence of different forms of local production systems, using concrete examples. Despite their

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differences, all these classifications point to the contingent nature of clusters and their extreme variability in time and space, as does the typology developed by Torre (2008). But other classifications consider, on the contrary, that external relations can play an important part in clusters with low levels of localization and partly explain their functioning. For example, Hamdouch and Depret (2010) argue that inter-organizational and interpersonal networks are the basis for the formation of clusters, be it through the varying degrees of openness of the systems to outside actors or the relatively competitive or network nature of the relations they develop. On the one hand, a cluster understood as a localized comparative advantage distinguishes itself by its low degree of geographical openness and the essentially formal and competitive nature of the relationship, as in the case Silicon Valley. On the other hand, a system that is spatially organized according to the location of its value chain components is less dependent on the geographical proximity of its actors, although they are also characterized by competitive, and even coopetitive, relations (a mix of cooperation and competition) between productive actors. Finally, clusters as socially and spatially embedded or multiscale networks are primarily founded on a network-based rationale, with activities and interactions more or less locally embedded and more or less open to the outside. Thus, the requirements of cluster definitions have gradually loosened, with the increasingly explicit consideration given to relations with non-local actors and a growing tolerance of diversity in internal relations. The idea of a purely local system is undermined by the inclusion of different types of linkages, built by companies or laboratories of all sizes, either locally or via pipelines oriented towards other territories or countries (Bathelt and Schuldt 2008). These changes correspond to a rapid extension of the concept to cases that are not necessarily characterized by high-technology activities (the most famous example being that of a wine cluster in Chile (Giuliani and Bell 2005), as well as to the emergence of various types of local systems (Giuliani, Chapter 22, this volume; Lundvall, Chapter 29, this volume). Analytically speaking, many variations of the original definition have emerged; variations which extend both the scope of action and the characteristics of clusters, away from the initial blueprint of the concept. One example is that of local production arrangements, analytical concepts and economic policy tools, such as in Brazil rural areas. The term ‘arrangement’ here refers to a rationale of relationships that cannot quite be considered systemic because, in some cases, the interactions in those systems are still in the emerging stage. Thus, local production arrangements are defined, in broad terms, as concentrations of economic, political and social agents, located in the same territory, with a focus on a specific set of interrelated economic activities. These links may be weak or may need to be reinforced in territories where activities are dispersed (Cassiolato et al. 2003). The concentration and clustering of small firms, in particular, are important dimensions and, in some cases, play a more important role than interactions. (b) Industrial and Business Ecosystems: The Latest Offshoots from Clusters The original concept of clusters has now been extended much further, to the point where it sometimes includes fields other than just the industrial sphere, as in approaches centered on industrial and business ecosystems. Indeed, the organization and functioning of new local production and innovation systems must now involve the participation of actors;

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participation based on both local and global environmental and societal issues. Thus, the growing complexity of issues and interactions between stakeholders requires new forms of governance that can take account of both the more traditional productive and technological dimensions and the environmental issues – such as those pertaining to the energy transition – and which must involve greater participation of local populations to be successful. The business ecosystems approach, which has had some success in recent years, particularly in management science, has some similarities with the cluster approach, in that it goes beyond the context of the firm and considers the networks of complex exchanges and interactions in which industrial actors are embedded (refer, for example, to Thorelli 1985 for an approach that goes beyond the Williamsonian dichotomy between market and hierarchy). The definition of the ecosystem refers to ‘an expanded environment in which different types of actors with specific skills are likely to participate in varying degrees in a collective process of value creation steered by a company’ (Moore 1993). It also refers to a firm’s ability to have some measure of control over its economic environment (Teece 2007), through networks of reciprocal trust and exchange, but also through the implementation of a coopetitive process (Brandenburger and Nalebuff 1996), combining different types of relationships within a system of interactions. The primary objective of this type of ecosystem is the creation of value within the firm and with actors outside the firm. This involves developing an open innovation model, through which the company is able to exploit external innovations and combine them with its own technology creation capacities, by exploring and exploiting opportunities in the field. The principle of open, or systemic, innovation rests on a network structure, which involves interaction with a large number of actors (firms, laboratories, training institutions) and can manifest itself at a local level (see further down). Intermediaries then play a central role in aligning the interests, cognitive maps, rationalities, knowledge, know-how and skills of the different actors, and in filling the remaining structural gaps. These systemic and relational characteristics of the business ecosystems approach largely echo those of clusters, especially as the relations involved can occur at the local level (though without neglecting linkages with actors outside the territory), but also since the structures of interaction, which often play a central role, call for a different way of seeing the boundaries of the firm and the way it coordinates activities with partners. There are invariants common to both approaches, such as networks, modes of coordination, cooperation and competition, value chains, intermediaries, the myths around innovation and so on (Teece 2007), even though analyses of firms’ strategies and especially of their links with industrial consumers have received a central role, and the principles of co-evolution are brought to the forefront of the analysis. The local dimension is also frequently highlighted if only to extol the opportunities of interfaces or the role of gatekeepers. This conception is close to the industrial and productive notion of clusters, to which rather cosmetic adjustments and refinements are sometimes made. But the industrial ecosystem approach calls for a much more important conceptual and analytical leap by integrating dimensions related to environmental protection and to the recycling of production outputs, with the more ambitious objective of redefining the analysis in terms of territories and their functioning. Indeed, while traditional industrial systems are characterized by a succession of processing operations including the use of raw materials,

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the commercialization of products and the storage of waste, the concept of industrial ecosystem (Frosch and Gallopoulos 1989) proposes an integrated model taking into account the recycling and re-use of waste in the production cycle, and in which issues of industrial ecology are considered. This idea was already present in previous studies on clusters, but the emphasis is strongly put on this dimension in the ecosystem approach. The idea is to get closer to how natural ecosystems operate and to reverse the image of industrial activities as having negative impacts on the environment, by showing not only that industry can produce positive effects, provided important changes are realized, but also that synergies with the environmental dimensions can be built. This biological analogy between natural systems and industrial activities can be criticized for being reductionist, and should not be confused with an actual business operating model (Ehrenfeld 2003); however, this analogy is evocative, and industrial ecology – through its objectives and the local embeddedness of its activities – is strongly connected to the territorial dimension. Thus, the benefits of geographical proximity among productive actors are often put forward and include opportunities in terms of transportation cost reductions and of local material and energy flows. Organized or institutional proximity is also seen as a prerequisite for, or as a result of, cooperation between actors. Thus, the industrial and territorial ecology approach proposes to develop project areas that present certain similarities with clusters and generate economic spin-offs besides purely individual benefits; spin-offs related to cost savings in terms of energy, resources, or waste treatment, or economies of scale generated by the pooling of services. This dimension is particularly important in the case of eco-industrial parks, with the ‘Kalundborg Symbiosis’ ranking first among them (Jacobsen 2006). The latter has served as a model for many researchers and practitioners and constitutes evidence that it is possible to implement industrial ecology principles. This practical example of industrial ecology is founded on components such as trust and shared values among partners, the variety of interrelated technologies and geographical proximity, and is the basis for the creation of circular economy systems, which seek to reproduce the same characteristics as the Kalundborg eco-industrial park to optimize resource use and waste recovery. Although all components for a complete symbiosis are seldom obtained, a parallel can be drawn with the concept of clusters by highlighting the inter-dependencies between the production and recycling actors or the prevalence of the local dimension.

THE COORDINATION-ORIENTED APPROACH While coordination is only one of the many dimensions of clusters and their variations, it has become central in approaches adopted by the ‘School of Proximity’ and in studies on the ‘Geography of Innovation’. Indeed, these two approaches study the constraints of proximity in the relations between the different actors of innovation. How close to one another do they have to be to ensure their coordination in a joint innovation process, given that innovation activities are both spatially concentrated and scattered among different innovation zones? However, these approaches have both internal and external limitations. Their internal limitations lie in their difficulty in addressing the question of coordination discussed next, while their external limitations lie in a reductionist vision

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of innovation, one that has little relevance to the contemporary reality of innovation, as discussed thereafter. (a) The Economic Geography of Innovation: History and Limitations The economic geography of innovation has expanded considerably in the last 30 years (Ferru and Rallet 2016), both conceptually and empirically, as well as in terms of its contribution to public innovation policies. It initially focused on developing analytical frameworks to justify the concentration of activities, and naturally based its analysis on the concept of externalities, transposing Marshallian externalities into the context of an innovation economy based on knowledge exchange and co-production. The externalities driven by customer–supplier relationships in industrial systems, highlighted by the industrial complex approach (e.g. Perroux 1955; Hirschman 1958; Czamanski 1974) and further analyzed by Krugman (1991), have now been replaced by information externalities as a factor explaining the geographic concentration of knowledge-intensive activities such as research and development (R&D) (Ota and Fujita 1993; Gaspar and Glaeser 1998). More specifically, the authors in this school of thought have considered the characteristics of knowledge to be the key to understanding the geography of innovation. The distinction between tacit and codified knowledge served to explain the spatial concentration of innovative activities. Indeed, innovation processes are thought to be primarily based on the use of tacit knowledge, the sharing of which requires face-to-face interactions (Storper and Venables 2004). This hypothesis has, however, not been directly tested in this literature, especially because the data used are not relational. It provides a framework for interpreting the observed concentration of innovation processes; concentration measured through the degree of co-location of innovation actors, itself deduced from bibliometric (co-authorships) or patent (patent citations) databases. Moreover, the geographical scale used to determine whether actors are located in the same geographic zone (a city or a more or less large region) varies according to the database used. From this point of view, the interpretive framework based on knowledge externalities and on the tacit knowledge argument is more or less convincing because it is more or less well suited to the geographical scale. Moreover, this interpretation is weakened by the mobility of researchers who temporarily travel to share tacit knowledge, and the increasingly widespread use of information and communication technologies for all types of knowledge, including tacit knowledge (Rallet and Torre 1995; 2009). The main shortcoming of this externality-based approach is that it fails to explain or test the coordination mechanisms supposed to justify the geographical concentration of innovation activities and merely interprets co-location phenomena observed at specific stages of the innovation process (publications or patents). The proximity-based approach has the advantage of explicitly focusing on the coordination mechanisms in the innovation processes and on their spatial dimension, through the initial question it raises: What forms of and how much geographical proximity do innovation actors need to coordinate their action and co-produce knowledge? The goal here is to understand the relative concentration of innovation activities in space, without presupposing the existence of a territory, which, through some specific normative qualities (qualities which an innovative milieu or technological district must present in

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order to function as a localized innovation system) prompts innovation partners to colocate their activities therein. The proximity-based approach does not deny the existence of innovation milieus or districts but does not consider them as representing a general model of local development. It, therefore, contests the appropriateness of defining public policies on the basis of this model only, as indeed has been the case all over the world since the 1990s (policies promoting clusters and poles of competitiveness). This approach to the spatial concentration of innovation activities is not centered on territory per se but rather on proximity: proximity seen as facilitating the coordination of the innovation process. This approach is very close to relational economic geography developed in parallel by Bathelt and Glücker (2003; 2011): both are based on a focus on the relationships between micro actors rather than on territories, the relative role of geographical proximity, and the need to address local and non-local dimensions as intrinsically interlinked. Let us briefly describe its analytical tools, as developed by the so-called French School of Proximity. Proximity is a multidimensional concept. There are several types of proximity, and therefore the question ‘what needs in terms of proximity, does the coordination of innovation actors involve?’ may have several answers depending on the nature of the knowledge exchanged. In particular, in earlier work we make a distinction between geographical proximity and a non-geographical proximity (Torre and Rallet 2005), which we refer to as ‘organized proximity’ to indicate that it is not geographical in nature. Economic actors can be close to one another and thus coordinate better without necessarily being geographically close, provided means supporting coordination exist. Support may include the rules of action imposed in organizations or institutions, common value systems, shared cognitive maps or cultures, and so on. The spatial distribution of innovation activities can be explained by needs for both geographical and non-geographical proximity. This results in various configurations, ranging from localized clusters to forms of non-territorial coordination (or, in other words, long-distance coordination) (Henn and Bathelt, Chapter 39, this volume). Addressing this issue requires a more detailed explanation of the concept of ‘organized proximity’, which allows for an in-depth analysis of the localization of innovation partners, the spatial configuration of innovation systems being the result of a combination of various types of proximity. Researchers have defined categories of proximity which they consider important in explaining the geographical distribution of innovation partners by showing that the type of proximity needed by innovation actors depends on the characteristics of the sectors in which they operate. Boschma (2005) has played an important role from this perspective, by distinguishing five types of proximity. By thus operationalizing the concept of proximity, he gave rise to many quantitative empirical studies applied to a wide variety of sectors and territories and based on available databases (for a critical survey, see Ferru and Rallet 2016). Thus, a significant corpus of literature has been produced that provides a contrast to public innovation policies centered on the creation of or support to clusters. These works have done more than just illustrate the central role played by various types of proximity in different sectors and territories. They also adopted a dynamic approach to spatial innovation patterns, by examining territorial resilience from an evolutionary perspective (Boschma and Frenken 2010; 2011): path-dependent regional technological development, risks of lock-in, bifurcations and so on. However, a dynamic analysis of proximity (Balland et al. 2014) becomes difficult to achieve because of the complex

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combinations between multiple types of proximity. Distinguishing between five types of proximity, assumed to be independent of one another at time t, lends itself to static analysis but makes less sense in a dynamic approach where the various types become endogenous unless the temporal mechanism of this endogeneity is described in detail, which databases do not allow. This is why more recent studies seem to have abandoned this path and reverted to more traditional, more manageable concepts of industrial economy such as those of relatedness or related competencies (Boschma and Iammarino 2009; Boschma et al. 2014), which derive from concepts developed in the 1980s, such as technological proximity or knowledge proximity to analyze R&D-related spillovers (Griliches 1979; Jaffe 1986; 1989), product diversification (Pavitt et al. 1989), or the technological diversification of industries (Breschi et al. 2003). The economic geography of innovation is now flourishing. However, it also has important limitations in accounting for new forms of innovation. It gives the impression of being a flagship approach, in the wake of a growing number of applied studies along a path that has been successful but is simultaneously locked in through a path-dependency effect, while innovation is now heading towards new shores. (b) On the Necessity to Return to the Original Approach to Proximity The economic geography of innovation is confined to a primarily industrial vision of innovation, in terms of concepts and data (Glückler, Chapter 17, this volume). And in the latest empirical research, it privileges the notion of technological proximity. This involves, for example, testing the effects of skill-relatedness – that is, the sectoral and scientific relatedness of skills – on the growth of a firm or on the implementation of mergers and acquisitions (Ellwanger and Boschma 2013). The initial extension of the approach to five types of proximities has given way to a strictly technological approach to innovation, in terms of proximity of technology classes. Thus, the other forms of proximity – and especially their social relations and inter-organizational dimensions – are being neglected. This marks a return to the traditional industrial economy approach, and the value added of this lies in combining this approach to proximity with the possible effects of ‘geographical proximity’. This regressive evolution can be explained by the fact that existing empirical studies have been largely data driven. Indeed, available databases center on science-based (bibliometrics) and technology-based (patents) innovation. The fact that those studies rest entirely on related databases has caused a decrease in interest in the questions of interaction between actors, and in a more general way about the notion of local and distant coordination mechanisms. No coordination support or mechanism is tested because the databases are not relational. Hypotheses of coordination are derived from indicators of technological similarity versus diversity or from co-location. Technological and geographical proximity indicators are supposed to reflect the existence of effective coordination between actors, whether located in the same areas or not. Thus, lack of complementarity or similarity between technological areas is used to explain the need for or ease of coordination, while the co-location of firms is interpreted as allowing for effective coordination. Yet, in so doing, one forgets or denies the advantages of proximity- or geographybased approaches – that is to say, the importance of interactions and of institutions

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in the coordination of innovation processes. Indicators of relatedness only point to a possibility of coordination; they merely serve to indicate potential collaboration. They do not demonstrate the existence of coordination, which is indeed a social and economic mechanism, often embedded in social relations (Granovetter 1985; Grossetti 2008; Ferru 2010, Bouba-Olga et al. 2014), in environmental contexts (Torre and Zuindeau 2009) or in organizational control (Lévy and Talbot 2015), a mechanism that requires that the potentials identified of such indicators be activated. Analysis of the complexity of these mechanisms is abandoned in favor of technological reductionism. The economic geography of innovation is thus turning into a technogeographical analysis matching technological and geographical distances. The focus therein is back on distances at the expense of proximity. The notion of proximity was privileged in our original work over that of distance for good reasons; otherwise we would have continued or developed an analysis in terms of distance. Distance is inactive in itself. It is a state: a person is far from or near to someone else; one technology is more or less similar to another. It only becomes active when related to a coordination mechanism external to it. In other words, it needs to be activated: defining the stages of a joint innovation project, rallying the necessary partners, finding funding, specifying the coordination methods used, dividing and allocating the tasks, distributing the value created by the project among its members, or agreeing on a common technological standard and a common market design. Innovation necessitates that the coordination issues raised be addressed. The founding hypothesis of the Proximity School is that geography of innovation activities are explained by the impact of geographical proximity on the ability or inability to solve the coordination problems that emerge in geographic space. The mechanisms used to resolve the coordination issues associated with innovation, whether based on markets, social relations, formal institutions, professional communities, value systems or cognitive maps, are more or less affected by geographic distance. The spatial distribution of innovation activities appears to result from the varying ability of these mechanisms to deal with geographic distance. This is the core idea that lies at the intersection of geographical proximity and non-geographical proximity (organized proximity); it is non-geographical in that it can be defined as facilitating coordination in the absence of geographical proximity. Reducing the analysis to an interrelation of ‘distances’, at the expense of an examination of the means of coordination used to solve the coordination problems associated with innovation, constitutes a regression compared to the proximity-based approach. This is why it is compatible with a return to the traditional industrial or spatial economy approach, which, for a long time, has framed the analysis of industrial organization phenomena in terms of distance. We believe, on the contrary, that the proximity-based approach is superior in examining the varying ability of commercial, social, institutional or cognitive coordination mechanisms to manage geographical distance. A possible argument against this suggestion is related to the constraint of database availability. But the latter cannot dictate the appropriateness of an approach to a problem. Both quantitative and qualitative data are necessary but they must be coherent with the goals of a research program.

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TOWARDS NEW DEFINITIONS OF INNOVATION AND ITS GEOGRAPHIES Now that we have clarified the limitations of recent quantitative work on the geography of innovation, it is necessary to return to the very notion of innovation. Indeed, three elements prompt us to address this question and to suggest a new approach to innovation and its geography considering the pressing societal, digital and environmental changes of today. As discussed in consecutive sections below, the first element is the need to move beyond a purely technological innovation perspective. The second is the consideration of a new source of innovation, that is, the involvement, on a cooperative basis, of individuals, citizens or consumers in the production of innovations. The third is the ecosystemic context of innovation. These three characteristics are not in line with the prevalent conception of innovation and its relation to space. (a) The Necessity of Extending the Notion of Innovation to Non-Technological Dimensions As already pointed out, it seems today that innovation can no longer be restricted to its technological component. This can be justified as follows. The traditional way is to stress, in line with Schumpeter (1934), that not all innovations are technological, but commercial, financial, organizational and social innovations have become increasingly important in the contemporary economy that is dominated by services. As evidenced by innovation studies conducted in many countries, this has prompted efforts to identify and measure types of innovation other than scientific and technological innovation, as listed in the OECD Oslo Manual (OECD 2015). This enumerative approach to innovation does not, however, challenge the emphasis placed primarily on the technological dimension in much academic work. This is because the available data mostly pertains to the technological aspects of innovation. Other dimensions are recognized but in effect take a secondary place. It is, we believe, necessary to look beyond the technological dimension to articulate the various dimensions of innovation and to place emphasis on the crucial role of the financial, commercial, social, organizational, environmental and institutional aspects in the development of innovation, whether it is technological or not. By neglecting this role, the geography of innovation obscures the central role of these dimensions in the innovative capacity of a firm or territory. This comes as a consequence of reducing a multidimensional phenomenon to a uni-dimensional one, with erroneous conclusions that may arise. A good example of this is digital technology: technology is the driving force behind the dynamic development of this sector and its impact on the overall economy, but its effects are conditioned by the emergence of new forms of organization, new commercial circuits and new business models. The literature on the productivity paradox (Brynjolfsson and Hitt 2000) has shown that the impact of digital technology on business performance is conditioned by the existence of organizational changes. Similarly, many digital technologies result in economically unsustainable dynamics of production due to the lack of business models that could ensure their profitability. Here, technology is not what is lacking. A plethora of technological possibilities exists but their introduction

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in the market implies innovations in areas other than technology. Non-technological innovations are essential in converting technological innovation into actual products or services, for example into economic value (Glückler, Chapter 17, this volume). Reducing this process – which is dramatically altering the world economy – to a patent-based measurement of innovation boils down to ignoring the fundamental problems associated today with the conversion of technology into economic value. Issues of data availability poorly justify the central emphasis placed on technological innovation. Another example is that of environmental innovations. Such innovations undeniably have a technological dimension, but the lever that helps to foster them is not necessarily technological. The type of innovation proposed in the functional services economy (see the seminal work of Stahel and Giarini 1989), for example, is based on the idea of renting out the use of a good, rather than selling the good. Unlike a seller, who maximizes his/her profit by increasing product sales, a lessor increases his/her profit by reducing the resources necessary for providing a given amount of services. Thus a tire manufacturer no longer sells the tires to the customers but invoices them – that is, transportation firms – based on the kilometers driven. Under these conditions, the producer is encouraged to improve the durability of the tires through technological innovation, to increase the reliability and longevity of the product, minimize resource consumption, recycle end-of-life products and so on. Technological innovation here is the outcome of an economic innovation. This type of innovation has important implications for the sustainable development of territories, whether urban or rural. (b) The Involvement of Individuals as Autonomous Sources of Innovation The analysis of innovation from the angle of its technological dimension is based on a particular conception of knowledge, its production and implementation, as a linear process that starts with scientific invention and ends with its application in industrial processes. Emphasis is placed on how to improve the transfer of knowledge from the scientific domain upstream to the industrial field and downstream through appropriate legal instruments (i.e. intellectual property laws), suitable economic incentives (knowledge protection versus knowledge transfer), and organizational structures designed to facilitate the transfer process (technological inter-mediation agencies, clusters, etc.). This linear pattern applies to both innovation within a firm and innovation within a territorial context (local or national innovation systems) (Lundvall, Chapter 29, this volume). This conception was first called into question at the firm level by introducing feedback loops from the market downstream to design upstream (Kline and Rosenberg 1986). The literature of the 1980s and the 1990s on innovation networks (Lundvall 1992) stressed the importance of cooperation between firms or between firms and research institutes. The concept of open innovation (Chesbrough 2003) goes even further in that it emphasizes the need to involve users and consumers in the design of products and services. However, these feedback loops remain internal to the idea of a knowledge process originating from science and going to the product or service. They improve the representation of the process without calling into question its direction (from upstream to downstream). In this setting, innovation involves actors outside the firm and government, such as consumers, users and citizens. They are active participants in the innovation process in

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that they cannot be reduced to mere sources of information fostering feedback loops in firms. They are also autonomous sources of knowledge and must be recognized as such. This phenomenon is observed both in the digital transformation and in the energy transition. In the digital field, it materializes in the form of the central role played by users in the co-production of content, for instance facilitated by Web 2.0-based platforms (O’Reilly 2005) and the collaborative economy (Botsman and Rogers 2010; Gansky 2010). This results in a very different way of producing, distributing and consuming services (Benkler 2006). These are radical innovations that rest on the emergence of new actors of knowledge production. While these innovations are technology-based (intelligent terminals, connected devices, platforms, information processing capacity, etc.), their most striking characteristic is the fact that they involve the participation of new actors as producers of knowledge and no longer only as the endpoint of the above-mentioned linear process. This emergence of new, non-industrial and non-academic actors constitutes a broader phenomenon. Many examples demonstrate that there is a capacity, among local actors, for innovation and creativity, including in the less technology-intensive, so-called peripheral territories. These innovations result from the creativity of local populations, without necessarily involving a high level of industrial input or productive specialization. They reveal the vitality of territories, which demonstrate their dynamism and capacity for invention through the utilization of local strengths. One such example is that of the development of short distribution channels or of smallscale farming, in which the distance between producers (generally farmers) and consumers is shortened, the origin of products is identifiable, and the industrial intermediaries – deemed too expensive or hazardous to health – are bypassed. Citizens participate in various ways such as getting involved in the productive process, public debates, participatory democracy and decision-making processes. Regarding the productive system, consumers are involved in product design, in the development of shorter industrial production channels, as with AMAPs (French abbreviation for associations of consumers tied to a local producer), Fab Labs and Living Labs, in line with but going beyond von Hippel’s (1988) theory of user-led production. Another example is that of local collaboration projects, such as crowdfunding, inspired by practices in developing countries such as small-scale local fundraising, collective project support, loans between individuals, local solidarity savings – which have become so popular that some national banks have growing interest in the concept – or even the establishment of new local currencies. Crowdsourcing is another type of innovative practice in which the efforts of a large number of people are combined to develop and implement a common project, enabling local people to create products and develop concrete solutions, to come together as a think tank and innovate in service of their territory. All these initiatives are systemic and often cooperative in nature. Shared or collaborative undertakings, activity and employment cooperatives, community transport organizations, healthcare pooling and parent-based child care all contribute to improving the resilience of territories in that they help to recreate proximity between local actors and maintain local solidarity, in complement to or substitution of technological innovation. Finally, the social and solidarity economy contributes to social or societal innovation (Moulaert et al. 2013). It consists of local cooperation networks able to provide help and support to individuals, particularly in times of crisis.

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We would also emphasize the importance of citizen participation in decision-making concerning environmental issues. Thus, local decision-making processes are now being influenced by citizens’ demands, as evidenced by the development of public participation forums, or conflicts involving citizens opposed to infrastructure construction, or extension projects supported by the public authorities or large corporations. These interventions modify the representation of environmental and development problems, and influence how they are addressed and solved. These new forms of innovation in the field of environmental protection and local planning must be seen as a result of the involvement of civic organizations combined with the traditional actions of firms or public institutions. These examples all suggest the need for a broader definition of innovation. A novelty that results in changes in existing operating procedures is an innovation. It may be technical or technological, but it may also be organizational (corporate governance structures, ‘just-in-time’, short circuits, etc.), social (e.g. micro-credit, social and solidarity economy movements) (Klein et al. 2014) and institutional (involving civil society, new laws and regulations, changes of power structures, etc.). Innovation no longer merely involves the utilization of researchers’ or engineers’ scientific knowledge but also involves the knowledge generated by civil society, local stakeholders, public or private organizations and associations. (c) The New Innovation Context The concept of ecosystem has been developed to account for the current characteristics of innovation, including its multidimensional nature and the emergence of new actors, who contribute to its development as well as dissemination. Indeed, this concept places emphasis on the ability to manage the complexity of interactions between the different actors of the ecosystem involved in the innovation process. A wide variety of actors must be utilized and coordinated to bring technological innovations to the market: firms of all sizes, research institutes, intermediate agencies, institutions (local authorities, standardization committees, professional associations, etc.) and communities (i.e. users, consumers and citizens). The boundaries between sectors are dissolving as a result of lateral forces promoting interactions between actors from different sectors and disrupting their traditional roles. Many indirect network effects lead to ‘chicken-and-egg’-type problems; the validity of economic models is called into question; regulations are no longer adequate; and new ones are not yet clearly emerging. The above-mentioned concept of ‘ecosystem’ is suited to such situations where actors must define or redefine their economic environment (organization of relations with other actors, norms, standards, business models, actions to modify regulations, etc.) so as to be able to turn technological innovation into new products and new services. The concept refers to a situation characterized by the co-development of innovations and of their environment, whereas the concept used previously to describe the systemic nature of innovation, that of the network (OECD 2001), refers to a given environment – flexible and variable or not – that possesses traits conducive to innovation. A central question, therefore, is that of the capacity to construct and organize the ecosystem, bearing in mind that, depending on how an actor addresses and answers this crucial question, the ecosystem will have different perimeters, the models of value creation and distribution will vary, and the market designs and dynamics will be different.

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Thus, in the field of new digital services, for example, different ways of constructing and organizing the ecosystem give rise to different innovation dynamics. As a matter of fact, in mobility services, the organization of the ecosystem’s innovation matrix varies according to whether it is defined by the stars of the digital world (e.g. Google, Apple), the historical players (e.g. car manufacturers, transport operators) or by new entrants (e.g. telecom operators and start-ups such as Blablacar and Uber), even if the building blocks are the same. This question is also central to environmental issues. Challenges related to climate change, energy transition, the reduction of pollution and emissions, de-carbonization, or greenhouse gas production are causing significant changes in industrial and innovation processes (Sinclair-Desgagné, Chapter 47, this volume) – changes that have an impact on patterns of production at the territorial level. Thus, the adoption of new ‘clean’ and ‘green’ technologies or innovations has given rise to a green business model that finds expression in ‘green clusters’ (Hamdouch and Depret 2010). This also finds echoes in the policies for sustainable development and renewable energy management implemented by public authorities (Cooke 2012), and leads to further questions concerning energy transition technologies, local waste recycling, methanization processes, or the virtues of industrial and territorial ecology. The consideration of environmental issues, at the heart of the approach in terms of industrial ecosystems and eco-parks, and of the principles of circular economy, impose additional constraints which lead to the emergence of new stakeholders in the local system game (Sinclair-Desgagné, Chapter 47, this volume). It is necessary to involve the local populations, or to at least obtain their approval, in order to avoid serious deadlocks or conflicts. Thus civil society as a whole must be incorporated into the concept of local system, as the latter can only function if it complies with the projects and desires of local stakeholders. This adds another new and crucial dimension to the concepts of cluster, network or even ecosystem.

CONCLUSION In this chapter we have discussed the current characteristics of innovation, and of its processes of creation and distribution, which require that we move beyond contemporary empirical studies of the geography of innovation or clusters that have become too limited. Our discussion responds to these challenges by pointing our three necessities: ●

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The first necessity is to analyze how the various dimensions of innovation condition each other. The innovation process does not merely involve scientific and technological inventions gradually incorporated into the economy, but also social, organizational, economic and institutional innovations that open up new opportunities, including technological ones. The second necessity is the consideration of new sources of knowledge and innovation through the active involvement of consumers, users and citizens. The role of these actors has been dramatically underestimated, not only because they have been little called upon in reality, but also because the prevalent conception of innovation as being technologically inevitable has relegated them to a subordinate

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The Elgar companion to innovation and knowledge creation role. Placing emphasis on the importance of the non-technological dimensions of innovation helps give them an active role as sources of knowledge to be utilized. Finally, the third necessity refers to the multidimensionality of innovation and the variety of actors involved, and raises the question of coordination and therefore that of the construction of an ecosystem able to support coordination between all the actors. In this context, innovation can be redefined as the ability to solve nontrivial problems of coordination. This ability emerges as the key resource, from which a broader vision of innovation can be built, whereas technology appears more of a disruptive than an organizing force.

What conclusions can we draw from this analysis with regard to public innovation policy and the role of local actors? The analysis shows, first of all, that local actors can serve as facilitators of interactions in innovation processes, between entrepreneurs and innovators in the traditional sense, but also with other stakeholders in civil society. We know that markets fail in the presence of externalities and that they cannot easily organize interactions between a large variety of actors, some of whom are not driven by a commercial rationale. Territories can serve as platforms for ecosystems, both in the technical sense (they have infrastructures) and in the organizational sense (they coordinate the different actors), thus facilitating the implementation of innovation (Attour and Rallet 2014). In this respect, they are the places from which the various actors accept or reject inventions and their conversion into innovations – that is to say, into solutions to coordination problems – without necessarily being guided by a commercial rationale, but rather by positioning themselves as part of the local governance process. A second conclusion is that the complexity of innovation, which consists in producing new services and in building the framework necessary for services production, requires the implementation of local experiments. Of course, one can conceive of globally developed innovations as being applicable to all local contexts. But in the field of local services (such as environmental services, smart cities, smart areas or new infrastructure), global innovations are not easily applicable locally, as it is necessary to interact with local actors to produce innovations and to convert the initial inventions into innovations. Transaction costs are, consequently, high for global firms, giving non-global actors much leeway. Furthermore, the complexity of the innovation process in an ecosystemic framework requires the implementation of locally based experiments. Experimentation does not just consist in testing a technology, but also, and above all, in building an ecosystem that can effectively foster innovations, and reject or modify them. That is why many local experiments of this type are being implemented today, as illustrated by the development of Living Labs. Indeed, this very extensive concept incorporates the idea of bringing together, in an open and non-conventional setting, a variety of different partners, for the purpose of experimenting with new ways of generating innovations. This leads to many local experiments. The aim of innovation policies must be to support their development, but also to facilitate their application on a wider scale, and to other local contexts. In terms of public innovation policies, these new elements outline an alternative approach of building local experiments to ensure development, instead of large-scale project policies.

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REFERENCES Asheim, B. and Gertler, M. (2006) The Geography of Innovation: Regional Innovation Systems, Oxford: Oxford University Press. Attour, A. and Rallet, A. (2014) ‘Le rôle des territoires dans le développement des systèmes trans-sectoriels d’innovation locaux: le cas des smart cities’, Innovations, 43, 253–279. Aydalot, P. (1986) Milieux Innovateurs en Europe, Paris: GREMI. Balland, P.A., Boschma, R. and Frenken, K. (2014) ‘Proximity and innovation: From statics to dynamics’, Regional Studies, 49, 907–920. Bathelt, H. and Glückler, J. (2003) ‘Toward a relational economic geography’, Journal of Economic Geography, 3, 117–144. Bathelt, H. and Glückler J. (2011) The Relational Economy: Geographies of Knowing and Learning, Oxford: Oxford University Press. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28, 31–56. Bathelt, H. and Schuldt, N. (2008) ‘Between luminaires and meat grinders: International trade fairs as temporary clusters’, Regional Studies, 42, 853–868. Becattini, G. (1979) ‘Dal settore industriale al distretto industriale. Alcune considerazioni sull’unità di indagine dell’economia industriale’, Rivista di Economia e Politica Industriale, no. 1; in English: Industrial Districts: A New Approach to Industrial Change, Cheltenham and Northampton, MA: Edward Elgar Publishing, 2004, 7–17. Becattini, G. (1987) Mercato e forze locali: il distretto industriale, Bologna: Il Mulino. Becattini, G., Bellandi, M. and De Propris, L. (eds) (2009) A Handbook of Industrial Districts, Cheltenham and Northampton, MA: Edward Elgar Publishing. Becattini, G. and Rullani, E. (1995) ‘Système local et marché global. Le district industriel’, in A. Rallet and A. Torre (eds), Économie industrielle et économie spatiale, Paris: Economica, 171–192. Benkler, Y. (2006) The Wealth of Networks: How Social Production Transforms Markets and Freedom, New Haven, CT: Yale University Press. Boschma, R. (2005) ‘Proximity and innovation: A critical assessment’, Regional Studies, 39, 61–74. Boschma, R. and Frenken, K. (2010) ‘The spatial evolution of innovation networks: A proximity perspective’, in R. Boschma and R. Martin (eds), The Handbook of Evolutionary Economic Geography, Cheltenham and Northampton, MA: Edward Elgar Publishing, 120–125. Boschma, R. and Frenken, K. (2011) ‘The emerging empirics of evolutionary economic geography’, Journal of Economic Geography, 11, 295–307. Boschma, R., Heimeriks, G. and Balland, P. (2014) ‘Scientific knowledge dynamics and relatedness in biotech cities’, Research Policy, 43, 107–114. Boschma, R. and Iammarino, S. (2009) ‘Related variety, trade linkages, and regional growth in Italy’, Economic Geography, 85, 289–311. Boschma, R.A. and Martin, R. (eds) (2010) The Handbook of Evolutionary Economic Geography, Cheltenham and Northampton, MA: Edward Elgar Publishing. Botsman, R. and Rogers, R. (2010) What’s Mine Is Yours: The Rise of Collaborative Consumption, New York: Harper Collins. Bouba-Olga, O., Grossetti, M. and Ferru, M. (2014) ‘How I met my partner: Reconsidering proximities’, in A. Torre and F. Wallet (eds), Regional Development and Proximity Relation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 223–239. Brandenburger, A.M. and Nalebuff, B.J. (1996) Co-opetition, New York: Doubleday. Breschi, S., Lissoni, F. and Malerba, F. (2003) ‘Knowledge-relatedness in firm technological diversification’, Research Policy, 32, 69–87. Brynjolfsson, E. and Hitt, L.M. (2000) ‘Beyond computation: Information technology, organizational transformation and business performance’, Journal of Economic Perspectives Perspectives, 14, 23–48. Camagni, C. (ed.) (1991) Innovation Networks: Spatial Perspectives, GREMI, Belhaven Press, 1–9. Cassiolato, J.E., Lastres, H.M.M. and Maciel, M.L. (2003) Systems of Innovation and Development: Evidence from Brazil. Cheltenham and Northampton, MA: Edward Elgar Publishing. Chesbrough, H. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Boston, MA: Harvard Business School Press. Cooke, P. (2012) ‘Transversality and transition: Green innovation and new regional path creation’, European Planning Studies, 20, 817–834. Courlet, C. and Pecqueur, B. (1992) ‘Les systèmes industriels localisés en France: un nouveau mode de développement’, in G. Benko and A. Lipietz (eds), Les Régions qui gagnent. Districts et réseaux: les nouveaux paradigmes de la géographie économique, Paris: PUF, 81–102. Czamanski, S. (1974) Study of Clustering of Industries, Halifax: Institute of Public Affairs, Dalhousie University.

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Ehrenfeld, J.R. (2003) Putting a spotlight on metaphors and analogies in industrial ecology’, Journal of Industrial Ecology, 7, 1–4. Ellwanger, N. and Boschma, R. (2013) ‘Who acquires whom? The role of geographical proximity and industrial relatedness in Dutch domestic M&As between 2002 and 2008’, Papers in Evolutionary Economic Geography (PEEG), no. 1319. Feldman, M.P. (1994) The Geography of Innovation, Dordrecht: Kluwer Academic. Feldman, M.P. and Massard, N. (2002) Institutions and Systems in the Geography of Innovation, Dordrecht: Kluwer Academic. Ferru, M. (2010) ‘Formation process and geography of science–industry partnerships: The case of the University of Poitiers’, Industry and Innovation, 17, 531–549. Ferru, M. and Rallet, A. (2016) ‘Proximity dynamics and the geography of innovation: Diminishing returns or renewal?’, in R. Shearmur, C. Carrincazeaux and D. Doloreux (eds), Handbook on the Geographies of Innovation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 100–122. Frosch, R.A. and Gallopoulos, N.E. (1989) ‘Strategies for manufacturing’, Scientific American, Special Issue ‘Managing Planet Earth’, 261, 144–152. Gansky, L. (2010) The Mesh: Why the Future of Business Is Sharing, New York: Portfolio Penguin. Gaspar, J. and Glaeser, E.L. (1998) ‘Information technology and the future of cities’, Journal of Urban Economics, 43, 136–156. Giuliani, E. (2017) ‘Industrial clusters in global networks’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 360–371. Giuliani, E. and Bell, M. (2005) ‘The micro-determinants of meso-level learning and innovation: Evidence from a Chilean wine cluster’, Research Policy, 34, 47–68. Glückler, J. (2017) ‘Services and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 258–274. Gordon, I. and McCann, P. (2000) ‘Industrial clusters: Complexes, agglomeration and/or social networks?’, Urban Studies, 37, 513–532. Granovetter, M. (1985) ‘Economic action and social structures: The problem of embeddedness’, American Journal of Sociology, 91, 481–510. Griliches, Z. (1979) ‘Issues in assessing the contribution of research and development to productivity growth’, Bell Journal of Economics, 10, 92–116. Grossetti, M. (2008) ‘Proximity and embedding effects’, European Planning Studies, 16, 629–642. Hamdouch, A. and Depret, M.-H. (2010) ‘Policy integration strategy and the development of the “green economy”: Foundations and implementation patterns’, Journal of Environmental Planning and Management, 53, 473–490. Henn, S. and Bathelt, H. (2017) ‘Transnational entrepreneurs and global knowledge transfer’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 638–651. Hirschman, A.O. (1958) The Strategy of Economic Development, New Haven, CT: Yale University Press. Jacobsen, N.B. (2006) ‘Industrial symbiosis in Kalundborg, Denmark: A quantitative assessment of economic and environmental aspects’, Journal of Industrial Ecology, 10, 239–255. Jaffe, A. (1986) ‘Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits and market value’, American Economic Review, 76, 984–1001. Jaffe, A.B. (1989) ‘Real effects of academic research’, American Economic Review, 79, 957–970. Klein, J.L., Laville, J.L. and Moulaert, F. (eds) (2014) L’innovation sociale, Toulouse: Eires Editions. Kline, S. and Rosenberg, N. (1986) ‘An overview of innovation’, in R. Landau and N. Rosenberg (eds), The Positive Sum Strategy, Washington, DC: National Academic Press. Krugman, P. (1991) ‘Increasing returns and economic geography’, Journal of Political Economy, 99, 483–499. Lagendijk, A. (2017) ‘Innovation, regional development and relationality’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 490–505. Lévy, R. and Talbot, D. (2015) ‘Control by proximity: Evidence from the “Aerospace Valley” competitiveness cluster’, Regional Studies, 49, 955–972. Lundvall, B.-A. (ed.) (1992) National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning, London: Pinter Publishers. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 472–489. Maillat, D. (1995) ‘Milieux innovateurs et dynamique territoriale’, in A. Rallet and A. Torre (eds), Economie industrielle et économie spatiale, Paris: Economica, 211–231.

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Markusen, A. (1996) ‘Sticky places in slippery space: A typology of industrial districts’, Economic Geography, 72, 293–313. Marshall, A. (1890) Principles of Economics, London: Macmillan. Martin, R. and Sunley, P. (2003) ‘Deconstructing clusters: Chaotic concept or politic panacea?’, Journal of Economic Geography, 3, 5–35. Maskell, P. (2001) ‘Towards a knowledge-based theory of the geographical cluster’, Industrial and Corporate Change, 10, 921–943. Moore, J.F. (1993) ‘Predators and prey: A new ecology of competition’, Harvard Business Review, 71, 75–86. Moulaert, F., MacCallum, D., Mehmood, D. and Hamdouch, A. (eds) (2013) International Handbook of Social Innovation: Collective Action, Social Learning and Transdisciplinary Research, Cheltenham and Northampton, MA: Edward Elgar Publishing. OECD (2001) Innovative Networks: Co-operation in National Innovation Systems, Paris: OECD. OECD (2015) Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd edn, Paris: OECD. O’Reilly, T. (2005) What is Web 2.0?, http://oreilly.com/web2/archive/what-is-web-20.html (accessed 30 May 2017). Ota, M. and Fujita, M. (1993) ‘Communications technologies and spatial organisation of multi-unit firms in metropolitan areas’, Regional Science and Urban Economics, 23, 695–729. Pavitt, K., Robson, M. and Townsend, J. (1989) ‘Technological accumulation, diversification and organisation in U.K. companies, 1945–1983’, Management Science, 35, 81–99. Perroux, F. (1955) ‘Note sur la notion de pôle de croissance’, Économie appliquée, 8, 307–320. Porter, M.E. (1998) ‘Clusters and competition: New agendas for companies, governments and institutions’, in M. Porter (ed.), On Competition, Boston, MA: Harvard Business School Press. Porter, M.E. (2003) ‘The economic performance of regions’, Regional Studies, 37, 549–579. Rallet, A. and Torre, A. (1995) Economie industrielle et économie spatiale, Paris: Economica. Rallet, A. and Torre, A. (2009) ‘Temporary geographical proximity for business and work coordination: When, how and where?’, Spaces Online, 7(2009-02), 1–25, www.spaces-online.com. RERU (Revue d’Economie Régionale et Urbaine) (1993) Economie de Proximités, Special issue, no. 3. Schumpeter, J. (1934) The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle, New Brunswick, NJ: Transaction Publishers. Shearmur, R., Carrincazeaux, C. and Doloreux, D. (eds) (2016) Handbook on the Geography of Innovation, Cheltenham and Northampton, MA: Edward Elgar Publishing. Sinclair-Desgagné, B. (2017) ‘Innovation and the global eco-industry’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 771–786. Stahel, W. and Giarini, O. (1989) The Limits to Certainty: Facing Risks in the New Service Economy, Dordrecht: Kluwer Academic Press. Storper, M. and Venables, A.J. (2004) ‘Buzz: Face-to-face contact and the urban economy’, Journal of Economic Geography, 4, 351–370. Teece, D.J. (2007) ‘Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance’, Strategic Management Journal, 28, 1319–1350. Thorelli, H. (1985) ‘Networks, between markets and hierarchy’, Strategic Management Journal, 7, 37–51. Torre, A. (2008) ‘First steps towards a critical appraisal of clusters’, in U. Blien and G. Maïer (eds), The Economics of Regional Clusters, Networks, Technology and Policy, Cheltenham and Northampton, MA: Edward Elgar Publishing, 29–40. Torre, A. and Rallet, A. (2005) ‘Proximity and localization’, Regional Studies, 39, 47–59. Torre, A. and Wallet, F. (eds) (2014) Regional Development and Proximity Relations, Cheltenham and Northampton, MA: Edward Elgar Publishing. Torre, A. and Zuindeau, B. (2009) ‘Proximity economics and environment: Assessment and prospects’, Journal of Environmental Planning and Management, 52, 1–24. von Hippel, E. (1988) The Sources of Innovation, Cambridge, MA: MIT Press.

27. Urban bias in innovation studies Richard Shearmur

The city . . . has long since been recognized as the birthplace of innovation and creativity. (Camagni, 2011, p.183) [C]ities speed innovation by connecting their smart inhabitants to each other . . . [They are] the places where their nation’s genius is expressed. (Glaeser, 2011, p.7)

INTRODUCTION A pervasive, yet not always explicit, idea underpins the study of innovation and its geography: innovation occurs more readily in clusters or cities. Geographic agglomerations of activity that have reached a critical mass generate dynamic interactions between agents, and innovation is most likely to occur there. Even when external networks are recognized, it is buzzing cities that connect with each other, leaving the space outside of cities and pipelines unexamined (Bathelt et al., 2004). When Glaeser (2011) writes of the Triumph of the City he taps into the idea that innovation is essentially urban. Kennedy (2011), who writes that ‘cities are at the vanguard of the new post-industrial age’, p.3), does so more explicitly. Currid (2007) emphasizes the connection between urban density and creativity, Camagni (2011) sees cities as the birthplace of innovation, and many others have elaborated agglomeration-related concepts now standard in the innovation literature such as clusters (Porter, 2003), innovative milieu (Maillat et al., 1993), learning regions (Florida, 1995) and so on. Although this idea rests on observation and theory, it also reflects a longstanding ‘urban bias’ (Lipton, 1977; Jones & Corbridge, 2010) whereby activities taking place in cities are overvalued or, conversely, activities and people in remote settings are not taken seriously, in particular by (predominantly urban) scholars. Karl Marx (1852) himself observed that there was ‘no diversity of talent, no wealth of social relationships’ (p.62) in rural France, and likened peasants in rural areas to ‘potatoes in a sack’ (p.62), co-existing without any interaction. Whether or not this is an accurate description of French peasantry at the time, such an attitude does not leave much room for the careful consideration of agrarian technology or of innovations in forestry (Perdue, 1994; Mumford, 1934). Indeed, Jacobs has gone so far as to claim that cities preceded, and were the source of, agricultural innovation (Jacobs, 1969), something that archaeologists – using evidence that is mundane, but that has not percolated through to innovation studies – have been at pains to refute (Smith et al., 2014). The purpose of this chapter is not to argue that cities and clusters do not play a distinct role in the innovation process; rather, it is an attempt to examine why cities and clusters are considered quintessentially innovative and why, conversely, small towns and peripheral areas are understood to be essentially non-innovative – notwithstanding the fact that establishments and firms regularly innovate in non-urban and non-clustered environments. 440

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Indeed, this is a recurring result from my own studies in the province of Quebec (Shearmur, 2011, 2012a; Shearmur & Doloreux, 2009, 2015), but also a result that is reproduced elsewhere (Grillitsch & Nilsson, 2015; Fitjar & Rodriguez-Pose, 2011; Petrov, 2011; Suarez-Villa & Walrod, 1997). Thus, a central idea that permeates the geography of innovation (and that guides its urban bias) – that firms innovate in an open fashion and benefit from the opportunities for interaction and information-gathering that are inherent to cities – needs to be reexamined, taking seriously, not just as exceptions but as empirical facts in need of explanation, innovation that occurs outside cities and clusters. The chapter is structured as follows. In the next section I outline three practical sources of urban bias in studies of the geography of innovation: motivation, patent data and the swamping of non-urban observations. These sources of bias are inherent to some of the data used, to some assumptions made, and to the way innovation is geo-localized. Although these biases may be rooted in more deep-seated power relations that determine what is socially relevant (Lipton, 1977), this section focuses on the empirical consequences of practical choices made by researchers. Of course, it is not all studies that suffer from such bias, but empirical evidence of small-town and peripheral innovation has not so far been incorporated into innovation theory. In the third section, I suggest this is because prevailing theories of innovation – in particular the idea of innovation as an open process, requiring ease of access to information and interlocutors – reinforce the urban bias that inheres in the empirical approaches that corroborate it. To simplify only slightly, there has been an almost tautological approach to the study of innovation’s geography: (urbanbiased) empirical evidence corroborates (urban-biased) theory, which itself furthers the credibility of (urban-biased) evidence. In the fourth section I attempt to break out of this circular logic, and outline some theory and concepts that explain how establishments in more isolated locations can innovate. The argument rests on the fact that there are different types of innovator: some innovators, relying more on internal capacity, strategically identified interlocutors, and internal capacity and knowledge, are able to operate away from urban areas and clusters. Indeed, the key question is not whether isolated firms innovate – evidence shows that they do – but how they can innovate given our current understanding of the processes of innovation. Another question raised by this argument is as follows: if establishments are indeed innovative in all types of location (including isolated and peripheral ones), why are many isolated and peripheral locations in economic decline (Polèse & Shearmur, 2006)? This question is also addressed. These concepts and ideas need further exploring, and definitive evidence to support them cannot be presented in this chapter; however, in section four exploratory results from a recent survey conducted in Quebec are also shown. These results corroborate the idea that open innovation processes differ across space, and that different types and degrees of openness prevail in central and remote regions. To conclude, some research avenues that these ideas and observations open up are articulated.

SOME SOURCES OF EMPIRICAL BIAS In this section I briefly outline three reasons why empirical work on the geography of innovation has been biased towards identifying innovation in cities and large clusters.

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Motivation for Studying the Geography of Innovation The first source of bias stems from the motivations that propel the study of innovation’s geography. There are two principal motivations. The first, which characterizes much of the research by economists, as well as national-level policy work, on innovation (McCann & Ortega-Argiles, 2013; Strand & Leydesdorff, 2013), is to understand how localities and regions contribute to the national economy. The focus is upon national competitiveness, and the geography of innovation is examined in order to understand if different spatial configurations of resources and economic agents are more effective at promoting worldfirst innovations that will further national economic growth. Many large firms and public knowledge institutions are located in cities, often leading to the conclusion that these cities are key to the development of radical innovations (Van Noorden, 2010; LaFlamme, 2011). However, the reasons why large firms and public knowledge institutions in large cities are rarely examined: it is assumed that their location is connected with the dynamic effects of agglomeration. However, more prosaic reasons such as government decisions (Van Noorden, 2010) or static agglomeration economies and access to labor (Puga, 2010; Shearmur, 2012a) could also explain why these large institutions are found in cities. Thus, even if world-first technological breakthroughs may emerge from cities, this is not necessarily because cities are inherently more innovative: it could be because political choices, public investment and labor requirements dictate that large knowledge institutions and corporations locate there. Indeed, when such institutions are located outside of agglomerations (such as Cornell University in Northern New York, the Los Alamos National Laboratory in New Mexico, the Jackson Laboratory in Bar Harbor, Maine) they remain innovative. The second motivation for exploring the geography of innovation is the desire to understand regional development processes: it is believed that innovation in local enterprises will help the local economy to develop (Bradford & Wolfe, 2013). This approach rests upon the transposition of endogenous growth theory – that is, the idea that the economy can grow by way of internal processes of learning and innovation – to the regional level – that is, the idea that regions and localities will develop by way of internal learning and innovation processes (Martin & Sunley, 1998; Cooke et al., 2004). From this perspective, whether or not innovations are world firsts is of minor importance: of more relevance is whether firms within a locality can remain competitive by introducing innovations, whether incremental, process, organizational or imitative. Whereas studies by geographers have adopted both perspectives, most economists and management scholars (as well as most national innovation policies) have focused upon the first motivation. This tendency to focus on world-first innovations, and their concomitant association with large firms and institutions, has highlighted urban areas as the loci of innovation – without, however, clearly identifying whether it is the dynamics of cities themselves, or merely the convenience they offer (such as proximity to a large workforce, infrastructure – such as airports – and proximity to political power), that are important. Conversely, the downplaying of innovations aimed principally at maintaining competitiveness (which may or may not be world firsts) has shifted focus away from the type of innovation that is more likely to occur in smaller cities and in more isolated areas, introducing urban bias.

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Indicators of Innovation A second source of urban bias is the use of patent activity as an indicator of the geography of innovation (Acs et al., 2002; Rothwell et al., 2013). Whilst it is true that for specific technologically-oriented industries patents may be a reliable indicator for comparing cities (Acs et al., 2002), it is doubtful whether low levels of patenting activity in peripheral and isolated regions can be interpreted as lack of innovativeness. Patents incorporate a variety of biases, most of which point to over-estimation of innovation in cities and to under-estimation in more isolated regions. The first source of bias is the most straightforward: patents only record patentable innovations. Thus, patents can only record technological innovations, principally those associated with products: whilst processes can be patented (and sometimes are), it is extremely difficult to protect a process innovation since competitors’ production facilities are usually inaccessible. This has two consequences: first, if, as Duranton and Puga (2001) suggest, and as Shearmur (2011) shows for Quebec, product innovation is more common in large cities, with process innovation more common in smaller, specialized towns, then larger cities will have more patent activity than smaller towns. Second, if the type of innovation that prevails in isolated places is incremental or organizational, then it simply cannot appear in patent records, further downplaying innovative activity outside of cities. A second source of bias linked to patent data stems from the fact that large corporations tend to locate close to larger cities or in clusters (Holmes & Stevens, 2002; Lafourcade & Mion, 2003) – for reasons of market access, labor requirements and logistics, not necessarily because of dynamic agglomeration economies (Puga, 2010). This does not mean that these firms locate within cities. It is headquarter offices and research and development (R&D) departments that often locate in cities themselves. Large manufacturing establishments have a tendency to locate in smaller cities within about one hour’s drive of a metropolitan area (Table 27.1): they are in the metropolitan shadow, and benefit from ‘borrowed’ agglomeration economies (Phelps et al., 2001). Thus, researchers whose names are on patent applications generated by activities within these large establishments will often be city dwellers, and the patent-holding corporations are city-based, biasing patents towards cities. Another urban bias that results from this geographic configuration is caused by the fact that corporations increasingly use patents to block competitors rather than to register ideas that they intend to develop and market (Heller, 2008; Jaffe & Lerner, 2007). This leads to a disconnection between patents (which register an idea) and innovation (developing and marketing an idea). To the extent that more of these defensive patents are associated with cities (which house the corporations and R&D departments) than with peripheral areas, this will over-state city innovativeness. It should be noted that Acs et al. (2002), who examine the connection between new products and patents, show that – contra this argument – patents and new products were, in the 1990s, geographically correlated across US metropolitan agglomerations of over 100 000 people; however, their study reveals nothing about what was happening in cities of less than 100 000 people, nor in more peripheral areas. Also, their study does not examine the location of establishments within the patenting corporations, nor does it consider non-patentable innovations. A final source of patent-related urban bias, also associated with the location of large corporations, is the fact that small enterprises tend to patent less, relying more on secrecy (Brouwer & Kleinknecht, 1999). This reliance on secrecy is rational for small

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Table 27.1 Manufacturing establishments, by size category, in the Canadian urban system, 2014 Employees 9 or less

10–99

100+

Metropolitan areas, 500K+ Central towns, 100–500K Central towns, 50–100K Central towns, 25–50K Central towns, 10–25K

56.3 50.8 49.8 49.9 50.4

38.0 41.4 41.3 39.2 38.1

5.7 7.8 9.0 10.9 11.5

Peripheral towns, 100–500K Peripheral towns, 50–100K Peripheral towns, 25–50K Peripheral towns, 10–25K

56.6 57.0 56.9 61.4

38.6 37.3 37.5 31.6

4.8 5.7 5.6 6.9

Towns within 1 hour of a metropolitan area

Towns beyond 1 hour of a metropolitan area

Note: This table shows the percentage of all manufacturing establishments within each city class that are of a particular size. It illustrates the underrepresentation of large establishments in peripheral areas, and their overrepresentation close to metropolitan areas. Within metropolitan areas themselves the situation differs – fewer large production facilities locate there, but there are more (small and medium-sized) headquarters and R&D departments. Source: Canadian Business Patterns, location counts by CMA (census metropolitan area)/CA (census agglomeration) and Employment size ranges, June 2014.

and medium-sized enterprises (SMEs) that operate in local markets, and that do not have the financial means to protect their ideas internationally: registering a patent is tantamount to publicizing an innovation, which may then be appropriated without the SME realizing it, or without the SME being able to protect its rights. Not only are SMEs more preponderant in isolated regions (Table 27.1), their very isolation makes secrecy a more viable way of protecting intellectual property (at least in the short term). Patents have been used to study the geography of innovation for a variety of reasons. One of them relates to why innovation is studied: given that the motivation behind many economic and national-policy studies is international competitiveness (particularly for manufacturing), then patents are an adequate, if imperfect, indicator of technological innovativeness. This indicator, legitimate for cross-country industrial studies, has been used to study the sub-national geography of innovation writ large, for which its use is questionable. Another reason why patents have been used is simply that they are one of the few (in Europe), and sometimes the only (in the USA), sources of information about the geography of innovation: notwithstanding the urban biases outlined above, there is often no alternative data for area-wide geographic analysis. There is a danger, however, that exploring the geography of innovation using patent data is tantamount to looking for lost keys under a street lamp just because it is brighter there. The Swamping of Non-Urban Observations Although patents are often used to study the geography of innovation, in Europe and in Canada regular innovation surveys have been conducted. In theory these surveys

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overcome most of the biases just noted for patents – whilst introducing others, in particular the fact that innovation is self-reported. The self-reporting bias has yet to be fully explored; however, there is less reason than for patents to believe that this bias will follow the urban/periphery continuum, particularly if surveys are performed at the establishment level. The advantages of these data – even though they do not pick up world-first innovations – is that they cover a wide range of different types of innovation (technological, marketing and organizational), and enable insight to be gained into the innovation processes of SMEs (the type of firm more prevalent in remote regions). However, from a geographic perspective these data suffer from a problem: they are not representative. In Europe the data are representative across industries and countries, but not across regions (Eurostat, 2014); this is partly due to uneven geographic sampling, but also to the fact that the CIS (Community Innovation Survey) is a firm-level survey, with all innovation assigned to firm headquarters. Thus ‘there is a risk that regions without head offices score lower on the CIS indicators as some of the activities in these regions are assigned to those regions with head offices’ (Eurostat, 2014, p.9) – a risk similar to that described above for patents. Notwithstanding these problems, CIS-type data are used to understand firms’ innovative behavior. Since these data are not adequate for detailed geographic study (even if they were representative at the second level of the EU’s Classification of Territorial Units for Statistics (NUTS2; see Eurostat, 2013), this would not differentiate urban from rural firms), observations cannot be disaggregated by their degree of isolation. Given that economic activity is now overwhelmingly urban (in Canada 72 per cent of people – and 74 per cent of establishments – are in cities of over 50 000 people, and only 11 per cent of all employment is in remote agglomerations – beyond an hour from a metropolitan area – of less than 50 000 people), innovation factors identified from these data will necessarily be driven by (and will reflect) urban and metropolitan-influenced observations. Only if the small population of more isolated firms could be studied separately would it be possible to determine whether ‘factors of innovation’ are generalizable across the urban/non-urban (or clustered/isolated) spectrum. As it stands, urban bias is caused by the fact that innovation factors identified from survey data are urban related (for example, diversity of information providers, obtaining information from consultants, variety of collaborators) because they are derived from data dominated by urban observations; it is then assumed (because it cannot be strictly verified) that innovation will be facilitated by location in an urban area. It is telling that when data with adequate geographic resolution and sampling are studied – such as those available in Norway (Fitjar & Rodriguez-Pose, 2011; Grillitsch & Nilsson, 2015), Quebec (Shearmur & Doloreux, 2009, 2015; Shearmur, 2011) or Britain (Lee & Rodriguez-Pose, 2013) – innovation by establishments in remote areas is not only identified, but specific behaviors are also revealed. Some illustrative evidence of this will be provided in later sections. In this section I have outlined three reasons why empirical work has tended to overlook non-urban, and especially peripheral, innovators, and has tended to reinforce the idea that innovation is an essentially urban phenomenon. In the next section I outline some theories that seem to support the idea that innovation occurs principally in urban areas.

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A BRIEF OVERVIEW OF INNOVATION THEORY It is not my intention to provide detailed descriptions of the theories I refer to. Rather, the purpose of this section is to highlight how a variety of concepts that underpin the study of innovation processes reinforce the idea that urban areas are its key locus. Maybe the most pervasive – and persuasive – conceptualization of the process of innovation is ‘open innovation’ (Chesborough, 2003). This is an umbrella concept, beneath which have been gathered a wide variety of ideas about innovation that developed during the 1980s and 1990s (Huizingh, 2011; Boltanski & Chiapello, 1999), all of which point towards innovation as social process. In order to innovate, firms of course rely upon their internal capacities (Cohen & Levinthal, 1990), but also rely upon information obtained from external sources – clients, suppliers, consultants, universities, competitors – and upon collaborations with key partners. Innovative firms develop new knowledge, both by combining external information with internal expertise, but also by developing knowledge collaboratively. Indeed, certain geographic contexts are considered to be more conducive to this than others: clusters of firms that engage in collaborative behavior can develop knowledge bases (Asheim & Coenen, 2006), which become associated with particular regions. Such clusters are more likely to develop in places where there exists a critical mass of firms – sometimes within a particular sector, sometimes across related activities (Boschma et al., 2013). Many of the geographic concepts that evolved during the 1980s and 1990s (such as regional innovation systems, learning regions, innovative milieu – see Moulaert & Sekia, 2003) are variations on the idea that regional concentrations of activity – most often in larger towns and cities (Crevoisier & Camagni, 2000) – can lead to dynamic interactions, and hence to innovation. This idea can, in turn, be traced back to Marshall (1920) who theorized how agglomerations of economic activity could lead to dynamic (knowledge) externalities, referred to as ‘mysteries’ (Brown & Duguid, 2000). Although these geographic concepts are not specifically urban, most empirical examples of innovative regions have tended to be in or close to cities: exemplars such as Silicon Valley, the M4 corridor, the Raleigh-Durham Research Triangle and SophiaAntipolis are in (or close to) large cities or densely populated areas such as San Francisco, London/Oxford, Raleigh Durham CSA, or Nice. Another body of literature has developed around the idea that creativity is a specifically urban phenomenon. A key proponent has been Jacobs (1969), who has argued that cities are the quintessential location for innovation because they allow people to meet, to interact, and to confront their ideas with others. The meeting of different minds leads to new combinations of old ideas, to the juxtaposition of different potentials and possibilities, and to the generation of new ideas. Cities enable the creative spark to occur by multiplying the number and variety of interactions. A similar idea, expressed more technically, has been put forward by Lucas (1988) and Romer (1990), who’s endogenous growth theories suggest that cities are sites of dynamic externalities, i.e. they are places in which innovation is generated by processes of interactive learning and emulation. More recently this idea has been re-articulated by Florida (2011), who argues that there exist certain people who are particularly creative and who are attracted to the openness and tolerance that cities foster. These ideas are important because they provide a coherent theoretical architecture in support of the view that urban processes generate innovation. Such thinking spans the

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last century, gathers Nobel Prize winners and popular authors, and is in harmony with concepts – such as open innovation and endogenous growth – that have been developed by researchers unconcerned with geographic phenomena (Boltanski & Chiapello, 1999). Not only do these ideas leave little doubt that innovation and creativity occur in cities, and that urban diversity, juxtapositions and dynamic externalities are important factors, they also reinforce (and have no doubt been influenced by) the urban bias that has lurked in popular culture, politics and political theory for centuries (Lipton, 1977). However, three points need to be borne in mind, and will be elaborated upon in the next section. First, much of the theory and many of the observations associating innovation and urban agglomeration were developed prior to the popularization of the internet. This (by now) mundane technology – by the very fact of its mundanity – is altering relations between urban and non-urban areas (Jones & Corbridge, 2010), and researchers such as MacPherson (2008), Moriset and Malecki (2009) and Shearmur and Doloreux (2015) provide examples of how isolated firms use the internet to overcome some of their locational disadvantages. Second, there are many examples of non-urban innovation – indeed, the industrial revolution began in rural textile factories (Hall, 1999). Even if current conceptualizations of innovation apply to a majority of innovators, the tendency to overlook exceptions (noted above when discussing empirical bias) downplays what is happening outside of cities: different concepts may be necessary to understand how innovation occurs in isolated locations. Finally, the theories just invoked assume a straightforward connection between location and innovation: innovation (or creativity) occurs where the innovator is. However, since innovation and creativity are processes that take place over time, the mobility of innovators – not only temporary mobility but also permanent changes in location – should be incorporated into innovation theory. Indeed, geo-localizing innovation is problematic for many reasons: as a social process involving numerous actors, assigning it to one particular place involves a fiction. Furthermore, the ‘place’ to which it is assigned is usually connected with the place of residence of the innovator – an administrative construct that may bear little relation to where the innovator spends his or her time, or to where the innovator generates his or her ideas (Shearmur, 2012b, 2017). Notwithstanding the problem of actually locating innovation, in the next section I assume that innovation in a single-establishment SME can be geo-located at the establishment, and discuss what happens if the firm moves, or if new establishments are opened, in order to exploit the innovation and grow the firm.

RECONCILING NON-URBAN INNOVATION WITH THEORY There are two principal obstacles to credibly arguing that firms in peripheral or isolated locations are just as likely to innovate as firms in cities. The first is that, if one accepts the idea of innovation as an open and social process, then – all else being equal – cities are better able to provide information, knowledge and collaborators. The second is that, even if it is accepted – for the sake of argument – that firms in isolated regions innovate, the question remains as to why isolated and peripheral regions (except those with high amenity value) have tended to decline over at least the last 30 to 40 years.

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Information, Interactions and the Isolated Innovator A first step in resolving the paradox of open innovation in isolated or peripheral locations is to allow that there exist different types of innovation and that, for many types of innovation, different innovators will operate in different ways. The question of how innovation can occur in isolated areas can then be reformulated to become: what type of innovation (or innovator) can successfully operate in isolated areas? Given the open innovation paradigm (which I do not question), innovation in isolated locations is likely to reflect a variety of characteristics (Shearmur, 2015). Although innovation always requires interaction and collaboration with external actors, these interactions will tend to be less frequent for isolated innovators than for city-based ones. This does NOT imply a lower variety of interlocutors (MacPherson, 2008; Shearmur & Doloreux, 2015; Tierlinck & Spithoven, 2008), but lower frequencies of interaction (McCann, 2007). The type of information and knowledge that will be gathered from external sources will not be excessively time dependent (Shearmur, 2015). In other words, innovators in isolated locations are unlikely to be involved in short market-driven innovation cycles (such as found in fashion or electronic gadgets). Rather, they will tend to require information of a more technical or scientific nature which does not lose value rapidly. This, in turn, means that innovators in peripheral regions will tend to target their interlocutors, and seek out strategic information sources and partners rather than relying upon informal contacts and serendipity (Morrison, 2008; Echeverri-Carroll & Brennan, 1999). Informal interactions in urban areas will tend to be between people who share social, cultural and financial capital (Bourdieu, 2005; MacPherson et al., 2001), whereas informal interactions in isolated areas will – by necessity – be premised only upon physical proximity (McManus et al., 2012), thereby crossing more social and cultural boundaries. Whilst less numerous than in urban areas, interactions in certain isolated contexts may – paradoxically – be more socially and culturally diverse. Some innovation in peripheral regions may be dependent on local knowledge, knowhow or problem identification (Shearmur, 2015). In other words, some types of innovation (in forestry, in soil management, in transporting people over snow etc.) are unlikely to occur in cities simply because they touch upon activities or problems that do not arise there. Innovators in peripheral regions may be more ‘introverted’ (Malecki & Poehling, 1999), and therefore more reliant on their internal capacities and resources. The profile that emerges is of a technologically oriented innovator – one who seeks out strategic interlocutors and is evolving in markets that are either local or specialized. Such innovators can operate in urban areas, but urban areas provide no decisive advantage, at least not in terms of inventing a product or process and bringing it to market. Such innovators will no doubt travel to acquire information and to meet with partners (Torre, 2008; Bathelt, 2011), and will rely more on social proximities than on geographic proximity to establish ties and transmit tacit information (Fitjar & Rodriguez-Pose, 2011). These innovators will be found in isolated locations for two distinct reasons (Shearmur, 2015). First, local entrepreneurs adapt to their local context: since innovation driven by buzz, serendipity and high-intensity interactions is less feasible in isolated locations,

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alternative processes of innovation are adopted. Whereas isolation may have been a handicap to innovation until the early 1990s, since then internet access has enabled isolated entrepreneurs to remain informed, to target specific interlocutors, and to establish contact with sources of information, collaboration and markets prior to travelling (MacPherson, 1997, 2008). In other words, in order for firms to innovate (and survive) in isolated locations they have little choice but to adopt this type of innovation strategy. The second reason that innovators may locate in isolated areas is by choice: an innovator who does not require an urban location may seek isolation for reasons of cost, lifestyle, or because some regions allow the innovator’s skills and knowledge to be put to use. For instance, an innovative mountain equipment manufacturer may choose to locate close to big climbs, a maple syrup specialist may thrive in remoter regions of Canada, and an explosives innovator will seek to locate away from densely populated areas. Other innovators may simply prefer to operate in secrecy, and avoid large cities because of the interactions that occur there. The profile just outlined is speculative, based on anecdotal evidence and logic. There is, however, some systematic evidence that the processes just described do occur. Figure 27.1 shows the proportion of innovators that have above-median frequencies of interaction with their service providers, and that have above median ratios of market to non-market sources of information. These data are derived from a survey of 804 manufacturers (of which 376 introduced at least one type of new-to-market innovation) conducted in 2011 by David Doloreux and myself, in which establishments were asked about their innovation behavior (in particular about the sources of information they rely on) and about their external high-order service providers. The dependence of innovators on market sources of information (clients, consultants, suppliers, personnel, etc. as opposed to universities, colleges, government laboratories, internet etc.), and the frequency with which innovators interact with their interlocutors, both decline significantly as one moves beyond an hour or so from metropolitan areas. Reliance on market-sourced information is high in metropolitan and peri-metropolitan areas, and low in remote regions. Frequency of interaction is average in metropolitan areas, high in peri-metropolitan areas and low in remote areas. These results, further discussed in Shearmur and Doloreux (2016), confirm that a specific behavior characterizes innovators in remote areas (they are more introverted and less market oriented than elsewhere). It should be noted that – in accord with the basic premise of this chapter – the propensity to innovate does not vary significantly with distance from a metropolitan area (Figure 27.1). Regional Innovation and Regional Decline In the previous sub-section I outlined some of the expected characteristics of isolated innovators, and show that, in Quebec at least, there is corroborative evidence for them. There remains a problem with the argument developed in this chapter: if, as Figure 27.1 shows, there is an almost equal probability of innovating in metropolitan, perimetropolitan and peripheral areas, then how can one explain – in light of the widely held idea that local innovation is a key factor of local development – that many of Canada’s isolated regions are in decline (Polèse & Shearmur, 2006, Table 2)? The juxtaposition of Figure 27.1 and Table 27.2 suggests that the connection between local innovation and local growth is absent.

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65.00 60.00 55.00

Percent (%)

50.00 45.00 40.00 35.00 30.00

Frequency of interactions above median, n = 376, DF = 3, p(chi2 = 0) = 0.0001

25.00

Ratio of market to non-market information sources above median, n = 376, DF = 3, p(chi2 = 0) = 0.026 Proportion of innovators, n = 804, DF = 3, p(chi2 = 0) = 0.23

20.00 0–20 km

20–50 km

50–130 km

over 130 km

Note: The survey from which these data are drawn is described in Shearmur and Doloreux (2016). Full details of these specific calculations, and of the classification of information sources as market and nonmarket, are available in that paper.

Figure 27.1 Frequency of interactions and market sources of information, by distance from metropolitan areas, Quebec, 2011

Table 27.2 Employment change in Quebec, 2004–2013

Metro areas, about 0–20 km Central areas, about 20–130 km Peripheral areas, about 130 km+

2004

2013

Change

1739.2 1734.0 542.3

1918.0 1917.7 529.4

10.3% 10.6% –2.4%

Note: These figures are based upon Quebec’s administrative areas. Metropolitan areas are Outaouais, Montréal, Capitale Nationale, Laval. Central areas are Chaudières Appalaches, Lanaudière, Laurentides, Montérégie, Centre-du-Québec and Estrie. The remaining six regions are peripheral. Source: Statistique Canada (SC), Enquête sur la population active, 2013, adapté par l’Institut de la statistique du Québec (ISQ).

Urban bias in innovation studies Stages of the innovation process Stage 1: Entrepreneurial innovation New product brought to market New process introduced Stage 2: Firm growth, market expansion, higher efficiency Human and capital resources High-order services, advice Marketing Possible local job losses as efficiencies kick-in

Figure 27.2

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Can occur in remote and peripheral regions If the entrepreneur does not decide to grow his/her firm or exploit the innovation, little impact on regional development Tends to involve a partial or complete movement towards an urban area because the resources for growth are often absent in remote or peripheral regions Bringing in outside partners Opening of production and/or marketing facilities in an urban area Need for good access to markets via airports, highways etc. Firm purchase and relocation

Two-stage innovation process and its geographic implications

The key to reconciling Figure 27.1 and Table 27.2 in the light of the purported connection between local development and innovation is to decompose the innovation process into two stages. These stages do not necessarily occur in the same place (Figure 27.2). Indeed, a hypothesis that accounts for the apparently contradictory observations is that innovators tend to move away from remote regions if they want their innovation to diffuse and their company to grow. The first stage illustrated in Figure 27.2 is the standard entrepreneurial innovation process that proceeds, with feedbacks, from initial idea, through research, development and prototyping to initial marketing (Malecki, 1997). The outcome of this stage is a marketed product or an implemented process, but at a small scale, aimed either at a niche global market or at a local, geographically circumscribed, market. At this point, one of two things can happen: ●

●

The entrepreneur can choose to do nothing: the company will not grow, revenues will not rise, but he or she will have fended off competition and ensured the survival of the firm. Many entrepreneurs in smaller towns have such an attitude, and do not wish innovation to disrupt their lifestyle. The innovation does not proceed beyond stage 1. The entrepreneur can decide to grow the firm by promoting the innovation, thus moving to stage 2. This can be done in a variety of ways, but will involve marketing and various types of advice on managerial, fiscal, human resource and production matters. This advice will usually be sought in larger cities towards the top of the urban hierarchy (Shearmur & Doloreux, 2015).

From a geographic perspective, moving to stage 2 can entail a variety of outcomes. Indeed, given the paucity of resources in isolated regions (in particular buildings, labor and physical access to markets), growing the firm will often involve opening facilities in urban or peri-urban locations. It could even mean moving the firm entirely. Another possibility is that an outside investor may become aware of the innovation and purchase it, or the company itself. Again, this will most often entail moving the firm towards an urban area.

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If the innovation is a new process leading to higher productivity, then the establishment may remain in its initial location, but will shed jobs unless it can expand its market share faster than it increases productivity (Shearmur & Bonnet, 2011). Remote regions do not have the resources or diversity to internalize processes of creative destruction – so job losses due to efficiency gains often entail job losses in the region. Stage 2 therefore occurs if the entrepreneur decides to grow (or sell) his or her company on the strength of the innovation. This will most often involve a partial or complete move towards an urban area which can provide material resources, services and know-how. At this stage a city’s buzz can become important for disseminating the innovation, adapting and perfecting it for larger markets, and identifying market opportunities. From this perspective the unique role of cities in the innovation process is not the generation of innovation, but its marketing and scaling up. This contributes to the urban bias discussed above, because it will often appear as if an innovation has emerged in a city, even if the initial innovation was developed and introduced elsewhere. Tornquist (2012) makes a similar argument when he discusses the way in which Nobel Prize winners seem to cluster in major universities. He shows how most Nobel Prize winners perform their prize-winning research early in their careers in small universities, often tied to the laureate’s country of origin or particular academic history. It is usually only when their careers are established that world-class universities offer them research Chairs and attractive positions. Notwithstanding this ‘marketing’ role of large universities, it is they that become associated with the Nobel Prize winner, rarely the smaller university where his or her ideas were developed. Cities, like world-class universities, play a gate-keeping role. For an innovation to take off it takes champions; these champions will tend to be well-connected urban actors with some influence over tastes and choices. This adds another element of tautology to the notion that cities are the font of innovation, because it is often city actors and city markets that determine whether or not an innovation becomes widely adopted and successful, and these actors and markets will, quite naturally, be more attuned to the type of innovation that addresses a city problem or meets city-driven demand. This argument – that it is the market (and marketing) power of certain urban dwellers that socially constructs innovation (and biases the concept towards urban requirements and tastes) – touches upon Lipton’s (1977) concept of urban bias, which is more political than the empirical acceptation of the idea developed in this chapter.

CONCLUSION In this chapter I argue that there is an inherent urban bias to innovation studies: agglomeration, whether in terms of urban areas or in terms of clusters (which tend, in any case, to be close to large cities) is deemed fundamental to generating the dynamic externalities and wider global connections that underpin innovation. This bias has been entrenched in the data and methods used to understand how firms innovate, and these data and methods have in turn reinforced theoretical understandings of innovation’s geography. A further source of bias, only touched upon in this chapter, is the fact that most gate-keepers – those who identify and promote innovation at a global scale – are urban based and are sensitive to urban-related tastes, problems, processes and solutions.

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Of course, there does exist a category of innovators who thrive within clusters and buzzing environments: the innovation processes that have been observed in cities, and that transpire from the analysis of urban-dominated databases, indeed occur. However, there has been a tendency to consider that innovation can only occur in this way, and that therefore – with minor exceptions that have not been seriously theorized – innovation is quintessentially urban and cannot occur in remote or isolated locations. A number of recent studies have begun to investigate, in a systematic way, innovation in peripheral regions. These are based upon data that are not spatially biased and that comprise enough observations to look closely at the behavior of establishments in nonurban and remote environments. They also emanate from countries – such as Norway and Quebec – with small towns and regions that lie outside the zone of influence of large cities, though Lee and Rodriguez-Pose (2013) have also worked on Britain. These studies not only reveal that innovation occurs in peripheral and remote regions, but that the behavior of innovators in such locations differs from that of innovators in clusters and cities. It should be noted, though, that whereas city-based firms can innovate in the same way as those in peripheral areas if they so choose, the reverse is not true: remote areas reduce accessibility to fast-changing market information and to certain types of interlocutor, so innovators that require these will necessarily gravitate towards urban areas. Innovators who are more technologically oriented and somewhat more introverted (which Shearmur & Doloreux (2016) have called slow innovators) can locate in cities, but, as the data presented in the chapter show, can – and do – operate in isolated areas as well. These observations lead to a paradox: if innovation can and does occur in all types of location, why are remote locations in decline? This question is premised upon the assumption that local innovation leads to local growth. In this chapter I propose a model, already presented in slightly different guises elsewhere (Shearmur & Bonnet, 2011; Shearmur, 2015), that distinguishes the initial stage of innovation (invention and first marketing) from subsequent stages (firm growth and dissemination of the innovation). It is the first stage that can – and does – occur in all types of region. Very often, unless the firm is rooted in a locality because of resource constraints or strong embeddedness, the second stage will entail the opening of offices and production facilities in cities (if not moving there entirely), thereby contributing to city growth and not to the growth of remote regions. The arguments and initial evidence presented in this chapter strongly suggest that the geography of innovation needs to engage with new research questions. Two key areas require investigation. The first concerns how innovators in remote regions innovate. In this chapter – and in the papers cited – tentative and piecemeal evidence is presented. This needs to be augmented and theorized in order to widen the scope of innovation theory so that it fully encompasses what have, until recently, been considered anomalies. The second key area of investigation concerns the mobility of innovators and the geography of innovation’s impacts: in this chapter I suggest that even if innovation occurs in remote areas, its growth impacts will often be felt in or around cities, which will benefit not only from the opening of offices and production facilities but also from purchases of services and other urban-based inputs (high-order services being predominantly sourced in cities, Shearmur & Doloreux, 2015; MacPherson, 2008). It is therefore both the permanent mobility of innovators (as discussed in this chapter) and their temporary mobility to conferences, fairs and meetings (Torre, 2008; Bathelt, 2011) that need to be integrated into the

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way we understand, and measure the local impacts of, innovation. This has implications for local and regional development policy, suggesting that its focus should shift from promoting local innovation and localized networks to facilitating local innovators’ expansion, growth and connections to the outside. Acknowledgements I would like to thank Harald Bathelt for inviting me to synthesize some of my ideas on the geography on innovation. I would also like to thank David Doloreux: our ongoing collaboration has provided much of the evidence (and many discussions) that these ideas attempt to make sense of. I am solely responsible for the ideas put forward.

REFERENCES Acs, Z., Anselin, L. and Varga, A. (2002) ‘Patents and Innovation Counts as Measures of Regional Production of New Knowledge’, Research Policy, 31: 1069–85. Asheim, B. and Coenen, L. (2006) ‘Contextualising Regional Innovation Systems in a Globalising Learning Economy: On Knowledge Bases and Institutional Frameworks’, Journal of Technology Transfer, 31: 163–73. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and Knowledge: Local Buzz, Global Pipelines and the Process of Knowledge Creation’, Progress in Human Geography, 28.1: 31–56. Bathelt, H. (2011) ‘Innovation, Learning and Knowledge Creation in Co-localised and Distant Contexts’, in A. Pike, A. Rodriguez-Pose and J. Tomaney (eds) Handbook of Local and Regional Development, London: Routledge, 149–61. Boschma, R., Balland, P.-A. and Kogler, D. (2013) ‘Relatedness and Technological Change in Cities: The Rise and Fall of Technological Knowledge in U.S. Metropolitan Areas from 1981 to 2010’, Papers in Evolutionary Economic Geography 13.16, Utrecht: Utrecht University. Bourdieu, P. (2005) The Social Structures of the Economy, Cambridge: Polity Press. Boltanski, L. and Chiapello, E. (1999) Le nouvel esprit du capitalisme, Paris: Gallimard. Bradford, N. and Wolfe, D. (2013) ‘Governing Regional Economic Development: Innovation Challenges and Policy Learning in Canada’, Cambridge Journal of Regions, Economy and Society, 6.2: 331–347. DOI: 10.1093/ cjres/rst006. Brouwer, E. and Kleinknecht, A. (1999) ‘Innovative Output, and a Firm’s Propensity to Patent: An Exploration of CIS Micro Data’, Research Policy, 28: 615–24. Brown, J. and Duguid, P. (2000) ‘Mysteries of the Region: Knowledge Dyamics in Silicon Valley’, in C.-M. Lee, W. Miller, M. Hancock and H. Rowen (eds) The Silicon Valley Edge, Stanford, CA: Stanford University Press, 16–39. Camagni, R. (2011) ‘Creativity, Culture and Urban Milieux’, in L. Girard, T. Baycan and P. Nijkamp (eds) Sustainable City and Creativity, Farnham, UK: Ashgate, 183–98 Chesborough, H. (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, Cambridge, MA: Harvard Business Press. Cooke P., Heidenreich, M. and Braczyk, H.-J. (eds) (2004) Regional Innovation Systems: The Role of Governance in a Globalized World, London: Routledge. Cohen, W. and Levinthal, S. (1990) ‘Absorptive Capacity: A New Perspective on Learning and Innovation’, Administrative Science Quarterly, 35.1: 128–52. Crevoisier, O. and Camagni, R. (eds) (2000) Les Milieux Urbains: Innovation, Systèmes de Production et Ancrage, Neuchâtel: Institut de Recherches Économiques et Régionales. Currid, E. (2007) The Warhol Economy: How Fashion and Art Drive New York City, Princeton, NJ: Princeton University Press. Duranton, G. and Puga, D. (2001) ‘Nursery Cities: Urban Diversity, Process Innovation, and the Life-Cycle of Products’, American Economic Review, 91.5: 1454–77. Echeverri-Carroll, E. and Brennan, W. (1999) ‘Are Innovation Networks Bounded by Proximity?’ in M. Fischer, L. Suarez-Villa and M. Steiner (eds) Innovation, Networks and Localities, Berlin: Springer-Verlag, 28–49. Eurostat (2013) ‘Overview: NUTS - Nomenclature of Territorial Units for Statistics’, Eurostat, http://ec.europa. eu/eurostat/web/nuts (16 May 2017).

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Eurostat (2014) Regional Innovation Scoreboard 2014, Brussels: European Union, Directorate General for Enterprise and Industry. Fitjar, R. and Rodriguez-Pose, A. (2011) ‘Innovating in the Periphery: Firms, Values and Innovation in Southwest Norway’, European Planning Studies, 19.4: 555–74. Florida, R. (1995) ‘Toward the Learning Region’, Futures, 27.5: 527–36. Florida, R. (2011) The Rise of the Creative Class Revisited, New York: Basic Books. Glaeser, E. (2011) The Triumph of the City, New York: Penguin. Grillitsch, M. and Nilsson, M. (2015) ‘Innovation in Peripheral Regions: Do Collaborations Compensate for Lack of Local Knowledge Spillovers?’ Annals of Regional Science, 54.1: 299–321. DOI: 10.1007/s00168-014-0655-8. Hall, P. (1999) Cities in Civilization, London: Phoenix Books. Heller, M. (2008) The Gridlock Economy, New York: Basic Books. Holmes, T. and Stevens, J. (2002) ‘Geographic Concentration and Establishment Scale’, Review of Economics and Statistics, 84.4: 682–90. Huizingh, E. (2011) ‘Open Innovation: State of the Art and Future Perspectives’, Technovation, 31: 2–9. Kennedy, C. (2011) The Evolution of Great World Cities, Toronto: University of Toronto Press. Jacobs, J. (1969) The Economy of Cities, New York: Vintage Books. Jaffe, A. and Lerner, J. (2007) Innovation and Its Discontents: How Our Broken Patent System Is Endangering Innovation and Progress, and What to Do about It, Princeton, NJ: Princeton University Press. Jones, G. and Corbridge, S. (2010) ‘The Continuing Debate about Urban Bias: The Thesis, Its Critics, Its Influence and Its Implications for Poverty-Reduction Strategies’, Progress in Development Studies, 10.1: 1–18. Lafourcade, M. and Mion, G. (2003) ‘Concentration, Spatial Clustering and the Size of Plants: Disentangling the Sources of Co-location Externalities’, CORE Discussion Paper No. 2003/91, http://ssrn.com/abstract5981397 (18 June 2015). LaFlamme, M. (2011) ‘Agglomeration vs. Isolation for Science Based Economic Development’, Newgeography. com, 18 January, http://www.newgeography.com/content/001996-agglomeration-vs-isolation-science-basedeconomic-development (18 June 2015). Lee, N. and Rodriguez-Pose, A. (2013) ‘Original Innovation, Learnt Innovation and Cities: Evidence from UK SMEs’, Urban Studies, 50.9: 1742–59. Lipton, M. (1977) Why Poor People Stay Poor: A Study of Urban Bias in World Development, London: Temple Smith. Lucas, R. (1988) ‘On the Mechanics of Economic Development’, Journal of Monetary Economics, 22: 3–42. MacPherson, A. (1997) ‘The Role of Producer Service Outsourcing in the Innovation Performance of New York State Manufacturing Firms’, Annals of the Association of American Geographers, 87.1: 52–71. MacPherson, A. (2008) ‘Producer Service Linkage and Industrial Innovation: Results of a Twelve Year Tracking Study of New York State Manufacturers’, Growth and Change, 39.1: 1–23. MacPherson, M., Smith-Lovin, L. and Cook, J. (2001) ‘Birds of a Feather: Homophily in Social Networks’, Annual Review of Sociology, 27: 415–44. Maillat, D., Quévit, M. and Senn, L. (eds) (1993) Réseaux d’innovation et milieux innovateurs: un pari pour le développement régional, Neuchâtel: Groupe de Recherche Européen sur les Milieux Innovateurs. Malecki, E. (1997) Technology and Economic Development: The Dynamics of Local, Regional and National Competitiveness, London: Routledge. Malecki, E. and Poehling, R. (1999) ‘Extroverts and Introverts: Small Manufacturers and Their Information Sources’, Entrepreneurship and Regional Development, 11: 247–68. Marshall, A. (1920) Principles of Economics, London: Macmillan. Martin, R. and Sunley, P. (1998) ‘Slow Convergence? The New Endogenous Growth Theory and Regional Development’, Economic Geography, 74: 201–27. Marx, K. (1852) The Eighteenth Brumaire of Louis Napoléon, Marx Engels Internet Archive, https://www.marx ists.org/archive/marx/works/download/pdf/18th-Brumaire.pdf (18 June 2015). McCann, P. (2007) ‘Sketching Out a Model of Innovation, Face-to-Face Interaction and Economic Geography’, Spatial Economic Analysis, 2: 117–34. McCann, P. and Ortega-Argiles, R. (2013) ‘Modern Regional Innovation Policy’, Cambridge Journal of Regions, Economy and Society, 6: 187–216. McManus, P., Walmsley, J., Argent, N., Baum, S., Bourke, L., Martin, J., Pritchard, B. and Sorensen, T. (2012) ‘Rural Community and Rural Resilience: What Is Important to farmers in Keeping Their County Towns Alive?’ Journal of Rural Studies, 28.1: 20–29. Moriset, B. and Malecki, E. (2009) ‘Organization versus Space: The Paradoxical Geographies of the Digital Divide’, Geography Compass, 3.1: 256–74. Morrison, A. (2008) ‘Gatekeepers of Knowledge within Industrial Districts: Who They Are, How They Interact’, Regional Studies, 42.6, 817–35. Moulaert, F. and Sekia, F. (2003) ‘Territorial Innovation Models: A Critical Survey’, Regional Studies, 37: 289–302.

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Mumford, L. (1934) Technics and Civilization, New York: Harcourt Brace. Perdue, P. (1994) ‘Technological Determinism in Agrarian Societies’, in M. Roe-Smith and L. Marx (eds) Does Technology Drive History? Cambridge, MA: MIT Press, 169–200. Petrov, A. (2011) ‘Beyond Spillovers: Interrogating Innovation and Creativity in the Peripheries’, in H. Bathelt, M. Feldman and D. Kogler (eds) Beyond Territory, London: Routledge, 168–90. Phelps, N., Fallon, R. and Williams, C. (2001) ‘Small Firms, Borrowed Size and the Urban–Rural Shift’, Regional Studies, 35.7: 613–24. Polèse, M. and Shearmur, R. (2006) ‘Why Some Regions Will Decline: A Canadian Case Study with Thoughts on Local Economic Development’, Papers in Regional Science, 85.1: 23–46. Porter, M. (2003) ‘The Economic Performance of Regions’, Regional Studies, 37: 549–78. Puga, D. (2010) ‘The Magnitude and Causes of Agglomeration Economies’, Journal of Regional Science, 50.1: 203–19. Romer, P. (1990) ‘Endogenous Technological Change’, Journal of Political Economy, 98.5: 71–102. Rothwell, J., Lobo, J., Strumsky, D. and Muro, M. (2013) Patenting Prosperity, Invention and Economic Performance in the United States and Its Metropolitan Areas, Washington, DC: Brookings Institution. Shearmur, R. (2011) ‘Innovation, Regions and Proximity: From Neo-regionalism to Spatial Analysis’, Regional Studies, 45.9: 1225–44. Shearmur, R. (2012a) ‘The Geography of Intra-metropolitan KIBS Innovation: Distinguishing Agglomeration Economies from Innovation Dynamics’, Urban Studies, 49.11: 2331–56. Shearmur, R. (2012b) ‘Are Cities the Font of Innovation? A Critical Review of the Literature on Cities and Innovation’, Cities, 29.2: S9–S18. Shearmur, R. (2015) ‘Far from the Madding Crowd: Slow Innovators, Information Value and the Geography of Innovation’, Growth and Change, 46.3: 424–442. DOI: 10.1111/grow.12097. Shearmur, R. (2017) ‘The Millennial Urban Space-Economy: Dissolving Workplaces and the De-localization of Economic Value-Creation’, in M. Moos, D. Pfeiffer and T. Vinodrai (eds) The Millennial City: Trends, Implications, and Prospects for Urban Planning and Policy, London: Routledge. Shearmur, R. and Bonnet, N. (2011) ‘Does Local Technological Innovation Lead to Local Development?’ Regional Science Policy and Practice, 3.3: 250–70. Shearmur, R. and Doloreux, D. (2009) ‘Place, Space and Distance: Towards a Geography of Knowledge Intensive Business Services Innovation’, Industry and Innovation, 16.1: 79–102. Shearmur, R. and Doloreux, D. (2015) ‘KIBS Use and Innovation: High-Order Services, Geographic Hierarchies and Internet Use in Quebec’s Manufacturing Sector’, Regional Studies, 49.10: 1654–1671. DOI: 10.1080/00343404.2013.870988. Shearmur, R. and Doloreux, D. (2016) ‘How Open Innovation Processes Vary between Urban and Remote Environments: Slow Innovators, Market-Sourced Information and Frequency of Interaction’, Entrepreneurship and Regional Development, 28.5–6: 337–57. Smith, M., Ur, A. and Feinman, G. (2014) ‘Jane Jacobs’ “Cities First” Model and Archaeological Reality’, International Journal of Urban and Regional Research, 38: 1525–35. Strand, O. and Leydesdorff, L. (2013) ‘Where Is Synergy Indicated in the Norwegian Innovation System? Triple-Helix Relations among Technology, Organization, and Geography’, Technological Forecasting and Social Change, 80: 471–84. Suarez-Villa, L. and Walrod, W. (1997) ‘Operational Strategy, R&D and Intrametropolitan Clustering in a Polycentric Structure: The Advanced Electronics Industries of the Los Angeles Basin’, Urban Studies, 34.9: 1343–80. Tierlinck, P. and Spithoven, A. (2008) ‘The Spatial Organization of Innovation: Open Innovation, External Knowledge Relations and Urban Structure’, Regional Studies, 42.5: 689–704. Tornquist, G. (2012) The Geography of Creativity, Cheltenham and Northampton, MA: Edward Elgar Publishing. Torre, A. (2008) ‘On the Role Played by Temporary Geographical Proximity in Knowledge Transmission’, Regional Studies, 42.6: 869–89. Van Noorden, R. (2010) ‘Cities: Building the Best Cities for Science: Which Urban Regions Produce the Best Research – and Can Their Success Be Replicated?’ Nature, 467: 906–8.

28. National and regional innovation systems Harald Bathelt and Sebastian Henn

INTRODUCTION Past research has shown that innovation typically does not take place in isolation but is associated with and enabled by ongoing interaction processes between firms and organizations, such as users, suppliers, research institutes and universities (Rosenberg 1982). Since the late 1980s, related ensembles of individual and collective actors and their interaction processes have been conceptualized as ‘systems of innovation’ (Edquist 2006). Generally, the notion of the ‘innovation system’ refers to ‘all important economic, social, political, organizational, institutional and other factors that influence the development, diffusion and use of innovations’ (Edquist 1997, 14). The innovation system approach can be considered a ‘unifying framework’ (Asheim et al. 2016) for a number of conceptualizations developed in different domains and disciplines (for common characteristics see Edquist 1997). One strand of literature on innovation systems, which has evolved in the field of management and technology studies, deals with ‘sectoral innovation systems’ that consist of a group of firms ‘active in developing and making a sector’s products and in generating and utilizing a sector’s technologies’ (Breschi and Malerba 1997, 131). A closely connected body of work is concerned with ‘technological innovation systems’: networks ‘of agents interacting in a specific economic/industrial area under a particular institutional infrastructure’ (Carlsson and Stankiewicz 1991, 111). While neither approach adopts a spatial perspective, territorially defined innovation systems, which have been discussed particularly in economic geography and regional economics, put an explicit focus on innovation-related actors and linkages at the national (Freeman 1988; Lundvall 1988; Nelson 1988; 1993), regional (Cooke et al. 1997; Asheim and Isaksen 1997; 2002; Cooke 1998; 2001) or metropolitan level (Fischer et al. 2001; Revilla Diez 2002a). In the last two decades, the idea of territorial innovation systems has attracted great interest from policymakers and social scientists – a development that has occurred in spite of some conceptual shortcomings. Particularly, the relationship between different territorial approaches and between territorial and other innovation system approaches has not been sufficiently explored. This is problematic, especially since these approaches aim to guide economic and technology policy. The goal of this chapter is to critically review the concepts of regional (RIS) and national innovation systems (NIS) and draw conclusions regarding their application. We argue that research on NIS and RIS has largely been dominated by empirical and policy-oriented studies which do not take account of the relationships between these two kinds of innovation systems. It will be shown that NIS can be regarded as self-referential systems capable of driving and guiding their own development, while true RIS in this respect are an exception. Compared to NIS, the RIS approach provides less of an explanatory analytical framework and is primarily policy-driven. 457

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The following section provides a short overview of the development of the RIS and NIS literature, before discussing the conceptual relationship between the two approaches. We then introduce the notion of the ‘social system’ and emphasize the feedback relations that exist between institutions, social interaction and innovation within a system context. It is argued that NIS can be understood as territorial innovation systems that are capable of shaping their own development path, while only exceptional regions have that capacity. The chapter concludes by drawing brief conclusions with respect to innovation policy.

REVISITING REGIONAL AND NATIONAL INNOVATION SYSTEMS In the early 1980s, the idea of national innovation systems developed almost simultaneously in Europe and the United States (Freeman 1982; Lundvall 1985). The key intuition underpinning this approach is that interrelated actors and institutions within the borders of a national state determine the innovative performance and growth of the respective national economy. Since the very beginning, the literature on NIS has been characterized by two major approaches (Lundvall, Chapter 29, this volume), and competing understandings continue to exist (e.g. Niosi 2002, 292). The approach put forward by Freeman (1988) and Nelson (1993) emphasizes the role of scientific and technological progress in innovation and seeks to map ‘indicators of national specialization and performance with respect to innovation, research and development efforts, and science and technology organization’, focusing on the science and technology infrastructure that enables innovation (Lundvall et al. 2009, 2). In contrast, Lundvall adopts a broader perspective on NIS by focusing on the interaction processes between firms and organizations engaged in innovation. This perspective emphasizes ‘social institutions, macroeconomic regulation, financial systems, education and communication infrastructures and market conditions’ as determinants of a country’s innovative potential (Lundvall et al. 2009, 3). The NIS approach was developed in response to existing policies supporting economic growth and development (Lundvall 2005, 4) that paid insufficient attention to innovation and the wider institutional context and support system. The idea of a NIS was inspired by empirical studies during the 1970s and 1980s about innovation processes and outcomes in different contexts. Through these studies, important elements, such as the chainlinked innovation model (Lundvall 2007), entered the NIS approach and were utilized to develop it further. However, since the NIS approach was originally designed to meet policy needs, it remained somewhat ‘undertheorized’ (Edquist 2006, 181) and has been criticized as being conceptually diffuse and ambiguous (Edquist 1997, 27f.). Important points of criticism include a lack of specificity regarding the elements to be included in empirical analyses and an inconsistent characterization of institutions (Edquist 2006). The NIS approach does not constitute a formal theory ‘in the sense of providing specific propositions regarding causal relations among variables’ (Edquist 2006, 186), but rather resembles something that could be termed a ‘conceptual framework’ (Edquist 2006, 186), a ‘social engineering approach’ (Lundvall 2005, 4) or a ‘focusing device’ (Lundvall 2007, 98). Despite this, the approach has attracted great interest as an analytical concept and policy tool for international organizations engaged in promoting economic growth,

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such as the Organisation for Economic Co-operation and Development (OECD 1997), the United Nations Conference on Trade and Development (UNCTAD 2011) and the World Bank (Goel et al. 2004), as well as many national governments. It has been applied to developed countries such as Belgium (Capron and Meeusen 2000), Canada (Niosi 2000), Finland (Veugelers 2009), Germany (Keck 1993), Japan (Freeman 1988), the United States (Mowery 1992) and Britain, New Zealand and Australia (Martin and Johnston 1999), as well as developing countries like Mexico (Ramírez and Unger 1998), India (Wessner and Shivakumar 2007) and Thailand (Intarakumnerd et al. 2002; for a debate on NIS in developing countries, see Lundvall et al. 2009). Without doubt, the NIS approach has had a strong impact within academia and policy because it offers a framework for understanding the competitiveness of domestic firms beyond the effects of cost advantages. One approach that spun off from the NIS debate in the mid-1990s is that of the regional innovation system (RIS) (Asheim and Isaksen 1997; Cooke et al. 1997), which has been understood in a similar way as involving ‘a set of interacting private and public interests, formal institutions, and other organizations that function according to organizational and institutional arrangements and relationships conducive to the generation, use, and dissemination of knowledge’ (Doloreux and Parto 2005, 134f.). An important reason for the development of the RIS approach was the growing empirical evidence of successful endogenous development processes in a number of regions such as Emilia Romagna, Baden-Württemberg and Silicon Valley. The emerging RIS approach drew on related work about industrial districts (e.g. Pyke et al. 1990), innovative milieus (e.g. Camagni 1991) and clusters (Porter 1990; Bathelt et al. 2004) that also highlighted the relevance of regional networks and geographical proximity for knowledge generation and economic growth. While RIS have been analyzed in different geographical contexts, including border and peripheral regions (Doloreux 2003), some argue that particularly ‘metropolitan regions offer firms spatial, technological and institutional proximity and specific resources whose exploitation generates significant externalities’ (Revilla Diez 2002b, 66). This view emphasizes the importance of metropolitan innovation systems (MIS) as a distinct class of (regional) innovation system (Fischer et al. 2001; Revilla Diez 2002a; 2002b). Followers of the RIS approach suggest that the approach, similar to NIS, ‘does not only exist as a framework for studying economic and innovation performance but . . . also . . . as a concrete tool for policymakers to systematically enhance localized learning’ (Asheim and Coenen 2006a, 144). Investigations of RIS have mostly been based on qualitative analyses in various regional contexts, while theoretical questions, for example with respect to specific characteristics and minimum requirements of an RIS, have remained unanswered (Doloreux and Parto 2005). It appears that many studies of RIS have adopted a perspective that aims to ‘articulate both generalities and particularities of specific regions, analyze new development trends and the resulting policy implications’ (Doloreux and Parto 2005, 138) or focused on in-depth analyses of individual case study regions (for an overview see Doloreux and Parto 2005; Asheim et al. 2016). Some studies have investigated the role of specific organizations, such as knowledge-intensive businesses (Muller and Zenker 2001), large local R&D-intensive businesses (Agrawal and Cockburn 2003) and universities (Gunasekara 2006), or analyzed specific linkages within the RIS, such as relations between enterprises and universities (Fritsch and Schwirten

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1999) or co-operative relationships between manufacturing firms (Fritsch 2001). In geographic terms, RIS have been primarily studied in industrialized countries such as Canada (Wolfe and Gertler 1998), Germany (Cooke and Morgan 1994) and Norway (Asheim and Isaksen 1997). Some studies on RIS in Eastern Europe (e.g. Radosevic 2002) and in developing countries and emerging markets (Chaminade and Vang 2008) have been published more recently. Interestingly, it appears that, when systematically comparing influential publications about NIS and RIS (according to Google Scholar citations), most studies taking one approach develop their arguments in relative isolation of the other. While references to the respective other approach are sometimes made in passing, papers about NIS either completely neglect the concept of RIS or simply view investigations on RIS as a different research tradition that has been stimulated by successful NIS work (e.g. Lundvall, Chapter 29, this volume). The connection between both approaches is similarly unclear in RIS studies, even in recent work (e.g. Asheim et al. 2016). Contributions to the literature on RIS do sometimes refer to similarities between NIS and RIS, for example by referring to the fact that both approaches take the chain-linked innovation model as a conceptual reference point or view innovation as a result of networked actors that are territorially embedded (e.g. Revilla Diez 2002b, 65; Asheim and Coenen 2006b, 166f.). They emphasize that RIS do not exist in isolation but have been influenced by NIS approaches, since the national level plays a crucial role in defining the framework conditions in terms of general laws and regulations and the research and educational infrastructure (e.g. Revilla Diez 2002b, 66). Chung (2002) suggests that RIS may even be able to effectively create NIS but does not provide a sufficient explanation to support this conclusion. It is striking that, apart from very general arguments – for instance that ‘close proximity between actors and organisations strongly facilitates the creation, acquisition, accumulation and utilisation of knowledge’ (Asheim and Coenen 2006b, 167) – almost no theoretical explanation is given as to why and under which conditions RIS can be expected to emerge, which types of innovations are produced in such systems and whether RIS can be conceptualized in a similar way as NIS. In fact, our research of the literature on NIS and RIS reveals that both approaches are not closely linked conceptually and that the latter has not been derived analytically from the former. Instead, it appears that RIS scholars have simply ‘transferred the systemic elements of a national innovation system to the regional level’ (Revilla Diez 2002b, 65), thereby implicitly assuming that a regional system dimension of innovation exists a priori (Lundvall 2005). Regardless of this critique, both approaches have made a broad impact on academia. As Figure 28.1 shows, the number of Google Scholar hits in searches of these terms in the title or text body of publications has increased exponentially since the mid-1980s. Studies of both approaches are now being cited several thousand times every year, with work on NIS approaches being cited nearly twice as often as that on the RIS approach. Our following analysis argues that NIS and RIS approaches are only loosely linked to each other in that they both emphasize the importance of innovation in territorial contexts. It will be shown that while the NIS approach describes a territorially defined open social system, work on RIS does not normally relate to such a social system. It is primarily driven by a policy agenda to strengthen or stimulate innovation processes at the subnational level and mobilize regional actors to support such activities. Only in very specific cases can regions be expected to have the qualities of a social system of innovation.

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Figure 28.1 Number of article publications on national and regional innovation systems, 1985–2015

THE NOTION OF THE ‘SYSTEM’ AS ‘SOCIAL SYSTEM’ As indicated above, one of the problems of existing research on innovation systems is that the existence of a system is often assumed, simply because the individual and collective actors in a region or national state are interlinked through network relationships (Lundvall 2007). To clarify the conceptualization behind NIS and RIS approaches, we introduce the concept of the ‘social system’. The German sociologist Luhmann (1984a; 1984b) defines social systems in analogy to living organisms as autopoietic, or self-referential, as if they were separated from their environment and closed in terms of the reproduction of their basic internal structures. In other words, they are capable of developing their own dynamics endogenously. As opposed to traditional systems theory, which draws attention to the internal relationships in a system but has difficulty defining its boundaries, Luhmann’s approach focuses on a system’s ability to reproduce its basic dynamics and the difference between its interior and exterior (Luhmann 2000; Kaufmann and Tödtling 2001). Following this line of reasoning, it can be argued that an innovation system as a social system has to have the capacity to continually reproduce itself and actively define the boundaries with its environment and with other systems. In the theory of social systems, society is viewed as a supersystem of ongoing

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communication that enables itself to constantly be reproduced (Luhmann 1984a; 2000). Social subsystems exist within this supersystem that are characterized by special semantics, through which their specific systemic operations can be distinguished from general communication (Klüter 1986). These special semantics are important as sorting mechanisms because social systems are organized based on the concept of meaning. In an innovation system, such special semantics develop around the interaction and problemsolving activities between actors connected in a technological field that result in innovation. Symbolic meanings help to establish order within a system through the definition of roles, priorities and routines. As a consequence, a shared set of interpretations and values is created which allows a boundary to be drawn between operations inside the subsystem and those outside, and to distinguish between meaningful and less relevant interaction (Luhmann 1984a; 1984b). This helps reduce the complexity of the action frameworks of individual and collective actors in the system. It is not straightforward, however, to simply transfer this conceptualization of a system and subsystem to the context of territorial innovation systems, as it assumes operative closure of a system and also focuses on the process of communication instead of the actors who communicate. Luhmann’s notion of operative closure is quite rigid in that it does not allow for a system to operate outside of its environment. This is problematic, as innovation processes have international linkages beyond specific territorial boundaries. We have to keep in mind though that Luhmann (1982) did not think of territorial systems when conceptualizing a social system. In reacting to critique regarding the existence of external linkages in social systems, Luhmann (2000, 79) later weakened his argument of operative closure. According to this revised understanding, which we adopt, systems can be defined by the potential to reproduce their internal mechanisms and the capability to actively maintain a distinction between their interior and exterior, while still having linkages with their environment. In what follows, we use this understanding of social systems (e.g. Willke 2000) as a starting point to reinvestigate NIS and RIS.

INSTITUTIONS, INTERACTION AND INNOVATION Luhmann’s (1984a; 2000) theory of social systems does not discuss or define institutions in any detail. This is not necessary because his theory is not based on agents, their intentions and patterns of behavior, but rather focuses on communication. In contrast, the strength of the NIS approach with its focus on firms and organizations can particularly be seen in its emphasis of the role of institutions, which provide a coherent reference frame for economic interaction. Institutions can be understood as stabilizations of economic interaction (Bathelt and Glückler 2014). They define roles and the specific tasks which are associated with these roles (Willke 2000) and provide the basis for interfirm collaboration and learning (Lundvall 1992b; Johnson 1992). A joint institutional framework enables specialized users and producers to discuss and solve particular problems (Hodgson 1988; North 1991). It helps firms understand the actions and strategies of other actors in innovation processes and thereby allows them to develop reasonable expectations, build trust and reduce uncertainty in economic transactions (e.g. Lorenz 1999; Lundvall 1999). In the context of innovation, an institutional framework does not exist spontaneously,

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but is the result of previous interaction and is modified through social practices in dayto-day interactions between the actors. In these interactions, joint problem-solving and experimentation lead to preliminary fixes which must be robust in order to survive the next series of interactions. These fixes are constantly being updated or adjusted to new goals in the innovation process (Storper 1997). Through this, conventions and routines are modified and updated and, in the context of the firm, provide an ‘organizational memory’ for future interactions (Nelson and Winter 1982). Of course, institutions can also block off innovation if they are too rigid and do not allow for adjustments or lead actors to overlook changes in the economic context (Johnson 1992; Edquist and Johnson 1997; Glückler and Bathelt, Chapter 8, this volume). Co-presence and co-location serve as powerful means of facilitating participation in the process of creating institutions (Bathelt et al. 2004). Due to the fact that fundamental conditions for the formation of institutions are created, legislated and regulated at the level of the national state, the associated institutional (Gertler 1993) and/or cultural affinity (Elam 1997; Freeman 2002) within a national territory make interaction within the respective boundaries easier than across them. As such, the national state generates important preconditions for firms to engage in interactive learning and knowledge creation in innovation. Actors and firms benefit from sharing the same language, attitudes toward technology and interpretative schemes (Gertler 2001). This will be discussed further in the context of NIS as self-referential systems.

NATIONAL INNOVATION SYSTEMS AS SELF-REFERENTIAL SYSTEMS One important implication in the work of Lundvall (1988; 1992a), Edquist (1997) and Freeman (2002) on NIS is that the institutional framework generated by the national state creates the potential for the development of self-referential, yet open systems. As opposed to views which assume convergence between different national economies, the NIS approach implies that differences between national states persist and that increasing specialization takes place as interrelations between production, innovation and institutions at the national level stimulate positive feedback loops. These reflexive relationships generate the conditions for the self-reproduction of national economic structures. Specific national patterns of innovation develop as existing specializations in the economy pre-structure the types of problems and bottlenecks in production that are recognized to be of crucial importance (Lundvall and Maskell 2000). Feedback mechanisms between production, innovation and institution-building ultimately produce specific national systems. The institutional framework supports and enables particular ways of interacting and influences the pathway of the innovation process (Archibugi et al. 1999). Patterns of interaction depend on the specific division of labor that has developed in prior interaction, on existing technological competencies or on the reproduction of specialized skills (Gertler 1993). As a consequence of the interdependence between production, institutional arrangements and knowledge creation, firms find it easy to choose partners in innovation from within their national context because these have the same understanding, are

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familiar with the specific technology context, ‘speak the same language’ and share similar experiences in problem-solving. This serves to establish social and cognitive affinity between the respective firms and provides a basis for specific interaction and learning dynamics between them. If we weaken Luhmann’s (1984a; 2000) original notion of operative closure and understand social systems based on their capability to reproduce their internal dynamics and actively maintain a difference between internal and external operations (Willke 2000), it is justifiable to view NIS as social systems (Bathelt and Depner 2003; Bathelt 2003). We argue that such systems exist, but not that every country necessarily develops its own particular innovation system or is capable of sustaining it based on its own regulatory structures. For instance, small countries and those that are highly dependent on other countries may be part of another country’s NIS. In such cases, the institutional settings within a country could be weak or the structures contradictory, preventing the formation of a self-referential system (Lundvall and Maskell 2000). Generally, however, national borders mark the boundaries of a political system which defines fundamental institutional settings for economic structures and processes within its territory. A NIS enables past communication and decisions to inform and influence communication and decisions in the future. Reflexive processes of problem identification, experimentation and problem-solving along with collective adjustments of action frameworks serve to establish identity and shared meaning. The consequence of such processes is that communication within a system’s borders occurs almost automatically, while external interaction involves more friction because it requires that differences in semantics and institutional arrangements, which are sometimes fundamental, be overcome. This does not, of course, exclude transborder interaction or globalization processes, but it does make external linkages ceteris paribus more complex and costly to arrange than internal ones, suggesting that reflexive linkages between production, institutional arrangements and innovation within a national context have the potential to generate a specific national dynamic. Not all economic sectors within a country follow the same problem-solving and knowledge generation dynamics because individual sectors have different requirements, due to different technological features and a specific division of labor. For instance, the German economy is especially well known for quality-based innovations in industries such as mechanical engineering or automobiles that often proceed in a stepwise manner, while the United States is characterized by more radical innovation patterns that proceed with discontinuities, especially in high-technology industries. It is those sectors that align best with the national institutional peculiarities that have the most opportunities for growth and innovation (e.g. Hall and Soskice 2001). Other industries may play an important supportive role for the respective core sectors but do not necessarily have the same innovation dynamics. For this reason we should not understand NIS as an approach that characterizes an entire country but as a combined sectoral–territorial approach that refers to the core sectors in that country (Bathelt 2003) – a specification that is not usually made explicit in the respective literature. With increasing globalization, one may question, of course, whether NIS still carry the same importance they once had and whether the above logic still holds. Ohmae (1995), for instance, suggested that regional states will replace today’s national states and develop into dominant political and economic entities in the future, due to the economic forces

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of globalization. Others have predicted strong convergence at the national level (Baumol et al. 1994; Abramovitz and David 1996). According to this literature, national states lose ‘power’ and their ability to fix economic boundaries (Dicken 2003), potentially giving rise to a world society without barriers to communication (Bahrenberg and Kuhm 1999). We believe that this conclusion is premature, however, as national states remain quite strong and define the basic building blocks of economic interaction (Gertler 1993; Boyer 2000; Campbell and Pedersen 2014). The national context does not disappear but its relevance changes and it reinvents itself by taking over new roles, such as enabling and supporting global economic integration through the establishment of new institutional settings (Gregersen and Johnson 1997; Boyer 2000). Often countries, even within the European Union, do this in their own distinctive ways. It is these distinctive adjustments of the institutional settings and innovation practices that lead to incremental changes in the NIS and generate an evolutionary dynamic – a topic that has not received enough attention in the literature. This evolutionary dynamic involves ongoing specialization of national systems rather than processes of convergence (Storper 1997).

REGIONAL INNOVATION SYSTEMS – SOME CRITICAL COMMENTS In the literature on RIS, the notion of the system is often used in a pragmatic way to emphasize the importance of localized institutional settings and research organizations, such as universities, technology transfer organizations and industry associations, in the innovation process (Cooke 1998). Systems in this context are not defined as social systems that are self-referential. Although it is frequently mentioned that not all regions can automatically be considered as innovation systems, regional systems are, at least implicitly, viewed as being a fairly normal or frequent phenomenon – and the impression is given that regions can become home to such a structure. This is related to the fact that the RIS literature tends to view regional systems as phenomena similar to NIS at a different spatial scale. One of the problems of the RIS concept is that it would require that the region as a social entity hosts a substantial part of an economic value chain and has a governance structure of its own, distinct from its environment and from other systems, to be able to drive and maintain its own dynamic. However, regions with such characteristics are not easy to find (Bathelt and Depner 2003). Even in national states with a decentralized governance structure, such as Germany, regions lack major political decision-making competencies. They largely depend on the regulatory framework defined at the national scale. Additionally, only a few regions can be characterized as economically self-sufficient, hosting a full-fledged ensemble of related industries and services which could serve as a basis for the establishment of an innovation system. And even if autonomous economic entities and strong institutional capability exist, the territorial reach of both may differ substantially. Much of the debate about the relevance of RIS has focused on exceptionally successful empirical cases of innovation clusters such as Silicon Valley and Baden-Württemberg or regions with a strong regional state such as the Basque region and Northern Ireland (Gertler 1993; Malmberg 1997), while neglecting the majority of rural, peripheral and

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‘normal regions’ (Storper 1997; Shearmur, Chapter 27, this volume). It is problematic to generalize from these kinds of positive cases. Other regions do not fulfill the criteria of self-sustained economic specialization and political governance that are characteristic of a social system (Howells 1999; Thomi and Werner 2001). Most regional ensembles of production, innovation and institutions do not establish autopoietic systems that generate and sustain their own innovation dynamic. This may be due to the fact that the institutional framework is primarily defined at the national, instead of the regional, level. According to Braczyk and Heidenreich (1998: 439), ‘some of the regions . . . have virtually no say in the organization of their institutions and very little politico-administrative autonomy’. Regions may also lack a critical mass of innovation-relevant actors or do not host an extended agglomeration of value-chain-linked activities. Instead, they may be part of global value chains and strongly depend on global decision-making centers and external linkages (Van Assche, Chapter 45, this volume). Since it is not that easy to identify general rules and regularities of regional innovation processes (Koschatzky and Sternberg 2000; Fischer et al. 2001; Revilla Diez 2002a), it is necessary to exercise care using the RIS approach. In realizing the wide variety of regional structures, Cooke and Morgan (1998) distinguish different types of regional ‘systems’ and even include a ‘no-innovation-system’ type. Instead of assuming that self-referential regional systems exist, which are hard to identify empirically, we need to be aware that economic value chains extend across regional borders and that decisive institutional conditions are regulated beyond the regional level (Bathelt and Depner 2003). This is not to say that the RIS approach should be abandoned. In fact, it has become an important policy tool to support innovation in local and regional contexts. However, when drawing close parallels between NIS and RIS, the impression is given that RIS can be conceptualized as social systems in the same way as NIS. And wrong policy conclusions may follow from this regarding the self-referential capabilities of regional entities. The point is that the notion of the ‘system’ can be easily misinterpreted when used in a regional context and can cause analysts to over-estimate regional capabilities. After all, regional configurations of production and innovation are deeply embedded in national systems and the global economy (Lundvall, Chapter 29, this volume). As a corollary of the argument developed above, the national system should be viewed as a suprastructure for regional ensembles that generates linkages to the global economy, rather than just being an aggregate of the respective regional entities (Freeman 2002). Along with Kuhm (2003), one could view regions as structural couplings of functional systems that have their own reproduction mechanisms. Processes affecting the spatiality of these couplings are not primarily regional, but national in character, or have strong sectoral/ technological components beyond any distinct spatial scale. Therefore, ‘regionalized national systems of innovation’ (Asheim and Herstad 2005) or ‘regional thickenings’ of technological systems might be much more common than true regional systems.

CONCLUSION As discussed in this chapter, NIS and RIS have developed into mature approaches that have been widely used in the social sciences (see Figure 28.1) to describe innovation processes in territorial contexts. However, both approaches have been characterized by

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some stagnation in recent years, especially in conceptual terms, even if recent discussions about innovation ecosystems (Cohendet et al. 2014) and knowledge ecologies in localized settings (Bathelt and Henn 2014) can be viewed as attempts to generate new conceptual dynamics and debate. NIS and RIS approaches have remained largely separate in the literature, with minimal direct engagement with each other. The argument put forward in this chapter suggests that the two approaches should be viewed as different conceptual and analytical tools that fulfill different purposes. Whereas the NIS approach is a conceptual tool to analyze and understand the nature of innovation systems at the national level, the RIS is more of a normative political device to mobilize innovation in localized contexts. While it is reasonable to conceptualize social systems of innovation at the country level, this is not a straightforward process at the regional scale. Through ongoing feedback processes between economic structure, institutional set-up and innovation processes, NIS are capable of reproducing their basic dynamics and to differentiate themselves from their environment. This is not to say, however, that NIS would exist forever or never change. Through feedback loops, the institutional framework for interaction is continuously furthered. In contrast, stable social systems are not typical phenomena at the regional scale, especially in rural, peripheral and ‘normal regions’ outside the large metropolitan areas. To assume that small-scale innovation systems are widespread phenomena bears the risk of underestimating the importance of institutions at the national and sectoral/ technological level, which are not decided upon locally. From the perspective of public policy, this suggests that a strong focus on regional linkages may generate false expectations for future development if the conditions for self-referentiality are not fulfilled. In most regions, a major challenge to development is not, it seems, to establish a self-referential innovation system, but to adapt to changes in national and global economic, technological, political and institutional conditions without losing regional competitiveness – all the more so as the capacity of regional decision-making and governance is often limited. If we accept that RIS can only exist under specific conditions that are not widespread, regional policy initiatives which focus on a region’s existing internal strengths and the establishment of regional networks between economic, political and scientific organizations could easily fail. While the strength of the RIS approach has always been the mobilization of localized actors and resources, innovation policy at whatever level needs to systematically support agents in developing linkages and networks with external agents, technologies and markets in their value chain (Bathelt et al. 2004). This is, for instance, currently done in the German support program on international cluster networks (‘Cluster-Netzwerke-International’) or in the ‘Cluster Go International’ platform for collaboration funded under the EU program for the ‘Competitiveness of Small and Medium-sized Enterprises (COSME)’. Such thinking is supported by current research on global cluster networks (Bathelt and Li 2014) and knowledge creation over geographical distance (Bathelt and Henn 2014). Of course, this does not suggest that regional innovation policies are obsolete. Caution should simply be exercised in prioritizing local capabilities over non-local opportunities in conceptualizing innovation systems. Acknowledgements Parts of this chapter summarize the argument developed in Bathelt (2003) and Bathelt and Depner (2003), and develop it further. We would like to thank Christiane Büttner and

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Nadine Schmidt for their research support and Daniel Hutton Ferris for excellent critical comments and suggestions on an earlier draft.

REFERENCES Abramovitz, M. A. and David, P. A. (1996) ‘Convergence and deferred catch-up: Productivity leadership and the waning of American exceptionalism’, in R. Landau, T. Taylor and G. Wright (eds) The Mosaic of Economic Growth, Stanford, CA: Stanford University Press, 21–62. Agrawal, A. and Cockburn, I. (2003) ‘The anchor tenant hypothesis: Exploring the role of large, local, R&Dintensive firms in regional innovation systems’, International Journal of Industrial Organization, 21, 9, 1227–1253. Archibugi, D., Howells, J. and Michie, J. (1999) ‘Innovation systems and policy in a global economy’, in D. Archibugi, J. Howells and J. Michie (ed.) Innovation Policy in a Global Economy, Cambridge: Cambridge University Press, 1–17. Asheim, B. and Coenen, L. (2006a) ‘The role of regional innovation systems in a globalizing economy’, in G. Vertova (ed.) The Changing Economic Geography of Globalization, Abingdon: Routledge, 139–155. Asheim, B. and Coenen, L. (2006b) ‘Contextualising regional innovation systems in a globalising learning economy: On knowledge bases and institutional frameworks’, Journal of Technology Transfer, 31, 1, 163–173. Asheim, B., Grillitsch, M. and Trippl, M. (2016) ‘Regional innovation systems: Past – present – future’, in R. Shearmur, C. Carrincazeaux and D. Doloreux (eds) The Handbook on the Geographies of Innovation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 45–62. Asheim, B. T. and Herstad, S. J. (2005) ‘Regional innovation systems, varieties of capitalism and non-local relations: Challenges from the globalising economy’, in R. A. Boschma and R. C. Kloosterman (eds) Learning from Clusters: A Critical Assessment from an Economic-Geographical Perspective, Dordrecht: Springer, 169–202. Asheim, B. and Isaksen, A. (1997) ‘Location, agglomeration and innovation: Towards regional innovation systems in Norway’, European Planning Studies, 5, 3, 299–330. Asheim, B. T. and Isaksen, A. (2002) ‘Regional innovation systems: The integration of local “sticky” and global “ubiquitous” knowledge’, Journal of Technology Transfer, 27, 1, 77–86. Bahrenberg, G. and Kuhm, K. (1999) ‘Weltgesellschaft und Region – Eine systemtheoretische Betrachtung (World society and region – A systems theory approach)’, Geographische Zeitschrift, 87, 3–4, 193–209. Bathelt, H. (2003) ‘Geographies of production: Growth regimes in spatial perspective 1 – Innovation, institutions and social systems’, Progress in Human Geography, 27, 6, 763–778. Bathelt, H. and Depner, H. (2003) ‘Innovation, Institution und Region: Zur Diskussion über nationale und regionale Innovationssysteme (Innovation, institution and region: About the discussion of national and regional innovation systems)’, Erdkunde, 57, 2, 126–143. Bathelt, H. and Glückler, J. (2014) ‘Institutional change in economic geography’, Progress in Human Geography, 38, 3, 340–363. Bathelt, H. and Henn, S. (2014) ‘The geographies of knowledge transfers over distance: Toward a typology’, Environment and Planning A, 46, 6, 1403–1424. Bathelt, H. and Li, P.-F. (2014) ‘Global cluster networks – Foreign direct investment flows from Canada to China’, Journal of Economic Geography, 14, 1, 45–71. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28, 1, 31–56. Baumol, W. J., Nelson, R. R. and Wolff, E. N. (1994) ‘Introduction: The convergence of productivity, its significance and its varied connotations’, in W. J. Baumol, R. R. Nelson and E. N. Wolff (eds) Convergence of Productivity: Cross National Studies and Historical Evidence, Oxford: Oxford University Press, 3–19. Boyer, R. (2000) ‘The political in the era of globalization and finance: Focus on some regulation school research’, International Journal of Urban and Regional Research, 24, 2, 274–322. Braczyk, H.-J. and Heidenreich, M. (1998) ‘Regional governance structures in a globalized world’, in H.-J. Braczyk, P. Cooke and M. Heidenreich (eds) Regional Innovation Systems: The Role of Governances in a Globalized World, London: UCL Press, 414–440. Breschi, S. and Malerba, F. (1997) ‘Sectoral innovation systems: Technological regimes, Schumpeterian dynamics, and spatial boundaries’, in C. Edquist (ed.) Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter, 130–156. Camagni, R. (ed.) (1991) Innovation Networks: Spatial Perspectives, London, New York: Belhaven Press. Campbell, J. L. and Pedersen, O. K. (2014) The National Origins of Policy Ideas: Knowledge Regimes in the United States, France, Germany, and Denmark, Princeton: Princeton University Press.

National and regional innovation systems

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Capron, H. and Meeusen, W. (2000) The National Innovation System of Belgium, Heidelberg: Physica-Verlag. Carlsson, B. and Stankiewitz, R. (1991) ‘On the nature, function and composition of technological systems’, Journal of Evolutionary Economics, 1, 2, 93–118. Chaminade, C. and Vang, J. (2008) ‘Globalisation of knowledge production and regional innovation policy: Supporting specialized hubs in the Bangalore software industry’, Research Policy, 37, 10, 1684–1696. Chung, S. (2002) ‘Building a national innovation system through regional innovation systems’, Technovation, 22, 8, 485–491. Cohendet, P., Grandadam, D., Simon, L. and Capdevila, I. (2014) ‘Epistemic communities, localization and the dynamics of knowledge creation’, Journal of Economic Geography, 14, 5, 929–954. Cooke, P. (1998) ‘Introduction: Origins of the concept’, in H.-J. Braczyk, P. Cooke and M. Heidenreich (eds) Regional Innovation Systems: The Role of Governances in a Globalized World, London: UCL Press, 2–25. Cooke, P. (2001) ‘Regional innovation systems, clusters, and the knowledge economy’, Industrial and Corporate Change, 10, 4, 945–974. Cooke, P. and Morgan, K. (1994) ‘The regional innovation system in Baden-Wurttemberg’, International Journal of Technology Management, 9, 3–4, 394–429. Cooke, P. and Morgan, K. (1998) The Associational Economy, Oxford: Oxford University Press. Cooke, P., Uranga, M. G. and Extebarria, G. (1997) ‘Regional innovation systems: Institutional and organizational dimensions’, Research Policy, 26, 4–5, 475–491. Dicken, P. (2003) Global Shift: Reshaping the Global Economic Map in the 21st Century, 4th edn, New York, London: Guilford. Doloreux, D. (2003) ‘Regional innovation systems in the periphery: The case of Beauce in Quebec’, International Journal of Innovation Management, 7, 1, 67–94. Doloreux, D and Parto, S. (2005) ‘Regional innovation systems: Current discourse and unresolved issues’, Technology in Society, 27, 2, 133–153. Edquist, C. (1997) ‘Systems of innovation approaches – Their emergence and characteristics’, in C. Edquist (ed.) Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter, 1–35. Edquist, C. (2006) ‘Systems of innovation: Perspectives and challenges’, in J. Fagerberg and D. C. Mowery (eds) The Oxford Handbook of Innovation, Oxford: Oxford University Press, 181–208. Edquist, C. and Johnson, B. (1997) ‘Institutions and organizations in systems of innovation’, in C. Edquist (ed.) Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter, 41–63. Elam, M. (1997) ‘National imaginations and systems of innovation’, in C. Edquist (ed.) Systems of Innovation: Technologies, Institutions and Organizations, London: Pinter, 157–173. Fischer, M., Diez, J. R. and Snickars, F. (2001) Metropolitan Innovation Systems: Theory and Evidence from Three Metropolitan Regions in Europe, Berlin: Springer. Freeman, C. (1982) ‘Technological infrastructure and international competitiveness’, Draft paper submitted to the OECD Ad hoc-group on Science, technology and competitiveness, August 1982. Sussex: Sussex University. Freeman, C. (1988) ‘Japan: A new national system of innovation?’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds) Technical Change and Economic Theory, London, New York: Pinter, 330–348. Freeman, C. (2002) ‘Continental, national and sub-national innovation systems – Complementarity and economic growth’, Research Policy, 31, 2, 191–211. Fritsch, M. (2001) ‘Co-operation in regional innovation systems’, Regional Studies, 35, 4, 297–307. Fritsch, M. and Schwirten, C. (1999) ‘Enterprise–university co-operation and the role of public research institutions in regional innovation systems’, Industry and Innovation, 6, 1, 69–83. Gertler, M. S. (1993) ‘Implementing advanced manufacturing technologies in mature industrial regions: Towards a social model of technology production’, Regional Studies, 27, 7, 665–680. Gertler, M. S. (2001) ‘Best practice? Geography, learning and the institutional limits to strong convergence’, Journal of Economic Geography, 1, 1, 5–26. Glückler, J. and Bathelt, H. (2017) ‘Institutional context and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 121–137. Goel, V. K., Koryukin, E., Bhatia, M. and Agarwal, P. (2004) ‘Innovation systems: World Bank support of science and technology development’, World Bank Working Paper 32, Washington, DC: The World Bank. Gregersen, B. and Johnson, B. (1997) ‘Learning economies, innovation systems and European integration’, Regional Studies, 31, 5, 479–490. Gunasekara, C. (2006) ‘Reframing the role of universities in the development of regional innovation systems’, Journal of Technology Transfer, 31, 1, 101–113. Hall, P. A. and Soskice, D. (2001) ‘An introduction to varieties of capitalism’, in P. A. Hall and D. Soskice (eds) Varieties of Capitalism: The Institutional Foundations of Comparative Advantage, Oxford, New York: Oxford University Press, 1–68.

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Hodgson, G. M. (1988) Economics and Institutions: A Manifesto for a Modern Institutional Economics, Cambridge: Polity Press. Howells, J. (1999) ‘Regional systems of innovation?’, in D. Archibugi, J. Howells and J. Michie (eds) Innovation Policy in a Global Economy, Cambridge: Cambridge University Press, 67–93. Intarakumnerd, P., Chairatana, P. and Tangchitpiboon, T. (2002) ‘National innovation system in less successful developing countries: The case of Thailand’, Research Policy, 31, 8–9, 1445–1457. Johnson, B. (1992) ‘Institutional learning’, in B.-Å. Lundvall (ed.) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter, 23–44. Kaufmann, A. and Tödtling, F. (2001) ‘Science–industry interaction in the process of innovation: The importance of boundary-crossing between systems’, Research Policy, 30, 5, 791–804. Keck, O. (1993) ‘The national system for technical innovation in Germany’, in R. R. Nelson (ed.) National Innovation Systems: A Comparative Analysis, New York: Oxford University Press, 115–157. Klüter, H. (1986) Raum als Element sozialer Kommunikation (Space as Element of Social Communication), Gießen: Geographisches Institut, Universität Gießen. Koschatzky, K. and Sternberg, R. (2000) ‘R&D cooperation in innovation systems – Some lessons from the European regional innovation survey (ERIS)’, European Planning Studies, 8, 4, 487–501. Kuhm, K. (2003) ‘Die Region – Parasitäre Struktur der Weltgesellschaft (The region – Parasitic structure of the world society)’, in T. Krämer-Badoni and K. Kuhm (eds) Die soziale Konstruktion des Raums (The Social Construction of Space), Opladen: Leske & Budrich, 175–196. Lorenz, E. (1999) ‘Trust, contract and economic cooperation’, Cambridge Journal of Economics, 23, 3, 301–315. Luhmann, N. (1982) ‘Territorial borders as system boundaries’, in R. Strassoldo and G. Delli Zotti (eds) Cooperation and Conflict in Border Areas, Milan: Angeli, 235–244. Luhmann, N. (1984a) Soziale Systeme: Grundriss einer allgemeinen Theorie (Social Systems: Foundations of a General Theory), Frankfurt: Suhrkamp. Luhmann, N. (1984b) ‘Die Wirtschaft der Gesellschaft als autopoietisches System (The economy of society as an autopoietic system)’, Zeitschrift für Soziologie, 13, 4, 308–327. Luhmann, N. (2000) Organisation und Entscheidung (Organization and Decision-Making), Opladen, Wiesbaden: Westdeutscher Verlag. Lundvall, B.-Å. (1985) Product Innovation and User–Producer Interaction, Aalborg: Aalborg University Press. Lundvall, B.-Å. (1988) ‘Innovation as an interactive process: From producer–user interaction to the national system of innovation’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds) Technical Change and Economic Theory, London, New York: Pinter, 349–369. Lundvall, B.-Å. (1992a) ‘Introduction’, in B.-Å. Lundvall (ed.) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter, 1–19. Lundvall, B.-Å. (1992b) ‘User–producer relationships, national systems of innovation and internationalisation’, in B.-Å. Lundvall (ed.) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter, 45–67. Lundvall, B.-Å. (1999) ‘Technology policy in the learning economy’, in D. Archibugi, J. Howells and J. Michie (eds) Innovation Policy in a Global Economy, Cambridge: Cambridge University Press, 19–34. Lundvall, B.-Å. (2005) ‘National innovation systems – Analytical concept and development tool’, Paper presented at the Druid Tenth Anniversary Summer Conference on ‘Dynamic of Industry and Innovation: Organizations, Networks and Systems’, June 27–29, Copenhagen. Lundvall, B.-Å. (2007) ‘National innovation systems – Analytical concept and development tool’, Industry and Innovation, 14, 1, 95–119. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 472–489. Lundvall, B.-Å. and Maskell, P. (2000) ‘Nation states and economic development: From national systems of production to national systems of knowledge creation and learning’, in G. L. Clark, M. P. Feldman and M. S. Gertler (eds) The Oxford Handbook of Economic Geography, Oxford: Oxford University Press, 353–372. Lundvall, B.-Å., Vang, J., Joseph, K. J. and Chaminade, C. (2009) ‘Innovation systems research and developing countries’, in B.-Å. Lundvall, K. J. Joseph, C. Chaminade and J. Vang (eds) Handbook of Innovation Systems in Developing Countries: Building Domestic Capabilities in a Global Setting, Cheltenham and Northampton, MA: Edward Elgar Publishing, 1–30. Malmberg, A. (1997) ‘Industrial geography: Location and learning’, Progress in Human Geography, 21, 4, 573–582. Martin, B. R. and Johnston, R. (1999) ‘Technology foresight for wiring up the national innovation system: Experiences in Britain, Australia, and New Zealand’, Technological Forecasting and Social Change, 60, 1, 37–54. Mowery, D. C. (1992) ‘The U.S. national innovation system: Origins and prospects for change’, Research Policy, 21, 2, 125–144.

National and regional innovation systems

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Muller, E. and Zenker, A. (2001) ‘Business services as actors of knowledge transformation: The role of KIBS in regional and national innovation systems’, Research Policy, 30, 9, 1501–1516. Nelson, R. R. (1988) ‘Institutions supporting technical change in the United States’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds) Technical Change and Economic Theory, London, New York: Pinter, 312–329. Nelson, R. R. (ed.) (1993) National Innovation Systems: A Comparative Analysis, Oxford: Oxford University Press. Nelson, R. R. and Winter, S. G. (1982) An Evolutionary Theory of Economic Change, Cambridge, MA: Harvard University Press. Niosi, J. (2000) Canada’s National System of Innovation, Montreal: McGill-Queen’s University Press. Niosi, J. (2002) ‘National systems of innovations are “x-efficient” (and x-effective): Why some are slow learners’, Research Policy, 31, 2, 291–302. North, D. C. (1991) ‘Institutions’, Journal of Economic Perspectives, 5, 1, 97–112. OECD (1997) National Innovation Systems, Paris: Organisation for Economic Co-operation and Development. Ohmae, K. (1995) ‘Putting global logic first’, Harvard Business Review, 73, January–February, 119–125. Porter, M. E. (1990) The Competitive Advantage of Nations, New York: Free Press. Pyke, F., Becattini, G. and Sengenberger, W. (eds) (1990) Industrial Districts and Inter-Firm Co-operation in Italy, Geneva: International Institute for Labour Studies. Radosevic, S. (2002) ‘Regional innovation systems in Central and Eastern Europe: Determinants, organizers and alignments’, Journal of Technology Transfer, 27, 1, 87–96. Ramírez, J. C. and Unger, K. (1998) ‘Mexico’s national innovation system in the 1990s: Overview and sectoral effects’, in R. Anderson, T. Cohn, C. Day, M. Howlett and C. Murray (eds) Innovation Systems in a Global Context, Montreal: McGill-Queen’s University Press, 108–126. Revilla Diez, J. (2002a) Betrieblicher Innovationserfolg und räumliche Nähe: Zur Bedeutung innovativer Kooperationsverflechtungen in metropolitanen Verdichtungsregionen – Die Beispiele Barcelona, Stockholm und Wien (Economic Innovation and Spatial Proximity: The Importance of Innovative Interactions in Metropolitan Areas Using the Examples of Barcelona, Stockholm and Vienna), Münster: Lit. Revilla Diez, J. (2002b) ‘Metropolitan innovation systems: A comparison between Barcelona, Stockholm, and Vienna’, International Regional Science Review, 25, 1, 63–86. Rosenberg, N. (1982) Inside the Black Box: Technology and Economics, Cambridge: Cambridge University Press. Shearmur, R. (2017) ‘Urban bias in innovation studies’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 440–456. Storper, M. (1997) The Regional World: Territorial Development in a Global Economy, New York, London: Guilford. Thomi, W. and Werner, R. (2001) ‘Regionale Innovationssysteme: Zur territorialen Dimension von Wissen und Innovation (Regional innovation systems: On the territorial dimension of knowledge and innovation)’, Zeitschrift für Wirtschaftsgeographie, 45, 3–4, 202–218. UNCTAD (2011) A Framework for Science, Technology and Innovation Policy Reviews Helping Countries Leverage Knowledge and Innovation for Development, New York: United Nations. Van Assche, A. (2017) ‘Global value chains and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 739–751. Veugelers, R. (2009) Evaluation of the Finnish National Innovation System. Full Report, Helsinki: Helsinki University Press. Wessner, C. W. and Shivakumar, S. J. (eds) (2007) India’s Changing Innovation System. Achievements, Challenges, and Opportunities for Cooperation. Report of a Symposium, Washington, DC: The National Academies Press. Willke, H. (2000) Systemtheorie I: Grundlagen. Eine Einführung in die Grundprobleme der Theorie sozialer Systeme (Systems Theory I: An Introduction to the Theory of Social Systems), 6th edn, Stuttgart: UTB. Wolfe, D. and Gertler, M. (1998) ‘The regional innovation system in Ontario’, in H.-J. Braczyk and P. Cooke (eds) Regional Innovation Systems: The Role of Governance in a Globalized World, London: UCL Press, 99–135.

29. National innovation systems and globalization Bengt-Åke Lundvall

INTRODUCTION Today the term national innovation system appears in several different domains within social science and engineering and is widely used in policy circles all over the world. The concept reflects an assumption that the pattern of innovation differs between countries and that such differences can be explained by systemic features: the components of the innovation system are different and they are linked differently to each other and such differences in economic structure and institutional setup are reflected in the rate and direction of innovation. We will take as starting point ideas presented in the very first contributions that made use of the innovation system concept, Freeman (1982/2004) and Lundvall (1985). There is some overlap between them but the perspectives are quite different. Freeman’s analysis refers to macro-phenomena and to international trade and development while Lundvall (1985) refers to the micro level where innovation is seen as shaped by user–producer relationships. We will argue that they are complementary and that they can be used to span and dissect important themes in the more recent literature on innovation systems and global value chains. The concept ‘national innovation system’ may be seen as a new combination of two different perspectives, one developed within the Innovation, Knowledge and Economic Dynamics (IKE) group at Aalborg University and one developed at the Science Policy Research Unit at Sussex University. The concept came out of bringing together an understanding of innovation as rooted in the production system (Aalborg) and an understanding of innovation as rooted in the science and technology system (Sussex). The Aalborg approach was inspired by the concept ‘national production systems’ as it was used by French Marxist structuralists such as Palloix (1969) and Bernis (1966). In Andersen et al. (1978), Esben Sloth Andersen criticized and developed these ideas by introducing an evolutionary perspective with focus upon innovation with the aim to overcome the limitations of what he saw as a too static framework. Another important inspiration for the Aalborg group’s work on innovation systems came from Björn Johnson (1992), who linked innovation and learning to the socioeconomic characteristics of national institutions. Lundvall (1985) took inspiration from early work by Andersen and Johnson when studying user–producer interfaces – these were analyzed from both a structuralist and an institutionalist perspective. Scholars at the Science Policy Research Unit were involved in a series of empirical projects that brought forward the interaction that took place in connection with innovation processes in industrial enterprises (Rothwell 1972; 1977). One of Freeman’s favorite themes in lectures in the early 1980s was about how innovation studies could overcome the apparent contradiction between supply and demand driven innovation through understanding innovation as an interactive process. While the IKE group started 472

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from the production system and developed its understanding of innovation and learning on this basis, the Science Policy Research Unit pioneered the mapping, comparing and analysis of national science and technology systems – a concept used by the Organisation for Economic Co-operation and Development (OECD) already at the beginning of the 1980s. This is reflected in Freeman (1982/2004), where the focus is upon the role of technological infrastructure. It is important to note that the two first contributions that made use of the concept (Freeman 1982/2004 and Lundvall 1985) aimed at understanding national economic performance in terms of competitiveness and economic growth and that the analysis was critical both to mainstream economics and dominant economic policy prescriptions. They were critical to development strategies based upon ‘pure markets’ and night watcher states and to discourses that presented lower wages as the best cure for weak competitiveness. Both of these contributions were placed in the tradition of political economy and the power dimension was taken explicitly into account. Freeman (1982/2004) referred to differences between the rich and the poor countries in terms of their capacity to set the global rules of the game and pointed to the important role of state intervention to close technological gaps, while Lundvall (1985) analyzed how gaps in competence and economic resources between users and producers led to ‘unsatisfactory innovations’ when either the user or the producer took a dominant position. In the ensuing diffusion and use of the innovation system concept these critical dimensions were almost lost and they were definitely marginalized. Scholars at business schools and technological universities as well as economists in international organizations such as the OECD and the World Bank used the concept in a more technocratic way, neglecting the power dimension.

TECHNOLOGICAL INFRASTRUCTURE AND INTERNATIONAL COMPETITIVENESS Around 1980 the OECD Directorate for Science Technology and Industry (DSTI) established a group of experts to analyze ‘Science, technology and competiveness’ with Sir Ingram as chairperson and Francois Chesnais as secretary. After a series of meetings the group finalized a report in 1983 that introduced the concept ‘structural competitiveness’. The report demonstrated that short-term variations in wage costs and currency rates had only limited effects on trade performance and that such differences reflected an ‘absolute advantage’ of certain countries. The report concluded that investments in knowledge infrastructure and in human capital were crucial for the long-term economic performance of the national economy. The report’s conclusions were controversial for the OECD and it was never published (officially due to limited printing capacity), but some of the main results were presented in an article in STI Review several years later (Chesnais 1987). The group invited a number of external experts to write papers that gave insights into the link between science, technology and competitiveness. Christopher Freeman contributed with a paper on ‘Technological infrastructure and international competitiveness’ (Freeman 1982/2004). In this paper he made what might be the very first reference to ‘the national innovation system’ (p. 550) and outlined arguments for why national systems of innovation (NSI) and especially technological infrastructure matter for the competitiveness of nations.

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The paper starts by making an important distinction between two different perspectives on international trade: one, prominent in standard economics, where the focus is upon comparative advantage and trade specialization and a second where the focus is upon absolute advantage and competitiveness. The aim of the paper is to address issues related to the second perspective. The paper refers to the Leontief paradox (Leontief 1953) and to attempts to dissolve the paradox by analyzing the role that technology plays for the patterns of trade specialization (Posner 1961; Hufbauer 1966; Gruber et al. 1967). Freeman then moves on to a discussion of the literature on the role ‘non-price factor’ in trade, citing works by Kravis and Lipsey (1971) indicating that factors related to quality and reliability are more important than price for users’ selection of means of production. He also refers to ‘the Kaldor paradox’ (Kaldor 1978) showing a ‘perverse’ relationship between national cost levels and export shares for the 1960s and 1970s. As Freeman points out, the empirical results that he quotes, with the exception of Kaldor’s paper, operate at the sector level, showing that in most sectors technology (as reflected in R&D-intensity and patenting) is an important factor when it comes to explaining international specialization. They only indirectly address the question of why countries remain in a dominating position for a longer period when it comes to trade and economic growth through innovation. In order to respond to that question, he uses economic history and shows how technological and economic world leadership has shifted from Great Britain to Germany. He also gives a detailed analysis of how Japan, on the basis of investment in knowledge and innovation, is successfully engaged in catching up with the US and the leading European countries. One original and interesting element in Freeman’s paper is his reading of Friedrich List (Freeman 1982/2004, pp. 552–557). He recognizes the well-known fact that List challenges the free trade ideology of Adam Smith and that List argues in favor of protecting infant industries. But he also shows that List’s most severe criticism of Adam Smith is that Smith neglects the importance of ‘mental capital’ and the quality of the labor force: ‘his free trade theory takes into account present values, but nowhere the powers that produce them’ (List 1845, p. 208). According to List, it is only when you take into account the learning processes in the production sphere that you can understand why, under specific circumstances, the principle of freedom of trade may need to be subordinated to the need to foster competences in the production sphere. A related argument for protecting the domestic market is that it will attract foreign tangible and intangible capital contributing to the formation of mental capital. In both cases List’s focus is upon the dynamics of innovation and competence building: The present state of nations is the result of the accumulation of all discoveries, inventions, improvements, perfections and extortions of all generations which have lived before us; they form the mental capital of the present human race, and every separate nation is productive only in the proportion in which it has known how to appropriate these attainments of former generations, and to increase them by its own acquirements. (List 1845, p. 183)

Freeman concludes the paper by arguing that the international monetary system needs to recognize that there are no mechanisms that will automatically overcome major trade disequilibria since these reflect structural factors difficult to change in the short run.

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In the absence of a new international economic order where surplus countries agree to transfer technologies and support the building of strong innovation systems in the deficit countries, the outcome at the global level will be deflationary. At the national level he points to the need for public investments in education and research and in technological infrastructure. The ideas of structural competitiveness and the importance of national innovation systems for international competitiveness became more widely accepted in the 1990s and in the first year of the third millennium – at least in public discourse. The euro crisis and the EU’s response to it, with a competitiveness pact that puts all the burden of adjustment on lowering wages and living standards in the south of Europe, is tragic evidence that those in charge of European economic policy have no understanding of the real dynamics of competitiveness (Lundvall and Lorenz 2012). Freeman ends the paper by arguing that investments in knowledge and technological infrastructure need to be combined with a new emphasis on understanding what kinds of ‘coupling’ mechanisms linking education systems, scientific institutions, engineering, business and marketing characterize the countries that have been successful in catching up. In this context Freeman makes a reference to the research program of the IKE group at Aalborg University: ‘The research at Aalborg on the interdependencies between various groups of firms in promoting technical progress in certain key sectors of the Danish economy is also highly relevant here’ (Andersen et al. 1981; Freeman 1982/2004, p. 550). This reference points indirectly to the second early contribution to the development of the NSI concept (Lundvall 1985).

PRODUCT INNOVATION AND USER–PRODUCER INTERACTION In the period 1980–1984 the Aalborg group hosted a major project on the impact of the use of micro-electronics on international competitiveness – the MIKE project. At the time there were many parallel national projects going on using various methods to capture the impact on productivity, employment and balance of payment. Some of those used macroeconomic models and input–output tables, while others studied specific sectors and the impact at the level of the firm. The MIKE project defined the units of analysis as ‘industrial complexes’ and analyzed four ‘industrial complexes’ that constituted important components of the Danish economy (Agro-, Office automation-, Environmental-, and Textile-industrial complexes). The project gave special attention to the interface between users and producers of means of production that embodied information technology and studied how the specific characteristics of the user–producer relationships shaped the technologies developed and used. The project demonstrated several cases of producer dominance and pointed to the importance for national economic performance of giving users, including workers and consumers, stronger competences to cope with the new technologies. The analysis of the interaction between producers and users of innovation in Lundvall (1985) was inspired by the results obtained in the MIKE project. The analysis addressed two sets of issues, one related to economic theory and one related to the understanding of the innovation process. Lundvall (1985) presented innovation as an interactive process where the feedback from users’ experiences was seen as crucial for the success of

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innovation, and on this basis it demonstrated that an economy characterized either by ‘pure markets’ or ‘pure hierarchies’ would experience little (product) innovation. Hence it was argued that markets where new products are introduced are ‘organized’ markets or semi-hierarchies. The analysis pointed to the limits of neoclassical economics but also to the limits of transaction cost economics as presented in Williamson (1975). Second, with reference to the MIKE project’s analysis of industrial complexes it gave several examples of ‘unsatisfactory innovation’ reflecting a combination of uneven market power and uneven distribution of competence between the producer and the user. It also broadened the use of the concept of user–producer interaction to include universities as producers and industrial enterprises as users, showing why this interaction would always be disharmonious since the user and producer operated with different modes of learning. In this context appeared what might be the first printed reference to ‘innovation system’ (Lundvall 1985, p. 36). There is some overlap between these first two contributions to the understanding of innovation systems. As mentioned above, while analyzing the role of international specialization and competitiveness, Freeman points to the importance of ‘coupling’ from invention to innovation and from original innovation (creation) to diffusion and use as well as to the complex process of ‘matching scientific and technological opportunities with the needs of potential users of innovation’. The analysis in Lundvall (1985) is built upon studying ‘Danish’ industrial complexes, but three of the four cases refer to industrial complexes that are quite dependent on imports when it comes to the technologies used (this is especially the case for textile machinery). The paper also introduces ideas similar to what can be found in recent literature on global value chains: The world economic system might be regarded as a complex network of user–producer relationships connecting units dispersed in economic and geographical space. (Lundvall 1985, p. 34) International specialization might be regarded as reflecting competition between verticals of production rather than competition between national industries. (Lundvall 1985, p. 34)

Some years later (in Lundvall 1988) the patterns of user–producer relationships were presented as a micro-foundation for the concept of national innovation systems. It was argued that the interaction with domestic users is facilitated by short distances in terms of geography, culture and language. This general argument was supported by empirical analysis of trade specialization showing that there was a correlation between the specialization in a specific commodity group and the specialization of machinery to be used in the same sector. Home markets were important for those developing new production technologies (Fagerberg 1988). The first major book on national innovation systems (Lundvall, 1992a) may be seen as an attempt to combine and further develop the two perspectives presented in Freeman (1982/2004) and Lundvall (1985). In the first part of the book the focus is upon the role of economic institutions and structure in national innovation systems. The second part analyzes different domains within the innovation system (work organization, cluster formation, finance, public sector and STI institutions). The third part is explicitly on the openness of national systems and refers to trade, integration and foreign direct investment (FDI).

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Chapter 3 in the book used the user–producer perspective to explain why national systems remain quite resistant to the trend toward globalization (Lundvall 1992b). It is argued that domestic interaction benefits from a shared language and from nationspecific economic institutions since they reduce transaction costs and raise the returns from interactive learning. In the introduction to the book it is emphasized that all national systems are becoming increasingly open. But this is not seen as a reason not to further develop and use the NSI concept. It is argued that globalization makes it even more necessary to understand the historical role as well as the ongoing transformation of national innovation systems.

EACH OF THE ORIGINS GIVES RISE TO NEW STREAMS OF ANALYSIS Each of the two pioneer contributions has stimulated specific research efforts related to innovation systems. The literature on catching up may be seen as a logical follow-up to Freeman’s reference to List and to his macro-perspective on economic development. While Freeman (1982/2004) points to the difficulties of establishing quantitative empirical analysis given the lack of data for less developed countries, much of the work on catching up has been empirical and aimed at testing his hypotheses. More specifically this literature has tested the relative importance of ‘openness’ versus factors related to the strength of the national innovation system. In the next section we summarize the main results from this literature. The literature on cluster formation and regional innovation systems developed by economic geographers may be seen as a follow-up to the analysis of user–producer interaction in Lundvall (1985). To begin with, this literature gave major emphasis to the importance of local interaction (Bathelt and Henn, Chapter 28, this volume). Later on it developed the analysis and pointed to complementarity between global (pipelines) and local (buzz) interaction. This evolution of ideas about interaction in space was interconnected with an analysis of distinct kinds of knowledge and different forms of learning. Below we will focus upon how this literature has developed its view of the role of distance in connection with the interaction that characterizes the innovation process.

WHAT ARE THE PREREQUISITES FOR CATCHING UP? Fagerberg’s contributions on competitiveness and catching up may be seen as following a trajectory that was outlined in Freeman’s paper from 1982. While Freeman, with reference to lack of data especially for the least developed countries, used qualitative and historical arguments to indicate the importance of technology for national economic development, Fagerberg, starting with his PhD thesis (1988), has engaged in a life-long effort to analyze quantitative data in order to sort out what are the main factors that contribute to economic growth and international competitiveness in countries at different levels of development (Fagerberg 1993; 1994; 2010; 2011). His works show that technological capabilities (the national innovation system) and factors having to do with ‘governance’ are crucial for economic development while factors

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cherished within the Washington consensus such as ‘openness’ (to trade and FDI) and the prevalence of Western political institutions do not favor economic development – especially not in the least developed countries. In Fagerberg (2010), where he summarizes much of his work on why growth rates differ, he demonstrates that a broader definition of ‘openness’ is significant for economic development. Openness to ideas, to entrepreneurial effort and to people (including tolerance toward minorities) are positively correlated with national economic performance. These conclusions are in line with the main results presented in Fu et al. (2011). Their analysis aims at understanding the role of national and international sources of knowledge and innovation. It is built upon an extensive literature review on the impact of FDI and it refers to a different type of data than the empirics used by Fagerberg (their evidence is from case studies of global value chains in emerging economies). They find that ‘the evidence suggests that, despite the potential offered by globalization and a liberal trade regime, the benefits of international technology diffusion can only be delivered by parallel indigenous innovation efforts and the presence of modern institutional and governance structures and a conducive innovation system’ (Fu et al. 2011, p. 1210). They conclude that ‘without indigenous innovation the income gap between rich and poor countries will never be closed’. These results support Freeman’s 1982 analysis where he, with reference to Friedrich List, argues that building national technological infrastructure and a strong national knowledge base should be a major focus for development strategies. Fagerberg’s analysis adds to that perspective the importance of governance (rule of law, intellectual property rights and corruption) as well as openness to ideas. Neither Fagerberg’s analysis nor the analysis of value chains in emerging economies indicate that the least developed countries would benefit from engaging in ‘free trade’ and giving more free access for foreign capital without simultaneously building technological capabilities and ‘upgrading’ national governance.

INTERACTIVE LEARNING IN REGIONAL SYSTEMS OF INNOVATION Jan Fagerberg’s work may be seen as following the trajectory outlined in Freeman (1982/2004). Economic geographers used some of the core ideas in Lundvall (1985) in a similar way to develop further the analysis of why certain activities tend to be located together in a specific region. The analysis of processes of innovation, and not least the diffusion of innovation, was of course not new for this interdisciplinary discipline. Torsten Hägerstrand’s seminal contributions on time and space models were linked to an analysis of innovation diffusion in space including reflections on the importance of face-to-face interaction. His dissertation (Hägerstrand 1953) represented a major milestone. At the start of the 1990s, Krugman and colleagues (Krugman 1991; Krugman and Venables 1995) presented quantitative growth models that signaled what they referred to as ‘the new economic geography’. Their models took on board most of the main assumptions characterizing neoclassical economics, but loosened up some of them – most importantly they allowed for increasing returns to scale, oligopolistic competition and

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costs of transport. This invasion of a rather narrow economics perspective, where it was assumed that regional agglomerations could be explained by the rational behavior of fully informed agents, left many economic geographers uncomfortable. Neither did it match well with the classical approach of Hägerstrand who, while using quantitative modeling, always emphasized the human and cultural dimensions of geography and preferred to work within an evolutionary perspective where uncertainty is seen as fundamental for outcomes. In this climate many economic geographers and experts on regional development saw the new perspectives emerging within innovation studies developed by heterodox economists as a more relevant inspiration for their research. The concept of interactive learning was used in Cooke and Morgan (1990) to explain economic agglomeration in Europe. The combination of the specific focus on user–producer interaction (Lundvall 1985) and the more general concepts of innovation systems (Lundvall 1988) and ‘the learning economy’ (Lundvall and Johnson 1994) inspired concepts such as the learning region and regional innovation systems. Nordic scholars such as Maskell and Malmberg (1997) and Asheim (1996) took some of these concepts as basis for developing theoretical and empirical work in new directions (Bathelt and Henn, Chapter 28, this volume). Further developments by Aalborg economists of the understanding of different types of knowledge and in modes of innovation (Lundvall and Johnson 1994; Jensen et al. 2007) also influenced this literature. A major argument for why proximity between users and producers was critical to innovation was that important components of knowledge were tacit (for in-depth analysis see Gertler 2007 and Asheim and Coenen 2005). But it was also recognized that knowledge may be more or less codified in different sectors and in different technologies and that this fact was important for understanding differences between industries in degrees of localization and internationalization. One of the most important contributions was the paper by Michael Storper (1995), which made use of ideas from (Lundvall 1985) to introduce ‘untraded interdependencies’ as a key concept aimed at giving regional economics a new theoretical foundation. Here Storper argued that vertical linkages such as those between producers and professional users were only one example of ‘untraded interdependencies’. Others were related to the employment contracts and reflected on informal labor market institutions at the regional level. Such relationships could be more or less hierarchical and more or less built upon trust. While most other scholars in economic geography draw rather practical implications from the analysis, using it primarily to argue that proximity is important and to explain the formation of clusters, Storper brought forward and developed further the underlying theoretical ideas. He summarizes his conclusions in three points: 1. 2. 3.

Technological change is path dependent. It is path dependent because it involves interdependencies between choices made over time – choices are sequenced in time, not simultaneous, and often irreversible. These choices have a spatial dimension, which is closely tied to their temporal uncertainty and interdependence. Some inter-organizational dependencies within the division of labor, that is input–output or network relations, involve some degree of territorialization. But in all cases where organizations cluster together in territorial space in order to travel along a technological trajectory, they have interdependencies

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Storper (1995) was not unaware that globalization was an ongoing process but he argued that those who saw it as a hegemonic trend that met with little resistance had given too much attention to techno-economic input–output relations and too little to untraded interdependencies including those not related to user–producer interactions. He used the concept of localized ‘economic conventions’ related to the knowledge system and to labor markets as signifying such interdependencies. The work by Edward Lorenz and colleagues on national differences in the organization of work may be seen as a follow-up of these ideas. Such differences constitute an important but neglected dimension of Europe’s national innovation systems and learning economies. In Lorenz and Valeyre (2006) it is demonstrated that work is organized quite differently in different national systems within Europe and that workers have very different access to jobs offering access to learning. In Arundel et al. (2007) it is demonstrated that there is significant correlation between national performance in terms of innovation and the predominant forms of work organization. These differences typically reflect both differences in formal institutions surrounding the labor markets and ‘conventions’ strongly rooted in national systems. The first wave of research on regional clustering taking Lundvall (1985) and Lundvall (1988) as inspiration emphasized the forces that lead to agglomeration, and often it was assumed that agglomeration could be explained by the character of knowledge exchange in connection with local input–output or user–producer relationships. At the level of national innovation systems, it was also assumed that user–producer relationships could explain the relative stability in international specialization. Empirical work did not always support this perspective, and increasingly it was found that: 1.

2.

The vertical couplings between firms within a regional cluster were not highly developed. Increasingly the vertical division of labor in product chains was further developed and different steps were distributed at different locations, sometimes at locations across the globe. While the interaction with domestic customers and suppliers was more frequent when developing new products, the less frequent interaction with distant customers and suppliers outside the national system played an important role especially in connection with path-breaking and more radical innovations. Firms and clusters that combined ‘local buzz’ with ‘global pipelines’ were more viable and performed better than those depending only on local interaction.

These observations emphasized the need to combine a national perspective with a wider view, a need reinforced by the globalization of financial markets, by economic integration in Europe and by the increasing number of firms that behave as if they are footloose. In the introduction to the Oxford Handbook of Economic Geography (Clark et al. 2000), these are the main arguments for why a national perspective is insufficient. But the conclusion is still that national systems matter. It is actually said that ‘as representatives of political agency they may be more important than ever’. The editors of the book especially see a

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weakening of the role of national systems in the tendency toward decoupling between private economic interest and enterprise and the home nation.

THE GLOBAL VALUE CHAIN APPROACH It is interesting to note that in the Handbook on Economic Geography (Clark et al. 2000) there is only one reference to ‘global commodity chains’ in spite of the fact that the introduction argues that global and sub-national economic processes should be given more attention. This reflects that the community of scholars who developed the global value chain approach came from the field of development studies, a sub-discipline clearly separated from regional studies and from the community of scholars working on issues related to economic geography in the global North. The main research question in recent global value chain research is: How does the character of the global production chain contribute to or hinder the upgrading of activities in firms located in less developed economies? The complementary question is: How does the character of the chain affect the distribution of value produced along the chain? This leads to the third question: Does the integration of local firms into global chains contribute to economic development in developing countries? One early major contribution to this field of research was the edited book Commodity Chains and Global Capitalism by Gereffi and Korzeniewicz (1994). The book brought together contributions by scholars with different backgrounds. Some of the contributions were case studies while others were historical or theoretical. The main theoretical references were to Immanuel Wallerstein’s contribution on the world system and global commodity chains (Wallerstein 1974) and to Michael Porter’s work on competition and innovation (Porter 1990). The most important analytical step taken was Gereffi’s distinction between producer-driven and user-driven value chains. This constituted the beginning of a discourse on ‘governance’ that later became dominated by references to transaction cost analysis. Another important reference is to Humphrey and Schmitz (2002). Those two scholars are affiliated to the Institute of Development Studies (IDS) at Sussex University. During the 1990s their focus was upon how the new understanding of industrial districts and cluster formation developed in Europe could inspire strategies for industrial development in developing countries (Humphrey 1995; Schmitz 1995, 1999; Humphrey and Schmitz 1998). Schmitz introduced the concept of ‘collective efficiency’ as characterizing successful clusters, a concept close to untraded interdependencies and shared economic conventions. Humphrey and Schmitz (2002) is an important paper since it marks a bridge between the global value chain literature and the cluster literature as it emanated from IDS at Sussex University. It is also important since on a few pages it introduces some fundamental concepts that have shaped the value chain discourse onwards. First it makes the distinction between four forms of industrial upgrading (Giuliani, Chapter 22, this volume; Van Assche, Chapter 45, this volume): 1. 2.

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The Elgar companion to innovation and knowledge creation New function New sector

As compared to the innovation literature the third form of upgrading is of special interest since it goes beyond technical innovation. It may be seen as a form of innovation resulting in a ‘new organization’. In the context of the global value chain literature it has a more specific connotation and it is assumed to be of great strategic importance. The value chain is seen as encompassing different functions spanning from exploitation of natural resources and manufacturing to R&D and marketing. It is assumed that firms that control the R&D and marketing functions can extract more value than those firms that are engaged exclusively in natural resource extraction or manufacturing. Even when firms succeed in developing new products and more efficient processes, they might gain little in terms of value if they remain producers without access to R&D or without a strong position in end-user markets. For the demand-driven chains the most important factor is the control of end-user markets, including establishing a strong brand. For the producer-driven chains the most important form of function upgrading is related to the building of R&D capacity. Multinational firms that control these functions are assumed to be able to dominate and ‘organize’ the whole value chain. The second conceptual contribution relates to different degrees of dominance and it refers to the governance of networks. The analysis takes Oliver Williamson’s transaction cost theory (Williamson 1975) as its starting point. It is argued that four types of relationships can be distinguished in the value chain: 1. 2. 3. 4.

Arm’s length market relations Networks Quasi hierarchies Hierarchy

The dominating form will depend upon a series of factors. Quasi hierarchies may reflect a combination of monopoly position of the buyer, need for speedy response among suppliers, limited capacity of suppliers and complexity in the product. It is argued that in a dynamic perspective the entrance of local firms into quasi hierarchies may support upgrading at least in terms of products and processes. The paper points to the importance of understanding the role of global linkages for firm-level upgrading. But it also specifies that in order to be successful integration needs to be combined with investing in knowledge within the firm and that the more demanding forms of upgrading require a strong innovation system and active innovation policies. A further step toward developing the understanding of governance of global chains was the work by Sturgeon on modular production networks. Sturgeon (2002) argues that the modularization of information technology production chains should be seen in the light of transaction cost theory. By standardizing and codifying interfaces between those producing components and the major computer firms it has been possible to reap scale economies in production without imposing prohibitively high transaction costs. It is argued that this is ‘a new American model of industrial production’ that can be applied in other sectors and sets new global standards for the organization of value chains.

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Gereffi et al. (2005) take these ideas into account and propose five different modes of governance: 1. 2. 3. 4. 5.

Hierarchy Captive Relational Modular Market

It is assumed that the further down we get on this list, the lower the degree of dominance for the lead firm in the value chain. As compared to the categories used by Humphrey and Schmitz (2002), captive corresponds to semi-hierarchical while the network category has been divided into two types of networks – relational and modular. Three different factors are used to explain why a transaction interface takes on a specific form: 1. 2. 3.

The complexity of information and knowledge transfer The extent to which the information can be codified The capabilities of suppliers

What is new as compared to Humphrey and Schmitz (2002) is that complexity now is explicitly related to information and knowledge and especially the emphasis upon the codifiability of the information. This is a theme that Aalborg economists have addressed in a number of papers where the emphasis has been upon the limited codifiability of crucial elements of knowledge – especially codifiability is limited for the knowledge forms that they refer to as ‘know-how’ and ‘know-who’ (Lundvall and Johnson 1994; Johnson et al. 2002).

RELATING THE GLOBAL VALUE CHAIN APPROACH TO THE ORIGINAL NSI CONTRIBUTIONS The global value chain literature may be seen as combining elements from the two original NSI contributions referred to above. It makes an attempt to address the fundamental question raised by Freeman in connection with his interpretation of Friedrich List. Under what circumstances does participation in trade and openness to FDI have a positive impact upon the knowledge base of the economy? There is also much overlap between the global value chain literature and Lundvall (1985). Lundvall (1985) does propose that most markets are organized and that they are infiltrated by hierarchical relationships – uneven access to resources and competence are seen as resulting in ‘unsatisfactory innovation’ especially when technologies are systemic. Other important overlaps are the references to Oliver Williamson’s ‘transaction cost analysis’ and the idea that the character of knowledge as more or less codified – or technologies as more or less modularized – matters for the predominant form of governance. Therefore, combining the innovation system perspective and the value chain perspective

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may be a way of reestablishing the critical intentions in the original contributions by Freeman and Lundvall. As mentioned, much of the more recent literature and policy prescriptions have become technocratic and have marginalized issues related to social phenomena such as power and trust. But there are of course important differences as well. While the analysis of Freeman aimed at pursuing analysis at the aggregate level, something that was followed up in Fagerberg’s work, most of the empirical work in the global value chain community is located at the level of the firm, the cluster or the value chain as a whole. As Adrian Wood (2001) has pointed out, there is a need to establish an analytical link from upgrading at the level of the single firm to the development of a whole economy. Without such a link there is no way that one can conclude that the upgrading of a single firm or one single cluster of firms will contribute to economic development at the country level. This ‘fallacy of composition’ may actually be the weakest point in the global value chain analysis. What might be good for the single firm might not be good for a cluster, a region or a national economy. When it comes to the micro-foundation for innovation systems and value chains there are also important differences. Lundvall (1985) and especially the economic geographers who made use of and further developed his ideas have insisted upon in-depth analysis of why specific activities become located together. Here the focus has been upon the character of knowledge and learning processes as well as upon localized ‘institutions’ and ‘economic conventions’. The global value chain literature tends to give less emphasis to analyzing cultural, economic and political geography. This reflects that globalization is seen predominantly as bringing institutional convergence between national economies. This contrasts with the innovation system perspective where globalization is seen as a process that might make specific national patterns more disparate, leading to divergence not only in terms of economic structure but also in terms of institutions. The value chain analysts tend instead to give more weight to relative costs. Their starting point is empirical observations of increasingly global commodity chains, and to some degree they seem to take for granted that national governments have to respect the principles of comparative advantage. It is paradoxical that value chain analysis developed mainly by sociologists has ended up with a somewhat uncritical use of relative cost and transaction cost theory.

ON THE IMPORTANCE OF BUILDING A STRONG NATIONAL INNOVATION SYSTEM Another issue where the two streams of thought diverge in terms of emphasis relates to the relative importance of domestic technological capacity and outcomes of participation in global value chains. The paper by Giuliani et al. (2005) is interesting since it makes an attempt to present a picture of local versus global interaction in Latin America on the basis of no less than 40 case studies. Their conclusions are that you find elements of ‘collective efficiency’ in most clusters while the form it takes depends on sector as well as regional and national context. They also confirm that in order to explain how integration in global value chains affects upgrading in the firm you need to take into account the

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characteristics of regional and national systems of innovation and especially the firms’ own efforts to engage in capacity building (Giuliani, Chapter 22, this volume; Van Assche, Chapter 45, this volume). This corresponds to what is found in Malerba and Nelson (2011). Studying ‘catching up’ in six sectoral innovation systems they find that industries differ in terms of how they link up with international firms. In some successful cases of catching up (automobiles in Korea) the access to foreign technology was crucial, while in other cases (software, semiconductors and agro-food) multinationals operated as customer lead firms in global value chains. But again, in order to explain success and failure in catching up – a phenomenon that could be referred to as ‘sectoral upgrading’ – they find that it is necessary to link the analysis of sector performance to the characteristics of the national innovation system. But the analysis of a wider set of cluster developments or of sectoral systems does not solve the ‘fallacy of composition’ problem. Even if it can be shown that most clusters can benefit from firms’ integration in global value chains and that specific sectors in a national system are characterized by catching up, it does not follow that this will contribute to economic and social upgrading at the national level. This is not to degrade the importance of case studies and sector studies. But it is a strong argument for combining different methods, including analysis at the macro level, in order to make it possible to establish links from micro- and meso-levels to what happens at the national level.

CONCLUSIONS The two first papers that made use of the concept ‘innovation system’ (Freeman 1982/2004 and Lundvall 1985) had in common a critical perspective on economic theory and on economic policy. They introduced the concept in two different contexts. Freeman analyzed the importance of building a strong technological infrastructure at the national level while Lundvall analyzed the interaction taking place at the level of the market between users and producers of new products. Freeman (1982/2004) has inspired Fagerberg’s work on catching up at the level of national systems. Fagerberg has developed methods to analyze in quantitative terms what Freeman derived as hypotheses on the basis of historical material. Lundvall (1985) inspired economic geographers such as Morgan, Cooke, Gertler, Maskell and Asheim, who developed further the analysis of forms of knowledge in the context of geographic space. Michael Storper enriched the analysis by linking ‘nation specific conventions’ to ‘untraded interdependencies’. The global value chain literature overlaps the two original contributions to the innovation system analysis. It shares Freeman’s assumption that capacity building (upgrading) is crucial for economic development and his concern that not all participation in international trade will contribute to that. It shares with Lundvall (1985) the assumption that most markets are organized (taking the form of networks) with patterns of dominance, and it also links the degree of codification to transaction cost analysis. There is of course a tension between the two perspectives and this tension can be linked to the issue of convergence versus divergence among national systems of innovation (Pietrobelli and Rabellotti 2011). Transnational value chains would be easier to establish if national systems become less disparate in terms of institutional setup and mode of

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innovation. On the other hand international diversity in terms of specialization in production and knowledge and in terms of income/cost levels can be seen as an underlying driver of the formation of global value chains. In order to understand the dynamics of convergence and divergence the most important step might be to analyze in some detail the evolution of codes of communication used in more or less local or global forms of interaction. There is little doubt that the multinational enterprises (MNEs) that play the most active part in shaping the value chain also engage in developing codes that can overcome gaps in culture and competence. An interesting question is how this affects competence building world-wide. Codification of tacit knowledge is not costless. Literature on the codification of expert systems show that what comes out of the codification process is less rich in terms of complexity and nuance than the original expert knowledge. The global value chain approach and the national innovation system approach differ also when it comes to focus and level of analysis. While the focus of the system of innovation approach has been on the role of governments in building national infrastructure and upon the role of domestic linkages, the focus of global value chain analysis has been on trade policies and transnational linkages. Freeman’s insistence (see Sharif 2006) that innovation system analysis should give more weight to understanding macro-phenomena rather than just doing case and sectoral studies has not been taken up on a big scale among those working on innovation systems. As Freeman put it in an interview: most of the people working on Innovation Systems prefer to work at the micro level and they are a bit frightened still of the strength of the neoclassical paradigm at the macroeconomic level, and I think that’s where they have to work. You have to have an attack on the central core of macroeconomic theory. It is happening but not happening enough, not strongly enough argued. (Sharif 2006)

Among those who have done it most systematically we find Fagerberg, Dosi and Verspagen. To link the transformation of economic structure to the process of economic growth and development is a major methodological challenge, and it is of major importance for the design of trade, industry and technology policy. In classical development economics the growth of manufacturing activities (assumed to be characterized by increasing returns to scale and steep learning curves) was seen as crucial prerequisite for high rates of aggregate growth. This was presented as motivation for trade and industry policy aiming at import substitution. An interesting and promising recent approach is to link national economic performance not to specific sectors but to the characteristics of the technology predominant in the domestic high-growth sectors (Lee 2013). An open and critical discussion between the national innovation system proponents and the global value chain scholars may prove fruitful when it comes to building an agenda for development research and when it comes to designing strategies for development. This assumption takes inspiration from the fact that the few countries that have been successful in catching up (Korea, Taiwan, Japan and China) have followed strategies where they gave attention both to building strong national innovation systems and to joining global value chains. One ambitious goal for the research agenda could be to follow up on Freeman’s interpretation of Friedrich List and develop a distinction between patterns of participation

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in the global economy that strengthen the national knowledge base (enhance mental capital) and patterns that undermine it. It could also address another question: Under what circumstances will the participation in global value chains contribute to learning and upgrading at the level of the firm (what is required in terms of strategy and absorptive capacity), at the level of a sector and to economic and social development at the national level (development strategy and strength of the national innovation system)? Such an analysis would of course need to recognize that context matters (the capacity of government, size of the economy, access to natural resources, world political position and level of income). The idea propagated by neoliberal economists that every single entrance of a domestic firm into a global value chain is promoting national economic development is of course utterly naïve. Acknowledgements Another version of this chapter appears in Lundvall, B.-Å. (ed.) The Learning Economy and the Economics of Hope, New York: Anthem. The author thanks Anthem and Edward Elgar for permitting this arrangement.

REFERENCES Andersen, E.S., Dalum, B. and Villumsen, G. (1981) ‘The importance of the home market for the technological development and the export specialisation of the manufacturing industry’, Technical Innovation and National Economic Performance: An IKE-Seminar, Aalborg: Aalborg University Press. Andersen, E.S., Johnson. B. and Lundvall, B.-Å. (1978) ‘Industriel udvikling og industrikrise’, Serie om industriel udvikling no. 4, Aalborg, Aalborg University Press. Arundel, A., Lorenz, E., Lundvall, B.-Å. and Valeyre, A. (2007) ‘How Europe’s economies learn: a comparison of work organization and innovation mode for the EU-15’, Industrial and Corporate Change, 16(6): 1175–1210. Asheim, P. (1996) ‘Industrial districts as “learning regions”’, European Planning Studies, 4: 379–400. Asheim, B.T. and Coenen, L. (2005) ‘Knowledge bases and regional innovation systems: comparing Nordic clusters’, Research Policy, 34(8): 1173–1190. Bathelt, H. and Henn, S. (2017) ‘National and regional innovation systems’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L. (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing: 457–471. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28(1): 31–56. Bernis, G.D. de (1966) ‘Industries industrialisantes et contenu d’une politique d’intégration régionale’, Economique Appliquée. Chesnais, F. (1987) ‘Science, technology and competitiveness’, STI Review, 1: 85–129. Clark, G.L., Feldman, M. and Gertler, M. (2000) The Oxford Handbook of Economic Geography, Oxford: Oxford University Press. Cooke, P. and Morgan, K. (1990), Learning through Networking: Regional Innovation and the Lessons of BadenWurttemberg, RIR Report No. 5. University of Wales, Cardiff. Fagerberg, J. (1988) Technology, Growth and Trade: Schumpeterian Perspectives, D.Phil thesis, University of Sussex, Brighton. Fagerberg, J. (1993) ‘A technology gap approach to why rates differ’, Research Policy, 22(2): 87–99. Fagerberg, J. (1994) ‘Technology and international differences in growth rates’, Journal of Economic Literature, XXXII: 1147–1175. Fagerberg, J. (2010) ‘The changing global economic landscape: the factors that matter’, in Solow, R.M. and Touffut, J.-P. (eds), The Shape of the Division of Labour: Nations, Industries and Households, Cheltenham and Northampton, MA: Edward Elgar Publishing: 6–31. Fagerberg, J. (2011) ‘Domestic demand, learning, and the competitive advantage of nations: an empirical analysis’, in Huggins, R. and Izushi, H. (eds), Competition, Competitive Advantages, and Clusters, Oxford: Oxford University Press: 131–147.

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Freeman, C. (1982) ‘Technological infrastructure and international competitiveness’, Draft paper submitted to the OECD Ad hoc-group on Science, technology and competitiveness, August 1982, mimeo. Later published as Freeman, C. (2004), ‘Technological infrastructure and international competitiveness’, Industrial and Corporate Change, 13: 540–569. Fu, X., Pietrobelli, C. and Soete, L. (2011) ‘The role of foreign technology and indigenous innovation in the emerging economies: technological change and catching-up’, World Development, 39: 1204–1212. Gereffi, G., Humphrey, G.J. and Sturgeon, T. (2005), ‘The governance of global value chains’, Review of International Political Economy, 12: 78–104. Gereffi, G. and Korzeniewicz, M. (1994) Commodity Chains and Global Capitalism, Santa Barbara, CA: ABC-CLIO. Gertler, M.S. (2007) ‘Tacit knowledge in production systems: how important is geography?’, in Polenske, K.E. (ed.), The Economic Geography of Innovation, Cambridge: Cambridge University Press: 87–111. Giuliani, E. (2017) ‘Industrial clusters in global networks’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L.  (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing: 360–371. Giuliani, E., Pietrobelli, C. and Rabellotti, R. (2005) ‘Upgrading in global value chains: lessons from Latin American clusters’, World Development, 33: 549–573. Gruber, W., Dileep, M. and Vernon, R. (1967) ‘The R&D factor in international trade and international investment of United States industries’, Journal of Political Economy, 75(1): 20–37. Hägerstrand, T. (1953) Innovation Diffusion as a Spatial Process (English translation by Pred, A., 1967), Chicago: University of Chicago Press. Hufbauer, G.C. (1966) Synthetic Materials and the Theory of International Trade, Cambridge, MA: Harvard University Press. Humphrey, J. (1995) ‘Industrial reorganization in developing countries: from models to trajectories’, World Development, 23: 149–162. Humphrey, J. and Schmitz, H. (1998) ‘Trust and inter-firm relations in developing and transition economies’, Journal of Development Studies, 34(4): 32–61. Humphrey, J. and Schmitz, H. (2002) ‘How does insertion in global value chains affect upgrading in industrial clusters?’, Regional Studies, 36: 1017–1027. Jensen, M.B., Johnson, B., Lorenz, E. and Lundvall, B.-Å. (2007) ‘Forms of knowledge and modes of innovation’, Research Policy, 36: 680–693. Johnson, B. (1992) ‘Institutional learning’, in Lundvall, B.-Å. (ed.), National Innovation Systems: Towards a Theory of Innovation and Interactive Learning, London: Pinter Publishers. Johnson, B., Lorenz, E., Lundvall, B.-Å. (2002) ‘Why all this fuss about codified and tacit knowledge’, Industrial and Corporate Change, 11: 245–262. Kaldor, N. (1978) ‘The effect of devaluations on trade in manufactures’, in Further Essays on Applied Economics, London: Duckworth: 99–118. Kravis, I. and Lipsey, R.E. (1971) Price Competitiveness in World Trade, New York: Columbia University Press. Krugman, P. (1991) ‘Increasing returns and economic geography’, Journal of Political Economy, 99: 483–499. Krugman, P.R. and Venables, A.J. (1995) ‘Globalization and the inequality of nations’, Quarterly Journal of Economics, 110: 857–880. Lee, K. (2013) Schumpeterian Analysis of Economic Catch-Up: Knowledge, Path-Creation and the Middle Income Trap, Cambridge: Cambridge University Press. Leontief, W. (1953) ‘Domestic production and foreign trade: the American capital position reexamined’, Proceedings of the American Philosophical Society, 97: 332–349. List, F. (1845) The National System of Political Economy, London: Longmans, Green and Co. Lorenz, E. and Valeyre, A. (2006) ‘Organizational forms and innovation performance: a comparison of the EU15’, in Lorenz, E. and Lundvall, B.-Å. (eds) How Europe’s Economies Learn, Oxford: Oxford University Press: 140–160. Lundvall, B.-Å. (1985) Product Innovation and User–Producer Interaction, Aalborg: Aalborg University Press. Lundvall, B.-Å. (1988) ‘Innovation as an interactive process: from user–producer interaction to the national innovation systems’, in Dosi, G., Freeman, C., Nelson, R.R., Silverberg, G. and Soete, L. (eds), Technology and Economic Theory, London: Pinter Publishers: 349–369. Lundvall, B.-Å. (ed.) (1992a) National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning, London: Pinter Publishers. Lundvall, B.-Å. (1992b), ‘User–producer relationships, national system of innovation and internationalisation’, in Lundvall, B.-Å. (ed.) National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning, London: Pinter Publishers: 47–70. Lundvall, B.-Å. and Johnson, B. (1994) ‘The learning economy’, Journal of Industry Studies, 1(2): 23–42. Lundvall, B.-Å. and Lorenz, E. (2012) ‘Social investment in the globalising learning economy: a European

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perspective’, in Morel, N., Palier, B. and Palme, J. (eds), Towards a Social Investment Welfare State? Ideas, Policies, Challenges, Bristol: Policy Press: 235–260. Malerba, F. and Nelson, R.R. (2011) ‘Learning and catching up in different sectoral systems: evidence from six industries’, Industrial and Corporate Change, 20: 1645–1675. Maskell, P. and Malmberg, A. (1997) ‘Towards an explanation of regional specialization and industry agglomeration’, European Planning Studies, 5: 25–44. Palloix, C. (1969) Problèmes de croissance en économie ouverte, Paris: Maspero. Pietrobelli, C. and Rabellotti, R. (2011) ‘Global value chains meet innovation systems: are there learning opportunities for developing countries?’, World Development, 39: 1261–1269. Porter, M. (1990) The Competitive Advantage of Nations, London: Macmillan. Posner, M.V. (1961) ‘International trade and technical change’, Oxford Economic Papers, 3(October): 323–341. Rothwell, R. (1972) ‘Factors for success in industrial innovations: Project SAPPHO – A Comparative Study of Success and Failure in Industrial Innovation’, Science Policy Research Unit, University of Sussex, Brighton. Rothwell, R. (1977) ‘The characteristics of successful innovators and technically progressive firms’, R&D Management, 7: 191–206. Schmitz, H. (1995) ‘Small shoemakers and Fordist giants: tale of a supercluster’, World Development, 23: 9–28. Schmitz, H. (1999) ‘Global competition and local cooperation: success and failure in the Sinos Valley, Brazil’, World Development, 27: 1627–1650. Sharif, N. (2006) ‘Emergence and development of the national innovation systems concept’, Research Policy, 35: 745–766. Storper, M. (1995) ‘The resurgence of regional economics, ten years later: the region as a nexus of untraded interdependencies’, European Urban and Regional Studies, 2: 191–221. Sturgeon, T.J. (2002) ‘Modular production networks: a new American model of industrial organization’, Industrial and Corporate Change, 11: 451–496. Van Assche, A. (2017) ‘Global value chains and innovation’, in Bathelt, H., Cohendet, P., Henn, S. and Simon, L.  (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing: 739–751. Wallerstein, I. (1974) The Modern World System, New York: Academic Press. Williamson, O.E. (1975) Markets and Hierarchies: Analysis and Antitrust Implications, London: Macmillan. Wood, A. (2001) ‘Value chains: an economist’s perspective’, in Gereffi, G. and Kaplinsky, R. (eds), IDS Bulletin Special Issue on The Value of Value Chains, 32(3): 41–46.

30. Innovation, regional development and relationality Arnoud Lagendijk

INTRODUCTION Since the 1970s, the field of regional studies has developed a strong focus on innovation. Both along academic and policy lines, regions have been presented as ‘cradles’ and ‘engines’ of innovation and hence of wealth creation, through the way in which they attract firms, nurture business development, foster specialization, build networks among firms, create knowledge centers and shape ‘innovation systems’ (Cooke 1992; Porter 1996; Bathelt and Henn, Chapter 28, this volume). The result has been a proliferation of regional innovation concepts, from the early notion of ‘clusters’ to the latest fruits of ‘smart specialization’ (Foray et al. 2011). This conceptual proliferation has been accompanied, moreover, by a mass of supporting and qualifying evidence, as well as by counterevidence and even fundamental criticism. On the one hand, a broad spectrum of studies, from systematic-quantitative to anecdotal-qualitative, has unearthed many ‘secrets’ of regional innovations, resulting in ever-more sophisticated abstractions as well as detailed regional innovation biographies. On the other hand, from ‘clusters’ onwards, concepts have been considered as being fuzzy, reified, interest-driven and unfounded (Markusen 2003), Accordingly, the literature has been accused of presenting a too closed and too bounded perspective of the region, resulting in a rather artificial divide between ‘local’ and ‘nonlocal’ dimensions. This turbulent half-century of regional studies’ fixation on innovation is reviewed here by adopting an ontological-relational perspective, which differs from a relational approach that stresses actual forms of interaction (Faulconbridge, Chapter 41, this volume). Rather than assuming a basic spatial logic or model underpinning the region–innovation nexus, such a relational perspective sees this nexus emerging in a more constructivist, evolutionary manner. The starting point of this discussion is threefold. First, innovation is considered as a concrete phenomenon constituted by ideas and practices reaching the region through circulation. Second, the way such ideas and practices become established in a region, and shape particular contexts of innovation, is place- and time-specific. The result is, to use another relational term, the unfolding of unique regional ‘trajectories’. Third, both circulation and trajectories are captured and directed by academic and policy views and interpretations, and the logics and rationales emerging from them. An ontological-relational perspective, in summary, focuses on the practical constitution (or ‘assemblage’) of regions as sites of innovation, rather than on the actualization of a basic model that can be understood in universal terms. Both objects and their causal mechanisms of becoming emerge from the same ‘flat ontology’, evolving together. The chapter is structured in three main sections. The argument starts with a historical account of the region–innovation nexus, identifying four stages. This account focuses on the emergence and prevalence of a more bounded and closed regional perspective. The

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second section reflects on this history, organized around the three steps suggested above: circulation, trajectories and logics. The final section concludes.

FOUR STAGES IN THE REGION–INNOVATION NEXUS Corporate Divisions of Labor Our history starts in the 1970s, when regional development was viewed primarily against the background of investment, location and innovation decisions made by large, often multinational, corporations. In line with ‘Schumpeter Mark II’, innovation was located primarily within the firm, notably large multi-plant firms with internal research and development (R&D) capacities (Dicken 1976; Young and Stewart 1986). These firms could either be major capitalist corporations, evolving through market and spatial expansion, or state-led enterprises formed through industrial policy. The impact on regional development took shape primarily through functional allocations. Regional wealth depended primarily on the functions firms allocated to regions, manifesting clear core–periphery patterns. ‘Core’ regions hosted central ‘control’ (headquarter) and ‘development’ (research, marketing) functions, boosting innovation. ‘Peripheral’ regions, in contrast, hosted ‘cheap’ highly standardized production processes executed by ‘branch plants’. Between core and periphery, a category of intermediate regions emerged (Lagendijk and Van der Knaap 1993). Intermediate regions jockeyed for positions as regional or national ‘control’ centers, as ‘innovative’ production sites (adaptation, limited R&D) innovation, or as ‘high-valueadded’ supply centers (Massey 1984). Regions, in summary, were primarily seen as bundles of location factors (labor, education, land, regulation, etc.), acquiring positions in the ‘global division of labor’ dominated by corporate hierarchies. The orientation towards innovation became stronger when, in time, the attention shifted from mere locational concerns to a richer gamut of firm–place relations (Dicken 2000), and even a move away from the firm towards the industry (Taylor 1995). The literature started to see firm–place relations as stemming from complex and dynamic processes of mutual interaction, stressing the impact of investments on location factors, processes of negotiation and governance, and the development of advanced assets by both firms and regions. Moreover, from the early 1980s onwards, scholars observed how the processes of innovation became rooted in industrial networks (or ‘value chains’), as well as within the territories in which such networks were anchored (Dicken 2000). This shift was triggered by an increased recognition of the role of networks, organized along industrial and territorial lines, in shaping ‘flexibly specialized’ economic activities. In the words of Storper and Walker (1989, p. 126): ‘Industries and territories are the dominant modes of organizing production, not giant enterprises.’ This resulted in a new, imaginative perspective, labeled ‘geographical industrialization’, in which the locus of innovation shifted from the firm to the industry. New Industrial Spaces ‘Geographical industrialization’ enriched the engagement between the region and innovation in two ways, namely along transactional and technological lines. First,

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instead of zooming in on the internal structure and operations of large enterprises, the emphasis shifted to the key role of inter-firm linkages and labor relations (Langlois 1992). The key questions became how territorial networks are organized (via the market place, subcontractors or in-house) and through which spatial form (proximate, regional, national, global). In an innovative and hence uncertain environment, economic agents were seen as seeking ‘smart’ solutions incurring the lowest costs and risks, and gaining the highest benefits from their investments in transaction-specific assets, that is, in resources needed for the production of dedicated components or services). In short, economic agents aimed for what was called ‘dynamic efficiency’. Langlois and Robertson (1995) elaborated a dynamic perspective on transaction cost theory. They convincingly argued that there is a variation of effective business ownership and coordination, from integrated firms to decentered industrial districts, corresponding to different spatial forms of production, from industrial districts to global networks. This strand of thinking underpinned Markusen’s (1996) well-known typology of innovative regions, ranging from the ‘classical’ industrial district to the ‘hubs and spokes’ and ‘satellite platform’ models. Second, rather than simply being fueled by the R&D hothouses of global enterprises, industries were now shaped through technological trajectories, characterized by radical shifts in industrial standards and ways of innovation and production (Dosi 1982). This highlighted the way in which, in line with Schumpeter’s model of creative destruction, technology-driven, ‘strong’ competition prompted the complete overhauling of industrial chains, organizationally and spatially. The spatial dimension was seen as particularly important in the most innovation-intensive phases of such transitions, when urban agglomerations and regions evolved as core sites or ‘milieus’ supporting dense social, institutional and economic interactions between firms, research centers, support organizations (including the state) and consumers. Such rich regional fabrics became characterized by ‘untraded interdependencies’, underpinning advanced technological trajectories (Storper 1995). Regions, in this perspective, were not considered to ‘host’ industries; they were created by industries and, vice versa, gave rise to ‘new industrial spaces’. ‘Geographical industrialization’, accordingly, downplayed the role of the large firm (Walker 1989), while remaining focused on spatial-functional divisions of labor. In the words of Storper and Walker (1989, p. 153), the new innovation territories are ‘situated within wider and deeper regional, national and international divisions of labor. Indeed, it is precisely their insertion into this richer division of social labor that permits the re-specialization of production upon which the new type of territorial organization is premised’ (p. 153). This perspective, in retrospect, was primarily focused on technological and organizational dynamics, and how these dynamics became interwoven with spatial economic forms. Territorial Innovation Models In the third perspective, the locus of innovation nestled further into the regional economic, social and political fabric, presenting the region as a significant, categorical site of innovation. The region–innovation nexus, accordingly, gained full strength through the development and proliferation of ‘territorial innovation models’ (TIMs) (Moulaert and Sekia 2003). Core TIM examples were ‘industrial districts’, ‘clusters’, ‘regional innovation

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systems’ and ‘learning regions’. Drawing on a combination of endogenous development theory and concepts of social-spatial ‘embedding’, the emphasis shifted from intra- and inter-firm transactions and technological trajectories to regional networks as channels of knowledge circulation, and vehicles for specializing and clustering. Echoing the early writings of Schumpeter, most TIMs feature the role of the entrepreneur, and hence the importance of vibrant and well-connected webs of small and medium-sized enterprises (SMEs), yielding a more decentered perspective on innovation. Importantly, it is the entirety of economic, social and institutional features of such webs that constitutes the nature and impact of ‘embedding’, and that has inspired the broad gamut of initiatives and policies supporting SME development, networking and clustering. The strengthening of the region–innovation nexus through concepts of TIMs had two major consequences. First and foremost, the significance of regional economies was expanded and deepened along economic, social, cultural, institutional and political lines (Moulaert and Sekia 2003). Classical Marshallian factors of localization (supplier links, knowledge spill-over, development of specialized labor and resources) were complemented by advanced ‘cluster’ assets. The latter stemmed primarily from institutionalized and systemized forms of knowledge creation (research centers), embodiment (education) and application (Porter 2000). Additionally, cultural and institutional characteristics further helped to strengthen cognitive and entrepreneurial capacities, fostering a climate of trust and mutual support, shaping regional ‘innovative milieus’ (Camagni 1991; Malecki and Spigel, Chapter 38, this volume). Locally situated, so-called associative forms of governance – enrolling local state actors, business associations, educational institutes, intermediaries and so on – served to build strategic intelligence, develop shared visions and engage in collective action (Cooke and Morgan 1998). Second, the innovative region was presented primarily as a ‘bounded territory’, emerging from a rather closed relation between innovation and regional development. The region encompassed an elaborate chain of innovation largely confined to a specific regional setting. External links were considered, but conceptualized and analyzed as different categories, separating the ‘non-local’ from the ‘local’ (Oinas and Malecki 2002). Marshallian districts evolved as distinct places in wider economic spaces. ‘Innovative milieus’ performed as nodal sites in global ‘filières’ (Belussi and Pilotti 2003). Such a separation was also part of Bathelt et al.’s (2004) perspective on knowledge circulation distinguishing between local ‘buzz’ and external ‘pipelines’, where buzz mostly entailed ‘tacit’ knowledge and pipelines carried more ‘codified’ knowledge. Closed does not mean here that processes captured by ‘milieu’ and ‘buzz’ are, in a strict sense, spatially bounded. Rather, they are embedded and anchored within a singular regional setting, turning the latter into a causal factor as well as a bearer of agency. The latter has been particularly important from a policy perspective, as will be discussed below. This distinction resulted in a categorical focus on the region, in which regional differences are principally understood as variations of a common ‘model’ (TIM). What happened at other spatial levels, such as spatial polarization at national and global levels, resulted in this perspective from the divergent performances of constituent regions. Neither corporate locational preferences and strategies, nor the unfolding of ‘geographical industrialization’ explained regional positions. Rather, regions occupied different, autonomous positions on the route towards advanced specialization and clustering (Massey 2005). A similar emphasis on internal ‘knowledge’ dynamics can be found in recent work in evolutionary

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economic geography (Boschma and Frenken 2009). This work started to explore how regional trajectories advanced through the development of routines sustaining innovation in particular sectors, and developed in a path-dependent, and sometimes pathbreaking way. The implication is that regional trajectories exhibited high levels of ‘related variety’, of new specializations being grounded on old specializations. Although there has been some recognition of ‘non-local’ interdependencies, the basic premise remained that regions follow certain evolutionary paths grounded in the development of internal routines and capacities. The bounded perspective on regional innovation has exerted enormous appeal, in academic as well as policy circles. In part, this appeal can be attributed to the rise of what is called neoliberal thinking, with its emphasis on ‘endogenous’ development, competitiveness and self-reliance, fueling a managerial attitude to economic development and economic policy-making (Cooke and Morgan 1998; Moulaert and Sekia 2003). In academic debates, the endogenous perspective has been enriched with the help of theoretical work on knowledge creation and ‘flows’, entrepreneurial cultures, associative governance, supply chain and networking capacities. The endogenous perspective also made it a suitable object for quantitative research. Where ‘geographical industrialization’, with all its transactional and technological variety, did not lend itself for sweeping statistical analysis, the categorical interpretation of the region clearly did. The result was a proliferation of studies regressing innovative and economic output indicators on a wide list of regional assets, routines and other characteristics. Over time, this has evolved into advanced multi-level approaches revealing the simultaneous impact of determinants at local and (inter)national levels. For qualitative researchers, on the other hand, the endogenous perspective has provided unlimited scope for detailed regional case studies and comparative analysis. Research thus became primarily oriented to explaining regional economic success and, to a lesser extent, failure, from within the region. While significant patterns emerged from these studies (as they did while analyzing corporate geographies), the matching of regional economic performance and ‘endogenous’ capabilities said little about underlying processes and relations, and hence about causality. As Sassen (2006) argued, one should beware of the ‘endogeneity trap’, the attribution of performance primarily to factors internal to the performing entity. Apart from data and analytical problems, such as selection biases and misspecification of variables, such a trap primarily results from the ignorance of the wider embedding of ‘local’ factors in non-local settings and networks. More fundamentally, it may lead to spurious and false abstractions, that is, to interpretation of general findings as valid universal characteristics of a population under study, rather than as the consequence of concrete forces impacting upon that population. The endogenous perspective has also played into the hands of policy-makers, offering them scope for improving the position of laggard regions by innovation-oriented ‘self-help’. Under the neoliberal doctrine, bottom-up ‘self-help’ was strongly preferred to top-down redistribution of resources and wealth between regions. As discussed above, policy-making has been forging particularly close links between innovation and spatial policies, also as part of ambitions to turn local lessons into ‘best practices’. It has also been a good ‘story’ for consultants working in the field of regional policy, who started circulating enticing prospects of advanced specialization and clustering, based on regional ‘foresight’ and strategic governance. Selling such ‘silicon dreams’ turned out to be a more

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interesting and commercially viable than helping regions to fight bidding wars for foreign investment (Kooij 2015). Likewise, in what Lagendijk (2001, p. 83) describes as an ‘uneasy co-habitation of a functional logic and local community aspirations’, the ‘self-help’ message would even inspire community workers and ‘grassroots’ organizations. Another prominent example is the way in which the European Union (EU) has turned the region into a core vehicle to achieve ‘balanced development’ across its territory, combining the aims of boosting innovation as well as social cohesion (Tewdwr-Jones and Morais Mourato 2005). The Organisation for Economic Co-operation and Development (OECD) and other international organizations have framed regional development in similar ways (OECD 2010). The problem here is what Agnew (2010) has called the ‘territorial trap’, the idea that state territoriality provides a given setting from which problems affecting that territory can be addressed. The idea, in other words, that regional issues can be best solved through regional governance. However, the literature has not been silent on these issues and ‘traps’. It has been replete with discussions on multi-level innovation (Malecki and Oinas 1999), the importance of external relations (Bathelt et al. 2004; Moreno and Miguélez 2012; Giuliani, Chapter 22, this volume), and dilemmas concerning competitiveness and regional institution building (Uyarra 2007). Political accounts have paid much attention to the role of elites in economic governance (Amin 1999; Swyngedouw 2000). Similar accounts have also pointed out the naivety of the idea that peripheral regions could simply catch up through ‘self-help’ policies (Lovering 1999). Studies on regional policy have regularly called for customized, bottom-up approaches, resisting the craving for political uniformity and accountability (Cooke and Morgan 1998). The results have been twofold. It has infused discussions on the nature and relevance of our thinking in the field of regional studies. It has also prompted the rise of new, more strategic, perspectives, such as ‘smart specialization’, the last perspective to be discussed here. Smart Specialization Fueled by the discussions summarized so far, a recent offspring of the TIM family is the concept of ‘smart specialization’ (Foray 2009). Elaborating, in particular, on the academic and policy work on ‘innovative clusters’, ‘smart specialization’ seeks to advocate a truly ‘place-based’ approach to regional economic development, yet in a way that is less closed, less imitative and less politically naive, and therefore potentially escaping the traps highlighted so far. More openness is derived from an acknowledgment that innovation processes generally occur at multiple spatial levels and manifest marked divisions of labor between global knowledge ‘hubs’, mostly core metropolitan areas, and intermediary regions specializing in knowledge ‘applications’ as part of global knowledge circuits. Hubs are the sources, in this view, of key enabling technologies (KETs), shaping entire new product ranges or even sectors, feeding specialization elsewhere. Such specialization is guided by a process of ‘entrepreneurial discovery’, a concerted and policy-driven activity that should align the regional political, institutional and administrative contexts with the need to boost regional innovative and competitive capabilities (Foray et al. 2012). While the question remains to what extent more peripheral regions can embark on such processes of discovery and specialization, it opens the door for a more spatially differentiated view on spatial-economic development.

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Ideally, thus, smart specialization combines a more open, multi-level geographical perspective on regional-economic performance with a more institutionally and spatially sensitive policy approach. Rather than aspiring to become another ‘Silicon Valley’ and falling into the ‘endogeneity trap’, this should help regions to find their own destiny, through becoming tactically and strategically (‘smartly’) embedded in global knowledge and value chains. Yet, while moving beyond some key caveats of previous perspectives, smart specialization still appears to hold on to a neoliberal doctrine of ‘self-help’, in which regions are equipped to shape territorial capacities for innovation and its commercial application to compete in a global ‘market place’. Providing such equipment is the gist of key documents such as the ‘RIS 3’ policy guidelines (Foray et al. 2012). Hence, there is no full escape from the ‘territorial trap’. There is indeed some recognition though of the process of technologydriven ‘geographical industrialization’ in line with the ‘new industrial spaces’ literature discussed before. Yet, the broader spatial implications of such a perspective, such as the shaping of structural inequalities and interdependencies, are hardly taken on board. As an academic idea, ‘smart specialization’ may embody some novelty; as a policy, however, it seems to amount to not much more than ‘old wine in new bottles’.

A REFLECTION: THE REGION–INNOVATION NEXUS FROM AN ONTOLOGICAL-RELATIONAL PERSPECTIVE Three decades of debates on the region and innovation have yielded an intense, but also somewhat categorical and closed view of the region. The extent to which regions are innovative and creating wealth is traced back to certain factors and conditions, and their combinations. In time, this way of reasoning has become more and more elaborate, including all kinds of non-linearities, interdependencies, feedback loops and other complexities. Yet the basic message has remained very similar. The world is populated by an essential category ‘regional economies’, within which certain determinants produce, with the right equipment, given effects (i.e. innovation). Whether employing advanced econometrics, indepth case studies, or innovative ‘mixed methods’ of quantitative or qualitative analysis, the study of regional innovation generally aims to reveal ‘universal’ capacities of regional economies, as well as the policy prostheses to meet these. To put these ideas in perspective, this section will review them, making use of ontological-relational thinking captured by actor-network and assemblage theories (DeLanda 2006), and the seminal work of Massey (2005). Such approaches feature a more empirical perspective on spatial phenomena, by considering general logics, factors and models not as abstract representations, but as concrete synthetic stories which perform by helping to shape the very object they describe. How they perform, moreover, depends on a wider set of historical and geographical conditions. The various approaches discussed above, with their specific views on regional innovation, thus present different stages in which changing circumstances have given rise to new performative concepts, which, in turn, have shaped regional forms and functions. An ontological-relational approach, accordingly, serves to empirically ground and contextualize different theoretical perspectives. To do so entails three key aspects: (1) an emphasis on circulation of practical stories and scripts and their performativity, (2) the notion of regional trajectories (accounting for historical

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and geographical conditions) and (3) the role of underlying academic logics and policy rationales (to contextualize distinct theoretical perspectives). Table 30.1 summarizes the outcome of this three-fold approach. Circulation First, the idea of circulation, fueled by the concept of actor-networks, points out that preferences, intentions and forms of action do not come to social actors as general mechanisms, which can be captured in abstract forms. Rather, they come in ‘concrete forms’, as practical knowledge, ‘building’ stories and scripts (Lagendijk and Cornford 2000). An iconic example here is the Regional Technology Plan Guide Book through which the EU sought to foster regional innovation (European Commission 1994). It is not through given ‘intrinsic properties’ that regional assets nurture innovation and wealth, but through the way they are enacted by coding, relayed by agents involved in regional ‘construction’ (Paasi 2012). To examine circulation, we have to map the development and circulation of ‘building’ stories and scripts, and to trace the nodes where they pass, translated and (re) articulated. This kind of work goes much further than the analysis of ‘policy transfer’. It involves the whole range of ‘mobile policies’ and other ‘vehicular ideas’ (institutional, cultural, psychological, etc.) infusing and shaping territorial development (Gibbs et al. 2013; Temenos and McCann 2012). In doing so, an ontological-relational perspective gives more substance to the notion of ‘embedding’, by disclosing how seemingly generic processes (such as innovation) are locally conceived and shaped. The tracing of circulation also draws the attention to the significance of nodal actors, the points where stories and scripts are (re)articulated and (re)transmitted. In the field of regional innovation, the world of circulation is dominated by experts whose daily work it is to observe, monitor and evaluate the link between regional characteristics and outcomes, and to point out better ways of benefitting from locally available assets, resulting in new or modified scripts. It constitutes a veritably global industry of reflexive knowledge creation, populated by massive numbers of academics, consultants and policy-makers, and with a dominant role played by international organizations such as the EU, OECD and World Bank. A major factor is the production of ‘imaginaries’, convincing and compelling grand narratives that, reflecting the current ‘Zeitgeist’, set out the envisaged features and directions of future societies. Today’s crowning (neoliberal) imaginary is that of the ‘knowledge-based economy’ (KBE), fueling prospects and luring images of innovative clusters, technopoles, innovation campuses and so on. Importantly, these imaginaries are not mere hypes. They present and prioritize particular ways of action in view of the myriad of possibilities at hand (Kooij et al. 2014). Imaginaries become compelling and vehicular with the aid of academic research (often backed by major funding programs), the expansion of consultancies (often making global careers out of single imaginaries), policy development (giving rise to extensive, multi-level policy arrangements) and all kinds of media communication (conferences, websites, policy networks, handbooks, etc.). All of these feed, in one way or another, back to strengthening the portrayal and lure of the innovative region, from imaginative portrayals (‘the next Silicon Valley’) to detailed policy scripts. This, in turn, triggers waves of stories on investments in clusters, university spin-offs, incubators, campuses and so on. Far from mere hypes, imaginaries are performative and productive.

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(Practices of) flexible specialization, new forms of transacting

Shifting modes of organizing production, untraded interdependencies

(Socio)technological trajectories, ‘science park’ development Local agglomeration induced by geographical industrialization

Circulation of stories and scripts

Regional trajectories

Academic logics and policy rationalities

‘Buzz and pipelines’

Networking, proximity, shaping local strengths

(Practices of) activitybased interaction, targeting and support, clustering scripts Flexible SME networks inducing localized specializations

Industrial district/ clustering

‘Strategic turn’

Innovative milieu

Regional innovation Smart specialization (RIS 3) systems (RTP/ RIS) ‘RIS 3’ script focused RTP guide book (Practices of) on ‘entrepreneurial instructing collective discovery’ and ‘SWOT’, targeting learning, collaborative projects and collaborative cognition and projects governance Finding global positions Institutions-based Culturalin knowledge circuits development institutional and value chains coalition around change fostering key technological innovation and and commercial entrepreneurship projects Technology-driven ‘selfInstitutional Social-spatial help’ rationale change, ‘Triple embedding, Helix’, ‘learning cognition, by interacting’ communication Regional specialization Shared, local milieu Local collaborative within (global) KETsystem in global in global setting based value chains competitive (‘filières’) environment

Territorial Innovation Models (TIM)

RIS 5 Regional innovation systems; RTP 5 Regional Technology Plan; SWOT 5 strengths, weaknesses, opportunities, and threats.

Source: Own elaboration.

Note:

(Un)boundedness

New industrial spaces

Regional innovation perspective

Flexible specialization

Table 30.1 An ontological-relational approach applied to a selection of regional innovation perspectives

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Through such loops and performativity, the world of circulation can be understood in evolutionary terms of variation, selection and consolidation. By lending material and intellectual support and credibility to particular ways of thinking, and by aligning with current imaginaries, stories and scripts accumulate attention, interests and resources. The result is that, out of many possibilities, particular practices and imaginaries gain further prevalence, and become consolidated and institutionalized. These processes, however, unleash their own sources of lock-in and resistance. Consolidation and institutionalization lead to fixed protocols, assessment procedures and bureaucratization. Collective mindsets are slow in altering their basic stories and scripts and letting go of prevalent imaginaries. Academic research becomes locked-in once careers, funding and status depend on particular ways of thinking and following particular methodological scripts (often more leaning towards verification than falsification). Policy-makers become experts in churning out and reporting alwayssuccessful projects to protect their funding stream. Politicians, finally, prefer to hold on to proven models and interventions, until a major crisis and/or new ‘Zeitgeist’ prompts them to change (Lovering 1999). Regional Trajectories Regional development results from the way ideas and practices land in regional settings, shaping specific local contexts of innovation and wealth creation. Regional development, accordingly, is not defined by intrinsic properties of regional-economic characteristics and factors; the success of ‘Silicon Valley’ or, to give another recent example, ‘Brainport Eindhoven’ (Horlings 2014) cannot be attributed to one specific internal success factor, a hidden treasure that intense research, big data or policy tourism could reveal. Nor is it the combined effect of the factors and processes to which regions are exposed that explain such success stories. Attributes such as proximity, localization, urbanization, regional governance and leadership, and talent pools have no given effects; they only set possibilities and incentives, as well as limitations (Lagendijk and Pijpers 2013). What counts is the creative process of how stories and scripts locally land and are joined (‘assembled’), and the extent to which this constitutes the region as a whole, in which the sum is more than its constituent parts. It is the very process of construction, of becoming itself, that shapes the nexus between innovation and region. This approach also comes with sensitivity for strategic and political aspects. The basic motive for regional ‘construction’ is not a derivative of given spatial-economic preferences; it is the outcome of political will, contestation and strategic choices. While some regions (or more precisely, their core agents and leaders) are able to act, momentarily, in a visionary, inclusive and prudent manner (such as ‘Silicon Valley’ and ‘Brainport’), others suffer from a tendency among local elites for rent-seeking and short-term political gains. Accordingly, one consequence of viewing regions as unique entities created through assembling is that ‘outliers’ should not be seen as strong deviations from the mean, as a kind of statistical noise. On the contrary, outliers are unique sites experimenting with new ideas and practices, potentially infusing new mechanisms and trajectories of placebased innovation. Key examples of such outliers are the ‘craft-based’ innovative regions in middle Italy emerging from the 1970s onwards, reviving interest in SMEs and ‘industrial districts’, and Silicon Valley, sparking off a particularly powerful conceptualization of

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regional innovation. Outliers can also work in a negative sense, as manifested by the fate of old industrial, and over-specialized areas such as the Ruhr area, Detroit and the North of England. The portrayal of regions as assemblages reveals regional development as enabled and conditioned by historically shaped trends, tendencies and capabilities. From a historical perspective, regions are shaped as sites of innovation at the junctions of multiple lines of development or ‘trajectories’ (Massey 2005). Trajectories are manifested in the form of rounds of material investments (assets, facilities, infrastructures), as well as ideas, stories and scripts, that are articulated and circulated across and within regions. Regions present, in the words of Massey (2005, p. 208), a ‘momentary coexistence of trajectories, a multiplicity of histories all in the process of being made’. Such coexistence, moreover, has three key features: it is, in Massey’s view (1) always resulting from interaction, (2) presenting spheres of ‘coexisting heterogeneity’, and (3) always becoming, always under construction. Regions and regional visions emerge as such only because they are constructed and identified out of a myriad of territorial processes and events. In Paasi’s (1991, p. 243) words, ‘region formation is only one moment in the perpetual regional transformation’. The challenge, then, is to understand such processes of construction and identification in relational terms, tracing unfolding trajectories. Conventional approaches, as discussed above, tend to attribute region formation to external factors and logics, to which regions are subjected, captured by set models and explanatory factors. Regions are then cogs in the machinations of the ‘knowledge-based economy’, hosting mechanisms of networking, collaboration and maximizing local benefits. In an ontological-relational perspective, in contrast, region formation is not just a response to external imperatives and machinations. To a large extent, region formation results from within regions as political-administrative units to gain, to use Feldman and Martin’s (2005) apt terminology, ‘jurisdictional advantage’. Processes of innovation and production are equipped and anchored as to strategically pin down ‘value added’ and other economic benefits in their own regions, fueled by overarching visions, strategies and imaginaries. Academic Logics and Policy Rationales The early discussion on the half-century of regional studies highlighted how academic work has provided concepts, logics and models for understanding the region–innovation nexus. This resulted in conceptual shifts from an emphasis on business locations to the role of networks and embedding, from ‘hard’ (economic) to ‘soft’ (social-cultural) aspects, from ‘traded’ to ‘untraded interdependencies’, from classical notions of specialization to the role of governance and identity formation, and from structural to strategic approaches (‘self-help’, ‘smart specialization’). What should be emphasized is that, in doing so, academic stories have an important ‘performative role’ (Callon 1998; see also Callon, Chapter 36, this volume). Academic theories do not just represent social-material realities, but they help to shape particular stories and perspectives while bracketing others, reaching out beyond the academic domain itself (Chouliaraki and Fairclough 2000; Varró 2010). They thus co-create performative vehicular ideas, inform policy scripts and frame regional trajectories. That does not mean that academic tales should be seen as stories ‘just like any other’. On the contrary, they acquire and deserve a specific authority, and

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hence role, stemming from the way claims are constructed and validated. What should be acknowledged, nevertheless, is that academic tales, as outcomes of multiple trajectories, themselves present a ‘coexisting heterogeneity’ and a process ‘always becoming, always under construction’. It thus inevitably produces all kinds of tensions, controversies and ‘blind spots’ that make its role far from neutral. The prevalence of a more closed, bounded regional perspective arguably manifests the outcome of such a bias. A key mechanism for academic work to be performative is the exchange with policymaking, notably though the framing and adaptation of policy rationales that underpin concrete acts of policy-making and implementation (Laranja et al. 2008). It should be noted here that policy developments and ‘rationales’ manifest their own trajectories, with their own ‘building’ stories and scripts. The latter are, in particular, driven by the fact that policies need to be justified and framed on the basis of an acceptable definition, interpretation and presentation of the public interest (Allen and Cochrane 2007). (Inter) national policy programs thus tend to depict regions not just as sites, but as laboratories of innovation and wealth creation, of which the benefit should accrue not just to the experimenting region itself, but to a wider (potentially even global) territory. In EU parlance, local lessons are ‘mainstreamed’, translated into ‘good practices’ for outreach and absorption elsewhere in the community, and even worldwide (Pellegrin 2007; Taylor et al. 2001). Through their own channels of policy dissemination as well as academic channels (often financed under EU research programs), such lessons and ‘good practices’ provided legitimate and acceptable stories and scripts of regional policy support, visioning and strategy formation. Mainstreaming was primarily seen in the context of interregional learning, in which the forerunners would help to equip less favored regions to ‘catch up’, and in which the learning process would help the entire territory (e.g. the EU) to meet the needs of the increasingly competitive ‘knowledge economy’. For decades, this has been the perspective underpinning the EU’s regional and innovation policies, and the liaisons between these two (DG Regio 2002). A critical issue is the interaction between academic logics and policy rationales. Through the elaboration and proliferation of the TIM family of regional innovation concepts, research agendas have become more attuned to policy agendas. This move prompted Lovering (1999, p. 391) to lament that ‘the policy tail is wagging the analytical dog and wagging it so hard indeed that much of the theory is shaken out’. What was shaken out, in particular, was the way activities within regions are an integral part of multilocational, multi-scalar and multi-dimensional processes of value creation, innovation and governance. This pushed research more into the ‘endogeneity trap’. Moreover, there was a growing disregard of wider concerns about spatial and scalar interdependencies and polarization, the political processes behind regional strategy-making, and the way policy-making itself was dependent on particular practices of legitimatization and captive imaginaries (such as the ‘knowledge economy’ and ‘social cohesion’). This, in turn, fed the ‘territorial trap’, and the resulting support for ‘self-help’ policies. As a result, the political and administrative drive to boost regional competitiveness – driven by partisan interests as well as neoliberal state agendas – was tactfully masked behind academic logics and policy rationales working in concert to stress the need for regional innovation. The elaboration of ‘smart specialization’ has gone some way to counter the pitfalls of the territorial trap. However, as argued before, so far this has not resulted in a change of basic policy rationales.

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CONCLUSION For almost half a century, regions have been conceived as core sites of innovation and wealth creation, through a rich repertoire of perspectives and concepts. This chapter has reviewed the academic contributions to this development, adopting an ontologicalrelational perspective. Such a perspective emphasizes how academic inquiry, while seeking to provide overview and abstract logics, takes part in the production and diffusion of logics and rationales underpinning the construction of innovation regions. Academics thus co-create the very object they study. In this perspective, innovative regions cannot be seen as the concrete manifestations of abstract logics or models prescribing the universal nexus between space, innovation and wealth creation. Rather, innovative regions are constituted through, as well as contribute to, the circulation of ‘living’ stories and scripts, and ‘vehicular ideas’ that stem from, as well as feed, concrete local experiences. In other words, abstract logics and general mechanisms do not pre-date, but co-evolve and even emerge from the objects they help to shape. The academic role, accordingly, is not the distant reporter of objectively verifiable regional phenomena. Instead, academics may act as interpreters, sense-makers and mediators in processes of circulation with the help of synthetic reviews, conceptualizations and abstract logics. They can help shape unique regional trajectories as dynamic paths of ‘coexisting heterogeneity’ in which innovation is effectively fueled by the local assemblage of globally circulating ideas, practices and resources. So what is, following from this account, the state of synthetic reviews, conceptualizations and abstract logics on regional innovation? The discussion so far points towards rather mixed results. On the one hand, academic inquiry has yielded an impressive conceptual richness and versatility, with an effective capacity to bridge academic, policy and practitioners’ domains. On the other hand, academic inquiry itself tends to be increasingly fragmented and ‘restless’, and easily caught up in conceptual traps, like the territorial and endogeneity trap discussed above. While the field has always manifested a healthy diversity, ongoing academic specialization has had the effect of deepening and extending current strands of work, and widening the gap and fragmentation between them, for instance between fundamental and applied research, and between qualitative and quantitative approaches (Boschma et al. 2014). Restlessness stems from the variety of principles and anchors employed in the field. Much work, notably quantitative but also case study research, tends to hold on to a ‘distantiated’, objective approach, sidestepping what is seen here as a core characteristic of academic inquiry into regional development: the pervasively performative nature of the ‘vehicular ideas’ it produces. More engaged forms of scholarship, however, may still suffer from the ‘policy tail . . . wagging the analytical dog’, as suggested in the case of smart specialization. The need for focus and support hampers attempts to overcome critical forms of blindness and conceptual ‘traps’. A major challenge for the future, consequently, is to stay clear from formulistic abstractions as well as uncritical case study work, and to engage fruitfully and truthfully in the articulation of abstract logics and vehicular ideas regarding regional innovation and its translation into policy rationales and scripts.

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REFERENCES Agnew, J. (2010). Still trapped in territory? Geopolitics, 15(4), 779–784. Allen, J., & Cochrane, A. (2007). Beyond the territorial fix: Regional assemblages, politics and power. Regional Studies, 41(9), 1161–1175. Amin, A. (1999). An institutionalist perspective on regional development. International Journal of Urban and Regional Research, 22, 365–378. Bathelt, H., & Henn, S. (2017). National and regional innovation systems. In H. Bathelt, P. Cohendet, S. Henn & L. Simon (Eds.), The Elgar Companion to Innovation and Knowledge Creation (pp. 457–471). Cheltenham and Northampton, MA: Edward Elgar Publishing. Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1), 31–56. Belussi, F., & Pilotti, L. (2003). Knowledge creation and codification in Italian industrial districts. In F. Belussi, G. Gottardi & E. Rullani (Eds.), The Technological Evolution of Industrial Districts (pp. 173–194). Boston, MA: Kluwer Academic Publishers. Boschma, R., Feser, E., Henneberry, J., Iammarino, S., Lagendijk, A., Ma, L., . . . Pijpers, R. (2014). Editorial: Sailing in the ocean of knowledge, 2008–13. Regional Studies, 48(8), 1313–1318. Boschma, R., & Frenken, K. (2009). Some notes on institutions in evolutionary economic geography. Economic Geography, 85(2), 151–158. DOI: 10.1111/j.1944-8287.2009.01018.x Callon, M. (1998). The Laws of the Markets. Oxford: Blackwell. Callon, M. (2017). Markets, marketization and innovation. In H. Bathelt, P. Cohendet, S. Henn & L. Simon (Eds.), The Elgar Companion to Innovation and Knowledge Creation (pp. 589–609). Cheltenham and Northampton, MA: Edward Elgar Publishing. Camagni, R. (1991). Local ‘milieu’, uncertainty and innovation networks: towards a new dynamic theory of economic space. In R. Camagni (Ed.), Innovation Networks: Spatial Perspectives (pp. 121–144). London: Belhaven Press. Chouliaraki, L., & Fairclough, N. (2000). Discourse in Late Modernity: Rethinking Critical Discourse Analysis. Edinburgh: Edinburgh University Press. Cooke, P. (1992). Regional innovation systems – competitive regulation in the new Europe. Geoforum, 23(3), 365–382. Cooke, P., & Morgan, K. (1998). The Associational Economy: Firms, Regions and Innovation. Oxford: Oxford University Press. DeLanda, M. (2006). A New Philosophy of Society: Assemblage Theory and Social Complexity. London and New York: Continuum. DG Regio. (2002). Regional Innovation Strategies under the European Regional Development Fund Innovative Actions 2000–2002. Brussels: European Commission. Dicken, P. (1976). The multiplant business enterprise and geographical space: some issues in the study of external control and regional development. Regional Studies, 10(4), 401–411. Dicken, P. (2000). Places and flows: situating international investment. In G. L. Clark, M. P. Feldman & M. S. Gertler (Eds.), The Oxford Handbook of Economic Geography (pp. 275–291). Oxford: Oxford University Press. Dosi, G. (1982). Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Research Policy, 11, 147–162. European Commission (1994). Regional Technology Plan Guide Book (2nd ed.). Brussels: European Commission, DG XVI / DG XIII. Faulconbridge, J. R. (2017). Relational geographies of knowledge and innovation. In H. Bathelt, P. Cohendet, S. Henn & L. Simon (Eds.), The Elgar Companion to Innovation and Knowledge Creation (pp. 671–684). Cheltenham and Northampton, MA: Edward Elgar Publishing. Feldman, M., & Martin, R. (2005). Constructing jurisdictional advantage. Research Policy, 34(8), 1235–1249. Foray, D. (2009). Understanding ‘smart specialisation’. In D. Pontikakis, D. Kyriakou, & R. van Bavel (Eds.), The Question of R&D Specialisation: Perspectives and Policy Implications (pp. 14–26). Joint Research Centre Scientific and Technical Report, Luxembourg: Joint Research Centre and Directorate General for Research. Foray, D., David, P. A., & Hall, B. H. (2011). Smart specialisation: from academic idea to political instrument, the surprising career of a concept and the difficulties involved in its implementation. MTEI Working Paper, Ecole Polytechnique Federale de Lausanne. Foray, D., Goddard, J., Goenaga Beldarrain, X., Landabaso, M., McCann, P., Morgan, K., . . . Ortega-Argilés, R. (2012). Guide to Research and Innovation Strategies for Smart Specialisation (RIS 3). Brussels: European Union. Gibbs, D., Krueger, R., & MacLeod, G. (2013). Grappling with smart city politics in an era of market triumphalism. Urban Studies, 50(11), 2151–2157. Giuliani, E. (2017). Industrial clusters in global networks. In H. Bathelt, P. Cohendet, S. Henn & L. Simon (Eds.),

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The Elgar Companion to Innovation and Knowledge Creation (pp. 360–371). Cheltenham and Northampton, MA: Edward Elgar Publishing. Horlings, L. G. (2014). Leadership, governance and knowledge in an enterprising place: the case of Brainport Eindhoven in the Netherlands. In L. Pugalis & J. Liddle (Eds.). Enterprising Places: Leadership and Governance Networks (pp. 149–175), Contemporary Issues in Entrepreneurship Research, vol. 3. Bingley, UK: Emerald. Kooij, H. (2015). Space for innovation, innovation in space, PhD Thesis. Radboud University, Nijmegen. Kooij, H.-J., Van Assche, K., & Lagendijk, A. (2014). Open concepts as crystallization points and enablers of discursive configurations: the case of the innovation campus in the Netherlands. European Planning Studies, 22(1), 84–100. Lagendijk, A. (2001). Scaling knowledge production: how significant is the region? In M. M. Fischer & J. Fröhlich (Eds.), Knowledge, Complexity and Innovation Systems (pp. 79–100). Berlin: Springer-Verlag. Lagendijk, A., & Cornford, J. (2000). Regional institutions and knowledge – tracking new forms of regional development policy. Geoforum, 31, 209–218. Lagendijk, A., & Pijpers, R. (2013). Beyond the regional cradle and policy trap: proximity and embedding as development potentialities. European Planning Studies, 21(5), 631–636. DOI: 10.1080/09654313.2013.734457 Lagendijk, A., & Van der Knaap, G. A. (1993). Foreign involvement in the Spanish automobile industry: internalising versus networking. Environment and Planning A, 25, 1663–1676. Langlois, R. N. (1992). Transaction-cost economics in real time. Industrial and Corporate Change, 1, 99–127. Langlois, R. N., & Robertson, P. L. (1995). Firms, Markets and Economic Change: A Dynamic Theory of Business Institutions. London: Routledge. Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835. Lovering, J. (1999). Theory led by policy: the inadequacies of ‘The New Regionalism’. International Journal of Urban and Regional Research, 22, 379–395. Malecki, E. J., & Oinas, P. (1999). Technology Trajectories in Space: From ‘National’ and ‘Regional’ to ‘Spatial’ Innovation Systems, vol. 00-35. Florida: University of Florida, Center for International Business Education and Research. Malecki, E. J. & Spigel, B. (2017). Innovation and entrepreneurship. In H. Bathelt, P. Cohendet, S. Henn & L. Simon (Eds.), The Elgar Companion to Innovation and Knowledge Creation (pp. 625–637). Cheltenham and Northampton, MA: Edward Elgar Publishing. Markusen, A. (1996). Sticky places in slippery space – a typology of industrial districts. Economic Geography, 72(3), 293–313. Markusen, A. (2003). Fuzzy concepts, scanty evidence, policy distance: the case for rigour and policy relevance in critical regional studies. Regional Studies, 37(6–7), 701–717. Massey, D. (1984). Spatial divisions of Labour: Social Structures and the Geography of Production. London: Macmillan. Massey, D. (2005). For Space. London: Sage. Moreno, R., & Miguélez, E. (2012). A relational approach to the geography of innovation: a typology of regions. Journal of Economic Surveys, 26(3), 492–516. Moulaert, F., & Sekia, F. (2003). Territorial innovation models: a critical survey. Regional Studies, 37(3), 289–302. OECD (2010). Regional Development Policies in OECD Countries. Paris: Organisation for Economic Co-operation and Development. Oinas, P., & Malecki, E. J. (2002). The evolution of technologies in time and space: from ‘national’ and ‘regional’ to ‘spatial’ innovation systems. International Regional Science Review, 25(1), 102–131. Paasi, A. (1991). Deconstructing regions – notes on the scales of spatial life. Environment and Planning A, 23(2), 239–256. Paasi, A. (2012). Regions are social constructs, but who or what ‘constructs’ them? Agency in question. Environment and Planning A, 42(10), 2296–2301. Pellegrin, J. (2007). Regional innovation strategies in the EU or a regionalized EU innovation strategy? Conceptual and empirical underpinnings of the EU approach. Innovation, 20(3), 203–221. Porter, M. E. (1996). Competitive advantage, agglomeration economies and regional policy. International Regional Science Review, 19(1–2), 85–94. Porter, M. E. (2000). Locations, clusters and company strategy. In G. L. Clark, M. P. Feldman, & M. S. Gertler (Eds.), The Oxford Handbook of Economic Geography (pp. 253–274). Oxford: Oxford University Press. Sassen, S. (2006). Territory, authority, Rights: From Medieval to Global Assemblages. Basingstoke: Palgrave Macmillan. Storper, M. (1995). The resurgence of regional economies, ten years later: the region as a nexus of untraded interdependencies. European Urban and Regional Studies, 2(3), 191–221. Storper, M., & Walker, R. A. (1989). The Capitalist Imperative: Territory, Technology and Industrial Growth. New York: Basil Blackwell.

Innovation, regional development and relationality

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Swyngedouw, E. (2000). Elite power, global forces and the political economy of glocal development. In G.  L.  Clark, M. P. Feldman, & M. S. Gertler (Eds.), The Oxford Handbook of Economic Geography (pp. 541–579). Oxford: Oxford University Press. Taylor, M. (1995). The business enterprise, power and patterns of geographical industrialisation. In S. Conti, E. J. Malecki, & P. Oinas (Eds.), The Industrial Organisation and Its Environment: Spatial Perspectives (pp. 99–122). Aldershot, UK: Avebury. Taylor, S., Polverari, L., & Raines, P. (2001). Mainstreaming the Horizontal Themes into Structural Fund Programming. Glasgow: European Policies Research Centre, University of Strathclyde. Temenos, C., & McCann, E. (2012). The local politics of policy mobility: learning, persuasion, and the production of a municipal sustainability fix. Environment and Planning A, 44(6), 1389–1406. Tewdwr-Jones, M., & Morais Mourato, J. (2005). Territorial cohesion, economic growth and the desire for European ‘balanced competitiveness’. Town Planning Review, 76(1), 69–80. Uyarra, E. (2007). Key dilemmas of regional innovation policies. Innovation: The European Journal of Social Science Research, 20(3), 243–261. Varró, K. (2010). After resurgent regions, resurgent cities? Contesting state geographies in Hungary and England. PhD Thesis. University of Nijmegen, Nijmegen. Walker, R. A. (1989). A requiem for corporate geography: new directions in industrial organisation, the production of space and uneven development. Geografiska Annaler, 71(1), 43–68. Young, S., & Stewart, D. (1986). The regional implications of inward direct investment. In A. Amin & J. B. Goddard (Eds.), Technological Change, Industrial Restructuring and Regional Development (pp. 77–99). London: Allen & Unwin.

PART VI INNOVATION IN TEMPORARY AND VIRTUAL SETTINGS

31. Trade fairs and innovation Harald Bathelt

INTRODUCTION Few studies have been conducted thus far to investigate the role of trade fairs in industrial innovation processes. In discussions among scholars, diverging opinions can be found as to whether these events indeed have a significant impact on the development of new economic products and processes. Marketing scholars emphasize that trade fairs are promotional tools in selling goods (Meffert 1993). Some related literature, however, questions the value of trade fairs in targeting specific markets and customer groups. An example of this is the normative account by Nisen et al. (1984) in a book on how to successfully market software products. They argue that the huge and growing costs of trade fair participation make these events increasingly less attractive as marketing platforms. Manifold internet discussions and blogs suggest that there are more promising marketing strategies that directly and actively engage with customer groups. Although these views differ in evaluating the importance of trade fairs, they perceive them in the first place as marketing instruments, rather than events to support innovation. Clearly, such evaluations have a material basis because actual innovation and research and development (R&D) projects are not being conducted at trade fairs. However, taking a closer look, the realities of trade fairs are quite complex. Trade fairs establish a platform for communication and exchanges of knowledge and experiences between a large number of producers, suppliers, intermediaries, users and other observers. They generate complex knowledge ecologies (Bathelt and Schuldt 2008; Bathelt et al. 2014) similar to those that support innovation and enable interactive learning processes (von Hippel 1977; Lundvall 1988; Gertler 1993) with vertical and horizontal exchanges (Li 2014). Indeed, when conducting research about the nature of innovation processes, we regularly find that firms attend trade fairs and view these to be important in informing and directing their innovation processes. Anecdotal evidence supports this different view about the role of trade fairs in innovation, as the following quote from an interview with the manager of an American-owned machinery firm with 700 employees conducted at its Canadian subsidiary location in 2009 illustrates. He insisted that trade fairs would be crucially important as triggers for new ideas. Prompted about the advantages of such events, he explained that trade fairs lead to a huge advantage. Our trade guys and most of our engineers, they want to touch [new products and technologies]. It is about touching the technology, poking it, and seeing what happens if. It is about being able to talk to the person and ask questions one-on-one. So, there is only so much you can do with that over the internet. You just cannot pick up the technology and touch it and see what it does and watch everything that is going on around it. And even see and hear other people’s reactions because there is always something that someone will say that will trigger another idea. That is how it works here for a number of our innovations.

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Many professionals that attend trade fairs would agree with this observation, even if they can only ‘touch’ new technologies in a metaphorical way. It is within this context of different views about the relationship between trade fairs and innovation that this chapter discusses evidence and presents arguments about the role of trade fairs in the knowledge transfer and generation processes that crucially feed into product and process development. This chapter builds on a knowledge perspective of trade fairs as developed in recent studies in economic geography and industrial marketing (Borghini et al. 2004; Maskell et al. 2006; Bathelt et al. 2014), which emphasize that these events are temporary clusters that bring together a large number of representatives from different segments of an industry or value chain to inspect and discuss new developments surrounding the exhibited products and processes. While a prominent function of trade fairs is to make contact between exhibitors and existing or potential buyers, the knowledge ecologies or ‘global buzz’ at these events generate the basis for the establishment and maintenance of networks, the discussion and evaluation of new technological or design developments, and the circulation of new ideas regarding future innovation. This perspective allows us to draw implications about the knowledge creation potential of trade fairs. In an attempt to pull together empirical findings about the relevance of trade fairs for innovation, this chapter will begin by reflecting some debates regarding the role of these events as a source of information for corporate decision-making processes. In the main part, empirical findings will be discussed from the perspective of exhibitors at two North American trade fairs, before a broader industry perspective is presented, drawing from the Canadian survey of innovation. Based on this discussion, a synthesis is provided that highlights different types of influences of trade fairs on innovation, followed by a conclusion.

TRADE FAIRS AS SOURCES OF INFORMATION While trade fairs have been intensively investigated in marketing studies, especially in the area of industrial marketing, the relationship between innovation and these events has not been a main focus of related work. In evaluating the role of trade fairs, many scholars have primarily investigated the effects of promotional activities or the marketing function of trade fairs, as discussed below. In particular, the role of trade fairs in supporting industrial purchasing decisions has been analyzed in such studies. While there is general agreement that trade fairs are a significant marketing tool and element in the firms’ marketing mix (Kerin and Cron 1987; Bello 1992; Sharland and Balogh 1996), empirical studies come to different conclusions with respect to how important trade fairs are compared to other marketing tools (Bonama 1983). While some studies suggest that trade fairs are an important source of information (Bello 1992; Hansen 2000), an early investigation of industrial buying processes by Moriarty and Spekman (1984) is less optimistic regarding their effects. In a study of over 300 randomly selected firms that bought data terminals, they found that trade fairs were only ranked 11th out of 14 different information sources considered in their study. Moriarty and Spekman (1984) also found that the roles of different information sources varied depending on the stage of the decision-making process. Another study by Lilien (1983) suggested that – among other influences – trade fairs were more important as an

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information source for purchase decisions when products had a high degree of technological complexity, when their transaction frequency was low, and when they were in an early stage of their product life cycle. In a cross-industry survey of over 250 top purchasing managers in different metropolitan regions in the United States, Jackson et al. (1987) found evidence that the importance of marketing tools varied according to different product groups and buying situations. Of six types of promotional tools distinguished in the study, trade fairs were ranked third or fourth in decisions to acquire capital goods, but were less important, ranking fifth or sixth, for other product types. At the same time, however, other studies suggest that trade fairs can be quite decisive as information sources. A cross-industry study of over 1,000 decision makers conducted on behalf of the well-respected German Association of the Trade Fair Industry suggested that trade fairs play a crucial role in providing information for purchasing decisions (AUMA 2008). In comparing decision makers that attend trade fairs with those that do not, the study found clear differences. Unsurprisingly, managers that attended trade fairs evaluated the importance of these events much higher than those who did not participate. Managers who did not attend trade fairs were not in need of corresponding information flows, did not evaluate the potential value of such information highly, or simply did not know better. Conversely, attendees at trade fairs were quite aware of the significance of information accessible at these events and used trade fairs specifically to gather relevant information. When evaluating the significance of different types of sources of ongoing information flows in identifying purchasing opportunities, decision makers who attended trade fairs ranked these events higher than all other information sources, with 66 percent of the interviewees viewing them as ‘important’ or ‘very important’. Other highly ranked information sources were the vendor’s website (with 59 percent of the responses being ‘important’ or ‘very important’), specialized industry journals (51 percent) and field staff (47 percent). All other information sources were evaluated as clearly less important. The study also found that direct contact with the vendor/producer through field staff became the dominant information source when purchasing decisions were getting closer to being finalized. Generally, however, trade fairs were among the top three information sources in all stages of the decision-making process. A large share of the decision makers viewed these events as ‘important’ or ‘very important’ in acquiring information (72 percent), exchanging experiences with peers (71 percent), monitoring competition (61 percent) and supporting customer relations (52 percent) (see also AUMA 2010). While this literature primarily speaks to the marketing function of such events, an increasing body of work suggests that trade fairs take on important ‘non-selling’ roles that may be even more significant than sales or lead generation (Sharland and Balogh 1996; Hansen 2000; Blythe 2002; Borghini et al. 2006). The key argument of this chapter is that the importance of trade fairs also extends to innovation and knowledge generation processes – that is, an aspect that should not be neglected. The next section specifically addresses the role of innovation during trade fairs and presents empirical evidence from the perspective of the exhibitors at these events.

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IMPORTANCE OF TRADE FAIRS FOR INNOVATION I – AN EXHIBITOR PERSPECTIVE In aiming to explore the role of innovation during trade fairs, this section presents the results from a qualitative study that was designed to investigate technological search processes and related exhibitor behavior at trade fairs. In a series of questions, exhibitors were asked how important trade fairs were for innovation, whether they would particularly exhibit innovations, and how the events would impact corporate innovation processes. Firms were also asked whether they were interested in new developments shown by other exhibitors and how they would collect information about these trends during the events (Bathelt et al. 2014; Gibson and Bathelt 2014). Two North American trade fairs in the lighting industry were chosen to conduct this research: IIDEX/NeoCon Canada in Toronto in 2008 and LightFair International in New York in 2009. Both trade fairs are leading events in the North American lighting industry with about 400–500 exhibitors and 16,000–23,000 visitors each (IIDEX/NeoCon Canada 2008; LightFair International 2009). While IIDEX/NeoCon Canada can be described as a regional/national trade fair that is focused on the Canadian market, with neither a primary export nor import function, LightFair International is the major industry event in North America that is directed towards the entire continental market and has become a major platform for international producers entering this market (Bathelt et al. 2014). During these events, almost 90 interviews were conducted with exhibitors to explore the role of innovation. It should be emphasized that neither trade fair is specifically known for its innovation focus. In fact, these events are quite different from the leading global hub show Light + Building, which takes place every three years in Frankfurt/Main, Germany (Bathelt et al. 2014). Light + Building is a very large, truly global event, with almost 2,500 exhibitors and over 200,000 visitors in 2014 (Messe Frankfurt GmbH 2015). About half of the visitors and exhibitors in the event originate from overseas. Light + Building is a leading international flagship fair that is known to be the central innovation hub of the industry. The trade fair is very important in the industry’s innovation dynamics since many firms use the event as a deadline to finalize R&D projects and launch new products and processes. In prior research, it was shown that firms viewed Light + Building as a crucial event where they were expected to showcase their latest and most important innovations (Bathelt and Schuldt 2008). While IIDEX/NeoCon Canada and LightFair International clearly did not have a similar innovation focus, the interviews showed that the vast majority of exhibitors viewed these events as an opportunity to showcase their innovation capabilities and to receive feedback from industrial users and buyers. The exhibitors also learned about other new products and developments in the industry at the events. From the interviews conducted, it was possible to distinguish four types of firms according to their innovation and knowledge creation practices (for a more extensive discussion, see Gibson and Bathelt 2014): Type 1 – Innovation leaders: Despite the fact that the two North American events did not operate as primary innovation platforms, about 10 percent of the firms were identified as innovation leaders. These firms put a high priority on presenting their latest technologies and aimed to introduce new products at the events. Overall, the

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firms paid much more attention to innovation than expected, especially at LightFair International. Innovation leaders applied a highly structured approach to search for other technological innovations at the fair and had thoroughly prepared themselves in advance to do so. Innovation was also an important topic in the discussions with visitors and other exhibitors, which helped the firms to collectively make sense of new trends in the industry. Type 2 – Active innovation seekers: Although sometimes presenting innovations at the events, these firms, which made up 35 percent of the interviewees, did not view the two fairs as platforms to structure their R&D activities. Firms classified as active innovation seekers spent a substantial amount of time scanning other exhibits for new trends in innovation. Systematic observation of other exhibitors was aimed at producing new inspirations for innovation processes. This generated standards for the firms to compare themselves with the other producers. As such, innovation activities played a crucial benchmarking role for active innovation seekers. The firms were also interested, when interacting with other trade fair attendees, to learn about the best practices that were commonly used, and did not just focus on technology updates. Type 3 – Impromptu innovation explorers: These firms, which accounted for another 35 percent of the total, were also interested in generating ideas for innovation but did so in a less systematic way than active innovation seekers. They were classified as impromptu innovation explorers that did not necessarily present their latest innovations at the events, but aimed to get in contact with existing and potential users to receive feedback and ideas for product improvements and new developments in the future. They scanned other exhibits in more opportunistic and less structured ways than innovation leaders and innovation seekers, but nonetheless wanted to ‘match [themselves] with what is going on in the industry’, as one manager emphasized. Type 4 – Passive innovation reviewers: About 20 percent of the firms interviewed were less interested in innovation and usually did not aim to present new products and processes at trade fairs. Instead, they were focused on interacting with buyers and maintaining relationships with customers. Their attitude towards innovation was rather passive and they did not spend much time on scanning other exhibits during the events. As opposed to type 1 and type 2 innovators which actively introduced new products at the trade fair and systematically took note of the organizational field in terms of innovation activities, innovation reviewers practiced a more passive approach towards innovation and played the role of preservers and recipients of new technologies, rather than being their shapers and creators (Gibson and Bathelt 2014). While these findings relate to a specific industry case, it can be assumed that the general role of innovation and knowledge creation is similar in other industries (for a broad analysis of trade fairs in different industries, see Bathelt et al. 2014). Of course, the impact of trade fairs on innovation processes clearly differs according to the type of event and industry at hand. However, the above analysis shows that even events that are not known as global drivers in technology development can play a significant role for industrial

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innovation (Bathelt and Gibson 2015). A large number of firms use trade fairs to systematically showcase their latest innovation to potential customers, in an attempt to receive crucial feedback and generate market legitimacy (Cohendet et al. 2013). At the same time, firms acquire inspiration for future innovation activities by systematically checking other firms’ exhibits for new developments. We should exercise care, however, in generalizing these results too readily. A valid objection to such research is, of course, that the findings generated from it are subject to a selection bias and may, in the first place, only speak to those firms that actually attend trade fairs.

IMPORTANCE OF TRADE FAIRS FOR INNOVATION II – AN INDUSTRY PERSPECTIVE To overcome the selection bias that is always present when conducting research at trade fairs, this section presents the results of a nationwide survey of innovation conducted by Statistics Canada (2005a), which focused on understanding the importance of and circumstances leading to product and process innovation in selected service industries in Canada during the period from 2001 to 2003. The survey resulted in over 2,100 completed questionnaires and a response rate of about 70 percent (Statistics Canada 2005b). While the data does not cover manufacturing industries, it allows us to derive representative findings about the innovation behavior of service firms, especially knowledge-intensive services. The empirical findings presented in this section were computed from the firms’ responses to the following question: ‘During the last three years, 2001 to 2003, which of the following played an important role as sources of information needed for suggesting or contributing to the development of new or significantly improved products (goods or services) or processes (including improved ways of delivering goods or services)’ (Statistics Canada 2005c). Respondents were then asked to evaluate a list of 17 potential sources of information (Table 31.1) on a five-point Likert scale with ‘5’ standing for the highest and ‘1’ for the lowest degree of importance. The underlying survey population consists of managers of those service firms that introduced product or process innovations between 2001 and 2003. The data allows us to understand how important trade fairs are compared to other sources of information in the firms’ innovation processes and how the significance of trade fairs differs between various service industries. Table 31.1 shows the average evaluations of the importance of different sources of information for innovation processes. In the survey, three groups of information sources were distinguished. The first group consisted of specialized staff from different business units that represented internal sources. The second group of information sources included customers, suppliers and other services and research facilities as external sources. The last group of generally available sources of information included trade fairs and exhibitions, professional conferences, trade associations and the internet. The results in Table 31.1 are listed according to different knowledge-intensive service industries. When looking at the first group of information sources, Table 31.1 clearly highlights the importance of internal sources as inputs into the innovation process. These are sources closely connected to a firm’s capabilities and competence base. Not surprisingly, it turned out that R&D and management staff were of crucial importance in enabling and shaping service innovation in Canada. These information sources were especially highly evaluated

515 4.37 2.77 3.87 2.46 1.53 1.53 1.53 3.82 3.92 3.11 3.89

1.23 3.49 3.35 2.75 3.84

3.77

3.26 4.53 2.72 3.30 2.11 1.22 1.26

3.88 4.40 3.12 4.43 3.14

4.46 3.77 3.28 3.88 3.05

TELE SELL

3.66 3.35 3.40

3.53

1.37

3.60 2.47 3.24 1.74 1.21 1.07

3.84

3.50 3.60 2.73 4.14 3.43

CABL

2.82 2.26 3.55

2.78

1.21

3.53 2.43 2.70 1.50 1.21 1.21

3.61

3.45 3.26 3.33 3.65 2.66

DATA

3.21 2.61 3.80

3.29

1.31

4.38 2.20 2.74 1.72 1.31 1.20

3.02

4.20 3.52 3.29 3.67 2.53

COMP DESG

2.83 2.57 3.32

3.43

1.31

3.88 2.24 3.00 2.19 1.80 1.65

3.19

3.03 2.83 3.14 3.79 2.43

3.16 3.34 3.38

3.42

1.16

4.15 2.58 2.81 2.10 1.16 1.57

3.83

3.19 3.62 3.40 3.84 3.35

GEO

3.39 2.86 3.88

3.51

1.45

4.20 2.89 3.73 2.43 1.82 1.39

3.83

4.31 3.64 3.54 4.36 3.13

IND DESG

3.19 2.85 3.83

3.77

1.94

3.95 2.31 3.17 3.12 2.42 2.14

3.04

4.69 3.48 2.76 3.55 2.92

R&D

3.33 3.13 3.11

2.78

1.83

3.89 3.13 3.00 2.25 2.00 1.71

3.44

3.00 2.56 3.11 4.00 2.17

DRILL

(ii) Professional, scientific and technical services ENG

Source: Own computations from Statistics Canada (2005a).

Notes: 1 Responses of firms were provided on a Likert scale ranging from 1 (low importance) to 5 (high importance). Non-responses were not considered. Average evaluations were computed from this metric. SOFT 5 software publishers; TELE SELL 5 telecommunications resellers; CABL 5 cable and other program distribution; DATA 5 data processing, hosting and related services; COMP DESG 5 computer systems design and related services; ENG 5 engineering services; GEO 5 geophysical surveying and mapping; IND DESG 5 industrial design services; R&D 5 research and development in physical, engineering and life sciences; DRILL 5 contract drilling (except oil and gas).

Internal sources − R&D staff − Marketing staff − Production staff − Management staff − Other business units External sources − Software, hardware or equipment suppliers − Clients or customers − Consultancy firms − Competitors or other firms − Universities/higher education − Federal government research labs − Provincial government research labs − Private non-profit research labs Generally available sources − Professional conferences, meetings and journals − Trade fairs and exhibitions − Trade associations − Internet

SOURCES OF INFORMATION

SOFT

(i) Information and communication technology services

Average evaluations of information sources for innovation by service industry1

Table 31.1 Average evaluations of the importance of different information sources for innovation in Canadian service firms, by service industry, 2001–2003

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in knowledge-intensive services that are closely linked with manufacturing activities. In industries such as software publishing, computer systems design, industrial design services and R&D services, the average ratings for such sources of information were consistently high, in the range of 4.2–4.7 (maximum value of 5) for R&D staff and 3.6–4.4 for management staff (Table 31.1). Of the external information sources, clients and customers were ranked as equally important for innovation, indicating that the respective industries were part of an extended social division of labor that required systematic producer–user interaction and interactive learning (von Hippel 2001; Lundvall, Chapter 29, this volume). In contrast, universities, government and other research laboratories and specialized consulting services were viewed as less important for innovation. Evaluations were low, except for one industry, being in the range of 1.1–2.5. Compared to these influences, trade fairs and exhibitions were viewed as more important information sources for innovation and received relatively high values. Although evaluations were on average a little lower than those for the most important internal and external sources, they were still in the range of 2.8–3.9 (maximum value of 5). When considering that conferences were evaluated as almost equally important as trade fairs (Table 31.1), it becomes clear that professional events are quite significant for knowledge generation processes related to innovation, and not just tools to support marketing and direct sales. Table 31.2 further supports these findings by showing in more detail the evaluations of Canadian service firms in terms of how important trade fairs (and exhibitions) are as sources of information for innovation processes. In the ten knowledge-intensive service industries selected, between 60 and 100 percent of the firms surveyed suggested that trade fairs and exhibitions were of medium or high importance for innovation. The share of firms was highest in software publishing, telecommunications reselling, cable distributing, computer systems design, industrial design, R&D and contract drilling services (Table 31.2). In particular, 70 and 80 percent of industrial designers and telecommunication resellers, respectively, suggested that trade fairs were highly important for their innovation processes. Overall, Statistics Canada’s (2005a) survey of innovation provides convincing empirical findings about the significance of trade fairs, exhibitions and professional conferences as information sources for innovation processes. The findings support the statement of Allen et al. (1982, p. 6) that ‘new ideas seldom appear full-blown from a single source of information’ and that it is unrealistic to expect that trade fairs would be the single most important influence on the innovation process overall. The data provides a clear indication that these events are integral parts of the knowledge generation processes of service industries. They seem to be particularly important in information and communication technology and professional, scientific and technical services and are thus fundamental elements that support the dynamics of today’s knowledge economy (Bathelt et al. 2014). Allen et al.’s (1982) early study of innovation processes in 200 manufacturing firms in Ireland, Spain and Mexico showed that the importance of trade fairs is not limited to service industries. Suppliers and firms in the same industry were by far the most important sources of information for innovation in this study, providing 29 and 23 percent of the foundational ideas for innovation, respectively. These sources of information were followed by parent firms, trade fairs and trade journals, which each accounted for 6–8 percent of the ideas that led to new product and process development. While not the most prominent influence on innovation, trade fairs were clearly a crucial component in

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Table 31.2 Share of Canadian service firms using trade fairs and exhibitions as information sources for innovation, by degree of importance and selected services industries, 2001–2003 Selected Canadian service industries

Importance of information for innovation1) Low Medium High Important2) Average (% firms) (% firms) (% firms) (% firms) evaluation3)

(i) Information and communication technology services − Software publishers 24.9 27.7 − Telecommunications resellers 0.0 19.6 − Cable and other program 6.8 38.5 distribution − Data processing, hosting and 37.5 41.7 related services − Computer systems design and 27.9 31.4 related services (ii) Professional, scientific and technical services − Engineering services 38.9 27.7 − Geophysical surveying and 33.0 28.0 mapping − Industrial design services 19.2 10.3 28.1 30.6 − Research and development in physical, engineering and life sciences − Contract drilling (except oil 11.1 44.4 and gas)

47.4 80.4 54.8

75.1 100.0 93.2

3.35 3.92 3.66

20.8

62.5

2.82

40.6

72.1

3.21

33.4 39.0

61.1 67.0

2.83 3.16

70.5 41.3

80.8 71.9

3.39 3.19

44.4

88.8

3.33

Notes: 1) Responses of firms provided on a Likert scale ranging from 1 (low importance) to 5 (high importance) were transformed into the categories ‘low’, ‘medium’ and ‘high’ importance, while non-responses were not considered. 2) The category ‘important’ is the sum of ‘medium’ and ‘high’ importance. 3) Average evaluations were computed from the Likert scale metric. Source: Own computations from Statistics Canada (2005a).

the knowledge generation mix. Allen et al. (1982) also found that the role of trade fairs in innovation differed by industry and by country. This is supported in Simmie’s (2003) analysis of external knowledge sources for innovation in the United Kingdom, which indicated that trade fairs were apparently less important for innovation, compared to the findings of the Canadian survey of innovation.

TYPES OF IMPACTS OF TRADE FAIRS ON INNOVATION Altogether, the evidence presented in this chapter suggests that trade fairs are a unique and crucial platform to generate knowledge regarding innovation, to present new products and processes to market actors, and to solve technological problems. While those managers and firms that do not attend trade fairs may not need this information or simply do not know better, many attendees are aware and utilize the potential of such events to

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continuously support innovation processes. The discussion below distinguishes six different types of how the impact of trade fairs materializes in and triggers innovation processes (for more detailed discussions, see Bathelt et al. 2014): 1.

2.

3.

Presentation of innovations: The leading flagship fairs in an industry are especially crucial for producers because they are expected to present the latest generation of products and technologies to customers and users at these events. This can be the result of decentralized, firm-specific innovation processes or it can be the outcome of so-called concertation processes where innovation trends are predefined by trade fair organizers, industry associations and other powerful industry actors (Rinallo and Golfetto 2006; Golfetto and Rinallo, Chapter 32, this volume). On the one hand, the presentation of the innovations satisfies customers who expect to see the most recent and up-to-date technologies as a basis for making buying decisions. When being confronted with a large selection of advanced technologies and designs, visitors can get an impression of the firm’s specific capabilities, which helps them to narrow down the number of potential trade partners from multilateral to one-on-one market situations (Callon, Chapter 36, this volume). The Chinese fashion clothing producers at the former famous Canton Fair, now called the China Import and Export Fair, in Guangzhou, where exhibitors present their latest fashion designs, are a good example to illustrate this (Bathelt and Zeng 2015). The innovations presented at the event help international buyers to select specific partners with which they engage in one-on-one negotiations – but, in the end, exhibitors are expected to produce completely different designs according to their customers’ blueprints (Bathelt et al. 2017). On the other hand, firms use important hub trade fairs as deadlines to finalize product and process innovations and introduce them to potential buyers and other market actors at the event. Providing specific information about these innovations and giving trade fair visitors the opportunity to inspect these products helps diffuse crucial knowledge about these new developments and generates early market legitimacy (Cohendet et al. 2013). Acquiring information about user needs: Trade fairs are temporary marketplaces during which the exhibitors aim to engage in interactions with existing or potential buyers (Rinallo and Golfetto 2011). They present all sorts of information in order to market and sell their products but also to receive feedback about these products, as well as about new customer needs and potential bottlenecks in customer relations (Godar and O’Connor 2001; Schuldt and Bathelt 2011). The multitude of systematic and intensive, albeit short-term, producer–user interactions at trade fairs contribute to interactive learning processes and help producers make decisions regarding product or process improvements in the aftermath. Overall, this supports ongoing incremental innovation processes through producer–user interaction (Lundvall 1988). Collecting information about the market, technology and policy environment: Trade fairs are often accompanied by conference presentations and specific exhibits that provide a broad overview of market trends, technological developments and new policies and government regulations. Key news that are presented in conference talks or brought to the events in the form of rumors and gossip are intensively discussed in the manifold face-to-face encounters between firms from different parts of the value

Trade fairs and innovation

4.

5.

6.

519

chain and from different institutional contexts. This creates a setting that enables collective sense-making and helps firms to identify new innovation opportunities. Compiling information about competitor innovations: Trade fairs, especially large international events, bring together a large number of competitors in partially overlapping but also partially different market segments. As such, these events generate important horizontal learning platforms (Li, Chapter 24, this volume). They provide unique occasions for firms to inspect the innovations of their rivals. International hub events present an almost complete overview of the diverse innovation processes in the industry in one place (Rosson and Seringhaus 1995; Bathelt and Schuldt 2008), and generate opportunities for firms to compare themselves effectively with their competition. Evidence suggests that exhibitors spend a substantial amount of their time in inspecting the exhibits of other firms (Bathelt and Gibson 2015). Through this, trade fairs have an important benchmark function that helps firms make decisions regarding new directions for innovation or changes in development paths. Searching for problem solutions: While the impact of trade fairs on innovation processes is often indirect, they can also be sites for immediate problem solving and active searches for technological solutions. When observing visitor behavior during such events, one comes across cases where technical specialists look for complementary firms that can help find a specific solution to a technical problem (e.g. Bathelt and Schuldt 2008), be it in reaction to new technological opportunities or modified government regulations. By talking to many other specialists in a short time period, a set of potential solutions can quickly be narrowed down. In a similar way, trade fairs are used to search for partners in current or future innovation projects. While this happens at trade fairs, there is no reliable data that allows us to evaluate how important such processes are. However, there is evidence that the function of trade fairs as places where deals are being made and contracts signed is decreasing and that non-selling functions and atypical visitors are becoming a common phenomenon (Sharland and Balogh 1996; Borghini et al. 2006). An important group of non-buyers are intermediaries that do not place orders but preselect product and process solutions that they suggest to their existing and potential customers, who do not attend these events. At lighting fairs, for instance, architects are an important visitor group. They pick out lighting solutions, which will later be acquired and implemented in new housing projects by developers (Schuldt and Bathelt 2011). Coming across problem solutions: Finally, there is another less planned and less structured way in which trade fairs impact incremental innovation and progressive specialization in production. Similar to the garbage-can model of organizational choice (Cohen et al. 1972), trade fairs can be characterized as ‘organized anarchies’ with a constant coming and going of participants that neither have clearly specified goals nor a precise idea of how to select a technology; yet, they attend trade fairs and acquire certain technologies and products (Bathelt and Gibson 2015). Rather than a goal-driven search process for technical solutions, these searches are often opportunistic in the sense that the participants scan the trade fair exhibits for potential solutions and make choices when a solution fits or can solve an actually existing problem. In a study of four manufacturing technology trade fairs in North America (Bathelt and Gibson 2015), stunning evidence was found that few firms were aware what technologies they were looking for when attending a trade fair, or even which

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The Elgar companion to innovation and knowledge creation specific problem solutions they needed. Most interviewees insisted, however, that they knew what technologies or solutions to acquire once they came across them during the event. These search processes benefit from the fact that trade fairs are miniature representations of the market (Rosson and Seringhaus 1995) and that firms can be confident that they capture the diverse multiplicity of potential solutions when looking through the exhibits. Such search behavior encourages innovation along existing technological trajectories, as firms tend to select those solutions that fit problems in their trajectories.

CONCLUSION While the empirical evidence discussed in this chapter is not fully conclusive, strong support is presented in favor of the argument that trade fairs play an important role in supporting and driving industrial innovation. Former studies on the information function of trade fairs are often skeptical about the role of these events compared to other means of collecting information. Observers emphasize that trade fairs are marketplaces focused on advertising and selling products, rather than hubs for innovation. In contrast to these views, this chapter indicates that trade fairs have a significant impact on innovation and knowledge creation. This is shown in two stages. First, the results of a qualitative study of North American lighting fairs shows that the vast majority of trade fair exhibitors systematically use these events to present innovations to visitors and acquire information about new products and technologies showcased by other firms. This is especially noteworthy as the respective events are not specifically known as innovation hubs. Second, findings from a national survey of service industries in Canada suggest that trade fairs are consistently, and across many knowledge services, ranked among the important sources of information for product and process innovation, directly following the impacts of internal information sources and customers. Of course, we need to take into consideration that the role of trade fairs with respect to product and process innovation may differ between industries, since innovation processes and practices can vary substantially according to technology. We also have to take into account that trade fairs have different functions and do not contribute equally to innovation processes. We may further find that different types of firms in different countries are characterized by varying information search patterns (Borghini et al. 2006; Schuldt and Bathelt 2011). While trade fairs are certainly not the only or the most decisive influence on the development of new products and technologies, they generate unique opportunities for acquiring and generating knowledge and stimulating innovation. This is related to the specific knowledge ecology or ‘global buzz’ that develops at these events (Maskell et al. 2006; Bathelt et al. 2014), associated with fast sequences of high-intensity faceto-face interactions and dense observations in a highly condensed spatial and temporal form. Other types of events or occasions can hardly offer a similarly broad choice of options that directly impact innovation and knowledge generation processes – and all of this occurring simultaneously in planned and unplanned, deliberate and accidental, and expected and unexpected ways.

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Acknowledgements I would like to thank Alexandra Eremia, Rachael Gibson and Sufyan Katariwala for excellent research support and manifold comments and suggestions. Further, I also wish to thank my co-editors for providing encouraging comments which helped to clarify the arguments presented in this chapter.

REFERENCES Allen, T. J., Hyman, D. B. and Pinckney, D. L. (1982) Transferring Technology to the Small Manufacturing Firm: A Study of Technology Transfer in Three Countries, Working Paper WP# 1328-82. Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology. AUMA – Ausstellungs- und Messe-Ausschuss der Deutschen Wirtschaft e.V. (2008) Einstellung von Entscheidern zum Messebesuch (Attitudes of Decision Makers toward Trade Fair Participation), Schriftenreihe der Deutschen Messewirtschaft, Edition 28, Berlin: AUMA. AUMA (2010) AUMA_MesseTrend 2010 (AUMA_Trade Fair Trend 2010), Schriftenreihe der Deutschen Messewirtschaft, Edition 31, Berlin: AUMA. Bathelt, H. and Gibson, R. (2015) ‘Learning in “organized anarchies”: Technological search processes and knowledge flows at international trade fairs’, Regional Studies, 49: 985–1002. Bathelt, H., Golfetto, F. and Rinallo, D. (2014) Trade Shows in the Globalizing Knowledge Economy, Oxford: Oxford University Press. Bathelt, H., Li, P. and Zhu, Y.-W. (2017) ‘Geographies of temporary markets and the mythical “Canton Fair”’, European Planning Studies, 25: 1497–1515. Bathelt, H. and Schuldt, N. (2008) ‘Between luminaires and meat grinders: International trade fairs as temporary clusters’, Regional Studies, 42: 853–868. Bathelt, H. and Zeng, G. (2015) ‘Trade, knowledge circulation and diverse trade fair ecologies in China’, in H. Bathelt and G. Zeng (eds) Temporary Knowledge Ecologies: The Rise of Trade Fairs in the Asia-Pacific Region, Cheltenham and Northampton, MA: Edward Elgar Publishing, 154–174. Bello, D. C. (1992) ‘Industrial buyer behavior at trade shows: Implications for selling effectiveness’, Journal of Business Research, 25: 59–80. Blythe, J. (2002) ‘Using trade fairs in key account management’, Industrial Marketing Management, 31: 627–635. Bonama, T. V. (1983) ‘Get more out of your trade shows’, Harvard Business Review, 61: 75–83. Borghini, S., Golfetto, F. and Rinallo, D. (2004) Using Anthropological Methods to Study Industrial Marketing and Purchasing: An Exploration of Professional Trade Shows, presented at the Industrial Marketing Purchasing Conference, Copenhagen. Online. Available HTTP: (2 December 2013). Borghini, S., Golfetto, F. and Rinallo, D. (2006) ‘Ongoing search among industrial buyers’, Journal of Business Research, 59: 1151–1159. Callon, M. (2017) ‘Markets, marketization and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 589–609. Cohen, M. D., March, J. G. and Olsen, J. P. (1972) ‘A garbage can model of organizational choice’, Administrative Science Quarterly, 17: 1–25. Cohendet, P., Héraud, J.-A. and Llerena, P. (2013) ‘A microeconomic approach to the dynamics of knowledge creation’, in P. Meusburger, J. Glückler and M. el Meskioui (eds) Knowledge and Economy, Klaus Tschira Symposia: Knowledge and Space, 5, Berlin and Heidelberg: Springer, 43–59. Gertler, M. S. (1993) ‘Implementing advanced manufacturing technologies in mature industrial regions: Towards a social model of technology production’, Regional Studies, 27: 665–680. Gibson, R. and Bathelt, H. (2014) ‘Field configuration or field reproduction? The dynamics of global trade fair cycles’, Zeitschrift für Wirtschaftsgeographie (The German Journal of Economic Geography), 58: 216–231. Godar, S. H. and O’Connor, P. J. (2001) ‘Same time next year – Buyer trade show motives’, Industrial Marketing Management, 30: 77–86. Golfetto, F. and Rinallo, D. (2017) ‘Innovation through trade show concertation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 523–536.

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Hansen, K. (2000) ‘From selling to relationship marketing at international trade fairs’, Journal of Convention and Exhibition Management, 2: 37–53. IIDEX/NeoCon Canada (2008) IIDEX Mediaflash, press release, Toronto. Online. Available HTTP: (2 September 2008). Jackson, D. W. Jr, Keith, J. E. and Burdick, R. K. (1987) ‘The relative importance of various promotional elements in different industrial purchase decisions’, Journal of Advertising, 16(4): 25–33. Kerin, R. A. and Cron, W. L. (1987) ‘Assessing trade show functions and performance: An exploratory study’, Journal of Marketing, 51(3): 87–94. Li, P. F. (2014) ‘Horizontal vs. vertical learning: Divergence and diversification of leading firms in Hangji toothbrush cluster, China’, Regional Studies, 48: 1227–1241. Li, P. (2017) ‘Horizontal learning’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 392–404. LightFair International (2009) General Exhibit Information, New York. Online. Available HTTP: (21 April 2009). Lilien, G. L. (1983) ‘A descriptive model of the trade-show budgeting decision process’, Industrial Marketing Management, 12: 25–29. Lundvall, B.-Å. (1988) ‘Innovation as an interactive process: From producer–user interaction to the national system of innovation’, in G. Dosi, C. Freeman, R. R. Nelson, G. Silverberg and L. L. G. Soete (eds) Technical Change and Economic Theory, London and New York: Pinter, 349–369. Lundvall, B.-Å. (2017) ‘National innovation systems and globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 472–489. Maskell, P., Bathelt, H. and Malmberg, A. (2006) ‘Building global knowledge pipelines: The role of temporary clusters’, European Planning Studies, 14: 997–1013. Meffert, H. (1993) ‘Messen und Ausstellungen als Marketinginstrument’ (Trade fairs and exhibitions as marketing tools), in K. E. Goehrmann (ed.) Polit-Marketing auf Messen (Marketing Policy on Trade Fairs), Düsseldorf: Wirtschaft und Finanzen, 74–96. Messe Frankfurt GmbH (2015) About Light + Building, Frankfurt/Main. Online. Available HTTP: (27 September 2015). Moriarty, R. T. Jr and Spekman, R. E. (1984) ‘An empirical investigation of the information sources used during the industrial buying process’, Journal of Marketing Research, 21: 137–147. Nisen, W., Schmidt, A. and Alterman, I. (1984) Marketing Your Software – 26 Steps to Success, Amsterdam: Addison-Wesley. Rinallo, D. and Golfetto, F. (2006) ‘Representing markets: The shaping of fashion trends by French and Italian fabric companies’, Industrial Marketing Management, 35: 856–869. Rinallo, D. and Golfetto, F. (2011) ‘Exploring the knowledge strategies of temporary cluster organizers: A longitudinal study of the EU fabric industry trade shows (1986–2006)’, Economic Geography, 87: 453–476. Rosson, P. J. and Seringhaus, F. H. R. (1995) ‘Visitor and exhibitor interaction at industrial trade fairs’, Journal of Business Research, 32: 81–90. Schuldt, N. and Bathelt, H. (2011) ‘International trade fairs and global buzz, Part II: Practices of global buzz’, European Planning Studies, 19: 1–22. Sharland, A. and Balogh, P. (1996) ‘The value of nonselling activities at international trade shows’, Industrial Marketing Management, 25: 59–66. Simmie, J. (2003) ‘Innovation and urban regions as national and international nodes for the transfer and sharing of knowledge’, Regional Studies, 37: 607–620. Statistics Canada (2005a) Survey of Innovation 2003, Table 22A.2 – Percentage of Business Units Using Sources of Information Needed for Suggesting or Contributing to the Development of Innovation during the Period 2001 to 2003, Catalogue number 88-524-XCB2005001, Ottawa: Science, Innovation and Electronic Information Division, Statistics Canada. Statistics Canada (2005b) Survey of Innovation 2003, Methodology Note, Catalogue number 88-524-XCB2005001, Ottawa: Science, Innovation and Electronic Information Division, Statistics Canada. Statistics Canada (2005c) Survey of Innovation 2003, Questionnaire, Catalogue number 88-524-XCB2005001, Ottawa: Science, Innovation and Electronic Information Division, Statistics Canada. von Hippel, E. (1977) ‘Has a customer already developed your next product?’, MIT Sloan Management Review, 18(2): 73–74. von Hippel, E. (2001) ‘Innovation by user communities: Learning from open-source software’, MIT Sloan Management Review, 42(2): 82–86.

32. Innovation through trade show concertation Francesca Golfetto and Diego Rinallo

INTRODUCTION Innovation often requires collective action. As no firm is an island, the support of relevant partners is of utmost importance to firms engaged in new product development – particularly in the case of radical innovations. Studies of technological change have shown that competing firms or business networks often try to shape the market concurrently by proposing alternative innovation trajectories (Abernathy and Utterback 1978; Anderson and Tushman 1990; Tushman and Rosenkopf 1992; Gomes-Casseres 1994). In these cases, the dominant solution is not necessarily the best alternative from a purely technical point of view. Instead, the solution that successfully gains the support of key players in the industry (e.g., users, suppliers, competitors, producers of collateral products) through the early involvement of some actors and effective communication strategies in respect of all the others may assert itself over technically superior alternatives (Arthur 1994; Lee et al. 1995; Rosenkopt and Tushmann 1998; Dokko et al. 2012). In this chapter, based on our previous research in the context of the clothing fabric industry (Golfetto 2000, 2004; Rinallo et al. 2006; Rinallo and Golfetto 2006, 2011; Golfetto and Rinallo 2008, 2012; Bathelt et al. 2014), we report on a sophisticated process, which industry members call trend concertation. It is this concertation process that allows the participating actors (mostly small and medium-sized enterprises) to effectively cooperate and develop, season after season, dominant product designs in ways similar to those that the standard affirmation literature describes (Abernathy and Utterback 1978; Anderson and Tushman 1990; see Cappetta et al. 2006 for an application to style innovation). In this context, the literature shows that rival firms often cooperate by participating in formal standard committees that reduce uncertainty and avoid costly standard wars (Dokko et al. 2012; Farrell and Simcoe 2012; Simcoe 2012). We contribute to this literature by describing the little-understood standard-setting role of trade shows. These collective marketing events may be understood as commercialization networks that, particularly in the context of fragmented industries, mobilize the individual innovative efforts of a great number of competing firms and coordinate them towards common trajectories. When trade associations support trade shows, these events may act as platforms that provide the participating actors with promotional and commercial benefits. Consequently, such trade shows may obtain the key actors’ early support and thereafter that of a critical mass of participating producers. Similarly to other collective events (e.g., professional conferences, public business ceremonies), trade shows play an important – but still understudied – role in the social construction of markets and industries, and in the diffusion of new technologies and innovations (see also Callon, Chapter 36, this volume; Bathelt, Chapter 31, this volume). These events bring together all the leading actors in a field in the same location for a limited period. Through the participants’ interaction and collective sense-making, trade 523

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shows have major impacts on the fields in which they are embedded (Borghini et al. 2006; Rinallo et al. 2010). Traditionally conceived as marketing events through which firms operating in business markets can increase their sales and improve their brand image (e.g., Gopalakrishna and Lilien 1995; Smith et al. 2004), trade shows have only recently been studied in ways that do justice to their important market-configuration role. In this chapter, we concur with these research streams by suggesting that trade shows – and the industry associations supporting them – can shape the innovation trajectories in their underlying industries by coordinating the new product development efforts of participating actors. We do not, however, claim that all trade shows can play a similar role. As we discuss in the conclusion, orchestration mechanisms are more likely to occur in fragmented industries and intermediate products. Moreover, only organizers that leading trade associations control and which can exert influence on exhibitors can succeed in the concertation of innovation (Golfetto 1988, 2004; Golfetto and Mazursky 2004; Golfetto and Rinallo 2008, 2012, 2014; Bathelt et al. 2014). This chapter reports on Première Vision, Paris, the leading clothing fabric trade show in Europe. The data used here have been mainly derived from our extensive field investigations into Première Vision and the European textile industry during 2002–2004 and 2004–2007 (Golfetto 2000, 2004; Rinallo et al. 2006; Rinallo and Golfetto 2006, 2011; Golfetto and Rinallo 2008, 2012). They also benefit from subsequent updating and studies on similar industries (Bathelt et al. 2014). To capture the complexities of the concertation process, we employed an ethnographic approach (Arnould and Wallendorf 1994; Borghini et al. 2014) mainly based on participant observation, as well as on directive and non-directive interviews. The data-gathering activities included interviews with the trade associations involved in the concertation mechanism, together with participant observation of backstage events, such as meetings and workshops to explain the new trends to the exhibitors. Directive and non-directive interviews were carried out with the informants during Première Vision. Additional interviews were realized at other fabric industry events to include the perceptions of those companies not involved in the concertation process or that do not attend Première Vision. This chapter is structured as follows: In the next section, we provide some background by describing the main characteristics of innovation processes in the textile and clothing sectors. This is followed by a brief description of Première Vision and its history. Subsequently we provide an analysis of the concertation process and the key actors involved in each of its phases. We conclude with a discussion of the implication of this chapter for research on innovation.

INNOVATION IN FABRIC FOR FINE FASHION APPAREL In fashion and related industries, innovation processes are mainly about style (Cappetta et al. 2006). Style refers to creativity in cut, color, patterns, fabrics, processing, and finishing. These elements of style satisfy symbolic functions that allow consumers to express their individual identity, and may also signal social status. As in high technology (Abernathy and Utterback 1978; Anderson and Tushman 1990), fashion cycles include a ferment phase (during which many new, radical style innovations are proposed) followed by the selection of a dominant design and a stability phase (when innovations are incremental).

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However, due to the symbolic properties of fashion goods based on consumers’ needs for distinction, the dominant design co-exists with market niches for products that are radically different, but which some consumers nevertheless appreciate highly. Contrary to the widespread belief that a limited set of celebrity designers (e.g., Giorgio Armani or Miuccia Prada) mainly creates fashion style, the textile industry, which supplies materials and fabrics, is also a central hub for fashion innovation. In other words, upstream creativity, which is aligned with what consumers value and want their clothes to express, inspires designers. This means that fabric producers must anticipate consumer needs in order to develop solutions that complement the core competence of designers and apparel producers when creating fashionable end products (Golfetto and Mazursky 2004; Zerbini et al. 2007). As suppliers of the apparel industry, fabric producers provide both technical and creative content. Upstream actors (e.g., textile machinery or chemical component producers) mostly develop technical content, which is equally available to most competitors. Consequently, it cannot be a source of competitive advantage. To differentiate their goods, fabric producers must vary their bi-annual collections in terms of color (e.g., ruby, or clay furrow), structure (e.g., jacquard, satin, chiffon, or stretch), aspect (e.g., structured, light, washed-out, or opaque), touch (e.g., soft, warm, fluid, or compact), decoration (e.g., arabesque, cashmere, or irregular stripes), and treatment (e.g., burnt-out, washed, or gummy coating). These highly differentiated elements are deliberately designed to provide finished goods with symbolic and expressive properties that consumers will value (Rinallo et al. 2006; Entwistle and Rocamora 2006). Moreover, in the development of their new collections, fabric producers face serious modularity problems linked to the fragmented, non-integrated nature of the fashion industry supply chain, as well as to consumer behavior (consumers need to combine their clothes and accessories in aesthetically pleasant ways). To avoid the risk of innovations that do not fit the prevailing trends, firms must consider consumers’ future tastes, along with the innovation efforts of upstream and downstream partners, as well as those of complementary products producers, when preparing their collections. For example, a fabric firm must ensure that its range of colors will correspond with that produced by knitwear, velvet, and other semi-finished textile manufacturers, as well as by its competitors. Furthermore, all semi-finished textile products must fit the dominant apparel styles and cuts. Finally, from the purchaser’s point of view, it is important to consider the issue of compatibility, as consumers will combine their apparel with accessories (e.g., clothes with shoes or bags) to express their identity and to simultaneously signal their membership of specific reference groups (Cappetta et al. 2006). Standards are therefore needed to reduce uncertainty in innovation. Although issues of modularity and compatibility are not unique to this setting, fabric producers face additional threats to their survival. First, the fabric industry (especially in Europe) is highly fragmented in that it includes many firms within each specialized stage of the value chain. Most of these firms are small and medium-sized enterprises, and even the market leaders lack the resources and skills to establish their own innovative solutions as the dominant design (Cappetta et al. 2006). Second, because it is linked to the fashion system, the innovation cycle for fabrics is extremely short, with seasonal collections launched at least once every six months. Third, the fragmented nature of the manufacturing and retail system in the industry means that new fabric products must be presented

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far in advance of consumer purchases. In September 2012, for instance, fabric producers needed to present their proposals for the spring/summer 2014 apparel collections to their customers (i.e., the apparel producers); that is, 18 months before the end products were available in retail stores. Since consumer tastes are ever changing, new product development is inherently risky and requires some form of fashion forecast to reduce uncertainty.

PREMIÈRE VISION, PARIS Première Vision is currently the leading European clothing fabric trade show, and one of the most important worldwide (Bathelt et al. 2014). Its history and international success are intertwined with that of the concertation process, and the ability to provide the attendees with a guide to the main trends in the industry and future fashion. The event’s origins date back to 1973 when 15 Lyon weavers decided to create a new trade show to present their fabric collections. At the time, the leading event in the industry was Interstoff. Founded in 1959 by Messe Frankfurt GmbH (the organization managing the exhibition complex in Frankfurt), the German event was the largest and most internationalized trade show in the clothing fabric industry, attracting exhibitors and visitors from Europe and the rest of the world, and showcasing a broad range of fabrics for different uses and with varying quality and price levels. The Lyonnais weavers behind Première Vision were producers of high-quality fabric and felt that their products would go unnoticed in such a large and mixed exhibition. They therefore decided to present their collections to fabric buyers in a less distracting environment, first in Lyon and then, from 1979 onwards, in Paris. Other French textile companies had already been invited to join the initiative in 1977 and, in 1980, the organizers extended their invitation to a group of carefully selected exhibitors from Western Europe, particularly from Italy. Nevertheless, the organizing activity basically remained under the control of the French trade associations of textile manufacturers. As buyers favored the initiative, the trade show took off quickly. Despite continued pressure to admit exhibitors from all over the world, Première Vision maintained a relatively strict exhibitor screening policy that privileged highquality European producers. It was not until 2002 that the trade show accepted a small number of selected non-European producers, all of which were industry leaders in their respective countries. These exhibitors were admitted to Première Vision on the basis of their products’ quality and creativity and their firms’ financial stability (Golfetto and Rinallo 2012). Fabric distributors were not, however, allowed to exhibit, as the organizers believed that only producers could commit to presenting innovations at the fair. The fair visitors (mostly fashion designers and other clothing producer professionals) appreciate this gesture particularly, maintaining that the textile designers and developers at the fair facilitate the acquisition of useful knowledge and the exchange of views about the future of their activity (Golfetto 2000; Rinallo and Golfetto 2011). With respect to the trade show layout, the exhibitors were originally grouped by country of origin. After a visitor survey revealed a preference for a layout based on material types, the exhibition format was changed. Another change was introducing categories for styles and consumer markets, while other reorganizing was done to facilitate the visitors’ understanding of changes in supply and market trends. The trade show was strongly

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visitor-oriented to help buyers find what they were looking for as quickly as possible. In 2005, the managing director Daniel Fauré said: “The market has become more difficult, with buyers having to work faster . . . Buyers need to find specific products that fit into their business. This new segmentation will help buyers find what they need more quickly” (Drapers Record 2005). With respect to timing, Première Vision put a strategy in place that was consistent with its desire to position itself as the presenter of innovation in the fabric industry. The dates for the bi-annual event were set three to four weeks before the traditional Interstoff dates, offering designers and buyers a first look of the new collections. As a result of their respective strategies, Première Vision and Interstoff began to satisfy different needs. At that time, the French exhibition was viewed as the key event, presenting international innovation that was critical for learning about new trends in the sector, while its German rival was considered a commercial event useful for finding suppliers, often from developing countries, that offer good value for money (Golfetto 2004). Première Vision’s enormous success was mainly linked to the introduction of an important innovation in organization called trend concertation, which refers to a process of understanding consumer trends, and then orienting the producers’ innovations through the involvement of the key industry players (Rinallo and Golfetto 2006; see the next paragraph). This process led to the exhibition starting to simultaneously design and present fashion trends for the following years. This innovation made the trade show remarkably influential, not only in terms of the fabric buyers’ choices, but also in terms of the choices made regarding the downstream markets and collateral industries. Première Vision enjoyed continuing success during the 1980s and 1990s. Visitors were attracted in great numbers, because, beyond its commercial raison d’être, the show was perceived as a key source of fashion knowledge due to the information it provided on upcoming trends. European exhibitors, particular those high-quality fabric producers that passed the show’s strict selection processes, were increasingly attracted by the possibility of meeting relevant customers and obtaining timely information on fashion trends to guide their innovative efforts. Producers focusing on high-end products found that Première Vision was the place to be. At Interstoff, they increasingly met buyers only interested in lower-quality fabrics at more affordable prices. By exhibiting at Première Vision, fabric firms could also position themselves as innovators in the industry. For these reasons, some industry leaders stopped exhibiting at Interstoff and employed the resulting cost savings to present themselves better at Première Vision. This caused a chain reaction, with smaller firms following the leaders because their presence would attract key buyers. As a consequence, many exhibitors migrated from Interstoff to Première Vision during the 1990s, leaving the German exhibition crowded with non-European producers with a more commercial orientation (CERMES 1994). However, a new trade show also targeting non-European fabric producers, Texworld, opened in Paris in 1999. Interstoff lost its market space and was closed down by Frankfurt Messe at the end of that same year. In 2004, the organization managing Première Vision (Première Vision le Salon S.A.), which was owned by French textile associations, merged with the organizers of another exhibition in Paris, Expofil, dedicated to yarns, and similarly controlled by manufacturer associations and committed to forecasting fashion trends. Since 2005, the newly merged company – PVE S.A. – has been involved in promoting various clothing exhibitions in Paris under the collective name of Première Vision Pluriel. Such exhibitions include

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Le Cuir à Paris (i.e., semi-processed leather goods), Indigo (i.e., textile design), and Mod’Amont (i.e., buttons, embroidery, and accessories). Trend forecasting for the various trade shows is currently jointly coordinated and carried out. In 2007, two international event organizers – Eurovet and GL Event – became part of the PVE S.A. corporate structure. PVE also began staging a number of smaller trade shows outside Europe (in New York, São Paulo, Istanbul, and Shanghai). These events are also used to diffuse concerted trends, which are, however, officially launched during the main show in Paris.

THE CONCERTATION PROCESS Clothing fabric industry insiders use the term concertation to define their collective action aimed at forecasting and diffusing fashion trends. In political science, the concept identifies consensus-building processes during which the major interest groups are brought together and encouraged to close a series of deals about their future behavior (Shonfield 1965). While the term is usually employed in the context of industrial relations to refer to governments, employers, and trade unions’ bargaining activities regarding wages and labor regulation, it may also apply to the collective action of organizations that collaborate to defend the interests of a specific industry, although state agencies do not intervene (Molina and Rhodes 2002). Première Vision introduced the concertation of fashion trends to address two distinct sets of needs, which involved both exhibitor-manufacturers and visitor-buyers. As noted above, the main challenge for fabric manufacturers is to predict shifts in demand and fashion trends. Customers have generally become more demanding and expect a significant amount of creativity from suppliers. Yet, due to the highly fragmented nature of the textile supply chains, fabric manufacturers are required to develop collections almost two years before retailers present the clothes incorporating those same fabrics and make them available to the end consumers. Consequently, before the development of the concertation mechanism, the innovations that fabric manufacturers presented in the past rarely followed specific fashion trends (which could have emerged after the product presentations). Many of these innovative efforts were therefore rejected or poorly received. Not only did manufacturers have needs that had to be met, but buyers too had needs. More specifically, during the 1970s, large shows such as Interstoff left fabric buyers increasingly dissatisfied, as the vast array of offerings in different styles and qualities often meant that they returned confused and bewildered. The organizer of Première Vision initially dealt with their complaints by convening a number of meetings of manufacturers aimed at encouraging them to agree on the future innovation trajectories. This process became increasingly sophisticated over time, with special investments in research into consumption, as well as clothing manufacturers and retailers’ active participation, supporting it. Within a few years, this process succeeded in defining more consistent future product ranges that could be easily communicated to buyers. Over time, the concertation process became a fundamental driver of fashion trends, taking root throughout the textile-clothing chain, and even beyond, for instance, in the automotive and furniture industries (Rinallo and Golfetto 2006). In the fabric industry, concertation occurs twice per year and begins at least five to six

Innovation through trade show concertation Step 1 - Identifying trends - Consensus on future trends

Step 2 - Involving exhibitors in new trends

Step 3 - Exhibitors adapting and manufacturing collections

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Step 4 - Highlighting selected trends at the events and outside

6 months

- 25–30 key players in textile-clothing manufacturing/ distribution chain

- 700–800 fabric manufacturers

- 700–800 fabric manufacturers - 2,000 subsuppliers

- 30–40,000 buyervisitors (clothing manufacturers/ related sectors) - 2–3 million trade people (indirect audience through press)

Source: Adapted from Bathelt et al. (2014).

Figure 32.1 The trend concertation mechanism at Première Vision and the players involved months before the opening of Première Vision. It comprises four key stages involving a growing number of players (Figure 32.1). Forecasting and Selecting Consumption Trends The first stage in the concertation process is the selection of the fashion trends that the event will support. At this stage, the agents involved initially identify a broad number of scenarios and emerging trends in consumption. These trends are the result of ad hoc research and meetings of experts, including panels of sociologists, cool hunters, bureaux de style (Guercini and Ranfagni 2012), designers, associations in the textile chain, and representatives from the most innovative fabric producers. Over the years, Première Vision also set up an international observatory of trends, which acts as a global socio-cultural surveillance unit that aims to identify the trends in consumption that are most likely to influence the textile and fashion industries (Rinallo and Golfetto 2006). Increasing environmental awareness has, for example, encouraged product designs that respect the principles of sustainability, resulting in a choice of organic raw materials, natural colors, simpler clothing styles, and so on. Similarly, in the aftermath of 9/11, the world became a less secure place for many consumers and conspicuous consumption was no longer considered appropriate. Apparel and fabrics were, thus, required to suit the consumer’s desire for safety, which led to less blatantly luxurious designs. True concertation occurs in the next step, when a relatively small group of fashion trends is selected (Rinallo et al. 2006). These trends are subsequently translated into

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instructions for the development of new textiles, which are then passed on to the fabric exhibitors. This critical step occurs during concertation meetings, which bring representatives from all stages of the textile-clothing value chain together. At these concertation group meetings, the representatives and members of the fabric industry’s trade associations, in upstream and downstream markets, as well as in complementary products, have an opportunity to express their opinions on the future trends. Leading producers (those that launch their own trends), distributors, and bureaux de style may also be involved and make a specific contribution to the concertation process. During the concertation meetings, decisions tend to be more politically than technically motivated and generally involve no more than 25 to 30 leading firms. The relevant actors thereafter reach consensus on the future common trends (Golfetto and Rinallo 2008), often after extensive negotiations between the representatives from the main exhibitor countries, who often have quite different requests. In 1992, for example, “Germany insisted on nature-inspired colours, whereas Austria suggested rediscovering the five continents and their colours. France firmly backed melancholy to evoke energy, harmony, and light in a simple, well-motivated message about yellow” (Mazzaraco 1993, p. 23). Extreme ideas with little support are thus abandoned in favor of those with widespread appeal. Concertation meetings thus reduce the variety in new collections and increase compatibility on the purchasing side. Moreover, although there are important differences between the trends identified at Première Vision and those identified at other trade shows, they are similar in many ways and are largely compatible, as most of the interested parties are involved in different concertation groups and provide similar input. Involving Exhibitors in Concerted Trends Once consensus has been reached, special workshops inform the Première Vision exhibitors about the selected trends. The trends are given evocative metaphors and labels (e.g., “search for equilibrium,” “urban soul,” or “cyber culture”); color tones are also precisely specified according to the Pantone color system, which is one of the standards for the selection and the accurate communication of color across a variety of industries. During the workshop, special materials (such as color cards, trend notebooks, CD ROMs, etc.) are distributed, which become a work tool for the firms operating downstream in the fashion industry. One interviewed concertation expert referred to the presentation of trends by emphasizing that “we create a lot of poetry, here.” This hints at the inspirational nature and aesthetic quality of the materials that serve as an aesthetic trigger for the development of new fabric collections. For example, the directions for the future fashion innovation for the autumn/winter 2009/2010 collection presented at Première Vision included the following: “Electric shocks,” involving “deeply coloured darks contrast in a binary and high-voltage cadence with subversive and grating brights”; and “flamboyant resonance” described as “volcanic condensations intersect[ing], in an undulating and explosive harmony, with voluptuous incandescence.” The visual descriptions of these fashion innovations are similarly evocative (Première Vision 2008). The process of affirming the selected trends begins at this stage and involves all the exhibitors allowed to display their wares at the trade show. A total of 700 to 800 exhibitors are involved in each exhibition. Although these exhibitors are not required to adopt

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the proposed trends, the forecasts made and the trends that the organizers select certainly influence them (Rinallo and Golfetto 2006). Developing Collections on the Basis of Concerted Trends In the months that follow, exhibitors develop new fabric collections incorporating – albeit with individual adaptations and variations – the fashion trend guidelines they have received from the trade show organizers. At this stage, differences can be detected in the way the exhibitors, depending on their size and innovation competences, engage with the trends. A large percentage of Première Vision’s exhibitors are small and medium-sized enterprises with limited consumer-oriented design and research capabilities. The information materials on trends distributed at Première Vision workshops are especially valuable for such firms, because these make the need to carry out their own market research redundant and, at the same time, offer the opportunity to decide whether, and to what extent, to deviate from the main trends. Large firms, which often have their own style ranges, are another matter, because they have large financial resources and new product development capabilities; nevertheless, they often consider the guidelines they receive from the organizers’ concertation workshops. The exhibitors’ comments are interesting in this respect. As one interviewed Italian firm explained: “Our style director goes to the workshop [where trends are presented], takes notes, and when she returns to the factory, she discusses these with the production and marketing guys. It’s very useful for us to have the trend books and the color cards; it would be tough otherwise . . .” (Italian Exhibitor, 16 employees). Another Italian exhibitor said: “We follow the trends, but not always. We have our own brand image, and sometimes we propose new fabrics that aren’t entirely compatible with the trends. But customers won’t care, they’ll follow us” (Italian Exhibitor, 120 employees) (Rinallo et al. 2006). It is at this stage that the process of adopting the selected trends is extended from the exhibitors at the trade show to their suppliers, sub-suppliers, and collateral manufacturers, which are involved in the production in various ways. It has been estimated that at least another 1,500 to 2,000 firms become involved in the process at this stage (Rinallo et al. 2006). Highlighting Selected Trends at Première Vision The last stage of the concertation process occurs when the collections that the exhibitors have developed are presented at Première Vision. This is when forecasts are transformed into realities. The trends are explicitly visible in the trends areas, which display the most representative swatches of each trend artistically. These areas, with their strong visual impact, make it possible to grasp what is new at a glance, while simultaneously allowing the manufacturers, whose collections incorporate specific trends, to be identified. Further, the trend documents, which are distributed to the exhibitors beforehand, are also made available to the visitors during the trade show in order to make the trends easier to understand. Finally, the exhibition layout also highlights the fashion trends in the various product categories and for the various markets. At this last stage, the concertation process becomes a flow of communication that

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involves all visitors at Première Vision, representing some 30,000 to 40,000 firms. Furthermore, since media reports convey the fashion trend information to millions of readers, the communication process is expanded to a huge audience of potential buyers, which helps affirm the selected trends. Manufacturers from industry groups other than clothing also visit the trade show, usually those seeking to source information that will help them better align their production with fashion trends. This applies both to related products, such as knitwear, bags, shoes, and accessories, and to other products, such as furniture and cars. Even manufacturers of clothing fabrics that are not among the exhibitors at Première Vision tend to employ, or even imitate, the selected trends, thus contributing to their dissemination. The final result of the concertation process is that the forecasts made at the beginning of the process are put into practice and, thus, confirmed. This benefits all the trade show exhibitors, which, in addition to getting their collections right, also reap obvious rewards in terms of their image as innovators. In other words, manufacturers outside the trade show tend to be seen as imitators, not innovators (Golfetto and Rinallo 2008).

CONCLUSIONS This chapter examines the trend concertation process in the clothing fabric industry. European producer associations introduced this process at the trade shows they controlled to support their position in the industry against strong competition from emerging countries’ manufacturers. The process involves forecasting future social and consumer value developments with the goal of guiding style innovation in a way that ensures individual efforts are not dispersed. By developing knowledge about downstream markets and guiding its exhibitors in the development of market-oriented innovations, Première Vision – the first trade show to employ concertation processes – soon became a key site for learning and knowledge exchange in the global fashion business. As a consequence, the network of producers that gather at Première Vision support the product designs and the style standards in ways similar to those found in high-technology industries (i.e., computer, videogames, software; see Rosenkopf and Tushmann 1998; Schilling 2002), in which rival firms often cooperate by taking part in formal standard committees, thus avoiding standard wars (Dokko et al. 2012; Farrell and Simcoe 2012; Simcoe 2012). In sum, the concertation process we studied achieves the following goals: (i) it provides a competitive advantage to the group of firms that present what will become the winning standard (dominant design); (ii) it unites competitors to create a critical mass, that is, a number large enough to accelerate the adoption of the joint standards; (iii) it builds a favorable competitive environment, since the seasonal forecasts act as a selffulfilling prophecy and become reality; and (iv) it broadcasts the new trend very broadly (Rinallo et al. 2006). The social stratification of the marketplace is another outcome of the concertation mechanism; that is, there is a differentiation between leaders and followers and between innovators and imitators. This has important implications in terms of marketplace status and premium prices. Our chapter contributes to the literature on standard affirmation in technological and style industries (Abernathy and Utterback 1978; Anderson and Tushman 1990; GomesCasseres 1994; Cappetta et al. 2006) by highlighting the little-understood role of trade

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shows as platforms for innovation. Similarly to standard-setting committees (Dokko et al. 2012; Farrell and Simcoe 2012; Simcoe 2012), these commercialization networks are an understudied organizational solution for collective action geared towards innovation. The Première Vision case shows that trade shows can help industry actors, such as trade associations, to combine and mobilize competing firms to support collective action initiatives, including affirming the dominant designs. In terms of the generalizability of our findings, the case we studied is not unique: after Première Vision developed it, many other shows in the clothing fabric and other fashion-related industries (yarns, leather goods, clothes, bags, shoes, and eyewear) adopted similar concertation mechanisms. For example, an Italian show, Milan-based Moda In (which is currently presented with other textile shows under the umbrella name Milano Unica), adopted a full concertation mechanism similar to the one described in this chapter. Other shows, related to other stages of the textileclothing supply chain, do not have a complete concertation process, but only exhibit trend areas with the key exhibitors’ input. Clearly, not all trade shows can shape their underlying markets’ innovation trajectories and more comparative research is needed to identify the reasons why some are able to play such an important role. We can, however, speculate that some enabling conditions, recurring in the Première Vision case, might help identify other contexts where trade shows might facilitate collective action. First, the clothing fabric industry is fragmented into a great number of small and medium-sized enterprises with limited marketing and new product development capabilities. These firms might be more inclined than their larger and more competent counterparts to follow coordinating institutions’ input regarding the development of their innovations. Second, the case refers to producers of intermediate goods, which need to coordinate their innovation efforts with the producers of finished products and collateral goods, as well as having to keep up with trends in retailer and consumer markets. Under these conditions, the need for coordinated actions might be stronger than when producers of end products interact directly with consumers. Additionally, trade show organizers owned or controlled by leading trade associations are more likely to have the legitimacy and the influence to guide their members towards collective innovation projects. Nevertheless, the protectionist attitude typical of trade associations (Golfetto 2004; Rinallo and Golfetto 2011; Bathelt et al. 2014) may lead to the exclusion of relevant firms. Première Vision’s organizers had to fight a few internal battles when they decided to extend their exhibitor base, first to non-French and later to non-European fabric producers (Rinallo et al. 2006). These organizers’ (and trade associations’) farsightedness and their ability to overcome internal resistance are therefore the other facilitating factors that made concertation processes possible. A final enabling condition refers to the market leaders’ support of the trade-showcoordinated concertation mechanisms. These leading companies generally have structured research and development processes, and consolidated brand images. As in the case of standard committees (Gomes-Casseres 1994; Rysman and Simcoe 2008; Dokko and Rosenkopf 2010), the decisions taken in the concertation groups may have adverse technological, economic, and organizational consequences for these firms and reduce the value of their capabilities. Consequently, these producers have few incentives to cooperate in collective innovation activities. A key task of the trade show organizers (and the trade associations supporting them) is therefore to secure the contribution of the market leaders, as their lack of cooperation can severely undermine the collective action’s success.

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This can be done by offering these market leaders political benefits, such as board membership of the trade association, seats on the trade show’s steering committees, and inclusion in the concertation groups. In this manner, these companies can guide the collective action to favor them, which will result in better incentives for cooperation. More generally, this chapter also emphasizes trade shows’ role as platforms that stabilize cooperation between competitors. The strategic management literature suggests that cooperation between competing actors, otherwise known as coopetition (Brandenburger and Nalebuff 1996; Bengtsson and Kock 2000), is often unstable. Also the literature on standard-setting committees shows that such cooperative arrangements are conflict-laden (Ranganathan and Rosenkopf 2014). Trade shows unite competing firms serving the same market by defining a common place and time for their promotional activities. In doing so, they create a critical mass that attracts visitor-buyers for the benefit of all the competitors. Beyond the promotional element, trade shows also give rhythm to the suppliers’ innovation cycle (Golfetto 2004; Power and Jansson 2008). In this chapter, we demonstrate that trade shows can, under some circumstances, not only affect the timing, but also the content of the innovation, thus shaping the evolution of their underlying industries. Acknowledgements This chapter draws from our earlier work on the concertation process in the clothing fabric industry. We would like to acknowledge the following original sources and thank the publishers of the respective works for allowing us to use some of this material: Industrial Marketing Management, 35 (2006), 856–869 (Elsevier B.V. ); Economic Geography 87 (2011), 453–476 (Clark University and John Wiley & Sons, Inc. ); H. Bathelt, F. Golfetto and D. Rinallo (2014), Trade Shows in the Globalizing Knowledge Economy, chapters 11 and 12 (Oxford University Press ).

REFERENCES Abernathy, W.J. and Utterback, J.M. (1978) ‘Patterns of industrial innovation’, Technology Review, 80 (7): 40–47. Anderson, P. and Tushman, M.L. (1990) ‘Technological discontinuities and dominant designs: cyclical model of technological change’, Administrative Science Quarterly, 35: 604–633. Arnould, E.J. and Wallendorf, M. (1994) ‘Market-based ethnography: interpretation building and marketing strategy formulation’, Journal of Marketing Research, 31: 484–504. Arthur, W.B. (1994) Increasing Returns and Path Dependency in the Economy, Ann Arbor, MI: University of Michigan Press. Bathelt, H. (2017) ‘Trade fairs and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 509–522. Bathelt, H., Golfetto, F. and Rinallo, D. (2014) Trade Shows in the Globalizing Knowledge Economy, Oxford: Oxford University Press. Bengtsson, M. and Kock, S. (2000) ‘“Coopetition” in business networks’, Industrial Marketing Management, 29 (5): 411–426. Borghini, S., Golfetto, F. and Rinallo, D. (2006) ‘Ongoing search among industrial buyers’, Journal of Business Research, 59: 1151–1159. Borghini, S., Golfetto, F. and Rinallo, D. (2014) ‘Using anthropological methods to study industrial marketing and purchasing. An exploration of professional trade shows’, SPACES Online 12 (2). Online. HTTP: (4 September 2015).

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Brandenburger, A.M. and Nalebuff, B.J. (1996) Co-opetition, New York: Doubleday. Callon, M. (2017) ‘Markets, marketization and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing, 589–609. Cappetta, R., Cillo, P. and Ponti, A. (2006) ‘Convergent designs in fine fashion: an evolutionary model for stylistic innovation’, Research Policy, 35 (9): 1273–1290. CERMES – Centre for Research on Markets and Services (1994) Tendenze nella Domanda di Servizi Fieristici: Rapporto Moda-Persona (Trends in the Demand of Trade Fair Services: Report on the Fashion Industry), Unpublished Research Report, Milan: Bocconi University. Dokko, G., Nigam, A. and Rosenkopf, L. (2012) ‘Keeping steady as she goes: a negotiated order perspective on technological evolution’, Organization Studies, 33: 681–703. Dokko, G. and Rosenkopf, L. (2010) ‘Social capital for hire? Mobility of technical professionals and firm influence in wireless standard committees’, Organization Science, 21: 677–695. Drapers Record (2005) ‘Première Vision to reveal revolutionary new layout’, Drapers Record, 10 December: 4. Entwistle, J. and Rocamora, A. (2006) ‘The field of fashion materialized: a study of London Fashion Week’, Sociology, 40: 735–751. Farrell, J. and Simcoe, T. (2012) ‘Choosing the rules for consensus standardization’, RAND Journal of Economics, 43: 235–252. Golfetto, F. (1988) Il Sistema Fieristico Internazionale (The International Trade Fair System), Milan: Angeli. Golfetto, F. (2000) ‘Reti di imprese e meta-organizzatori: il ruolo delle fiere (Firm networks and meta-organizers: the role of trade shows)’, Sinergie, 18: 189–211. Golfetto, F. (2004), Fiere e Comunicazione: Strumenti per le Imprese e il Territorio (Trade Fairs and Communication: Tools for Firms and the Territory), Milan: EGEA. Golfetto, F. and Mazursky, D. (2004) ‘Competence-based marketing’, Harvard Business Review, 51 (November): 26. Golfetto, F. and Rinallo, D. (2008), ‘Reshaping markets through collective marketing strategies: lessons from the textile industry’, in K. Tollin and A. Carù (eds) Strategic Market Creation: Realizing Radical Innovation from a Marketing Perspective, Chichester: John Wiley & Sons, 97–123. Golfetto, F. and Rinallo, D. (2012) ‘Competition and collective marketing: a history of the European textile industry trade shows’, in S. Borghini, A. Carù, F. Golfetto, S. Pace, D. Rinallo, L. Visconti and F. Zerbini (eds) Prodotto, Consumatore e Politiche di Mercato Quarant’Anni Dopo. Scritti in Onore di Stefano Podestà (Product, Consumer and Market Policies Forty Years Later. Writings in Honor of Stefano Podestà, Milan: EGEA Online, 369–394. Golfetto, F. and Rinallo, D. (2014) ‘The evolution of trade show systems: lessons from Europe’, in H. Bathelt and G. Zeng (eds) Temporary Knowledge Ecologies: The Rise and Evolution of Trade Fairs in the Asia-Pacific Region, Cheltenham and Northampton, MA: Edward Elgar, 42–66. Gomes-Casseres, B. (1994) ‘Group versus group: how alliance networks compete’, Harvard Business Review, 72: 62–66. Gopalakrishna, S. and Lilien, G.L. (1995) ‘A three stage model of industrial trade show performance’, Marketing Science, 14 (Winter): 22–42. Guercini, S. and Ranfagni, S. (2012) ‘Creation of fashion trends and role of the bureau de style for textile innovation’, Journal of Global Fashion Marketing, 3 (1): 12–21. Lee, J., O’Neal, D.E., Pruett, M.W. and Thomas, H. (1995) ‘Planning for dominance: a strategic perspective on the emergence process’, R&D Management, 25 (1): 3–26. Mazzaraco, M. (1993) ‘Word is out: yellow is in for spring-summer ’94’, Women’s Wear Daily, January 20: 23. Molina, O. and Rhodes, M. (2002) ‘Corporativism: the past, present, and future of a concept’, Annual Review of Political Science, 5: 305–331. Power, D. and Jansson, J. (2008) ‘Cyclical clusters in global circuits: overlapping spaces in furniture trade fairs’, Economic Geography, 84: 423–448. Première Vision (2008) ‘Fashion info, September 2008 Salon’. Press Kit. Online. HTTP: (15 November 2013). Ranganathan, R. and Rosenkopf, L. (2014) ‘Do ties really bind? The effect of knowledge and commercialization networks on opposition to standards’, Academy of Management Journal, 57 (2): 515–540. Rinallo, D., Borghini, S. and Golfetto, F. (2010) ‘Exploring visitor experiences at trade shows’, Journal of Business and Industrial Marketing, 25 (4): 249–258. Rinallo, D. and Golfetto, F. (2006) ‘Representing markets: the shaping of fashion trends by French and Italian fabric companies’, Industrial Marketing Management, 35 (7): 856–869. Rinallo, D. and Golfetto, F. (2011) ‘Exploring the knowledge strategies of temporary cluster organizers: a longitudinal study of the EU fabric industry trade shows (1986–2006)’, Economic Geography, 87: 453–476. Rinallo, D., Golfetto, F. and Gibbert, M. (2006) ‘Consocia et impera: how the French and Italian fabric producers cooperate in order to affirm the “dominant design” in the fashion industry’, in M. Gibbert and T. Durand (eds) Strategic Networks, Oxford: Blackwell, 88–106.

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Rosenkopf, L. and Tushmann, M.L. (1998) ‘The coevolution of community networks and technology: lessons from the flight simulation industry’, Industrial and Corporate Change, 7: 311–346. Rysman, M. and Simcoe, T. (2008) ‘Patents and the performance of voluntary standard-setting organizations’, Management Science, 54: 1920–1934. Schilling, M.A. (2002) ‘Technology success and failure in winner-take-all-markets: the impact of learning orientation, timing and network externalities’, Academy of Management Journal, 45 (2): 387–398. Shonfield, A. (1965) Modern Capitalism: The Changing Balance of Public and Private Power, Oxford: Oxford University Press. Simcoe, T. (2012) ‘Standard setting committees: consensus governance for shared technology platforms’, American Economic Review, 102: 305–336. Smith, T.M., Gopalakrishna, S. and Smith, P.M. (2004) ‘The complementary effect of trade shows on personal selling’, International Journal of Research in Marketing, 21: 61–76. Tushman, M.L. and Rosenkopf, L. (1992) ‘Organizational determinants of technological change: towards a sociology of technological evolution’, Research in Organizational Behavior, 14: 311–347. Zerbini, F., Golfetto, F. and Gibbert, M. (2007) ‘Marketing of competence: exploring the resource-based content of value-for-customers through a case study analysis’, Industrial Marketing Management, 36 (6): 784–798.

33. Knowledge collaboration in hybrid virtual communities Gernot Grabher and Oliver Ibert

INTRODUCTION Knowledge collaboration, for a fairly long time, was primarily perceived as a matter of face-to-face interaction. Today, however, knowledge is co-created over large distances in virtual labs, and business opportunities are generated in mediated interaction between traders even though the involved actors are located at distanciated places and often have never met each other personally. As we have shown in greater detail elsewhere (Grabher and Ibert 2014, 105–110), widely dispersed and loosely coupled groups of enthusiastic users, semi-professionals and idealistic professionals who interact with one another online are able to share knowledge, generate new ideas and even self-organize quite demanding tasks of knowledge co-creation despite a fundamental lack of both physical co-location and relational proximity. Such communities circulate different types of knowledge, ranging from usage experiences to technical design details. In some instances, even procedural knowledge, the ability to effectively self-organize collective processes of knowledge creation, can be observed in virtual communities. Furthermore, such communities are highly interactive. Forms of feedback range from simple positive (and negative) reactions to making additional suggestions or even uttering constructive critique. Some of the connotations of the notion “virtual”, however, can easily be misleading when theorizing the affordances and limitations of mediated interaction. This chapter is particularly concerned with two of these widely shared misunderstandings. First, the notion “virtual” is often associated or even equated with immateriality. This implies the problematic assumption that knowledge circulates in virtual networks as a kind of immaterial entity which is separated from physical bodies and material objects. Second, “virtual” is often treated as something that is derived from the “real”. It exhibits some key properties (virtues) of the real thing but it will never be more than an incomplete reality, even if the most sophisticated and advanced technology is employed. This second assumption is particularly consequential for economic geographers who widely treat faceto-face interaction as a unique and highly efficient “communication technology” (Storper and Venables 2004) which is indispensable in knowledge collaboration. Hence, online encounters are mostly valued against this background and appear as deficient substitutes. They seem to lack the sensory richness of meeting face-to-face, of shaking hands and of sharing the mood of the moment. In this chapter we wish to question both assumptions about the nature of the virtual. By referring to an empirical investigation of nine online communities we, first, seek to better assess the interdependencies between online environments and the physical and material context. As we will show, these interrelations are far more complex and subtle than prevailing debates on the (limited) substitutability of face-to-face encounters through 537

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virtual exchange suggest. Second, our empirical material allows a detailed exploration of the specific contributions of virtually mediated collaboration to knowledge creation. The apparent effectiveness of online collaboration is not just an outcome of ever more sophisticated technical features that blur the boundaries between online and offline. We argue instead that we are presently witnessing the emergence of an unprecedented form of social practices of collective learning which take advantage of the interplay between online connectivity and practices embedded in offline environments and which exhibit social dynamics unattainable in face-to-face-only settings. Our chapter proceeds as follows. After introducing key notions of the empirical field and presenting our methodological approach, we first explore the interplay between material and virtual environments in particular. Second, rather than regarding virtual exchange as a deficient substitute of face-to-face interaction, we appreciate the unique qualities of online interaction in enabling (and limiting) knowledge collaboration.

KEY NOTIONS Knowledge Practices Our understanding of knowledge in this chapter builds on the rich body of research on communities of practice (Lave and Wenger 1991; Wenger 1998; Amin and Roberts 2008). We employ the term “knowledge practice” to emphasize that human knowledgeability is inseparable from social practice. Knowledge is not an object in its own right (Amin and Cohendet 2004), but a relational resource (Bathelt and Glückler 2005). A flute maker’s knowledge, for instance, is not an individual achievement, let alone a possession. Rather, it is distributed among the involved craftspersons’ brains and fingertips and the used materials, artifacts and tools assembled in the workshop (Cook and Brown 1999). Additionally, this understanding emphasizes that knowledge is the ability to act (Stehr 2001); it only becomes concrete and palpable when actively performed (Ibert 2007). Our analysis also draws on the analytical distinction between tacit and codified knowledge as elaborated by Michael Polanyi (1966). Polanyi argues that all human knowledge rests to a lesser or larger degree on implicit assumptions. This “tacit dimension” of knowledge eschews explication; either it is not explicated while relying on it or it is not explicable at all, even if one tries to (Gertler 2003). In our study we follow Polanyi in his understanding of tacit knowing. We thus do not treat tacit knowledge as a distinct type of knowledge that can be separated from codified knowledge. Rather we acknowledge that all knowledge has a tacit dimension. The ability of sharing knowledge and the potential of learning something new depend on the degree of similarity and dissimilarity in the underlying assumptions of the actors involved. For instance, members of the studied virtual communities circulate pieces of codified information, mainly in form of textual utterances or graphical representations (sketches or photographs). The adequate perception and correct understanding and interpretation of these pieces of information, however, depend on the shared tacit knowledge. Similarities or dissimilarities between the underlying assumptions of the sender and the receiver give rise to misunderstandings and tensions (Meusburger 2009), either in a productive (Stark 2009; Ibert 2010; Hautala 2011) or an unproductive fashion.

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Hybrid Virtual Communities Starting from these key assumptions on the nature of human knowledgeability, this chapter provides an in-depth exploration of collaborative knowledge practices in nine hybrid virtual communities. We approach the notion of virtual hybrid community by subsequently introducing its three constituent elements, communities, virtual and hybrid. We regard a community as an informal group of people who share a common practice and voluntarily adhere to common rules (such as rules of admission, exclusion and behavior). These shared practices and rules enact a system of relationships between people, activities and the world (Lave and Wenger 1991; Horrigan and Rainie 2001; Haythornthwaite 2009; Hercheui 2011, 5). Participation in a community is strongly rooted in intrinsic motivation; typically members feel passionate about using a product, contributing to a genre or undertaking a particular activity. The prevailing governance mechanism at work within a community is “sharing” (Belk 2010). Members contribute to a common pool of resources without the expectation of immediate reciprocity. Every member can legitimately use this pool according to his or her demand (Belk 2010). These mechanisms of distributing resources entail a sense of common responsibility, mutual recognition and considerate utilization of the joint pools of resources, time and mutual attention. Communities cultivate knowledge as a joint practice. Unlike most other resources knowledge is enriched rather than diminished through sharing (Belk 2010). This, however, does not mean that more strategic considerations are generally precluded. Rather, to the contrary, sharing might partially be motivated by career concerns and signaling effects (Lerner and Tirole 2002): community engagement entails the legitimate transformation of reputation capital into business capital. The jointly cultivated knowledge pool offers considerable incentives for the involved participants to act strategically by monopolizing a winning margin from the shared knowledge pool (Grabher et al. 2008). However, as the following accounts will reveal, compared to the prototypical case, knowledge sharing seems to be more strongly motivated by the desire of being acknowledged by peers (Stewart 2005; Wiertz and de Ruyter 2007) or by the involved firms that employ a repertoire of symbolical acknowledgements ranging from invitations to special corporate events to the formal acknowledgement by key corporate actors. Virtual communities are communities that mainly interact by using communication tools provided by the Internet and in publicly accessible online environments (Preece 2000; Amin and Roberts 2008). Although corporeal meetings of community members are possible, not even uncommon, virtually mediated communication is the quintessential form of interaction. Virtual communication, of course, has more recently been transformed fundamentally. From the early static websites, bulletin boards and listserv mailing systems, the evolution of the so-called web 2.0 affords new dimensions and qualities of interactivity (Napoli 2010). In fact, virtuality implies a novel mode of “two-way mass communication” – the one interacts directly with the few, and indirectly with the many (Gulbrandson and Just 2011, 1100). Hybrid communities (Kunz and Mangold 2004) denote a specific kind of community which encompasses the sphere of professional expertise on the one hand and the mundane world of ordinary users, laypersons, enthusiasts and hobbyists, on the other (Grabher et al. 2008). Characteristically, interaction in hybrid communities unfolds horizontal

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(expert-to-expert; user-to-user) and vertical dynamics (expert-to-user, professional-tolayperson). Scientific attention to hybrid communities so far has largely focused on sectors organized around digital products (like software, for example). More recently, however, these communities have also been apprehended in a much wider range of industries and knowledge domains like, among others, trend sports equipment (Shah 2005), cultural industries like fashion (Kawamura 2006), dance (Aoyama 2007), music (Pinch 2003; Jeppesen and Frederiksen 2006) and increasingly in the field of health care and pharmaceuticals (Boon et al. 2008; Yaqub and Nightingale 2012). Hybrid virtual communities can be subdivided into three distinct types, each of which is defined by the kind of relationship between commercial producers on the one hand, and users and enthusiastic laypersons on the other. Firm-hosted communities (Jeppesen and Frederiksen 2006) are initiated and maintained by professional and commercial producers. These firms set up the online-forum of exchange, employ the community’s moderators, define and police the norms of interaction and can, if deemed necessary, set the agenda by explicitly soliciting feedback on specific topics. Firm-related communities are launched by community members who create and enforce the rules of interaction in a self-organized process. However, these communities are not independent from professional producers. Rather, the object of common interest is associated with a distinct brand or even a specific product. In contrast to the partially solicited advice in firm-hosted communities, knowledge collaboration typically unfolds around questions and practices that emerge from daily utilization. Finally, independent communities (Grabher et al. 2008; Brinks and Ibert 2015) emerge and evolve without the incitement or assistance of professional or commercial organizations. Interaction dynamics are driven by the motivations and aspirations of community members alone. Relative to the two other types of communities, knowledge collaboration in independent settings is most rigorously focused on the “epistemic object” (Knorr Cetina 2001) whereas firm-related communities in particular unfold strong lateral dynamics around off-topic threads or simply “noise”. This typology, of course, represents a static classification. In the constantly shifting field of online environments and social media, virtual communities are, on the one hand, inherently dynamic. In particular firm-related and independent communities, rather than being static and sterile, are fuzzy and unruly social formations (Grabher et al. 2008, 270). Communities learn and forget, get bored or turn angry, consolidate or drift apart. Communities, in short, evolve, and over longer time horizons might migrate across the boundaries of our typology. On the other hand, these virtual communities are enmeshed in a wider evolving ecology of online media and social network sites with shifting boundaries and changing attributions and usage patterns. Facebook, for example, which in its early years was largely confined to all sorts of non-professional socializing and content sharing, increasingly provides socio-technical affordances for semi-professional and professional knowledge collaboration (see, for example, Beer 2008; Boyd 2010).

RESEARCH DESIGN To explore the evolving field of hybrid virtual communities empirically, we focus on goodpractice cases in which the respective collaborative procedures across distance are well established and the spatial dimension as well as the unique qualities of online collabora-

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tion are clearly observable. The selection, in other words, is explicitly not geared towards the average or representative case. This particular focus has to be taken into account in the interpretation of findings that cannot be generalized in a straightforward fashion. Case Selection According to this overarching goal, our strategy for case selection is highly selective. We approached the field in two successive steps. In order to be able to select appropriate cases, we conducted an online inquiry across highly diverse knowledge domains and were able to gather data on 121 different communities. We collected basic information about these communities that embraces the topic of knowledge practices, the relation to existing firms and brands, the existence and mode of moderation, the age of the community (date of foundation), its size (total number of contributors) and its present state of activity (approximated through the number of actually active members). Since the resulting sample offered a sufficient choice of goodpractice cases, we proceeded with the selection of communities for a closer investigation. To grasp the diversity of knowledge collaboration practices for the subsequent in-depth analysis, we sought to cover a broad spectrum of communities that met the following criteria. First, we conducted a series of spot tests to make sure that interaction within our selected communities is focused on epistemic objects that are developed further in a collaborative way. In other words, we excluded communities that were restricted to simple ratings and ephemeral subjective expressions of approval or disproval (like in the case of many fashion communities, for example). Second, for practical reasons of data availability, our research was restricted to then-active and publicly accessible communities. Third, in order to reduce variance in the institutional influences across the selected cases, we concentrated on English-speaking communities that are primarily active in the US. However, the selected communities were not restricted to a single national context. Rather, the protocols revealed that the community members collaborate over a wide range of places, regions and nations. Fourth, since we expected that the intensity of knowledge collaboration decreases with the degree of dependency of the community on the respective producers, we selected cases that cover all the typical constellations of community– firm relations identified in the second section above, and choose for each constellation three examples (Table 33.1). The proposed typology is based on the official policies and practices of firms with regard to online forums. Beyond the official codes of conduct, firms are actively manipulating online forums and, in particular, online product reviews. According to Hu et al. (2012), 10.3 percent of online product reviews are subject to manipulation. However, we assume that manipulation with regard to the communities studied is negligible for two reasons: (1) newly established and independent firms have disproportionally high incentives to manipulate online reviews whereas established brands of large producers (such as our cases) make disproportionally low gains from online manipulation (Mazylin et al. 2013); (2) through their involvement in continuous knowledge collaboration, members of the studied firm-hosted and firm-related communities already display a certain loyalty to brands and producers which in turn reduces the incentives of firms to manipulate communities through undisclosed practices. Fifth, when selecting cases we deliberately sought knowledge domains beyond those

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Huggies Babies

This forum, initiated by Kimberly Clark, offers the opportunity for young parents to share their experiences in raising their newborn children.

Name

Description

Type Swarm of Angels This community of artists has produced a feature film in a selforganized way and exclusively in an online environment. The physical production sites and the technical equipment (audio studios, cutting facilities, cameras) are distributed among community members.

BMW Touring This is a community of dedicated motorcyclists with a preference for BMW motorcycles.

This forum offers a platform for professional and hobby photographers who share their dedication to Nikon camera equipment.

This forum is mainly used by enthusiastic supporters of the IKEA philosophy to share ideas about furniture and interior design.

On this site, users of Dell computer hardware discuss technical issues with the firm’s technicians and with other users.

This forum encompasses mainly people with a passion for homecooking, who are, due to different circumstances in their daily lives, mostly unable to spend much time on it.

Firm-related Nikonians

IKEA Fans

Dell

Kraft

Firm-hosted

Table 33.1 Overview of analyzed cases Independent

A trend sports community that uses modified mono-skis to surf on sand dunes and hills.

This forum, an informal global research network that seeks to advance the development of a new drug against cancer (DCA, short for dichloroacetic acid), interacts with cancer patients and their relatives. As the DCA drug cannot be patented anymore, the pharmaceutical industry shows little interest in investing in product development.

Sandboarder DCA

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in which successful online collaboration is already well acknowledged. The emblematic cases, like Linux, suggest that distance can be overcome most easily when the epistemic object is immaterial (like software, for example) and when codified knowledge and professionalism predominate (Haythornthwaite 2002; Hew and Hara 2007; Dahlander et al. 2008). Our study, in contrast, includes cases of virtual communities that perform highly material practices (such as furniture construction or cooking), strongly rely on tacit knowledge (child care or film making) and are open for non-professional expertise. By proceeding in this manner, we arrived at a sample that promises novel insights into the dynamics of knowledge collaboration across a range of typical constellations between producers and users and encompasses different forms of expertise (from experiences in the use of products to technical expertise). Even though we focused on English-speaking communities, the forums cross territorial boundaries and thus offer valuable insights into interaction across larger physical distance and knowledge sharing in an international setting. However, our focus on good practice implies that our study is biased towards communities that have been able to evolve and consolidate over several years. Due to this “survivorship bias”, our account inevitably is skewed towards community formats that have proven to be able to reproduce themselves and towards knowledge domains in which virtual hybrid communities apparently are a viable mode of knowledge collaboration. Our study, in other words, remains silent about the universe of virtual hybrid communities that never took off or ceased to exist after a short period of time. Netnography We accessed these communities empirically using a netnographic approach (Kozinets 2002; Garcia et al. 2009). Analogous to an ethnographer, the netnographer gathers data by participating and directly observing practices while they are performed. We enrolled in the central forums of the communities we were interested in, which brought us into a position of being (silent) participants who could observe ongoing and past interaction between community members. However, virtual communities are fuzzy social phenomena with blurred boundaries. Community members usually interact in several forums, each of which has a different thematic scope and spatial reach (Baym 2007). For instance, the virtual community of Nikon enthusiasts interacts on such diverse forums as Nikonians, Nikonistas, Nikonistes, Nikoncafe or dpreview.com. We were thus not able to observe the entire spectrum of the communities’ online interaction. Rather, our observations were focused on (and restricted to) singular, though particularly relevant, forums. Therefore, we conducted several pre-tests in order to make sure that we selected the major platforms of the community (in terms of actually active members) that at that time were active and accessible for observation. In the case of Nikon camera users, for instance, we picked, according to these criteria, the Nikonians Forum. We then subscribed to the selected forums as ordinary members and thus acquired a role that allowed us to directly observe the publically visible interaction between community participants. We were not able to observe any private messages that might have been exchanged between individual community members. In the publically visible communication we did not find any indications of a significant share of privately sent or received messages among community members. This potentially relevant private dimension of virtual interaction within virtual communities remains obscure in our data. From June

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to September 2007 and from January to March 2008 we collected data from ten threads (a stream of contributions that centers on a single topic) for each of the nine communities. Streams rather than single messages can be regarded as the minimum “production units” of the community that result in the production of a collective epistemic object (Akrich 2010). Given the immense volume of data produced by communities, we were forced to analyze the ongoing interaction selectively. Before starting an in-depth analysis we first conducted a cursory pre-examination of threads in order to identify those streams of conversation that promised to be most product-related and oriented towards improvement and advancement. Due to our empirical approach we are only able to observe the active parts of the community. According to previous research only a small share of a community contributes frequently to ongoing discussions and thereby becomes visible for the netnographer. Stegbauer (2013) distinguishes between “lurkers”, participants who only watch and observe the forum’s activities (Preece et al. 2004), and “posters”, participants who actively contribute to the forum. According to a survey by Rubicon Consulting, “about 80% of content is produced by 9% of users . . . About 65% of web users are passive readers who contribute content only occasionally. They account for only about 20% of content . . . Another 9% of web users are pure lurkers, never contributing any content” (Rubicon Consulting 2008, 8). Within the same forum participants can clearly be classified as either a lurker or a poster. However, individuals might also switch between both roles when participating in different forums simultaneously (Stegbauer 2013). Thus, lurkers that remain invisible on one forum might become visible as posters on another forum. In these cases they might even turn into “cosmopolitans” (Dahlander and Frederiksen 2012), peripherally involved members who import relevant divergent ideas that are less likely to be circulated within the core group. All in all, we analyze 90 threads. Our observation data covers 1,470 posts (a post is a single contribution to a particular thread) provided by approximately 800 community members. This particular structure of the data offers distinct opportunities and limitations for an empirical analysis. On the one hand, it allows access to a qualitative investigation of a social phenomenon which is difficult to grasp as it connects diverse sites across the globe, is open for many contributors who use nicknames that hide their true identity, and usually only becomes manifest in asynchronous interaction. On the other hand, in such a socially diffuse, temporally fragmented and spatially dispersed setting, due to pragmatic reasons (limited accessibility of sites, the lack of identifiable representatives) the costs for collecting complementary qualitative data are exceedingly high, especially when – as in our case – the research design is explorative and the range of divergent communities is broad. Due to these pragmatic limitations, we ground our analysis solely on data as it is represented in online protocols. We are able to triangulate evidence, but only within the same set of data, and not across different sets of data.

MATERIALITY: SHARING PRACTICE WITHOUT SHARING CONTEXT Virtual interaction is often treated as if it is immaterial and thus almost completely freed from spatial constraints. In this section we wish to scrutinize this immaterial character in more detail and explore empirically the multiple ways in which virtual interaction is

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related to and depends on material preconditions. Our findings suggest that the desire for, or requirement of, organizing the co-presence of people as well as people and objects in knowledge-based interaction, which is discussed very prominently in the economic geographic literature (e.g. Storper and Venables 2004), turns out to be less important than expected, yet, materiality matters significantly through the ways in which the respective knowledge-related practices interact with their socio-material contexts. Co-presence of People and Objects In all observed communities, virtual interaction and personal meetings occur and often go hand in hand (Mok et al. 2010). Thus face-to-face encounters of community members are part of the repertoire and culture of the observed virtual communities. In firm-based communities, such gatherings are typically organized by the focal producer in the form of events. In firm-related communities, such meetings also take place in a self-organized manner without the intervention of a producing firm. Members of the IKEA Fans Forum, for example, occasionally organize meetings in the stores of their home regions. Physically co-located members of the Nikonians Forum occasionally meet personally to undertake photo excursions. However, our observations suggest that these physical meetings are almost dispensible for knowledge-generating activities. We did not find evidence that face-to-face meetings had been arranged for the purposes of intensified knowledge exchange or as a result of complaints about missing opportunities to discuss things personally in more detail. This suggests that personal meetings rather support informal and ephemeral socializing that, in general, tends to increase the affection with the community and the willingness to help other members (Sessions 2010). In relation to the ongoing, intense and cumulative knowledge exchange online, face-to-face socializing seems only to have a limited impact on the depth of knowledge exchange within these communities. On the one hand, offline gatherings may provide individual benefits for members as the development of relationships strengthens ‘bonding’ social capital (in the sense of Putnam 2000). On the other hand, however, these gatherings do not necessarily benefit the community at large as the resources found in weak ties (i.e. ‘bridging’ social capital) may be sacrificed as attendees of face-to-face meetings favor interaction with one another – at the expense of exchange with those who do not attend meet-ups (Sessions 2010). Moreover, membership in a community whose members are geographically widely dispersed offers opportunities for delegating a thorough inspection of products which otherwise would be out of reach. For instance, in the BMW Luxury Touring Forum one member located in Florida offered to examine more closely a used motorcycle a fellow motorcyclist located in Colorado wanted to buy. I noticed in your profile that you are from Jersey and in your post that the bike is in Florida. I am located in west central Florida and have several Florida dealers within easy drive of my location. Should it be within a reasonable distance I would be happy to go to the dealer and look it over and or take some pictures of it to send to you if you like. (BMW Luxury Touring Forum, thread #3)

Interestingly, the Colorado motorcyclist did not respond to this offer to get first hand information right from the site. Instead, after several supportive comments from the wider community, he decided to make the deal without physical inspection. Even though

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for participating netnographers it is impossible to ultimately verify the information about geographical locations provided in this quotation, it is noteworthy that this information has been provided at all. With references to places like in the quotation above, participants point at the possibility to complement information about facts provided online with richer sensations of the look and feel of the object that can only be experienced in corporeal interaction. The fact that the offer was not accepted hints at the possibility that physical co-presence, though practically possible in this case, was finally regarded as dispensable when it came to a purchasing decision. These findings on the overall low importance of immediate face-to-face (and face-toobject) encounters for knowledge collaboration, however, do not imply that the physical and material preconditions of virtual exchange in general are irrelevant. In fact, they are highly relevant, though in a way that is different to that usually conceived. Mediated Interaction Accommodated in Localized Practices Laboratory studies (e.g. Knorr Cetina 1981) and historical analyses by geographers of science (Livingstone 2003) reveal that human knowledgeability is deeply rooted in the social and material conditions provided by those localities where the respective knowledge practices are performed. This research highlights that engagement in practice is always situated in a shared local context (Wenger 1998). In an online environment, however, participants mutually engage each other in a shared practice even though a shared local context is not at their disposal. Virtual communities do not only involve members with similar expertise, interests or passions but also connect diverse places exhibiting more or less similar material contexts. Of course, with our research design we were unable to immediately observe all, or even some of the involved places, as the sheer number of dispersed locations involved in the observed interactions exceeded our research capacities. However, what we were able to observe are ongoing negotiations between community members, in which they explicate the particularities of their material surroundings and the relevance of specific local conditions for the validity of the insights expressed in their posts. The Sandboarders offer the most obvious examples for this kind of negotiations. As every sandboarding track is embedded into a unique landscape, members have to explicate the particular physical properties of their boarding spots to specify the wider relevance of their experiences and suggestions. First of all Wisconsin sand is not great for sandboarding so don’t expect it to fly there. Second, hot sand is slower than cool sand, so you might want to try it early morning or later in the year when the temp drops. (Sandboarder Forum, thread #2)

Contributions like that seek to identify critical parameters within which the valued knowledge of the community can be discussed in a meaningful way. Through interaction the community members jointly enact a framework of critical dimensions that contextualize their knowledge practices and at the same time neglect idiosyncratic particularities of the landscapes. For instance, the Sandboarders situate their knowledge practices in a framework that consists of hill gradients, sand granularity, weather conditions, ramps, board characteristics and so on.

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Similarly, before further discussing the issue of wobbling handlebars, a member of the BMW Luxury Touring Forum first asked for more detailed information to augment the context: However, for ‘discussion’ purposes.. can you give specs.. Total Mileage, Tire Mileage Front/Rear, Air Pressure Front/Rear. (BMW Luxury Touring Forum, thread #4)

In both cases, community members specify the parameters within which it makes sense to discuss the respective topics and thereby jointly constitute a shared practice without relying on a shared local context. Parts of the critical information stick to the locations where the practices are performed (von Hippel 1994), as the Sandboarder quote exemplifies, while other parts stick to the object, as the motorcycle example illustrates. Both kinds of information, however, have to be considered in conjunction with sharing practices. This theme is variegated in manifold ways by the observed communities. Two of the firm-related communities take advantage of the standardization efforts undertaken by global manufacturers. The IKEA fans, for instance, can be sure that all their fellow peers across the globe can use exactly the same materials, screws, pegs and pins – as long as they are obtained from IKEA. The same holds true for Nikonians, who frequently exchange detailed data on the properties of their gear: My list is as follows: NIKON D200 or D300 BODY, TOKINA AT-X 124 AF Pro DX 12-24mm f/4.0, TOKINA AT-X 280 AF 28-80 f/2.8, Cokin Z Pro filter holder. (Nikonians, thread #8)

With the help of such lists of specifications, members with the same camera equipment can team up as “gear twins”. The identical equipment can then be tested against the background of the divergent local conditions of the respective camera users. Exposition to multiple stress factors can provide a fuller and deeper understanding of the product’s performance properties (von Hippel 2005). Finally, sharing practice without sharing context entails interaction that seeks to determine the degree of generalization of the shared knowledge. The Nikonians exchange precise sets of data in order to make explicit under which local conditions (like, for example, climatic conditions) their experiences have been made and thus at the same time under which conditions their knowledge is most likely to be valid. Another case in point are IKEA fans who provide accounts about the age and the restlessness of their children in order to validate their suggestion for a particular design: if your kids are like mine, they’d never stay in place . . . With or without the legs, the cabinet is sturdy and has held up to three children who absolutely, positively can NOT sit still during a meal. Plus, I frequently find all three standing on the benches to write on the stick-on chalkboard above the bench. (IKEA Fans, thread # 3)

Moreover, the members of the forums do not only discuss the limits of their knowledge but also seek to find ways to expand the scope of generalization by altering the set of relevant framework conditions within which to discuss a particular issue: I am not a wedding photographer but obviously you are dealing with a huge range of different lighting conditions. However it is not just wedding photographers that have to cope with such changes. (Nikonians, thread #4)

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Within virtual communities, shared practice is not a result of socialization in a context of physical co-location, but rather of collective heedful engagement (see Weick and Roberts 1993) with similar but physically distanciated material contexts. Crucially, shared practice provides the preconditions for those interrelating activities that are critical for triggering moments of collective creativity (Hargadon and Bechky 2006): help seeking, help giving, reflective reframing (in which each actor in turn attends to and builds upon the comments and actions of others) and affirmation (e.g. through organizational values that support individuals’ seeking and providing help and reflective reframing).

THE ADVANTAGES OF NOT BEING THERE The hybrid virtual communities we studied do not only seem capable of producing deep insights and complex knowledge. In fact, we maintain that liberated from the limitations of physical neighborhood (Haythornthwaite 2002), these communities also afford technical opportunities, organizational practices and social dynamics that foster particular learning processes unattainable in face-to-face contexts. In other words, while the absence of face-to-face encounters and the dependence on online interaction mostly are perceived as bottlenecks for knowledge collaboration, we seek to appreciate the enabling character of these very circumstances. Low Multiplexity and Quasi-Anonymity Since members in hybrid virtual communities (except moderators with an explicit role description) are quasi-anonymous, online interaction is characterized by low degrees of multiplexity. In this sense, hybrid virtual communities can economize on the proverbial “strength of weak ties” (Granovetter 1973). Relations are almost purely informational (Grabher 2004, 1505–1506), less so in firm-hosted communities, in which a larger degree of lateral and off-topic communication occurs, but more so in firm-related and independent communities. In fact, this informational character of exchange is reinforced in the code of conduct of hybrid virtual communities that frequently precludes personal attributions and stress the open and, in principle, egalitarian constitution of the community. The status of quasi-anonymity is only legitimately suspended if additional information facilitates the validation of specific posts (like, for example, revealing the fact of having young children when discussing the robustness of furniture; IKEA Fans, thread #3). The condition of quasi-anonymity implies the absence of alternative cues of professional experience, disciplinary background and formal status or contextual clues such as office location, seating position or even clothing that exert influence on communication (see, for example, Bathelt and Turi 2011, 525; Dubrovsky et al. 1991). Under these conditions individual posts are valued according to their contribution to the specific problem at hand. Quasi-anonymity, in this sense, implies a redistribution of influence from formal status to competence, commitment and enthusiasm. In all our observed cases, reputation and credibility appear strictly community-specific: in general the standard format of interaction protocols only reveals the duration of community membership (“Join Date”) and the overall number of posts of the specific member as a proxy for commitment and knowledgeability.

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This emancipation from cues to formal status might not only give a voice to views that in more traditional face-to-face settings (such as patients in the setting of a conference of medical professionals, for example) could not be raised or would remain unheard, but it also motivates sustained engagement over long periods (without any tangible results) and thus nurtures (long-term) cumulative knowledge dynamics (Otto and Simon 2008). The DCA Forum, for instance, has managed to keep up momentum for several years despite any formal approval of their cancer therapy approach. In a sense, knowledge circulation in virtual hybrid communities comes rather close to the ideal of the “Mode 2”-type of knowledge production (Gibbons et al. 1994). In this mode the locus of knowledge collaboration shifts from the traditional institutional framework of disciplinary organized knowledge production to learning and knowledge creation in the “context of its application”. Each particular context of application implies its particular set of theories, analytical strategies, modes of validation and learning practices. Knowledge is not primarily valued for its scientific validity but rather for its usefulness in practice. Cumulative Learning, Selection and Memory Participation in hybrid virtual communities is largely driven by intrinsic motivations and the prospect of gaining reputation among peers or vis-à-vis the focal firm (Wiertz and de Ruyter 2007; Grabher et al. 2008). Even firm-hosted communities hardly ever offer monetary or material compensation beyond the occasional symbolic gesture of handing out a gift for particularly promising ideas. The particular incentive structure also shapes the ways in which knowledge is produced in virtual communities. Whereas the involvement of extrinsically motivated participants is more strategic, selective and situational, intrinsically motivated members typically are more experientially oriented, more enduring in their engagement, and relate to a broader spectrum of issues (Füller 2010, 106). The more sustaining engagement in the community is conducive to the cumulative dynamics of advancing knowledge that in turn is further enhanced by specific features of online exchange. Virtual hybrid communities are “hypertextual” (Gulbrandson and Just 2011, 1099). Texts that a reader selects are also linked to their own references and allusions. The reader hence can enter at any node and can choose any path through the network (Gaggi 1997). These possibilities encourage “writerly, active reading rather than passive consumption of what has been produced by a conventional authorial author” (Gaggi 1997, 104). It is common practice in virtual communities to explicitly refer to previous statements (by copying posts partially into the subsequent post). Originally Posted by nicasian: I’m building an extended window seat with fridge cabinets and was wondering how you attached the Akurum legs to the bottom? There are no built in holes on these wall cabinets so did you have to drill some? Thanks. You don’t need to drill holes to attach the legs. You can cut off the peg on the leg plate and screw the leg plate into the bottom of the cabinet. (IKEA Fans, thread #3)

Discussion threads persist for years due to the storage of messages, and message databases in most cases may be searched via electronic queries. Such a “rewinding” of time to

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accurately review and collectively re-elaborate a discussion thread seems hardly possible in a physical meeting (Akrich 2010, 6.4). The practice of following on from certain previous posts (while ignoring other statements) acts as an effective collective “selection environment” for ideas (Kozinets et al. 2008, 343). Moreover, this sort of explicit cross-referencing produces a collective memory, which is particularly valuable in the self-organization of communities who operate independently from firms. Further, due to this self-referentiality, virtual communities cultivate a certain sense of focus and accuracy that discourages spontaneous and unrelated utterances. More generally, heedful interrelating with ongoing interactions within the community fosters collective cognition that connects individual ideas and experiences in ways that both redefine and resolve the demands of emerging situations (Hargadon and Bechky 2006, 486). Heedfulness and on-topic professionalism (Ren et al. 2007) indeed is partially codified in the rules and codes of conduct of the communities. Across all threads of our sample, only 4.2 percent of all posts, or 0.6 posts per thread, can be classified as non-topic contributions. The shares are higher in firm-hosted communities and lower in independent communities. Finally, a discussion of certain topics can lead to preliminary epistemic objects that are isolated from the ongoing flow of exchanges: about half of the analyzed communities formalize products of knowledge collaboration by creating a space in which insights and suggestions of more general relevance are organized into a knowledge corpus. A particularly rich example is the site of the DCA Forum that over time has developed an increasingly differentiated register that offers access to relevant papers, studies, reports on dosage and side effects, alternative therapies and a ‘Frequently Asked Questions’ section that is continually extracted from ongoing debates (DCA Forum). This sort of experiential knowledge formalization fosters a culture of rational discussion in which individual experiences and perspectives are articulated and collectively re-elaborated with insights from science and technology (see also Akrich 2010, 6.1–6.10). Asynchronicity and Reflective Reframing Effort is required initially in arranging face-to-face encounters, but once an encounter has been arranged, it hardly stretches out beyond the moment of physical co-presence. In contrast, initiating a virtual communication can be done instantaneously and does not involve a large amount of effort. Moreover, subsequent contributions to online forums can potentially be posted whenever convenient and without much effort. However, this convenience seems not to be the main affordance of online interactions. Rather, our data suggests that the long-term engagement of intrinsically motivated participants and the possibility to continue debates on particular issues for a considerable period of time in order to delve deep into the subject matter seems to be the main advantage associated with asynchronous online interaction. In the Swarm of Angels Forum, for example, some threads last for almost two years (the most extensive thread went on for 596 days). These time spans allow interactive processes of “reflective reframing” (Hargadon and Bechky 2006) that are not available in the most intensive face-to-face encounters. In general, interaction within the observed forums is characterized by long response times. On average, across all the analyzed threads, contributors take about 114 hours to react to fellow peers’ suggestions. In general, the average response time is longer in independent forums and shortest in firm-hosted forums. Asynchronicity leaves more

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time for the participants to contemplate answers. For instance, in the IKEA Fans Forum contributors frequently test suggested modifications and versions before reacting on a contribution. In the DCA Forum contributors often delay posting answers to requests until reliable diagnostic material or test results are available. Longer response times, moreover, offer richer opportunities to support an argument with additional material, for instance with a sketch, a blueprint, a section from a research report, a CAT-scan, a set of supporting data or a photograph. Finally, asynchronicity allows community members who typically are simultaneously involved in a range of related communities (Dahlander and Frederiksen 2012) to triangulate information by consulting alternative forums. Asynchronicity thereby harnesses the problem-solving benefits of cognitively diverse perspectives (Brabham 2010). The practice of conveying ideas and suggestions through multi-dimensional crossreferencing (by inserting weblinks, comments, articles, audio interviews or videos), which prevails in virtual communities, opens up new avenues for associative mind-mapping and increases collective reflexivity. In general, thinking and tinkering through metaphors, analogies and comparisons enhances creative problem solving (Burroughs and Mick 2004, 404). Quite obviously, instantaneous face-to-face-encounters do not leave as much space for playful and experimental tinkering that seems perfectly legitimate in virtual communities (Kozinets et al. 2008, 343). In fact, the forums with the deepest knowledge and the most constructive forms of feedback (IKEA-Hackers, Swarm of Angels) are at the same time the communities with the longest-lasting threads and response times beyond average. Asynchronicity turns out to be beneficial in instances of elaboration and tinkering along a complex trajectory with a high level of “knowledge interdependence” (Molinari et al. 2009), like in the case of cancer treatment in the DCA Forum or the collaborative production of a feature film in the case of the Swarm of Angels Forum. Conversely, asynchronicity is less advantageous in cases with low knowledge interdependence, and in cases in which spontaneous responses produce decisions of relatively higher quality, that is, decisions in which an immediate impulse reflects accumulated (tacit) experience, as is the case of firm-hosted communities where members validate proposed ideas by spontaneously expressing dissent or consent (see also Kahnemann 2011).

CONCLUSIONS This chapter is concerned with two widely shared misunderstandings related to the notion of virtual knowledge creation. First, “virtual” is often associated or even equated with immateriality. As a consequence, virtual knowledge collaboration is mainly seen as a viable option in digital knowledge domains but not in more traditional fields. Second, “virtual” is often perceived as being derived from the “real”. Virtual interaction thus is treated as a deficient substitute for face-to-face interaction. Against this background, we seek to explore more systematically the material preconditions of virtual knowledge collaboration and specify some benefits of distanciated relations in learning processes that so far have been understated or even overlooked. We do so in charting an empirical field – hybrid virtual communities – in which knowledge practices, despite the unavailability of physical co-location and personalized trust, evolve in elaborate ways and accomplish quite demanding tasks.

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As our findings demonstrate, collaborative knowledge practices in virtual communities are not at all immaterial. Rather, their social cohesion is partly enhanced through organized forms of co-presence. Furthermore, information shared via mediated interaction has to be contextualized with local, social and material practices. For instance, community members establish a common framework within which important issues of the community can be negotiated meaningfully by jointly negotiating which elements of their surrounding material contexts are crucial to establishing a shared practice, and which of these elements represent only local idiosyncrasies (sharing practice without sharing context). Moreover, as the “gear twins” in the Nikonians community exemplify, community members frequently refer to product IDs and order codes to assemble almost identical material “constellations of practice” (Faulconbridge 2010) at geographically dispersed locations. The purposeful creation of similar material conditions allows a more meaningful in situ validation, exploration and variation of the virtually shared knowledge. Furthermore, collaboration in virtual hybrid communities, of course, lacks the media richness and the entire spectrum of non-verbal cues (like gestures, body language, language variety) through which face-to-face encounters ease interaction and mutual understanding (Bathelt and Turi 2011, 524–525). And yet, our empirical material suggests that virtual interaction is far more than only a deficient substitute for face-to-face interaction. Rather, distinct features of online interaction (quasi-anonymity, asynchronicity, virtual memory) do provide unique opportunities for collaboration, such as on-topic professionality and cumulative and problem-oriented learning. Asynchronicity seems to unfold particular advantages in cases of high degrees of “knowledge interdependence” (Molinari et al. 2009), like in the instance of cancer treatment in the DCA Forum or the collaborative production of a feature film by the Swarm of Angels Forum. Finally, we wish to direct attention to one conceptually promising but empirically unexplored issue. We agree that virtual exchange is more ambiguous than exchange in physical co-presence. Face-to-face interaction reduces the ambiguities of information exchange since interaction partners situated in the same context can more easily negotiate perceptions and interpretations (Song et al. 2007). In virtual communities, misunderstandings and misapprehensions might remain undetected for a longer time. However, whereas ambivalence and misunderstandings usually are perceived as undesired distortions, we suggest that both can unfold creative dynamics. Their settlement demands more explication (Mahr and Lievens 2012), contextualization and mutual confirmation (Bathelt and Turi 2011). By overcompensating the absence of sensory clues with verbal explication (Olsen and Olsen 2003), community members might reveal the taken-for-granted aspects of everyday problem solutions and afford the explication of knowledge and practices that otherwise might remain unconscious and in fact tacit. Explication invites a problematization, further exploration and de-contextualization of practices. Communication under conditions of equivocality, thus, triggers the generative moments of incompleteness (Garud et al. 2008); creative dynamics occur not despite but because of misunderstandings (Stark 2009, 193). We think that online interaction should offer rich opportunities for these generative moments of misunderstandings. However, the structure of our data does not allow us to support these ideas empirically. To reveal generative misunderstandings it would be necessary to perform diachronic analysis from longitudinal data, whereas our sample is designed for a synchronic comparison of different types of communities. Moreover,

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observational data of the collaborative dynamics on the community level would have to be confronted with in-depth interviews with individual community members to uncover divergent interpretations of identical instances. Hence, it will be up to future research to explore to what degree hybrid online communities might constitute a sort of “trading zone” (Galison 1997) in which divergent experiences and attributions are not “ironed out” but spark sustained engagement. Acknowledgements This chapter draws on ideas as developed first in Grabher and Ibert (2014). The empirical work has been supported by the German Research Foundation (GR1913/7-1).

REFERENCES Akrich, M. (2010) From communities of practice to epistemic communities: Health mobilizations on the Internet. Sociological Research Online, 15(2): 10. Amin, A., Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities and Communities. Oxford: Oxford University Press. Amin, A., Roberts, J. (2008) Knowing in action: Beyond communities of practice. Research Policy, 37: 353–369. Aoyama, Y. (2007) The role of consumption and globalization in a cultural industry: The case of Flamenco. Geoforum, 38(1): 103–113. Bathelt, H., Glückler, J. (2005) Resources in economic geography: From substantive concepts towards a relational perspective. Environment and Planning A, 37: 1545–1563. Bathelt, H., Turi, P. (2011) Local, global and virtual buzz: The importance of face-to-face contact in economic interaction and possibilities to go beyond. Geoforum, 42: 520–529. Baym, N. (2007) The new shape of online community: The example of Swedish independent music fandom. First Monday, 12(8). Beer, D. (2008) Social network(ing) sites . . . revisiting the story so far: A response to Dana Boyd and Nicole Ellison. Journal of Computer-Mediated Communication, 13: 516–529. Belk, R. (2010) Sharing. Journal of Consumer Research, 36: 715–734. Boon, W., Moors, E., Kuhlmann, S., Smits, R. (2008) Demand articulation in intermediary organisations: The case of orphan drugs in the Netherlands. Technological Forecasting and Social Change, 75(5): 644–671. Boyd, D. (2010) Social network sites as networked publics: Affordances, dynamics, and implications. In: Papacharissi, Z. (ed.) A Networked Self: Identity, Community, and Culture on Social Network Sites. London: Routledge, pp. 39–58. Brabham, D.C. (2010) Moving the crowd at Threadless: Motivations for participation in a crowdsourcing application. Information, Communication and Society, 13(8): 1122–1145. Brinks, V., Ibert, O. (2015) Mushrooming entrepreneurship: The dynamic geography of enthusiast-driven innovation. Geoforum, 65: 363–373. Burroughs, J.E., Mick, D.G. (2004) Exploring antecedents and consequences of consumer creativity in a problem-solving context. Journal of Consumer Research, 31: 402–411. Cook, S.D.N., Brown, J.S. (1999). Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science, 10: 381–400. Dahlander, L., Frederiksen, L. (2012) Core and cosmopolitans: A relational view of innovation in user communities. Organization Science, 23(4): 988–1007. Dahlander, L., Frederiksen, L., Rullani, F. (2008) Online communities and open innovation: Governance and value creation. Industry and Innovation, 15: 115–123. Dubrovsky, V.J., Kiesler, S., Sethna, B.N. (1991) The equalization phenomenon: Status effects in computermediated and face-to-face decision making groups. Human–Computer Interaction, 6: 119–146. Faulconbridge, J. (2010). Global architects: Learning and innovation through communities and constellations of practice. Environment and Planning A, 42: 2842–2858. Füller, J. (2010) Refining virtual co-creation from a consumer perspective. California Management Review, 52: 98–122. Gaggi, S. (1997) From Text to Hypertext: Decentering the Subject in Fiction, Film, the Visual Arts, and Electronic Media. Philadelphia: University of Pennsylvania Press.

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Galison, P.L. (1997) Image and Logic: A Material Culture of Microphysics. Chicago: University of Chicago Press. Garcia, A.G., Standlee, A.I., Bechkoff, J., Cui, Y. (2009) Ethnographic approaches to the Internet and computermediated communication. Journal of Contemporary Ethnography, 38(1): 52–84. Garud, R., Jain, S., Tuertscher, P. (2008) Incomplete by design and designing for incompleteness. Organization Studies, 29: 351–371. Gertler, M. (2003) Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there). Journal of Economic Geography, 3: 75–99. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., Trow, M. (1994) The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. London: Sage. Grabher, G. (2004) Architectures of project-based learning: Creating and sedimenting knowledge in project ecologies. Organization Studies, 25: 1491–1514. Grabher, G., Ibert, O. (2014) Distance as asset? Knowledge collaboration in virtual hybrid communities. Journal of Economic Geography, 14(1): 97–123. Grabher, G., Ibert, O., Flohr, S. (2008) The neglected king: The customer in the new knowledge ecology of innovation. Economic Geography, 84(3): 253–280. Granovetter, M.S. (1973) The strength of weak ties. American Journal of Sociology, 78(6): 1360–1380. Gulbrandson, I.T., Just, S.N. (2011) The collaborative paradigm: Towards an invitational and participatory concept of online communication. Media, Culture and Society, 33(7): 1095–1108. Hargadon, A.B., Bechky, B.A. (2006) When collections of creatives become creative collectives: A field study of problem solving at work. Organization Science, 17(4): 484–500. Hautala, J. (2011) Cognitive proximity in international research groups. Journal of Knowledge Management, 15(4): 601–624. Haythornthwaite, C. (2002) Creating and sustaining distributed learning communities. In: Renninger, K.A., Shumar, R.W. (eds.) Building Virtual Communities: Learning and Change in Cyberspace. Cambridge: Cambridge University Press, pp. 159–190. Haythornthwaite, C. (2009) Online knowledge crowds and communities. Paper presented at the International Conference on Knowledge Communities, University of Nevada, Reno, February 2009. Hercheui, M.D. (2011) A literature review of virtual communities: The relevance of understanding the influence of institutions on online collectives. Information, Communication and Society, 14(1): 1–23. Hew, K.F., Hara, N. (2007) Knowledge sharing in online environments: A qualitative case study. Journal of the American Society for Information Science and Technology, 58: 2310–2334. Horrigan, J.B., Rainie, L. (2001) Online Communities: Networks that Nurture Long-Distance Relationships and Local Ties. Pew Internet & American Life Project, Final Report. Washington, DC. Hu, N., Bose, I., Koh, N.S., Liu, L. (2012) Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3): 674–684. Ibert, O. (2007) Towards a geography of knowledge creation: The ambivalences between “knowledge as an object” and “knowing in practice”. Regional Studies, 41(1): 103–114. Ibert, O. (2010) Relational distance: Sociocultural and time-spatial tensions in innovation practices. Environment and Planning A, 42(1): 187–204. Jeppesen, L.B., Frederiksen, L. (2006) Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organization Science, 17(1): 45–64. Kahnemann, D. (2011) Thinking, Fast and Slow. London: Penguin. Kawamura, Y. (2006) Japanese teens as producers of street fashion. Current Sociology, 54(5): 784–801. Knorr Cetina, K. (1981) The Manufacture of Knowledge. Oxford: Pergamon. Knorr Cetina, K. (2001) Objectual practice. In: Schatzky, T.R., Knorr Cetina, K., Savigny, E. (eds.) The Practice Turn in Contemporary Theory. London: Routledge, pp. 175–188. Kozinets, R.V. (2002) The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, XXXIX: 61–72. Kozinets, R.V., Hemetsberger, A., Schau, H.J. (2008) The wisdom of consumer crowds: Collective innovation in the age of networked marketing. Journal of Makromarketing, 28: 339–353. Kunz, W.H., Mangold, M. (2004) Hybride Communities als Treiber des Kundenwertes. In: Herstatt, C., Sander, J.G. (eds.) Produktentwicklung mit virtuellen Communities. Kundenwünsche erfahren und Innovationen realisieren. Wiesbaden: Gabler, pp. 69–98. Lave, J., Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press. Lerner, J., Tirole, J. (2002) Some simple economies of open source. Journal of Industrial Economics, 52: 633–649. Livingstone, D. (2003) Putting Science in its Place: Geographies of Scientific Knowledge. Chicago: Chicago University Press. Mahr, D., Lievens, A. (2012) Virtual lead user communities: Drivers of knowledge creation for innovation. Research Policy, 41(1): 167–177.

Knowledge collaboration in hybrid virtual communities

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Mayzlin, D., Chevalier, J.A., Dover, Y. (2013) Promotional reviews: An empirical investigation of online review manipulation. NBER Working Paper No. 18340. Meusburger, P. (2009) Spatial mobility of knowledge: A proposal for a more realistic communication model. disP, 177: 29–39. Mok, D., Wellmann, B., Carrasco, J. (2010) Does distance matter in the age of the Internet? Urban Studies, 47(13): 2747–2783. Molinari, G., Sangin, M., Dillenbourg, P., Nüssli, M.-A. (2009) Knowledge interdependence with the partner, accuracy of mutual knowledge model and computer-supported collaborative learning. European Journal of Psychology of Education, 24(2): 129–144. Napoli, P.M. (2010) Revisiting “mass communication” and the “work” of the audience in the new media environment. Media, Culture and Society, 32(2): 505–516. Olsen, C., Olsen, J. (2003) Mitigating the effects of distance on collaborative intellectual work. Economics of Innovation and New Technology, 12(1): 27–42. Otto, P., Simon, M. (2008) Dynamic perspectives on social characteristics and sustainability in online community networks. System Dynamics Review, 24: 321–347. Pinch, T. (2003) Giving birth to new users: How the Minimoog was sold to rock and roll. In: Oudshoorn, N., Pinch, T. (eds.) How Users Matter: The Co-construction of Users and Technologies. Cambridge, MA: MIT Press, pp. 247–270. Polanyi, M. (1966) The Tacit Dimension. New York: Doubleday. Preece, J. (2000) Online Communities: Designing Usability, Supporting Sociability. Chichester: Wiley. Preece, J., Nonneke, B, Andrews, D. (2004) The top 5 reasons for lurking: Improving community experiences for everyone. Computers in Human Behavior, 20: 201–223. Putnam, R.D. (2000) Bowling Alone: The Collapse and Revival of American Community. New York: Simon & Schuster. Ren, Y., Kraut, R., Kiesler, S. (2007) Applying common identity and bond theory to design of online communities. Organization Studies, 28(3): 377–408. Rubicon Consulting (2008) Online Communities and Their Impact on Business: Ignore at Your Peril. Rubicon Consulting, Inc. Sessions, L.F. (2010) How offline gatherings affect online communities: When virtual community members “meetup”. Information, Communication and Society, 13(3): 375–395. Shah, S. (2005) Open beyond software. In: Cooper, D., DiBona, C., Stone, M. (eds.) Open Source 2.0: The Continuing Evolution. Sebastopol, CA: O’Reilly Media, pp. 339–360. Song, M., Berends, H., van der Bij, H., Weggeman, M. (2007) The effects of IT and co-location on knowledge dissemination. Journal of Product Innovation Management, 24(1): 52–68. Stark, D. (2009) The Sense of Dissonance: Accounts of Worth in Economic Life. Princeton: Princeton University Press. Stegbauer, C. (2013) Grenzen virtueller Gemeinschaften: Strukturen internetbasierter Kommunikationsforen. Wiesbaden: Springer. Stehr, N. (2001) A world made of knowledge. Society, 3/4: 89–92. Stewart, D. (2005) Social status in an open-source community. American Sociological Review, 70: 823–842. Storper, M., Venables, A.J. (2004) Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 4: 351–370. von Hippel, E. (1994) “Sticky information” and the locus of problem solving: Implication for innovation. Management Science, 40(4): 429–439. von Hippel, E. (2005) Democratizing Innovation. Cambridge, MA: MIT Press. Weick, K.E., Roberts, K.H. (1993) Collective mind in organizations: Heedful interrelating on flight decks. Administrative Science Quarterly, 38(3): 357–381. Wenger, E. (1998) Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press. Wiertz, C., de Ruyter, K. (2007) Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities. Organization Studies, 28(3): 347–376. Yaqub, O., Nightingale, P. (2012) Vaccine innovation, translational research and the management of knowledge accumulation. Social Science and Medicine, 75(12): 2143–2150.

34. Performativity and the innovation–replication dilemma Luciana D’Adderio

INTRODUCTION A fundamental issue in transferring organizational practices or routines across geographic settings concerns how organizations address potential trade-offs between pressures to replicate routines precisely, and pressures to innovate and adapt routines to their new context. In this chapter I draw on a case of complex routines transfer to call for a more dynamic characterization of transfer as an emergent achievement involving the effortful re-creation of routines at a new location. By building on recent advances in Routine Dynamics and Performativity Theory, I argue that routines transfer and re-creation can be usefully theorized as the (constantly challenged) outcome of ‘performative struggles’ amongst competing agencies and their performative programmes. The performative framework helps in advancing a more dynamic perspective on routines transfer while also focusing our attention towards the key role of (physical and digital) artefacts and communities in mediating routines transfer and replication. What Is Performativity In recent years we have witnessed a substantial upsurge of interest towards Performativity Theory as a means to capture and theorize the deeper sociomaterial dynamics underpinning the emergence and persistence of market and organizational structures and processes. Originally the brainchild of Economic Sociology (Callon 1998, MacKenzie 2006), where it has been developed to capture the effect of market theories over market creation and evolution, Performativity has been more recently brought to the attention of Management scholars, particularly in the fields of Organizational Studies and Organizational Theory (Beunza and Stark 2004, Ferraro et al. 2005, D’Adderio 2008 and 2011, Cabantous and Gond 2011, D’Adderio and Pollock 2014). In this chapter, I begin by introducing some insights into Performativity, subsequently proceeding to show how this can be used to inform a deeper understanding of routines transfer and replication, especially with respect to the role of materiality. Performativity Theory (Callon 1998 and 1999, MacKenzie 2006) suggests that theories and models are not simple descriptions of settings but they transform the settings which they describe. Economic Theory, for example, does not simply describe actual markets but it alters them. Performativity theorizes the influence of models and theories on markets as a dynamic and iterative process involving ‘disentangling’, ‘framing’ and ‘overflowing’ (Callon 1998, 1999). ‘Disentangling’ (Callon 1998) involves creating a model or theory by ‘detaching’ people and things from their local context (see also Callon, Chapter 36, this volume). Finance Theory, for example, is created by detaching agents and goods from 556

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the financial markets in which they are embedded. Disentangling is followed by ‘framing’ (Goffman 1974), by which a model or theory (i.e. Finance Theory) is able to influence reality (i.e. financial markets), thus making the theory more similar to the model. The framing process is typically mediated by a plethora of material tools and devices which contribute to framing transactions (Garcia, in Mackenzie et al. 2007). No matter how effective, however, Performativity warns us that framing is never complete. Relationships previously framed tend to spill out of the frame, introducing dissimilarity between the model and reality (referred to as ‘overflowing’ in Callon 1998). And the process does not end here. Overflowing is often followed by further ‘reframing’ which again prompts increasing similarity between the model and reality. According to Performativity, thus, economic theories and models are performed through iterative cycles of framing, overflowing and reframing which capture and regulate the dynamic adaptation of the model and reality. Routines and Performativity Recent advances in Routine Theory (D’Adderio 2008, 2011) have shown how the Performativity framework can be harnessed to improve our understanding of Routine Dynamics (Feldman 2000, Feldman and Pentland 2003, Pentland and Feldman 2005, Feldman et al. 2016). In the context of industrial rule-following, for example, D’Adderio (2008) has drawn on Performativity to theorize the mutual adaptation between, on the one hand, procedures/Standard Operating Procedures (SOPs) and rules (theories or models of routines), and, on the other hand, actual routinized performances. Dynamic adaptation in this context involves iterative cycles of framing, overflowing and reframing, through which SOPs and rules are performed and shape (as well as being shaped by) performances. More specifically, through framing, rules and procedures influence performances by selectively favouring some actions and action patterns at the expense of others; overflowing captures how performances inevitably tend to spill over or deviate from the rule or procedure, generating variations which in some cases may be preserved and embedded within the rule (often through artefacts); and through reframing (often modified) rules and procedures are able once again to frame actions. D’Adderio’s (2008) study of how engineers enact formal procedures and rules while performing design routines therefore shows how formal (in this case, software-embedded) rules and procedures frame the engineering design process; how engineers are able to – at least partially or temporarily – work around or modify the rule or procedure; and how and when the modified rule or procedure continues to frame performances.

PERFORMATIVITY AND ROUTINES TRANSFER The example above demonstrates that the performative framework can be usefully harnessed to theorize the emergence of routines out of dynamic cycles of mutual adaptation (including Performativity and Counter-Performativity, see MacKenzie 2006) amongst the routines’ constituent aspects (actions/performances and action patterns/ostensive aspects, Feldman and Pentland 2003) and their contextual features (e.g. physical and digital/virtual artefacts and communities, see D’Adderio 2011). The example shows how Performativity

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can provide vantage points to unravel the recursive dynamics by which models of routines (e.g. SOPs, rules) and the realities they frame (performances) emerge and co-evolve. This includes theorizing how routines may be made for example more or less similar, and how this might change over time, across organizational contexts, and through the use of tools and artefacts. By extrapolating from these insights, we can see how a performative framework may provide us with some useful theoretical tools to capture the emergent dynamics underpinning routines transfer and replication. Performative Programmes A first insight from Performativity concerns the role of agency in shaping routines. According to the performative framework, the dynamics by which routines emerge can be seen as the outcomes of struggles amongst competing performative programmes (Callon 2007). How does this work in practice? In the course of the struggle between competitive performative programmes, some agencies are able to inscribe their own actions and worldviews into tools and artefacts. These agencies are most likely to succeed in exerting their own influence over the routine. This is because enrolling artefacts tends to solidify actions and assumptions, making them relatively more stable and thus more difficult to oppose or deviate from. Performativity, in this sense, does not do away with human agency but rather helps in explaining how and why some organizational agencies are more influential than others in shaping routines. This in turn allows us to account for how not just individuals but also organizational groups, teams and communities (including epistemic cultures and communities of practice, Knorr Cetina 1999, Cohendet and Llerena 2003) perform with respect to transfer dynamics. Organizational communities, whose motivations and interests are often different and competing (Callon 2004), tend to enact contrasting and often incompatible goals and worldviews, holding important implications for transfer. And there is more. By focusing our attention on how the actors’ competences, views, values and motivations might shift during the course of transfer, Performativity can add to the recent work in Routine Theory which has called for a more dynamic theorization of routines (Feldman et al. 2016), while warning us about the dangers of taking routines’ stability and persistence for granted (see also Feldman 2000). Not only the actions, but also the goals, objectives, motivations and values of a team or community, for example, are never fixed but instead can – and often do – shift or become combined over time with those of another (Callon 2004). The boundaries, composition and forms of these communities are themselves fluid and constantly changing, bearing important implications for routines. The Notion of Agencement A second way in which Performativity can help us understand routines transfer is by affording a more nuanced way to characterize agency, which includes taking into careful consideration the effect of materiality on routines. A useful Performativity construct here is the notion of agencement. The term agencement comes from the French language, where it refers to the act of ‘arranging’ or ‘assembling’ heterogeneous materials together (Callon 1998). In the Performativity idiom, a socio-technical agencement is the assemblage of heterogeneous elements that is required for the assumptions contained in a model or formula to be actualized or become real (MacKenzie 2003, Callon 2007, Chapter 36, this

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volume). Agencements are therefore hybrid collectives, arrangements made up of human beings (e.g. bodies, emotions, intentions) as well as those material, technical and textual devices that take part in the action and in the cognitive process (Hutchins 1995). A crucial feature of these assemblages is their capacity to act in different ways, depending on their configuration (Çalışkan and Callon 2010). For this reason the notion of agencement can help us gain a deeper appreciation of routines transfer dynamics, as discussed further below. For the time being I want to emphasize how relinquishing the predominant emphasis on human actors shared by much of the Organizational and Organizational Routines literature, can provide us with the ability to account for the combined role of people and artefacts/artefactual representations in shaping routines. It can do so by shifting the emphasis from the individual actor seen as operating in isolation and often detached from its context, to focus on the heterogeneous ensembles of elements that are typically involved in performing routines. Rather than centring on the individual, the notion of agencement thus draws our attention towards the socio-technical networks of actors and actants which support the performance of a routine. This approach is inspired by the view that routine-following, as a key form of organizational action and cognition, is mostly a collective and distributed process (Narduzzo et al. 2000, Orlikowski 2002). Thanks to its potential to account for the influence on routines of diverse and distributed human and material agencies, operating at multiple levels and shifting over time, the notion of agencement can help us advance a more dynamic understanding of routines transfer. The Role of Materiality A third key aspect of the performative framework’s potential to capture routine transfer dynamics is that it helps in theorizing the role of rules and procedures (as artefactual instantiations of routines) by considering them endogenous to routines (in contrast with much of the extant literature which more or less implicitly relegates artefacts to the periphery of routines). In Performativity, economic models and theories are in fact not ‘external’ to the market but an intrinsic part of it. They are not ‘cameras’, or detached external representations of markets or market abstractions, but ‘engines’ that make the markets tick (MacKenzie 2006). A similar observation can apply to formal routines seen as process models which have a range of effects on routines. Procedures thus are not simple ‘guiding principles’ which lie outside the routine and set the boundaries of what can or can’t be done. They are instead constituent components of routines, directly (although never deterministically, see D’Adderio 2008 for a discussion) contributing towards shaping their course. D’Adderio (2011) made this point by showing how artefactual routines, including models, procedures and rules, can be usefully theorized as being endogenous to the routine’s generative system, thus adding to Feldman and Pentland’s (2003) and Pentland and Feldman’s (2005) original Routine Dynamics framework. This implies that artefacts and artefactual representations of routines play very central and active roles as mediators amongst routinized actions and action patterns, bringing materiality to the fore as an essential and core aspect of Routine Dynamics. But just how do artefacts contribute towards shaping routines? Performativity can help us capture the influence of artefacts on routines’ evolution (a role already suggested in Nelson and Winter 1982) by characterizing the specific ways in which artefacts as mediators shape the interactions between routines’

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aspects and artefacts. In contrast with an early view of SOPs and rules as either imperfect representations/passive descriptions of process that can be easily dismissed/disused, or as prescriptions that are compulsively and automatically reproduced (see Cohen et al. 1996 for a discussion), Performativity shows that – once embedded in a thick web of technological artefacts and organizational relationships – some kind of mutual adaptation (or ‘performation’) tends to occur (D’Adderio 2008). Performativity thus can help in moving beyond simplistic ‘on/off’ accounts of ‘best practice’ adoption or transfer, which characterizes much of the Management Theory’s mainstream approach to transfer, by unveiling some of the deeper dynamics involved in the simultaneous construction of routines and their artefacts. This is clearly important when trying to characterize what happens when artefactual routines (as templates) are uplifted from one site and transferred to another, and can therefore provide some interesting new insights over the micro dynamics involved in performing routines transfer.

THE TRANSFER OF A COMPUTER SERVER To illustrate the performative approach’s power to theorize Routine Dynamics, I draw from the valuable case of a complex product transfer at a high-technology organization. This is a leading US-based electronics manufacturer with a global workforce of 35,000 and an annual turnover of $10bn (at the time of fieldwork). I collected the data during a three-year ethnographic study of the $30m transfer of a computer and its manufacturing capability from the US to the UK. The product transferred was at the time the world’s most complex server, an imposing machine priced at around $2m.This case is particularly interesting for our purpose as, from the very outset, it revealed some strong tensions between the need to replicate routines identically and improving/adapting them to the new site, a tension also known as the ‘replication dilemma’ (Winter and Szulanski 2001). Recent research has shown how unravelling the innovation–replication dilemma can provide important new insights into Routine Dynamics (D’Adderio 2014). The transfer project was aimed at increasing the manufacturer’s production capacity to meet the forecasted escalating global demand for high-end computer products. The move entailed uplifting, transferring and validating the server product, its production process and the related production facilities from the US to the UK. The complexity of the product prompted the early decision to duplicate the origin capability identically through implementing Intel’s ‘Copy Exactly!’ philosophy. Often applied within the context of replicating food franchises such as McDonald’s (Love 1995, Iansiti and West 2003), this strategy involved reproducing the original template/working example precisely, while refraining from introducing any changes until the transfer had been completed and the transferred recipe at the new site had been validated. The common rationale behind copy exactly is that even the smallest uncontrolled and undetected changes applied to a template could generate so many complex and unpredictable interactions that it would be no longer possible to use the original site as a referent for diagnostics (Winter et al. 2012). In the case of our server, due to its high complexity, any changes applied to the template could have resulted in significant negative impacts on downstream product attributes such as its quality and reliability. If product complexity and the need for the server to manage mission-critical activities at customer sites called for

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the highest level of quality and reliability in the field, the need for high performance and customization resulted in competing pressures for innovation and improvement (including both the constant pressure to introduce new architectures and faster components, and the pressure for continuous process improvement). Below I examine how the tension played out as seen through the lens of the Performativity framework. This will in turn allow me to address how the (often) competing goals of replication and innovation, and the associated pressures to align and improve, developed within the course of the transfer, and how they shaped routines and their sociomaterial context. Transfer: Replication Prevailed The dominant force that was at play during transfer was replication, involving the quest for the closest possible alignment across sites. This was exemplified by the prevalence of the copy exactly/alignment goal and the supporting tools and communities, reflecting the dominant view that processes had to be transferred exactly. The alignment imperative was principally supported by project management at both sites. Its underlying assumption was that, during transfer, alignment between sites was more important than other factors such as product and process improvement and aligning to ‘best’ practice (i.e. ISO 9001, Six Sigma). As the philosophy behind copy exactly involved initially freezing all variables, the origin template (articulated, captured and embedded in digital form into a shared Computer Model) was allowed to change only minimally; and any potential or actual changes (such as those mandated by local health and safety regulations) had to be painstakingly controlled and tracked throughout. A range of tools and methodologies were introduced at this point to support the close alignment across the two sites. Tools and artefacts included the ‘Big Rules’, a set of highlevel imperatives that played a key role in enforcing control and coordination throughout the project. Rules were intended to ensure that everything was exactly the same, products were mirror image, processes were cut and pasted, and teams were carbon copied across sites. Tools also included an ‘Exception Approval’ methodology inspired by NASA, which established that any deviations from the model had to be detected and either eliminated or controlled through a strict cross-site and cross-functional approval process. In addition to tools, a number of inter-site and inter-functional communities were created to generate ‘spaces’ (Silke and Langley 2016) where differences could be detected, discussed and aligned or tracked. For example, a joint face-to-face committee, and associated online community, was set up including function representatives from both sites, to manage the transfer process. The team was named ‘Failure Is Not An Option’ (FINAO) from NASA’s Apollo 13 space mission, to reflect the importance of maintaining close alignment. This comprehensive and exacting set of management tools and organizational communities supported ‘copy exactly’ by standardizing the product, process and organization, orienting actions and action patterns towards alignment and minimizing or eliminating as far as possible any deviations from the template. Any differences that emerged during this process, whether due to origin template ambiguity, transfer inaccuracy, or resistance by local communities, were as far as possible controlled or migrated to match the template. Origin routines were able to closely and progressively frame performances at destination, making them more and more similar to those at origin. Despite the pressure for innovation remaining present throughout,

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replication and the related pressure for alignment prevailed during transfer. This however did not to last indefinitely. Post-Transfer: Innovation Prevailed As transfer moved towards completion, the emphasis began to shift away from replication and towards innovation. A thorough testing process, which confirmed that the two production sites were achieving statistically similar performance results (see also Winter et al. 2012), triggered a shift from alignment to improvement. The need for innovation and improvement, which was deeply felt within the engineering communities (especially at destination), could now be voiced. This supported the rationale that improving product and process was more important or urgent than maintaining alignment. If a product specification is still evolving, as is often the case in fast-changing high-technology environments such as high-end electronics, it makes little sense to copy exactly as this might imply copying a specification that is outdated or underperforming. Another example of the need for improvement was the need for aligning to global standards, such as ISO 9001. Up to this point, destination had been forced to ‘de-standardize’ by downgrading their procedures in order to align with origin, which was not yet ISO compliant. At the same time, UK engineers, who had been held back by alignment, could now draw upon their experience and expertise to begin to introduce product and process improvements. Up to this point their input had been frustrated by the mandate to support ‘same practice’ as opposed to ‘best practice’. As the template specification, turned into a set of SOPs and inscribed into the shared Computer Model’s repository, was incrementally released and opened to (controlled) changes, destination’s practitioners were able to explore ways in which their knowledge and skills could be finally used to improve on the original template. A range of new tools and communities were created at this point to replace the Big Rules, FINAO, and the Exception Process. The new tools introduced at this time were oriented towards facilitating innovation and change rather than replication and alignment. An example of a tool and associated community was the Change Review Board, whereby function representatives met to discuss, review and negotiate proposed changes. Another example was the cross-site online Engineering Forum community, which had been until then marginalized but was now able to propose initiatives directed at innovation and improvement. Also in this case, however, the emphasis on innovation and improvement coexisted with the still present contrasting imperatives supporting replication and alignment, giving rise to occasions during which the introduction of changes was significantly obstructed or simply slowed down. The perceived need for ‘agility’, for example, principally upheld by US management, continued to fuel resistance. Global standards were seen by this community as potentially reducing agility therefore posing a threat to competitiveness. The American emphasis on agility thus persisted, albeit coexisting with the initially European, but now globally acknowledged, call for global standards. Another example of resistance is the way the now widely acknowledged need for improvement found obstacles in the persistent emphasis on alignment which still dominated discussions, as evidence of new gaps at lower procedural levels were progressively brought out into the open. The increasing acceptance of the importance of allowing changes to foster engineering creativity, innovation and improvement had the marginal opposite effect of producing further (albeit only

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partially or temporarily successful) attempts to enforce alignment. The renewed efforts in favour of alignment involved a closer scrutiny of performances, for example through formal audits, which combined the search for aligning actions with that for aligning meanings. Audits however were resource consuming and were not bound to last in a climate which was increasingly oriented towards change and innovation. Performative Struggles between Innovation and Replication We can now draw on Performativity to theorize how this outstanding organization managed to harness complexity and uncertainty by modifying its routines to address the simultaneous presence of conflicting pressures pushing towards replication/alignment (exploitation, March 1991) and innovation/improvement (exploration, ibid.) at the same time. During the course of transfer, ‘copy exactly’ constituted a very strong imperative, one which was supported by management at both sites and enacted through the creation of a range of dedicated physical and digital/virtual tools (SOPs, the Big Rules, the Computer Model) and organizational teams or communities (FINAO). The original template, captured by engineers and painstakingly embedded in the shared Computer Model as a set of procedures and rules, was able at this point to frame performances at destination. This generated progressive similarity between routines at origin and those at destination (i.e. through ‘carbon copy’, ‘mirror image’ or ‘copy exactly’ rules). For example, changes were implemented to the UK machine-assembly procedures in order to align these to the US procedures. Aligning in this case involved changing the UK labour structure (whereby multi-skilled technicians were responsible for an entire machine assembly) to match the US (where specialized operators enacted short fragments of the assembly sequence). As a consequence, the number of changes allowed to the template, the overflowing in Performativity terms, was minimal. Deviations were due to the impossibility to fully codify the process into the Computer Model, which left gaps in the process definition at the micro level. In turn, gaps provided further opportunities for divergence, fuelled by differences in organizational culture and structure. Examples were small deviations from shared procedures at lower procedural levels which temporarily escaped detection. There were times when UK engineers, frustrated in their inability to bring their experience to the process and unable to accept the downgrade to non-ISO compliant procedures for the sake of alignment, took these gaps as opportunities to introduce much needed improvements. The ‘copy exactly’ imperative, however, began to give way to innovation during post-transfer. Once the statistical similarity between origin and destination had been verified, the alignment goal was loosened up by releasing the shared product and process definitions and allowing them to change, albeit still in a controlled fashion. From this point onwards the emphasis shifted from the replication/alignment to the innovation/ improvement imperative which was now finally able to shape or ‘frame’ performances. Acknowledging innovation involved creating scope for ISO compliance, cross-platform and cross-product standardization, and continuous improvement. While innovation and improvement increasingly framed performances, they nevertheless encountered resistance from persistent contrasting imperatives. The need for flexibility, for example, clashed with the need for standardization, while agility and innovation clashed with regulatory requirements for ISO compliance. As before, the coexistence of contrasting goals generated a

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renewed, if constrained and temporary, pull towards replication and alignment (again, overflowing), demonstrated by the set-up of audits as a last resort attempt to regain control over deviations.

TOWARDS A PERFORMATIVE PERSPECTIVE ON TRANSFER A performative approach becomes especially valuable when the issue at stake is how routines can be transferred, reproduced or even replicated identically. Performativity can provide vantage points to theorize how organizations might be able to replicate routines precisely despite uncertainty, complexity, discontinuity and conflict characterizing the reality in which contemporary organizations operate. In supporting a novel characterization of the relationship between routines aspects, artefacts and distributed agencies, a performative approach can provide new grounds to theorize Routine Dynamics. This includes understanding the role of transfer as the locus for performative struggle amongst competing goals and objectives; the role of distributed and diverse agencies in supporting or obstructing transfer and replication; and the role of tools and artefacts on the transfer and (exact) reproduction of routines. Performing Competing Goals Seeing routines transfer through the lens of Performativity Theory highlights the need to pay closer attention to the combined influence of contrasting organizational goals – which compete to configure actions and action patterns – over routines and their outcomes. Performativity provides fertile ground to capture the emergence and diffusion of organizational objectives, and these may shape routines during the course of transfer and beyond. The transfer story outlined in this chapter (but see D’Adderio 2014 for a more detailed and comprehensive theorization) shows how contrasting goals may be coexisting and attempting to configure routines in any organization at any point in time. The first of the imperatives highlighted by our evidence was ‘copy exactly’ or ‘replication’ and the associated pressure towards maintaining close alignment across transfer sites. In Performativity terms, for replication to be successful in shaping routines at destination, it had to generate a web of digital and physical objects and relationships which supported the assumptions of close alignment. In our case, this involved assembling a network comprising both people and artefacts which supported copy exactly while enacting routines, thus orienting them towards replication. From the outset, ‘copy exactly’ drove the capture and codification of routines at origin and their inscription into SOPs and the digital Computer Model. The level of control embedded in these as well as other sophisticated methodologies and (physical and digital/virtual) communities meant that the template was able to frame performances at destination, making them increasingly similar to those at origin. While there were of course minor instances of overflowing, due for example to engineers experimenting in their sandbox, the absence of any substantial modifications signalled that copy exactly was at that stage successfully framing routines supporting cross-site alignment. Replication, however, was not the only goal in attempting to frame routines during transfer. The pressure to improve on both product and process meant that innovation was

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challenging replication every step of the way. Artefacts and communities were oriented at this stage towards reframing routines so that any deviations would be controlled or eliminated, as far as possible. During post-transfer, innovation gained strength and managed to increasingly overshadow replication. In this instance, another dedicated set of (physical and digital/virtual) artefacts and communities were created and enacted to support improvement at the expense of close alignment. Innovation, in other words, progressively succeeded in framing routines during post-transfer, generating instances of overflowing whereby practitioners attempted to orient tools and communities back towards supporting alignment. The performative framework provides us with a new conceptualization of transfer as a continuously emergent achievement, the result of struggles amongst competing organizational goals and the heterogeneous webs of things and relationships which they are able to assemble and maintain. The Agencement as Agentic Unit Earlier in the chapter I explained how the fluid web of people and things enacting a goal or objective can be usefully captured through the Performativity notion of agencement (Callon 1998). This concept helps in focusing our attention towards the multiple goals that coexist at any time in organizations and the heterogeneous networks they subsume which might support them in their efforts to prevail over competing goals and shape routines. As the transfer study in this chapter illustrates, the sources of diversity that threatened alignment were abundant and pervasive both during transfer and beyond. Discrepancies in actions and action patterns, as discussed above, were often related to the often uncomfortable coexistence of multiple and diverse, physical and virtual, organizational communities, enacting different knowledge, goals, preferences, values and motivations. Functional communities (i.e. engineers), thus, could at times collide with project communities (i.e. the inter-functional transfer team). For example, sites might at times agree at the level of the individual counterparts (i.e. a US engineer and his/her UK counterpart) while at the same time disagreeing with the overall direction of the project. An engineer, for example, might choose to support ‘improvement’ even when it was in clear contrast with the project’s emphasis on ‘alignment’. An additional source of complexity consisted in the fact that alliances, competences and priorities were not stable but changed over time. For example, while initially US managers fiercely promoted ‘copy exactly’, seen as a means to retain control over their product, they later acknowledged the need for improvement. Our analysis thus reveals the presence of complex and ever-changing arrays of contrasting and complementary organizational communities and their artefacts, whose alliances towards one goal or another can significantly shift over time and across contexts, shaping routines and their outcomes. Artefacts as Mediators within Performative Struggles This finally takes us to the role of (physical and digital) artefacts and their effects over Routine Dynamics, a topic which I have analysed here within the specific context of transfer. According to Performativity, the agencies that are likely to prevail are those who are able to invoke tools and technologies to support their goals. Artefacts can help make some agencements and their goals stronger and more durable. The ‘replication’ agencement,

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for example, which supported alignment, enlisted SOPs, the Computer Model, and the Big Rules during transfer. In this case, SOPs, and the digital and physical tools in which they were codified, helped provide a ‘principle of materiality’ (see also Orlikowski and Scott 2008) which strengthened the ‘copy exactly’ dictum thus making it more durable and easier to enforce. The rules of course could be bypassed, and indeed to an extent they were, and yet deviations were mostly unable to generate impacts which escalated beyond the level of the local team or function. During post-transfer, (physical and digital/ virtual) tools and communities such as the Change Request and the Engineering Forum supported innovation and improvement by making changes easier to apply and validate across functions and sites. Initiatives aimed at limiting change were applied (as in the case of audits) but they were only able to succeed in temporarily slowing down the path towards improvement. Finally, our evidence also shows how the artefacts themselves evolved and changed as a consequence of their involvement in performative struggles. For example, while standard procedures initially reflected the ‘alignment’ goal, they were later modified to support ‘innovation’ and ‘improvement’. It is interesting to point out how, over time, artefacts such as the Computer Model and the procedures themselves changed, reflecting the outcomes of the performative struggles in which they were involved. In this case Performativity allows us to throw new light on the role of physical and digital artefacts beyond that of intermediaries (Star and Griesemer 1989), in highlighting their active role as key mediators (Latour 2005) participating in the creation and re-creation of routines.

CONCLUSIONS In this chapter I have addressed the fundamental issue of how organizations may learn to address the tensions which arise between pressures to replicate and pressures to innovate while transferring routines across highly diverse and distributed organizational settings. In so doing, I have drawn on a Performativity-inspired theoretical framework to capture routines transfer dynamics, including the role of context (physical and digital/virtual artefacts and communities) in shaping routines. The proposed approach adds to the replication stream in Organizational Theory (Szulanski 1996, Winter and Szulanski 2001, Iansiti and West 2003, Winter et al. 2012) by showing how replication can be usefully theorized as an act of re-creation of origin routines at destination. Transfer as re-creation can be usefully theorized as a dynamic and effortful process (Pentland and Reuter 1994, D’Adderio 2014, Danner-Schröeder and Geiger 2016) involving the enactment of different, and often competing, performative programmes (goals and the array of sociomaterial relationships which sustains them) which in turn shape routines and their outcomes. Drawing from Performativity Theory (Callon 1998, MacKenzie 2006), I have thus introduced a new framework which, while building on previous approaches to routines transfer, can shed new light on the situated (Lave 1988, Suchman 1987) and distributed (Hutchins 1995) organizational mechanisms that regulate the transfer and re-creation of routines at a new site. This approach points to some novel and relevant findings. First, the new framework characterizes routines transfer as a dynamic and effortful process involving performative struggles (Callon 1998, D’Adderio 2011, D’Adderio and Pollock 2014, Cohendet and Simon 2016) amongst organizational programmes which

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compete to configure routines. Performative programmes, as in this case of innovation and replication, typically span across organizational locations, functions and levels, and gain influence and viability by engaging a diverse and dispersed set of organizational teams and communities and heterogeneous sets of digital and physical tools and artefacts. Competing (and often equally strategically relevant) goals may pull the organization towards similar or opposite directions, with important implications for routines and their outcomes (e.g. alignment, improvement). In addition, the relative influence of a programme can – and often does – change over time (D’Adderio 2014, D’Adderio and Pollock 2014), again bearing important consequences for Routine Dynamics, in the context of transfer and beyond. Second, I have emphasized the potential usefulness of thinking in terms of the agencement (rather than simply agency) as the agentic focus for studying routines and routines transfer. While acknowledging the key role of individual agents, this notion allows us to account for the combined influence of actors and artefacts in shaping routines. We have therefore seen how replication prevailed in shaping routines during transfer through the creation of a series of (physical and digital/virtual) tools and communities which supported close alignment to the original template. During post-transfer, the innovation imperative gained strength over replication by similarly involving a set of (once again, physical and digital/virtual) artefacts and communities which supported improvement. This shows how organizational participants endowed with varying toolsets and technologies can support the emergence of different sociomaterial assemblages, which in turn may sustain different organizational theories (goals, strategies) thus fundamentally influencing organizational outcomes. Third, I have highlighted how artefacts (e.g. SOPs, Computer Models) can play key roles in routines transfer. In this context Performativity points to a much more active role for materiality than it has been allowed so far in the routines transfer literature, in showing how physical and digital artefacts, as active ‘engines’, rather than passive representations, can directly (albeit never deterministically) influence the course of routines. Artefacts and tools can be thus enrolled as mediators, not simply intermediaries, within the struggles amongst competing agencies and/or organizational communities and their performative programmes. They are therefore often critical in shaping performances and are themselves transformed as a consequence of their involvement in performative struggles. In conclusion, this chapter has advanced a novel characterization of routines transfer – based on Performativity Theory – which captures the more complex, micro-level dynamics underpinning the effortful re-creation of routines at a new location. While widely applicable to all cases of routines transfer, this approach is especially productive when the focus of our study is a complex transfer, involving the re-creation of routines across highly diverse and distributed organizational settings. Within this transfer context, Performativity can provide a more nuanced characterization of routines transfer than is currently available in the mainstream transfer literature. In so doing, a performative approach can help us lay the foundations for a new generation of transfer studies, one which sees organizations as the – strongly and continuously challenged – outcome of performative struggles amongst potentially and/or temporarily conflicting goals (in this case innovation and replication) through which routines are created and re-created.

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REFERENCES Beunza, D. and Stark, D. (2004) Tools of the trade: The sociotechnology of arbitrage in a Wall Street trading room. Industrial and Corporate Change, 13, 369–400. Cabantous, L. and Gond, P. (2011) Rational decision making as performative praxis: Explaining rationality’s éternel retour. Organization Science, 22, 573–586. Callon, M. (1998) An essay on framing and overflowing: Economic externalities revisited by sociology. In M. Callon (ed.) The Laws of the Markets. Blackwell, Oxford, 244–269. Callon, M. (1999) Actor-network theory – the market test. Sociological Review, 47(S1), 181–195. Callon, M. (2004) The role of hybrid communities and socio-technical arrangements in participatory design. Journal of the Center for Information Studies, 5, 3–10. Callon, M. (2007) What does it mean to say that economics is performative? In D. MacKenzie, F. Muniesa and L. Siu (eds) Do Economists Make Markets? On the Performativity of Economics. Princeton University Press, Oxford, 310–357. Callon, M. (2017) Markets, marketization and innovation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation. Edward Elgar Publishing, Cheltenham and Northampton, MA, 589–609. Çalışkan, K. and Callon, M. (2010) Economization, Part 2: A research programme for the study of markets. Economy and Society, 39(1), 1–32. Cohen, M.D., R. Burkhart, Dosi, G., Egidi, M., Marengo, L. Warglien, M. and Winter, S.G. (1996) Routines and other recurring patterns of organisations: Contemporary research issues. Industrial and Corporate Change, 5, 653–698. Cohendet, P. and Llerena, P. (2003) Routines and incentives: The role of communities in the firm. Industrial and Corporate Change, 12(2), 271–297. Cohendet, P. and Simon, L. (2016) Always playable: Recombining routines for creative efficiency at Ubisoft Montreal’s video game studio. Organization Science, 27(3), 614–632. D’Adderio, L. (2008) The performativity of routines: Theorising the influence of artefacts and distributed agencies on routine dynamics. Research Policy, 37(5), 769–789. D’Adderio, L. (2011) Artifacts at the centre of routines: Performing the material turn in routines theory. Journal of Institutional Economics, 7(2): 197–230. D’Adderio, L. (2014) The replication dilemma unravelled: How organizations enact multiple goals in routines transfer. Organization Science, 25(5): 1325–1350. D’Adderio, L. and Pollock, N. (2014) Performing modularity: Competing rules, performative struggles and the effect of organizational theories on the organization. Organization Studies, 35(12), 1813–1843. Danner-Schröder, A. and Geiger, D. (2016) Unravelling the motor of patterning work: Toward an understanding of the microlevel dynamics of standardization and flexibility. Organization Science, 27(3), 633–658. Feldman, M.S. (2000) Organizational routines as a source of continuous change. Organization Science, 11, 611–629. Feldman, M.S. and Pentland, B.T. (2003) Reconceptualizing organizational routines as a source of flexibility and change. Administrative Science Quarterly, 48, 94–118. Feldman, M., Pentland, B., D’Adderio, L. and Lazaric, N. (2016) Beyond routines as things: Introduction to the special issue on routine dynamics. Organization Science, 27(3), 505–513. Ferraro, F., Pfeffer, J., and Sutton, R.I. (2005) Economics language and assumptions: How theories can become self-fulfilling. Academy of Management Review, 30, 8–24. Goffman, E. (1974) Frame Analysis. Harper, New York. Hutchins, E. (1995) Cognition in the Wild. MIT Press, Cambridge, MA. Iansiti, M. and West, J. (2003) Experience, experimentation, and the accumulation of knowledge: The evolution of R&D in the semiconductor industry. Research Policy, 32, 809–825. Knorr Cetina, K. (1999) Epistemic Cultures: How the Sciences Make Knowledge. Harvard University Press, Cambridge, MA. Latour, B. (2005) Reassembling the Social: An Introduction to Actor Network Theory. Oxford University Press, Oxford. Lave, J. (1988) Cognition in Practice. Cambridge University Press, Cambridge. Love, J.F. (1995) McDonald’s: Behind the Arches (Rev. Ed.). Batnam Books, New York. MacKenzie, D. (2003) An equation and its worlds: Bricolage, exemplars, disunity and performativity in financial economics. Social Studies of Science, 33(6), 831–868. MacKenzie, D. (2006) An Engine, Not a Camera: How Financial Models Shape Markets. MIT Press, Cambridge, MA. MacKenzie, D., Muniesa, F. and Siu, L. (eds) (2007), Do Economists Make Markets? On the Performativity of Economics. Princeton University Press: Princeton.

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March, J.G. (1991) Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. Narduzzo, A., Rocco, E. and Warglien, M. (2000) Talking about routines in the field. In G. Dosi, R.R. Nelson and S.G. Winter (eds) The Nature and Dynamics of Organizational Capabilities. Oxford University Press, New York, 27–50. Nelson, R.R. and Winter, S.G. (1982) An Evolutionary Theory of Economic Change. Belknap, Cambridge, MA. Orlikowski, W.J. (2002) Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249–273. Orlikowski, W.J. and Scott, S.V. (2008) Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2, 433–474. Pentland, B.T. and Feldman, M.S. (2005) Organizational routines as a unit of analysis. Industrial and Corporate Change, 14(5), 793–815. Pentland, B.T. and Rueter, H.H. (1994) Organizational routines as grammars of action. Administrative Science Quarterly, 39(3), 484–510. Silke, B. and Langley, A. (2016) The interplay of reflective and experimental spaces in interrupting and reorienting routine dynamics. Organization Science, 27(3), 594–613. Star, S.L. and Griesemer, J.R. (1989) Institutional ecology, ‘translations’ and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19, 387–420. Suchman, L. (1987) Plans and Situated Action: The Problem of Human–Machine Interaction. Cambridge University Press, Cambridge. Szulanski, G. (1996) Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17, 27–43. Winter, S.G. and Szulanski, G. (2001) Replication as strategy. Organization Science, 12(6), 730–743. Winter, S.G., Szulanski, G., Ringov, D. and Jensen, R.J. (2012) Reproducing knowledge: Inaccurate replication and failure in franchise organizations. Organization Science, 23, 672–685.

35. Coworking and innovation Janet Merkel

INTRODUCTION When Richard Florida published and promoted his ideas on the “Creative Class” as the new economic driving force he was met with intense criticism. However, what has been less debated is his argument that “place has become the central organizing unit of our time, taking on many of the functions that used to be played by firms and other organizations” (2004, p. 6). While Florida’s argument referred to the coordinating function of urban agglomerations, it is one particular kind of place this chapter wants to focus on and that assumes these coordinating functions for independent, freelance workers: coworking spaces, a new type of flexible shared and managed workplace that has recently emerged in cities worldwide. These coworking spaces and the associated social practice of coworking exemplify a new form of work organization for freelance workers. However, these workspaces are not just flexible shared office spaces for professionals “working alone together” (Spinuzzi 2012). Instead, they promote a community-based approach to the organization of freelance work and creative sociability in cities. Coworking spaces bring together heterogeneous but often complementary knowledge bases among the individual coworkers and might thus facilitate creative and innovative processes. And coworking spaces are not just the context in which economic activities happen or provide the stage on which entrepreneurial activity is performed; coworking spaces might act as an opportunity structure where entrepreneurial opportunities can emerge. Until now the social phenomenon of coworking and coworking spaces has remained underresearched. We lack a systematic social-scientific analysis on the role of coworking spaces in the urban creative economy as well as on coworking and its assumed effects on knowledge sharing or collaborative activities, and how coworking relates to contemporary economic and social transformation processes. In the context of this Companion, this chapter aims to introduce coworking as a practice of “distributed, interorganizational, collaborative knowledge work” (Spinuzzi 2012, p. 400) and coworking spaces as “complex and heterogeneous relational innovation landscapes” (Schmidt et al. 2014, p. 245). It will explore how an understanding of coworking and coworking spaces can contribute to research questions in economic geography in three different ways. First, a look into coworking sheds light on a growing group of economic actors that have not been fully acknowledged in economic geography: freelancers. Second, it provides a comprehensive micro-perspective into social dynamics of knowledge generation because these spaces bring together heterogeneous groups of actors and different knowledge bases and could further illuminate the role of different types of proximity or distance and shared practice for knowledge generation and innovation. And third, coworking spaces themselves become meaningful actors within the urban creative economy mediating between freelancers, firms and organizations. As new and distinct knowledge sites, coworking spaces can provide an empirical lens into theoretical 570

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questions of relational and spatial proximity for creativity and innovation beyond the firm and within creative urban milieus (Boschma 2005; Amin and Roberts 2008; Desrochers et al., Chapter 14, this volume). In this chapter, I will combine sociological perspectives with recent research in economic geography on the social dynamics of knowledge creation, proximity and the spatialities of creative and innovative processes. The discussion will draw mainly on empirical literature from the field of culture and creative industries, where freelance work has been a subject of intense study for the past decade. However, that does not mean that all freelancers working in those shared offices are creative workers nor that all freelancers work in coworking spaces. My contribution is structured in four sections. First, the chapter introduces the rise of freelancers as independent economic agents in the dominant project-based production mode of creative industries to contextualize the emergence of coworking spaces as a collective coping strategy to deal with the uncertainties and risks associated with freelance work. The second part focuses on introducing coworking and coworking spaces as a new empirical phenomenon in creative urban economies. In the third section, the chapter discusses the forms of social learning, collaboration and innovative activities that coworking can facilitate by bringing together existing empirical research and theoretical discussions. The chapter will conclude by presenting further directions for research on coworking and the social dynamics of knowledge creation and innovation processes.

THE RISE OF THE INDEPENDENT, FREELANCE WORKFORCE Since the 1970s we have witnessed structural changes in labor markets with the erosion of Fordist structures of industrial organization that conveyed the rise of flexible work practices, the “individualization of labor in the labor process” (Castells 2001, p. 282) and the rise of atypical and precarious employment (Beck 1994; Ross 2006). Furthermore, since the 1980s we can see a rhetorical shift in political discourse that emphasizes risktaking and entrepreneurial activities with the construction of the “new economy” (see Thrift 2001; Neff 2013, pp. 39–52). For example, Neff explores in “Venture Labor” (2013) how those narratives and discourses of risk during the dot-com boom in the early 2000s made “individual risk acceptable and framed risk as cool” (p. 4) and, thus, how individual workers internalized and normalized risk and became an entrepreneurial self (Bröckling 2014). New information and communication technologies further enable remote and distributed work practices, such as teleworking from home, and change significantly how, when, and where people work (Rainie and Wellmann 2012; Martins 2015; Spinuzzi 2015). In creative industries, organizational restructuring to reduce costs and to outsource risk as well as regulatory changes (e.g., liberalization policies in the TV sector) have added to shifts in employment relations towards more flexible, nonstandard forms of work that have resulted in a rise of freelance workers (Christopherson 2002; Ekinsmyth 2002). A freelance worker can be defined as a “skilled professional who is neither an employer nor an employee, supplying labor on a temporary basis, under a commercial contract for services for a fee to a range of business clients” (Kitching and Smallbone 2008, p. 5, see also Cappelli and Keller 2013; Mould et al. 2014). Freelancers are independent economic actors, operating in complex “project ecologies” (Grabher 2004) between firms and organizations and in collaboration with other freelancers, often on a global scale (see, for

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example, Watson 2012). Yet, until now, there has been a distinct lack of empirical data about the exact number of freelancers in creative industries as well as in other economic sectors; that is why they are often referred to as the “invisible workforce” (see Freelancers Union 2012; Watson 2012; Mould et al. 2014). The American advocacy organization Freelancers Union estimates that in 2012 34 percent of the American workforce (53 million) have been working freelance, with 21.1 million doing full-time freelancing and 14.1 million engaged in “moonlighting”, doing freelance work besides a primary employed job (2012, p. 3). A report from the IPAG Business School on so-called iPROS (independent professionals) calculated that 8.9 million people are working as freelancers in the European Union, a growth of 45 percent since 2004 (Leighton and Brown 2012, p. 1). Berlin’s last Culture and Creative Industries Report (SenWTF 2014) claims that 42 percent of people working in these sectors are self-employed with no employees. Similar results have been reported for creative industries sectors in the UK (for an overview, see Mould et al. 2014, p. 2443). In culture and creative industries, a rich body of literature has developed that addresses the working conditions of those “experiencing the effects of the neo-liberalization of work” (Watson 2012, p. 621). While politicians present this shift to freelance work and self-employment gloriously as key economic drivers (see Bröckling 2014), the daily reality and subjective experience of freelance work can be a rather “complicated version of freedom” (Hesmondhalgh and Baker 2010, p. 4) with precarious employment, often low wages and unpaid work, portfolio-based careers, high personal investments in maintaining employability, dense social networking during work and non-work times, and intense competition (see Gill and Pratt 2008; Hesmondhalgh and Baker 2011; McRobbie 2013; Menger 2014). The specific organizational logics of cultural and creative industries additionally affect employment relations and conditions of creative labor. These industries are described as highly prone to uncertainties and risk (Caves 2000; Vinodrai and Keddy 2015) because they operate in an environment of rapidly changing consumer tastes and uncertainty due to the speed of technological innovation (Caves 2000; Hesmondhalgh 2007). Therefore, temporary projects have become the dominant organizational form in cultural and creative industries, based on flexible weak-tie networks that constitute complex project ecologies (Grabher 2004) in cities. In order to navigate these uncertainties and risks firms tend to agglomerate in cities to get access to these specialized thick labor markets and the surrounding “buzz” to facilitate higher innovation rates (see Bathelt et al. 2004; Storper and Venables 2004; Scott 2008; Shearmur, Chapter 27, this volume). For this “reflexive production system”, as Thiel (2017, p. 22) argues, cities then act simultaneously as resources, catalysts and compensation mechanisms since cultural and creative industries rely on more tacit forms of knowledge, craft and practical skills as well as on the cultural thickness of urban environments (see Drake 2003; Lloyd 2006). And, the specific social construction of markets in these industries (see, for example, Anand and Peterson 2000; Jones 2002; Currid 2007; Moeran and Pedersen 2011) blurs the lines of economic and non-economic spheres and of work and non-work for individual workers (Ekinsmyth 2002; Blair 2003; Neff 2005; Banks 2007; Becker 2008; Lingo and Tepper 2013). Taken together, these characteristics expose freelancers to a high degree of risk and entrepreneurial pressure, volatility, flexibility and precariousness in their work and labor market circumstances. Moreover, freelancers have to organize their own work environment, their training and skills development, and their search for new contracts and work

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opportunities, and have to cope with the sequencing of intense work schedules on projects and long off-times in between (Vinodrai and Keddy 2015). In addition, freelancers often lack sufficient social security support (e.g., pension, health insurance, sick pay, incapacity benefits or maternity/paternity pay) and, despite their growing economic importance, are marginalized in economic policy decision-making (Merkel and Oppen 2013; Mould et al. 2014). As research in cultural and creative industries highlights, networking and clustering are two dominant coping strategies of small and medium-sized businesses to deal with the uncertainties and risks in these economic sectors (Andres and Round 2015). The diversification of projects typifies a third strategy (Hirsch 1972; Caves 2000; Andres and Round 2015, p. 3). These three coping strategies can also be found for freelance workers on the individual level (see, for example, Lloyd 2006; Currid 2007; Felton et al. 2010). Recently, we have witnessed a fourth and distinct strategy for freelance workers: coworking in designated coworking spaces.

COWORKING: A NEW SOCIAL ORGANIZATION OF WORK While the idea of coworking has been around for more than ten years (see Deskmag 2013a), most of these self-organized workspaces have emerged since the economic crisis of 2008 when companies had to lay off workers and office spaces had to be vacated or sublet due to high rents (see, for example, Felton et al. 2010; Capdevila 2015). Ever since, coworking has grown into a reflexive, global movement with its protagonists exchanging experiences, knowledge and best practices in blogs, conferences, books or wikis, where coworking is presented as the ultimate social work experience in the “unoffice” (DeGuzmann and Tang 2011). Among scholars, there is a considerable debate on how to define coworking spaces and the specific practice of coworking (see, for example DeGuzmann and Tang 2011; Spinuzzi 2012; Capdevila 2013; Parrino 2013; Moriset 2014; Schmidt et al. 2014; Fuzi 2015). For instance, Clay Spinuzzi (2012, p. 400) asserts: “A coworking space is a place to get work done—specifically, knowledge or service work that originates outside the site in other intersecting activities.” Yet, at the same time, he acknowledges: “These spaces differ radically in ambience, amenities, location, and clientele—and as important, their proprietors and the coworkers who work there differ radically in how they describe coworking in their talk and in a great variety of texts, including business documents, collateral, advertisements, Web sites, and social media.” (p. 400). For Spinuzzi, coworking represents a case of “distributed, interorganizational, collaborative knowledge work” (2012, p. 400). However, in interrogating the user perspective he does not explore which role the coworking space and its management play in facilitating this kind of knowledge work and whether there are differences to other forms of distributed, interorganizational and collaborative knowledge work such as online cooperation networks (see for example, Grabher and Ibert 2014). The most comprehensive dataset on coworking so far, is produced by the German online magazine Deskmag. According to their Fifth Global Coworking Survey in 2015, the number of coworking spaces worldwide rose from 600 in October 2010 to 7800 in November 2015, with half a million people now working in those shared workplaces (Deskmag 2015). The majority of coworking users are male (62 percent), between 25 and

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44 years of age (74 percent), and work as freelancers (53 percent) (data on the demographics of coworkers can only be found in the Third Global Coworking Survey, see Deskmag 2013b). Their professional background is predominantly in culture and creative industries and in the information and communications sector (Schmidt et al. 2014)—namely those knowledge workers central to the “cognitive-cultural economy” (Scott 2008, p. vi) or what Florida (2004) broadly conceived as the “creative class”. The survey also reveals a distinct geography of coworking: more than two-third of these spaces can be found in Europe and North America, predominantly in inner city areas, with Barcelona, Berlin, London, New York and Paris each having more than 80 of those shared workspaces already (see also Coworking wiki 2015). Benefits of Coworking for Coworkers As freelance workers in creative industries are known to “interact reflexively with the relational, institutional, cultural and material conditions in which they are embedded” (Thiel 2017, p. 31), the emergence of coworking spaces can be interpreted as a bottom-up organizational practice to cope with the structural conditions in creative labor markets, the economic crisis, and the disadvantages of a freelance work situation. Temporary working in a shared workspace, instead of renting an office space or working from home, has become a strategy to minimize risks (e.g., financial risk around self-employment as less money is needed to start or personal risks by being surrounded by other freelancers who share similar challenges and problems and can provide support to each other or provide feedback on ideas). Those affordable workspaces match the financial situations as well as the necessary flexibility of freelance workers and, in addition, provide crucial resources for them to sustain a freelance livelihood in a volatile, highly mobile and fragmented labor market (Blair 2003). Besides the cost-effectiveness of sharing a common work infrastructure with the appropriate technical equipment (e.g., high-speed broadband, printer, meeting rooms), freelancers are attracted to these workplaces for the most part for professional and social reasons: they often mention the social isolation of the home office, the many distractions and problems of self-motivation in connection with that, and rather prefer socializing with like-minded people sharing the same challenges and problems (see DeGuzmann and Tang 2011). Many freelancers consider coworking a form of “boundary work” (Warhurst et al. 2008) that enables a regular structured office day with established routines and keeps a balance between their work and private life (Pohler 2012). Getting access to valuable knowledge and recognition among peers are further motivations for freelancers to engage in coworking (DeGuzmann and Tang 2011). Many report that coworking helped them to enlarge their professional network, to find support for work-related questions and to gain new collaborators among the other coworkers; many also reported that coworking increased their productivity (Spinuzzi 2012; Deskmag 2013b). Coworking is not just about “working alone together” (Spinuzzi 2012) in a socially animated environment; it also provides a cultural framing for freelance workers as coworking promotes a shared set of values among coworkers that shall become embodied in all spaces: community, collaboration, openness, diversity and sustainability (Coworking Manifesto 2014). The governance mode in these spaces is described as community-based (Capdevila 2013) with trust acting as the main governance mechanism. Trust enables

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“voluntary, nonobligating exchanges of assets and services between actors” (Uzzi 1996, p. 678; see also Spinuzzi 2012) and thus promotes what in relation to coworking is often described as “sharing” (DeGuzmann and Tang 2011). Sharing in the coworking context refers to a form of nonreciprocal contribution into a common pool of resources (e.g., relevant legal or tax-related information on the freelance work situation) that tends to foster “feelings of solidarity and bonding” (Belk 2010, p. 717) and enables mutual learning among its members (see Ibert and Stein 2012, p. 608f.). In self-descriptions, such as on blogs or coworking websites, the “collaborative approach” in coworking is highlighted as the distinctive feature that sets these shared workspaces apart from other forms of shared, flexible work setting such as satellite offices, hot desks, coffee shops, libraries or traditional business incubators. Yet the “collaborative approach” is directed at creating a better, supportive work environment rather than challenging the structural problems of the freelance work situation (e.g., through political claims for more social protection) or providing alternatives (e.g., in promoting forms of economic organization such as cooperatives, see Cheney et al. 2014). The overly positive discourse of coworking as a collective solution to the freelancers’ individualization problem cannot conceal that coworking is deeply embedded into the organizational logics of creative industries with its project-based work, the constant need for networking to maintain access to potential project partners and to exchange knowledge and, thus, the specific knowledge dynamics in those sectors for the constant production of novelties (e.g., Caves 2000; Capdevila 2013). Furthermore, coworking supports the specific creative production process, because freelancers in these sectors need the promotion and validation of their peers to build up a reputation (see Currid 2007; Hracs et al. 2013; Thiel 2017) and might find a critical and supportive infrastructure among other coworkers for the necessary recognition. Yet, this raises the question as to whether coworking is actually sustaining precarious working conditions for freelancers more than helping to overcome them? For example, Gandini (2015, p. 203) attributes a “contradictory nature” to coworking spaces. On the one hand, coworking exemplifies the adaptability of freelance workers as entrepreneurial subjects and depicts them as the epitome of neoliberal self-governance, as workers who are trained “to accept and reproduce for themselves the precise conditions of their subordination” (Banks 2007, p. 42) by internalizing those entrepreneurial expectations and optimizing themselves for it. Furthermore, coworking spaces with their fee policies and growing selection of members—which can be interpreted as exclusionary practices—can sustain existing inequalities in those sectors. On the other hand, this perspective misses the agency of those workers and their capacity to resist, and it oversees the potential for imagining other economic and social possibilities through collective action (see de Peuter 2014a; de Peuter and Cohen 2015). Here, coworking spaces as places of encounter and negotiation can facilitate, and already have, new forms of political representation for freelance workers (e.g., the European Freelancers movement, see Freelancers Europe 2014). There are examples of coworking spaces such as the Supermarkt in Berlin, the worldwide operating Impact Hubs, Hive at 55 in New York, or the growing numbers of coworking cooperatives that provide platforms for discussions on freelancers’ rights, the lack of social protection and alternative forms of economic organization (Cagnol 2013; de Peuter 2014a; Supermarkt 2014)—enabling this kind of reflexivity and giving these discussions a place holds a potential for emancipatory practices. Acknowledging the contradictory nature and the recent emergence of coworking, it remains to be seen

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“whether these initiatives . . . will act as crucibles of experimentation in the production of new economies of cooperation that would strive to minimize precarity in all its forms— rather than thrive off it, or mystify it, in the spirit of the Warhol economy” (de Peuter 2014b, p. 43). Discussion of Coworking in Academic Literature The growth of coworking spaces has been followed by a growing body of research that analyzes coworking and situates it within different academic debates. From the sociology of work perspective, scholars discuss coworking as a collective coping strategy with the insecurities and precariousness of freelance work (Pohler 2012; Merkel and Oppen 2013; Moriset 2014; Merkel 2015). In economic geography, the dynamics of knowledge exchange within coworking spaces (Parrino 2013), their role as innovative micro-clusters, intermediaries (Capdevila 2013, 2015), or as creativity and innovation labs (Schmidt et al. 2014), has been discussed. Economics, in contrast, interrogates coworking spaces as a new business model for office provision (Salinger 2013). As the phenomenon of coworking continues to diversify with a variety of collaboratively organized work spaces now claiming to be “creativity and innovation labs” (such as fab labs, maker labs, hackerspaces, incubators and accelerators, open innovation labs or coworking spaces), there is a need to better understand these “more complex and heterogeneous relational innovation landscapes” (Schmidt et al. 2014, p. 245). Consequently, Schmidt et al. (2014) propose a first heuristic typology for these different types of spaces according to their governance mode, objectives, users, industry focus, accessibility and other aspects. They differentiate between grassroots labs, coworking labs, firm-driven innovation labs, academic-driven innovation labs and incubators and accelerators as five “new spatio-temporal configurations in knowledge-driven economies” (p. 243) for interdisciplinary project teams. Since the phenomenon of coworking emerged only recently, there is as yet little critical understanding of coworking and its assumed effects on social learning, knowledge generation and innovation.

SOCIAL LEARNING, COLLABORATIVE WORKING AND INNOVATION IN COWORKING SPACES Empirical research on coworking has so far not explained what coworking as a social practice characterizes, and how and through which mechanisms collaboration and learning emerges or is facilitated in these spaces. Despite being referred to as collaborative workspaces, there remains the empirical question as to whether freelancers work together on self-determined projects and, if so, when and why they choose to do so and under what conditions. The existing research demonstrates that coworking spaces can facilitate and stabilize relationships and foster relational proximity between coworkers by providing an open-minded social and cultural context in which shared interest and mutual trust and understanding among coworkers can develop (see, for example, Brinks and Schmidt 2015). Working in these spaces then enables multiple forms of encounters and “varieties of knowing in action” (Amin and Roberts 2008). It can be assumed that coworking spaces can promote knowledge diffusion but may also facilitate knowledge generation,

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understood as a result of interactive learning opportunities (see, for example, Ibert 2007; Müller and Ibert 2014). Social Learning in Coworking Spaces The phenomenon of coworking brings together two crucial concepts in economic geography: physical proximity and community for learning and innovation processes. Often, already the names of these workspaces indicate the conjunction of community and space, and the emergence of new ideas, to signal collaborative orientations, practices and processes in knowledge generation (e.g., Affinity Lab, Camaraderie, Collective Agency, Common Spaces, Creative Density, Hub, Makespace, Thinkspace, The Urban Hive). With their unique social composition and their specific focus on professional skill development, coworking spaces could be learning sites for “practical knowledge”, “sensible knowledge” and “aesthetic judgments” (see Strati 2007; Gherardi 2009b), which means they become a valuable source for coworkers where they learn practice-based skills, specific industry standards and “appraisal vocabulary”, which refers to expressions or narratives that enable practitioners to communicate within their professional field and peer group and that are crucial for the construction and negotiation of professional identities (Gherardi 2009b, p. 543). One particular instance of “appraisal vocabulary” is “entrepreneurial storytelling” (Lounsbury and Glynn 2001). For example, young start-ups rely very much on entrepreneurial storytelling to enable beneficial resource flows for their venture—whether in the form of capital or human support. Mostly, these young companies have no product or prototype, no office, no functioning team, and no customers yet, so the creation of an appealing and coherent story becomes one of the most crucial assets to attract interest and can reduce the uncertainty typically associated with entrepreneurship (see Lounsbury and Glynn 2001). This function is particularly important for younger coworkers who become freelancers right after university and for whom industry-specific skill development is increasingly a problem for two reasons (see Grugulis and Stoyanova 2011). First, there is intense competition for apprenticeships, internships and trainee positions with the increased number of people that are educated in and interested in creative professions, often leading to unpaid work as a way of “getting in” these sectors (see Oakley 2009 for a discussion on oversupply in cultural labor markets; Siebert and Wilson 2013). And second, the organization of cultural production has become so specialized in small production companies that insights into actual work processes become limited because they happen outside the firms. Here, coworking spaces could provide the “missing middle” (Grugulis and Stoyanova 2011) between novices and experts by offering specific learning opportunities such as workshops, by engaging with other coworkers in self-determined project work or just by “being there” inside the “buzz” among other professionals (Asheim et al. 2007). However, the existing literature on the social dynamics of knowledge generation in economic geography has mainly studied the formal collaborative practices in and between firms or organizations, but not of individual agents. This literature takes for granted a shared context, an important precondition that embeds the collaboration and bridges cognitive distances between different knowledge bases, and raises the absorptive capacity of participants (see Nooteboom 2000). Do coworking spaces with their cultural framing provide such a shared context, do they enable the bridging of cognitive distances or do only

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professionals with similar backgrounds work together? The growing specialization of coworking spaces along professional activities (e.g., for musicians, bloggers, artists, designer, social entrepreneurs) suggests the latter (see, for example, Schmidt et al. 2014). This poses the additional question of what is actually shared in these spaces. We do not know what exactly it is that coworkers can share or exchange in terms of knowledge if their knowledge comes from different epistemic backgrounds. And, how can we conceptualize the learning and the knowledge production in these spaces? For example, “learning by socializing” is a basic mechanism that is described in detail in the community-of-practice literature (Brown and Duguid 1991; Wenger 1998; Amin and Roberts 2008; Müller and Ibert 2014; Roberts, Chapter 21, this volume). This perspective “places emphasis on learning as an inherently social, embodied and situated process, which takes place regardless of the intensity of information processing or formal qualification involved” (Müller and Ibert 2014, p. 8). Coworking spaces provide access, participation and engagement with other coworkers in the coworking community and thereby can constitute bases for “learning in doing” and “knowing in action” (Wenger 2000; Amin and Cohendet 2004; Amin and Roberts 2008; Vallance 2011). In assembling like-minded freelancers, coworking spaces foster a relational proximity through the shared practice of coworking and physical proximity. While relational proximity has been carved out as a precondition for interactive learning and collaboration in economic geography (Bathelt et al. 2004; Boschma 2005; Faulconbridge, Chapter 41, this volume), it raises more empirical questions: What kind of socialites does coworking as a knowledge practice create, how can we describe these emerging social forms in coworking spaces, and what characterizes them? Are these “communities of practice” (Wenger 1998), “collectivities of practice” (Lindkvist 2005), “networks of practice” (Brown and Duguid 1991), or “communities of practitioners” (Gherardi 2009a)? Coworking spaces might be characterized by the simultaneity of different socialities of “knowing in action”—as Amin and Roberts (2008) show there do exist varieties of situated learning that have been conflated in the community of practice discourse. Therefore, they differentiate four different types of collaborative work: craft- or task-based work, professional practice, epistemic or highly creative collaboration, and virtual collaboration. We might find all four of these types in coworking spaces simultaneously. In addition, collaboration is more than just interaction or communication (which was often brought up by coworkers and hosts as constituting collaboration in my own empirical research), so when, how and why do coworkers actually work together? For example, Capdevila (2014) differentiates three different collaboration logics in coworking spaces in Barcelona: cost-related collaboration (e.g., sharing an office space and lowering transaction and information costs), resourcebased collaboration (e.g., sharing and maintaining a common technical infrastructure as well as knowledge) and relational collaboration (e.g., creating synergies among coworkers and empowering the whole community to be innovative). Only the last two constitute collaboration if collaboration is understood as working together towards a shared, mutually defined objective (see Adler et al. 2008; Spinuzzi 2015, p. 5). Similarly, Parrino (2013) found in her two case studies in Milan that only one space—the managed coworking spaces and not the shared workspace on the site of a design agency—actually facilitated collaborative work. Both studies conclude that the organizational platform (Parrino 2013) or the manager (Capdevila 2014) in these spaces plays a pivotal role in facilitating and shaping collaborative activities among coworkers—a finding that is also supported by my own research on coworking hosts (see Merkel 2015).

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Curating Collaborative Working in Coworking Spaces What coworking hosts or community managers always emphasize is that interaction and collaborative activity does not develop between coworkers just because they sit next to each other. Most hosts claim that the physical proximity and simultaneous presence of coworkers will not necessarily lead to interaction, collaboration or relational closeness in the sense of common interests, shared values, worldviews and interpretative frameworks (see, for example, Ibert 2010). Rather, what is needed is a form of curation to engage coworkers in the social practice of working together collaboratively. For example, coworking hosts described their work as “conducting”, “mothering”, “community-building” and “social gardening” (Merkel 2015, p. 128). They apply two types of strategies that are intertwined. Material strategies focus on the design of the spaces and how it can facilitate communication and encounter. All spaces provide opportunities for social gatherings such as kitchen, lounges, meeting rooms, libraries, workshops, and a café. The physical design of the coworking space, with its open floor plan, arrangement of tables (e.g., to enable eye contact between coworkers), and the location of social areas, play an important role in turning the space into a collaborative one through influencing movement flows and interaction patterns between people, in providing affordances for social behavior (e.g., conference rooms, notice boards) and in shaping coworkers’ perceptions of the particular space as a collaborative one (see Fayard and Weeks 2007; Dorley and Witthoft 2012). As social strategies, hosts initiate different online and offline formats such as events and regular meetings, set up blogs for introducing coworkers to each other, or provide bulletin boards at the entrance so members can put up a profile and search for help and specific skills. Hosts try to create a hospitable atmosphere through talking to the coworkers, asking for their specific interests, and connecting them with other coworkers. Hosts claim that eating together is the most effective socialization mechanism, and they often organize informal events around food, such as breakfast or lunch meetings, where new members are introduced, specific projects discussed and coworkers can seek help from each other. There are also organized talks by members, as well as seminars, courses or regular consultation hours (e.g., on legal or tax issues) with invited experts. Most spaces have educational programs, which are often accessible for the wider public without a membership card, and encourage peer-to-peer-learning networks for professional skill development. General Assembly, a coworking space provider from New York which is currently located in 15 cities worldwide, even made a business model out of skills-based learning and provides freelancers with professional courses “on the most relevant skills of the 21st century” (GA 2015). To enable more synergies among coworkers, a growing number of hosts select their coworkers according to their professional “fit” with other coworkers (see Merkel 2015). Coworking as a New Innovation Model Coworking could represent a discrete innovation model since these spaces gather different professional groups or communities of practice (e.g., blogger, app developer, social entrepreneurs) in a socially animated environment and provide the creative friction or dissonance considered as a precondition for radical (product) innovation (see Stark 2009, Chap.1; Ibert 2010). If we follow Stark’s argument on innovation as a recombination

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emerging from different competing evaluative principles, then coworking spaces can be interpreted as a “heterarchically” organized places where socio-cultural dissonances can productively clash and new knowledge can be generated. And, as Stark (p.17) asserts, knowledge generation does not only favor weak ties but also strong ties with more intimate familiarity and more frequent diverse knowledge exchange (see also Sosa 2011 for the role of strong ties as catalysts of creative ideas). Since coworking spaces provide physical proximity for individual coworkers with like-minded others, it can be assumed that tie formation happens because of two reasons. First, social similarity is known as a crucial factor in tie formation and, second, coworking spaces as a shared cultural context can facilitate personalized trust, reciprocal exchange and collaboration with strangers (see Wenger 1998 and Daskalaki 2010 for patterns of tie evolution in creative networks). Through the constantly changing social composition within the space due to the flexible rent model, coworking spaces become “relay points of circulating knowledges” (Amin and Cohendet 2003, p. 5) bridging near and distant relational links with diverse knowledge bases. An example is the worldwide operating network of Impact HUBs which opened its first shared office space in 2005 in London Islington and grew through a franchise model into more than 70 hubs worldwide with over 11 000 members who all specialize in social entrepreneurship (Impact Hub 2015). Capdevila (2013) argues that coworking spaces act increasingly as intermediaries (see Howells 2006) within the creative milieu in a city, and beyond, in providing opportunities for interactions and brokering relationships between freelancers, firms and other organizations (e.g., through events, workshops or collaborations). This intermediary function can most clearly be seen for start-up scenes in cities. Here, coworking spaces provide crucial coordinating functions for young start-up entrepreneurs, venture capitalists, companies and policy-makers, and are often entry points into a cities’ start-up scene for non-locals. The recent growth in coworking can mainly be attributed to the fact that most newly established coworking spaces are “pre-incubators” for newly founded start-ups, occasionally accommodating more than a hundred early-stage tech start-ups and specializing in services for these businesses, rather than just a shared workspace for freelancers (see Moriset 2014, or Capdevila 2013). For example, WeWork, a provider of shared office spaces, now operates start-up hubs in 16 cities worldwide and was recently valued at 5 billion US dollars after starting out as a single coworking space in 2010 in New York (Gellman and Brown 2014; WeWork 2015). In summary, coworking spaces become specific knowledge sites that combine temporary situations of professional knowledge exchange for freelance workers as in project contexts (Grabher 2004), in “temporary clusters” such as conferences (Henn and Bathelt 2015, Bathelt, Chapter 31, this volume) with more tacit forms of knowledge as in informal social gatherings (Storper and Venables 2004; Currid 2007), and where local and global knowledge flows intersect. They also might become fertile grounds for new epistemic communities and radical innovations (Ibert 2010; Cohendet et al. 2014).

CONCLUSION: COWORKING SPACES AS NEW KNOWLEDGE SITES The current spread of coworking spaces as collaborative workspaces in cities epitomizes shifts in work practices and employment relations in contemporary labor markets. In

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addition, coworking gives freelance workers as independent economic actors an unprecedented visibility. In providing a cost-effective, resourceful and socially animated work environment for freelancers—which makes it easy to just “plug in” whenever needed— these coworking spaces evolve into new and distinct knowledge sites within and across (creative) urban economies. Being among like-minded people facing the same challenges and problems as freelancers, self-employed or young entrepreneurs; gaining access to valuable work-related knowledge and recognition; and enlarging one’s professional network, are all strong motivations to engage in coworking. The aim of this chapter was to sketch out coworking and coworking spaces as distinct objects of study since we lack a comprehensive understanding of the knowledge dynamics in these spaces, and how these spaces can bridge both spatial and relational proximities as well as distances. We have only little understanding of what characterizes coworking as a specific knowledge practice. Most empirical research is explorative and concentrates on a limited number of interviews with coworkers or hosts/managers and single case studies. However, in order to understand the practice of coworking, the learning processes, and the collaborative forms of creative and innovative processes that can emerge in these new knowledge sites, we have to find more robust methodological approaches to this object of study. For example, ethnography enables a more situated investigation into the actual practices of coworkers through combining participant observation with interview techniques, and, thus, can be used to interrogate how people actually engage in coworking, collaborative practices and community-building, and the meanings they assign to them (see, for example, Bechky 2006; Stark 2009; Beaulieu 2010; Capdevila 2015). Social network analysis could provide a “rich toolbox” (Ter Wal and Boschma 2008) for investigating the structure of interactions in and across coworking spaces and the content of relationships among coworkers. There is also a need for more comparative research across spaces, as Schmidt et al. (2014) have shown in their research on innovation and creativity labs, to get a nuanced understanding of the different social dynamics that can facilitate knowledge generation and innovative processes in this variety of spaces of collaborative work (see also Parrino 2013; Capdevila 2015). But, there is also a need to compare across time so as to not get trapped in the newness of the phenomena, as there have been different types of managed workspaces for the creative industries since the 1970s (see, for example, Montgomery 2007). Surveys are also crucial as we currently have only a limited understanding of the demographics of coworkers (age, gender, education and professional background), the frequencies of their use of coworking spaces, the interaction patterns among coworkers, and how firms, organizations and public policy engage with these spaces. Furthermore, we need approaches that account for the contingency and complexity of knowledge production in coworking spaces. Contingency refers here to the fact that collaboration or learning in these spaces is not purposefully searched for and part of an intentional and strategic activity but rather happens unanticipated as “moments of creative collaboration” (Hargadon and Bechky 2006) when coworkers interact in these places. We also need approaches that capture the interdisciplinary nature of coworking because of the different professional backgrounds, to better understand the ambiguous role of diversity as a productive condition and hindrance for the generation of new ideas (see, for example, Ibert 2007). Then, we need approaches that differentiate more carefully between co-location (physical proximity), co-presence (mutual entrainment) and collaboration (working together on a project) as modes of “being together”, because they constitute

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different types of social interaction and so the learning processes and the knowledge created in each of these forms of “togetherness” might differ. Finally, the specific role of the coworking host in curating these new work experiences needs some further elaboration as the host plays a crucial role in facilitating the community and thus in enabling collaboration and innovative activities. As we see increasing amounts of freelance work in specific industries, and as more companies are trying to emulate coworking spaces in their corporate offices (Waber et al. 2014), are sending employees or teams into coworking spaces for a temporary creative work experience (Gellman and Brown 2014) or are setting up their own creativity and innovation labs modeled after coworking spaces (Schmidt et al. 2014), this phenomenon is well worth studying for economic geography.

REFERENCES Adler, P. S., Kwon, S.-W. and Heckscher, C. (2008) ‘Professional Work: The Emergence of Collaborative Community’, Organization Science, 19(2): 359–376. Amin, A. and Cohendet, P. (2003) Geographies of Knowledge Formation in Firms, paper presented at the RUID Summer Conference 2003 on Creating, Sharing and Transferring Knowledge. Copenhagen June 12–14, 2003. Retrieved from http://www.druid.dk/conferences/summer2003/Papers/AMIN_COHENDET. pdf (20.08.2015). Amin, A. and Cohendet, P. (2004) Architectures of Knowledge: Firms, Capabilities and Communities, Oxford: Oxford University Press. Amin, A. and Roberts, J. (2008) ‘Knowing in Action: Beyond Communities of Practice’, Research Policy, 37(2): 353–369. Anand, N. and Peterson, R. A. (2000) ‘When Market Information Constitutes Fields: Sensemaking of Markets in the Commercial Music Industry’, Organization Science, 11(3): 270–284. Andres, L. and Round, J. (2015) ‘The Creative Economy in a Context of Transition: A Review of the Mechanisms of Micro-Resilience’, Cities, 45: 1–6. Asheim, B., Coenen, L. and Vang, J. (2007) ‘Face-to-Face, Buzz, and Knowledge Bases: Sociospatial Implications for Learning, Innovation, and Innovation Policy’, Environment and Planning C: Government and Policy, 25(5): 655–670. Banks, M. (2007) The Politics of Cultural Work, Basingstoke: Palgrave Macmillan. Bathelt, H. (2017) ‘Trade Fairs and Innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 509–522. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and Knowledge: Local Buzz, Global Pipelines and the Process of Knowledge Creation’, Progress in Human Geography, 28(1): 31–56. Beaulieu, A. (2010) ‘From Co-Location to Co-Presence: Shifts in the Use of Ethnography for the Study of Knowledge’, Social Studies of Science, 40(3): 453–470. Bechky, B. A. (2006) ‘Gaffers, Gofers, and Grips: Role-Based Coordination in Temporary Organizations’, Organization Science, 17(1): 3–21. Beck, U. (1994) World Risk Society, Cambridge: Polity. Becker, H. S. (2008) Art Worlds, Berkeley, Los Angeles, London: University of California Press. Belk, R. (2010) ‘Sharing’, Journal of Consumer Research, 36(5): 715–734. Blair, H. (2003) ‘Winning and Losing in Flexible Labour Markets: The Formation and Operation of Networks of Interdependence in the UK Film Industry’, Sociology, 37(4): 677–694. Boschma, R. A. (2005) ‘Proximity and Innovation: A Critical Assessment’, Regional Studies, 39(1): 61–74. Brinks, V. and Schmidt, S. (2015) Global Knowledge Communities in Temporary Spaces, paper presented at the Workshop Transience and Permanence in Urban Development (TPUD), University of Sheffield, January 14–15, 2015. Retrieved from http://www.sheffield.ac.uk/polopoly_fs/1.452220!/file/BrinksSchmidt. pdf (25.08.2015). Bröckling, U. (2014) The Entrepreneurial Self: Fabricating a New Type of Subject, London: Sage. Brown, J. S. and Duguid, P. (1991) ‘Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation’, Organization Science, 2(1): 40–57. Cagnol, R. (2013) ‘6 Reasons to Build a Cooperative of Coworkers’. Retrieved from http://www.deskmag.com/ en/6-reasons-to-build-a-cooperative-of-coworkers (24.01.2016).

Coworking and innovation

583

Capdevila, I. (2013) Knowledge Dynamics in Localized Communities: Coworking Spaces as Microclusters. Available at SSRN. Retrieved from http://ssrn.com/abstract52414121. (20.08.2015). Capdevila, I. (2014) Different Inter-Organizational Collaboration Approaches in Coworking Spaces in Barcelona, Available at SSRN. Retrieved from http://ssrn.com/abstract52502816 (20.05.2016). Capdevila, I. (2015) ‘Co-Working Spaces and the Localised Dynamics of Innovation in Barcelona’, International Journal of Innovation Management, 19(3). Cappelli, P. and Keller, J. (2013) ‘Classifying Work in the New Economy’, Academy of Management Review, 38(4): 575–596. Castells, M. (2001) Die Netzwerkgesellschaft, Opladen: Leske und Budrich. Caves, R. E. (2000) Creative Industries: Contracts between Art and Commerce, Cambridge, London: Harvard University Press. Cheney, G., Santa Cruz, I., Peredo, A. M. and Nazareno, E. (2014) ‘Worker Cooperatives as an Organizational Alternative: Challenges, Achievements and Promise in Business Governance and Ownership’, Organization, 21(5): 591–603. Christopherson, S. (2002) ‘Project Work in Context: Regulatory Change and the New Geography of Media’, Environment and Planning A, 34(11): 2003–2016. Cohendet, P., Grandadam, D., Simon, L. and Capdevila, I. (2014) ‘Epistemic Communities, Localization and the Dynamics of Knowledge Creation’, Journal of Economic Geography, 14(5): 929–954. Coworking Manifesto. (2014) Coworking Manifesto (global – for the world). Retrieved from http://wiki.coworking. com/w/page/35382594/Coworking%20Manifesto%20(global%20-%20for%20the%20world) (25.05.2017). Coworking wiki. (2015). ‘Coworking Space Directory’. Retrieved from http://wiki.coworking.org/w/page/ 29303049/Directory (25.08.2015). Currid, E. (2007) The Warhol Economy: How Fashion, Art, and Music Drive New York City, Princeton: Princeton University Press. Daskalaki, M. (2010) ‘Building “Bonds” and “Bridges”: Linking Tie Evolution and Network Identity in the Creative Industries’, Organization Studies, 31(12): 1649–1666. de Peuter, G. (2014a) ‘Beyond the Model Worker: Surveying a Creative Precariat’, Culture Unbound, 6(1): 263–284. de Peuter, G. (2014b) ‘Confronting Precarity in the Warhol Economy: Notes from New York City’, Journal of Cultural Economy, 7(1): 31–47. de Peuter, G. and Cohen, N. S. (2015) ‘Emerging Labour Politics in Creative Industries’, in K. Oakley and J. O’Connor (eds.), The Routledge Companion to the Cultural Industries, London, New York: Routledge, 305–318. DeGuzmann, G. V. and Tang, A. I. (2011) Working in the “Unoffice”: A Guide to Coworking for Indie Workers, Small Business, and Nonprofits, San Francisco: Night Owls Press. Deskmag. (2013a). ‘The History of Coworking in a Timeline’. Retrieved from http://www.deskmag.com/en/ the-history-of-coworking-spaces-in-a-timeline (28.08.2014). Deskmag. (2013b). ‘The Third Global Coworking Survey 2012’. Retrieved from http://de.slideshare.net/desk wanted/global-coworking-survey-2012?ref5http://blog.deskwanted.com/ (28.08.2014). Deskmag. (2015). ‘First Results of the New Global Coworking Survey’. Retrieved from http://www.deskmag. com/en/first-results-of-the-new-global-coworking-survey-2015-16 (18.01.2016). Desrochers, P., Leppälä, S. and Szurmak, J. (2017) ‘Urban Diversity and Innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 215–229. Dorley, S. and Witthoft, S. (2012) Make Space: How to Set the Stage for Creative Collaboration, Hoboken: Wiley. Drake, G. (2003) ‘“This Place Gives Me Space”: Place and Creativity in the Creative Industries’, Geoforum, 34(4): 511–524. Ekinsmyth, C. (2002) ‘Project Organization, Embeddedness and Risk in Magazine Publishing’, Regional Studies, 36(3): 229–243. Faulconbridge, J. R. (2017) ‘Relational Geographies of Knowledge and Innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 671–684. Fayard, A.-L. and Weeks, J. (2007) ‘Photocopiers and Water-Coolers: The Affordances of Informal Interaction’, Organization Studies, 28(5): 605–634. Felton, E., Gibson, M. N., Flew, T., Graham, P. and Daniel, A. (2010) ‘Resilient Creative Economies? Creative Industries on the Urban Fringe’, Continuum, 24(4): 619–630. Florida, R. (2004) The Rise of the Creative Class, New York: Basic Books. Freelancers Europe. (2014) ‘European Freelancers Movement 2014’. Retrieved from http://freelancers-europe. org/ (25.10.2014). Freelancers Union. (2012) ‘Freelancing in America. A National Survey of the New Workforce’. New York.

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Fuzi, A. (2015) ‘Co-Working Spaces for Promoting Entrepreneurship in Sparse Regions: The Case of South Wales’, Regional Studies, Regional Science, 2(1): 461–468. GA. (2015) ‘General Assembly. Our Story’. Retrieved from https://generalassemb.ly/about (20.08.2015). Gandini, A. (2015) ‘The Rise of Coworking Spaces: A Literature Review’, Ephemera: Theory and Politics in Organization, 15(1): 193–205. Gellman, L. and Brown, E. (2014) ‘Wework: Now a $5 Billion Co-Working Startup. Rich Price for Four-YearOld Office-Sharing Company with Big Ambitions’, The Wall Street Journal, 15 December. Retrieved from https://www.wsj.com/articles/wework-now-a-5-billion-real-estate-sartup-1418690163 (20.08.2015). Gherardi, S. (2009a) Organizational Knowledge: The Texture of Workplace Learning, Malden, Oxford: Blackwell. Gherardi, S. (2009b) ‘Practice? It’s a Matter of Taste!’, Management Learning, 40(5): 535–550. Gill, R. and Pratt, A. (2008) ‘In the Social Factory? Immaterial Labour, Precariousness and Cultural Work’, Theory, Culture and Society, 25(7–8): 1–30. Grabher, G. (2004) ‘Temporary Architectures of Learning: Knowledge Governance in Project Ecologies’, Organization Studies, 25(9): 1491–1514. Grabher, G. and Ibert, O. (2014) ‘Distance as Asset? Knowledge Collaboration in Hybrid Virtual Communities’, Journal of Economic Geography, 14(1): 97–123. Grugulis, I. and Stoyanova, D. (2011) ‘The Missing Middle: Communities of Practice in a Freelance Labour Market’, Work, Employment and Society, 25(2): 342–351. Hargadon, A. B. and Bechky, B. A. (2006) ‘When Collections of Creatives Become Creative Collectives: A Field Study of Problem Solving at Work’, Organization Science, 17(4): 484–500. Henn, S. and Bathelt, H. (2015) ‘Knowledge Generation and Field Reproduction in Temporary Clusters and the Role of Business Conferences’, Geoforum, 58: 104–113. Hesmondhalgh, D. (2007) The Cultural Industries, London: Sage. Hesmondhalgh, D. and Baker, S. (2010) ‘“A Very Complicated Version of Freedom”: Conditions and Experiences of Creative Labour in Three Cultural Industries’, Poetics, 38(1): 4–20. Hesmondhalgh, D. and Baker, S. (2011) Creative Labour: Media Work in Three Cultural Industries, London: Routledge. Hirsch, P. M. (1972) ‘Processing Fads and Fashions: An Organization-Set Analysis of Cultural Industry Systems’, American Journal of Sociology, 77(4): 639–659. Howells, J. (2006) ‘Intermediation and the Role of Intermediaries in Innovation’, Research Policy, 35(5): 715–728. Hracs, B. J., Jakob, D. and Hauge, A. (2013) ‘Standing out in the Crowd: The Rise of Exclusivity-Based Strategies to Compete in the Contemporary Marketplace for Music and Fashion’, Environment and Planning A, 45(5): 1144–1161. Ibert, O. (2007) ‘Towards a Geography of Knowledge Creation: The Ambivalences between “Knowledge as an Object” and “Knowing in Practice”’, Regional Studies, 41(1): 103–114. Ibert, O. (2010) ‘Relational Distance: Sociocultural and Time–Spatial Tensions in Innovation Practices’, Environment and Planning A, 42(1): 187–204. Ibert, O. and Stein, A. (2012) ‘Spatialities of Governance—Spatial Imaginations Associated with Market, Hierarchy, Networks and Communities’, Geography Compass, 6(10): 602–616. Impact Hub. (2015) ‘What Is Impact Hub’. Retrieved from http://www.impacthub.net/what-is-impact-hub/ (20.08.2015). Jones, C. (2002) ‘Signaling Expertise: How Signals Shape Careers in Creative Industries’, in M. Peiperl, M. Bernard Arthur and N. N. Anand (eds.), Career Creativity: Explorations in the Remaking of Work, Oxford, New York: Oxford University Press, 209–228. Kitching, J. and Smallbone, D. (2008) Defining and Estimating the Size of the UK Freelance Workforce. A Report for the Professional Contractors Group, London: Kingston University. Leighton, P. and Brown, D. (2012) Future Working: The Rise of Europe’s Independent Professionals (Ipros), Paris: IPAG Business School, France for European Forum of Independent Professionals (EFIP). Lindkvist, L. (2005) ‘Knowledge Communities and Knowledge Collectivities: A Typology of Knowledge Work in Groups’, Journal of Management Studies, 42(6): 1189–1210. Lingo, E. L. and Tepper, S. J. (2013) ‘Looking Back, Looking Forward: Arts-Based Careers and Creative Work’, Work and Occupations, 40(4): 337–363. Lloyd, R. D. (2006) Neo-Bohemia: Art and Commerce in the Postindustrial City, New York: Routledge. Lounsbury, M. and Glynn, M. A. (2001) ‘Cultural Entrepreneurship: Stories, Legitimacy, and the Acquisition of Resources’, Strategic Management Journal, 22(6–7): 545–564. Martins, J. (2015) ‘The Extended Workplace in a Creative Cluster: Exploring Space(s) of Digital Work in Silicon Roundabout’, Journal of Urban Design, 20(1): 125–145. McRobbie, A. (2013) ‘Fashion Matters Berlin; City-Spaces, Women’s Working Lives, New Social Enterprise?’, Cultural studies, 27(6): 982–1010.

Coworking and innovation

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Menger, P.-M. (2014) The Economics of Creativity: Art and Achievement under Uncertainty, Cambridge, MA: Harvard University Press. Merkel, J. (2015) ‘Coworking in the City’, Ephemera: Theory and Politics in Organization, 15(1): 121–139. Merkel, J. and Oppen, M. (2013) Coworking Spaces: Die (Re-) Organisation Kreativer Arbeit, WZBrief Arbeit 16. Moeran, B. and Pedersen, J. S. (2011) Negotiating Values in the Creative Industries: Fairs, Festivals and Competitive Events, Cambridge: Cambridge University Press. Montgomery, J. (2007) ‘Creative Industry Business Incubators and Managed Workspaces: A Review of Best Practice’, Planning, Practice and Research, 22(4): 601–617. Moriset, B. (2014) Building New Places of the Creative Economy. The Rise of Coworking Spaces, paper presented at the 2nd Geography of Innovation International Conference 2014, Utrecht January 23–25. Retrieved from https://halshs.archives-ouvertes.fr/halshs-00914075/document (20.08.2015). Mould, O., Vorley, T. and Liu, K. (2014) ‘Invisible Creativity? Highlighting the Hidden Impact of Freelancing in London’s Creative Industries’, European Planning Studies, 22(12): 2436–2455. Müller, F. C. and Ibert, O. (2014) (Re-)Sources of Innovation: Understanding and Comparing Innovation Dynamics through the Lens of Communities of Practice, working paper, Erkner: IRS. Neff, G. (2005) ‘The Changing Place of Cultural Production: The Location of Social Networks in a Digital Media Industry’, Annals of the American Academy of Political and Social Science, 597(1): 134–152. Neff, G. (2013) Venture Labor: Work and the Burden of Risk in Innovative Industries, Cambridge, MA: MIT Press. Nooteboom, B. (2000) ‘Learning by Interaction: Absorptive Capacity, Cognitive Distance and Governance’, Journal of Management and Governance, 4(1): 69–92. Oakley, K. (2009). ‘Art Works’—Cultural Labour Markets: A Literature Review, London: Creativity, Culture and Education. Parrino, L. (2013) ‘Coworking: Assessing the Role of Proximity in Knowledge Exchange’, Knowledge Management Research and Practice, 13(3): 261–271. Pohler, N. (2012) ‘Neue Arbeitsräume Für Neue Arbeitsformen: Coworking Spaces’, Österreichische Zeitschrift für Soziologie, 37(1): 65–78. Rainie, L. and Wellmann, B. (2012) Networke:. The New Social Operating System, Cambridge, MA: MIT Press. Roberts, J. (2017) ‘Community, Creativity and Innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 342–359. Ross, A. (2006) ‘Nice Work If You Can Get It: The Mercurial Career of Creative Industries Policy’, Work Organisation, Labour and Globalisation, 1(1): 13–30. Salinger, J. H. (2013) Economic Development Policies through Business Incubation and Co-Working: A Study of San Francisco and New York City. Retrieved from http://hdl.handle.net/10022/AC:P:20748. (20.08.2015). Schmidt, S., Brinks, V. and Brinkhoff, S. (2014) ‘Innovation and Creativity Labs in Berlin: Organizing Temporary Spatial Configurations for Innovations’, Zeitschrift für Wirtschaftsgeographie, 58(4): 232–247. Scott, A. J. (2008) Social Economy of the Metropolis: Cognitive-Cultural Capitalism and the Global Resurgence of Cities, Oxford: Oxford University Press. SenWTF. (2014) 3. Kreativwirtschaftsbericht Berlin: Entwicklungen Und Potenziale. Berlin: Gemeinsam herausgegeben von Senatsverwaltung für Wirtschaft, Technologie und Forschung, Der Regierende Bürgermeister von Berlin, Senatskanzlei—Kulturelle Angelegenheiten und Senatsverwaltung für Stadtentwicklung und Umwelt. Shearmur, R. (2017) ‘Urban Bias in Innovation Studies’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 440–456. Siebert, S. and Wilson, F. (2013) ‘All Work and No Pay: Consequences of Unpaid Work in the Creative Industries’, Work, Employment and Society, 27(4): 711–721. Sosa, M. E. (2011) ‘Where Do Creative Interactions Come From? The Role of Tie Content and Social Networks’, Organization Science, 22(1): 1–21. Spinuzzi, C. (2012) ‘Working Alone Together: Coworking as Emergent Collaborative Activity’, Journal of Business and Technical Communication, 26(4): 399–441. Spinuzzi, C. (2015) All Edge: Inside the New Workplace Networks, Chicago: University of Chicago Press. Stark, D. (2009) The Sense of Dissonance: Accounts of Worth in Economic Life, Princeton: Princeton University Press. Storper, M. and Venables, A. J. (2004) ‘Buzz: Face-to-Face Contact and the Urban Economy’, Journal of Economic Geography, 4(4): 351–370. Strati, A. (2007) ‘Sensible Knowledge and Practice-Based Learning’, Management Learning, 38(1): 61–77. Supermarkt. (2014) ‘Supermarkt Berlin’. Retrieved from http://www.supermarkt-berlin.net/ (15.09.2014). Ter Wal, A. L. J. and Boschma, R. A. (2008) ‘Applying Social Network Analysis in Economic Geography: Framing Some Key Analytic Issues’, Annals of Regional Science, 43(3): 739–756.

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Thiel, J. (2017) ‘Creative Cities and the Reflexivity of the Urban Creative Economy’, European Urban and Regional Studies, 24(1): 21–34. Thrift, N. (2001) ‘“It’s the Romance, Not the Finance, That Makes the Business Worth Pursuing”: Disclosing a New Market Culture’, Economy and Society, 30(4): 412–432. Uzzi, B. (1996) ‘The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect’, American Sociological Review, 61(4): 674–698. Vallance, P. (2011) ‘Relational and Dialectical Spaces of Knowing: Knowledge, Practice, and Work in Economic Geography’, Environment and Planning A, 43(5): 1098–1117. Vinodrai, T. and Keddy, S. (2015) ‘Projects and Project Ecologies in Creative Industries’, in C. Jones, M. Lorenzen and J. Sapsed (eds.) The Oxford Handbook of Creative Industries, Oxford: Oxford University Press, 251–267. Waber, B., Magnolfi, J. and Lindsay, G. (2014) ‘Workspaces That Move People’, Harvard Business Review. Retrieved from https://hbr.org/2014/10/workspaces-that-move-people (20.05.2017). Warhurst, C., Eikhof, D. R. and Haunschild, A. (2008) Work Less, Live More? Critical Analyses of the Work– Life Boundary, London: Palgrave Macmillan. Watson, A. (2012) ‘Sociological Perspectives on the Economic Geography of Projects: The Case of ProjectBased Working in the Creative Industries’, Geography Compass, 6(10): 617–631. Wenger, E. (1998) Communities of Practice: Learning, Meaning and Identity, Cambridge: Cambridge University Press. Wenger, E. (2000) ‘Communities of Practice and Social Learning Systems’, Organization, 7(2): 225–246. WeWork. (2015) ‘Wework. Create Your Life’s Work’. Retrieved from https://www.wework.com/ (20.08.2015).

PART VII INNOVATION, ENTREPRENEURSHIP AND MARKET MAKING

36. Markets, marketization and innovation Michel Callon

INTRODUCTION Marketization, which is gradually expanding the empire of commodities and imposing the financial world’s modes of evaluation on more and more sectors of activity, acutely raises the question of the role of markets in contemporary societies. In a recent article that draws on Hirschman’s (1982) seminal article, Fourcade and Healy (2007) point out that debates surrounding marketization have generated a set of arguments and standpoints that fall into three large categories: some maintain that markets promote peaceful and cordial relations – what in the 18th century was called doux commerce – and can be seen as civilizing forces, synonymous with efficiency and democratic standards; others argue, on the contrary, that markets are machines which destroy the social fabric and eventually themselves as well; and finally, more recently, and especially with the upsurge of neoliberal ideology, markets are perceived as fragile institutions threatened by conservative forces that are impeding their development and which must be fought. These contrasting arguments correspond to different attitudes towards marketization, and engage their proponents in a tug of war with, on the one hand, those who think that markets are a satisfactory solution to the problems arising in human communities and, on the other, those who see them as a problem. Ultimately, the two positions are irreducible, especially since they agree on one basic point: both take the notion of a market for granted, seeing it as unproblematic. In other words, they know what a market is, even if they disagree on its effects. My objective here is to contribute to changing the terms of the debate. To put an end to this tug of war and to renew reflection on marketization, we have to challenge this postulate and problematize the definition of markets. And rather than adding a new definition to the many that already exist, one that might be more appropriate and objective, I propose to set out from the actual usage of the word “market”. In particular, I wish to consider the way in which market and competition are closely associated. A thorough examination of the notion of market competition leads me to distinguish two ways of describing markets, depending on the role played by product innovations. In interface-markets, innovation strategies aim to reduce competitive pressure, while in market-agencements they are the expression of competition itself. In the former, the definition of market goods is secondary, whereas in the latter it is at the heart of the confrontation between economic agents. Empirical research on the innovation process confirms the greater realism of the market-agencement conception and leads to a new view of marketization and its implications. The competitive dynamics of market-agencements, which makes the establishment of new bilateral transactions and of product innovation the dominant rule, results in the constant expansion of the market sphere. The marketization process is thus at the core of markets’ functioning.

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MARKETIZATION AND THE MARKET Marketization as a Particular and Disputable Modality of Economization The notion of economization refers to a set of actions aimed at transforming and formatting institutions, behaviours, objects, affects and feelings so that they become economic, that is, so that they match a certain idea and a certain conception of what economy is. Hence, the reality that the word economy relates to, at a certain point in time and for certain actors, depends on (potentially) multiple conceptions guiding the economization process (Caliskan and Callon 2009, 2010). Nothing is inherently economic (“economy per se obviously does not exist”, Braudel 1985: 10), but everything can become economic. This may be the wise and measured management that Aristotle advocated. Or, on the contrary, the desired result may be to make profit-seeking and the optimization of the use of resources a universal rule. Highlighting economization processes allows for recognition of the historical and therefore variable and evolving dimension of economic activities. The potential diversity of modalities of economization of human activities should not make us lose sight of the fact that, depending on the circumstances and period, some of them are more influential than others. For the past fifty years, the idea that, for better or for worse, markets are the reference in the organization of economic life has gradually come to prevail, both in academic circles and among policy makers. Moreover, whereas the market concept is polysemic, one of its definitions, generally qualified as neoliberal, has become predominant (Mirowski 2009). The neoliberal version is now considered to be the dominant form of marketization, which is itself conceived of as the dominant form of economization. In this version, the market is defined as an economic organization revolving around individuals, the liberation of their creative and productive capabilities, and the assertion of their free will. It is opposed to the economic model of a command economy that paralyzes society and makes it ineffective by stifling individual initiative. The establishment of such markets and their extension requires the creation of institutions, without which they could not function (for a review of this question, see Fligstein 2001). These institutions are a constant, endless struggle. The result, over the past decades, has been the development of global policies of privatization, the liberalization of financial markets and international trade, state withdrawal, and the transformation of firms’ governance structures (see Rodrik 2006, on his summary of the Washington consensus as “stabilise, privatise, liberalise”). In parallel and complementary to these offensive strategies, preventive practices have been established, with the main objective of weakening all institutional change that may thwart the development of markets. This growing ascendancy of markets has taken two complementary forms: intensive marketization, denoting the mechanisms aimed at increasing and amplifying market pressure within existing markets, for example by tracking down all forms of organization that hinder their functioning; and extensive marketization, which can take more or less violent forms (Harvey 2003) and denotes the gradual commodification of goods and services that either did not formerly exist (Steiner and Trespeuch 2014) or, like work, land and money, had remained outside market mechanisms (Polanyi 1944). We can say that this view is predominant because, unchallenged, it polarizes debates

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on marketization. On the one hand there are those who argue that even though the market has shortcomings (such as producing externalities or under-investing in the production of public goods), it does nevertheless avoid the worst problems that led to the failure of command economies. On the other hand, there are those who see the growing ascendancy of markets, defined as such, as auguring the end of politics and the disappearance of altruism and of privacy, solidarity and community life, all of which are being undermined by unbridled individualism, the growth of injustice, and the deterioration of moral life. By choosing to be for or against marketization, one forgets that the neoliberal version of markets is not the only possibility. This opposition pushes into the background other debates on the nature of markets, which become virtually inaudible. To discuss marketization and its implications, we have to return to the market concept and admit that we do not really know what a market is or could be. Markets and Language Games What is a market? In answering this question, there is a strong temptation to turn to theoretical work and to seek an objective and consensual definition. This quest is however destined to fail as we encounter multiple and often incompatible conceptions. Elaborating a new definition as a substitute for the others will merely complicate the situation. Yet should we for all that give up trying to understand market activities, and thereby refuse to discuss marketization? Should we agree to consider that, because it is dominant, the neoliberal version of markets and marketization is the only possible one? Based on a suggestion of Colette Depeyre and Hervé Dumez (2010), I would like to show here that an alternative strategy is possible. Depeyre and Dumez look at the various language games in which the market concept appears, and thereby its different uses. The word “market” is everywhere; it circulates from one place to another, creating a space of possible confrontation and exchange. Everyone engages the word in courses of action, in analyses or in reflection that load it with specific meaning. Depeyre and Dumez point out, for example, that “the study of a real extreme case shows that one can talk of the market even when close relations of mutual dependence exist between the supply and the demand, even when there is not really a product, and even when there is no real price” (ibid: 226). They conclude that it would be fruitless and counterproductive to pretend that there could be a market concept or even, more modestly, that we could give it a definition, which, albeit imperfect, was nevertheless satisfactory. The Wittgensteinian notion of a language game is useful. Who would dare claim that it is possible to devise a definition of market activities that expresses what an Egyptian small farmer feels when discussing the price of his bale of cotton with a merchant from Cairo; and that simultaneously does justice to the work of Milton Friedman, Friedrich von Hayek, Oliver Williamson or Richard Nelson; and finally that also takes into account the manoeuvring of Monsanto as it uses all available means to eliminate traditional agriculture? On the other hand, who would deny that by referring to the market, each of these actors contributes to the constitution of a set of questions which divides them yet which they share, and that this notion helps to make more explicit? All of them talk of a market, each one focuses on different practices and issues, but each one, in their own way, recognizes the existence of common issues. The notion of language game encompasses

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this tension. It furthermore enables one to consider from the same point of view those who are in the market and those who study it from the outside; those who practice it and those who analyse it. In the different language games concerning the market, we often find the notion of competition. Whether we open an economics textbook, a treatise on economic sociology or a book on anthropology, whether we listen to a senior official or a company director, a trade union leader or a judge ruling on the regularity of markets, we almost inevitably encounter the subject of competition. From Smith, who, perpetuating a longstanding tradition saw competition as “the force tending to equate market and natural price” (McNulty 1967: 396), to more recent developments in economics, the study of markets has constantly revolved around the analysis of the modalities of competition between agents. To renew reflection on the modalities and implications of marketization, I therefore propose to start with the notion of market competition and the various conceptions thereof. Beyond the diversity of definitions, I believe it is possible to distinguish two contrasting ways of describing market competition, depending on the role that it gives to product innovation. Whereas in the one approach (the interface-market), innovation is a strategy designed to weaken the intensity of competition, in the other (marketagencement), competition is expressed in innovation strategies aimed at establishing bilateral monopolies. In the former, innovation tends to introduce imperfections in competition; in the latter it ensures its purity. I will present each of these two contrasting conceptions and highlight their opposition about the role and meaning of bilateral transactions in market exchange.

INTERFACE-MARKETS OR HOW TO DO AWAY WITH ACTIVITIES INVOLVING THE DESIGN AND QUALIFICATION OF GOODS An interface-market brings together supply and demand considered to be autonomous and separate, each of which comprises distinct spheres. Polanyi uses the suggestive term blocs (of supply and demand) to denote these spheres and to emphasize their separation. Supplies and demands in their multiplicity are represented by agents whose identity is defined by their role and the competences it implies – the main roles being those of sellers and buyers. Goods are Platforms that Articulate Blocs of Supply and Demand Irrespective of how agents and their competences are described, or how blocs and their constitution are analysed (networks, separate atoms, etc.), there is an assumption that the confrontation between these blocs concerns the goods offered by suppliers and sought by demanders. The goods are already there, available. They do of course have to have been designed to meet the demand, and to have been produced and put on sale, but these different activities do not interfere with the architecture of interface-markets. They participate in the constitution of the blocs and in the possibility of their encounter, but do not challenge the relevance of this structure. Goods are, or can be, defined by characteristics that are more or less easy to identify and to compare. Their description can be controversial.

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But apart from these difficulties, these goods are things that can be located in time and space, and whose existence is taken for granted. In traditional analyses, markets have only two sides, one for the supply bloc and one for the demand bloc. To take observed trends into account, the possibility of several sides is now envisaged with the notion of multisided markets (Rochet and Tirole 2003). The most well-known example is that of the free press. The daily that underground users are offered every morning on their way to work is financed by the advertising in it. Like many others, the newspaper articulates three blocs: the advertising agencies that buy the space; the users of public transport who read the newspapers; and the firm that designs, produces and distributes these dailies. To design this type of good, the function of which is to articulate several blocs to one another, economists have suggested the notion of a platform. Rochet and Tirole (2002) show, for example, that a credit card can be analysed as a platform articulating several blocs: banks, traders and cardholders. They give other examples such as gaming platforms. One of the aims of this type of approach is to study how prices are set. For instance, in the case of the press, the fact of being free of charge is but a possibility. The notion of a multisided market is a significant improvement, for it accounts more precisely for the complexity of the relations that interface-markets establish between goods, supplies and demands. A platform-good acts simultaneously as a link and as a service supplier. I propose that we extend this notion to configurations that involve only two blocs. In these markets, as in all multisided ones, the main function of goods is to articulate supply and demand outside of them. This is why it is appropriate to qualify them as platforms. The platform-good maintains the separation between blocs, while keeping them linked. Conversely, when the blocs of supply and demand are independent of one another, they require platform-goods for a market to exist. This combination of blocs and platforms constitutes the structure of interface-markets. Competition, Demographic Pressure and Market Structures: The Spectre of a Bilateral Transaction Interface-markets organize the encounter and confrontation between (at least) two blocs, with goods being the (passive) agent in this process. The confrontation leads to the setting of prices or, from a normative standpoint, to their “discovery”. This is the point at which the notion of competition comes in, as the level of prices depends partly on the modalities and intensity of the competition. To understand the role of market competition in interface-markets and to highlight its effects, it is useful to start from the notion of bilateral transaction. The bilateral transaction constitutes a reference situation in which there is a direct encounter between a supplier (only one) and a demander (only one) who wish to engage in a transfer of property in exchange for monetary compensation. The setting of the price at which the transaction will be concluded depends both on the limits that the agents set for themselves (the supplier decides not to sell below a certain price and the demander decides not to buy above a certain price) and their respective strength in the negotiation. A bilateral transaction is thus characterized by calculation and judgement corresponding to the agents’ preferences, and by power relations that depend primarily on available resources and information. It is often posited that the transaction is better from a moral point of view and

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preferable from an economic one if the impact of power relations is reduced in pricesetting mechanisms (whether it is to the supplier’s or the demander’s advantage). The introduction of competition is precisely what makes it possible to obtain this result. It rids the transaction of everything that is foreign to individual calculation or judgement and that stems from relations of domination. In practical terms, it consists in a gradual increase in the number of agents (supplies and demands) involved, or likely to be involved, and that transform the bilateral transaction into a multilateral one. Owing to competition, it is the market that bargains and alleviates, and sometimes eliminates, the arbitrary nature of power relations. Competition gives each individual agent the autonomy enabling them to escape the others’ ascendancy. Its establishment implies that the goods offered (and sought after) are similar. The mechanism that allows for this result is known as “the law of the market or the law of supply and demand”. This law can be summed up as follows: When people want more of a good than is currently being produced, its price will rise. This higher price increases producers’ profits and provides incentives for existing firms to expand production and for new firms to enter the industry. Conversely, if an industry is producing a good for which there is no market or a good that people no longer want in the same quantity, the result will be excess supply and the price of that good will fall. This outcome reduces profits or creates losses, providing for some existing firms to cut back the production and for others to go out of business. (Case, Fair et al. 2009: 759)

The validity of this law is based on a conception of competition that now seems to stand to reason. Yet it took a long time to be formulated clearly, as the hypotheses defining its validity were not self-evident (Schumpeter 1996: 611). These hypotheses are moreover the same as those that define the interface-market: the existence of blocs external to each other, whose encounter implies the existence of determined goods that form and articulate these sets. An elegant way of describing the role of competition in the formatting of market transactions is to introduce the notion of market structures or, perhaps more precisely to avoid the determinism of structures, of competitive configurations. Two extreme situations can typically be distinguished: those where competition is absent (two agents) and those in which a high level of competitive pressure exists (many agents). Between the two, intermediate situations exist, which are normally grouped together under the term “imperfect competition”. The description and analysis of these configurations have been examined in many studies. One of the variables most often considered is the number of agents involved, or likely to be as for instance in contestable markets (Baumol, Panzar et al. 1982). We can thus distinguish very roughly between multilateral transactions (with a large number of agents), bilateral ones (with two agents) and paucilateral ones (with few agents). These competitive configurations do of course result from these agents’ entry and exit strategies. Whether they are on the demand or the supply side, the agents play with these configurations, that is, with the intensity of demographic pressure so as to increase their respective weight in the transactions concerning them. Their objective is to turn the power relations to their advantage. They do so by creating imperfections in the competitive struggle. This is where innovation strategies come in. I now examine these strategies.

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Innovation Is an Effect of Competition In interface-markets, one of the strategies available to agents (of supply, especially) in order to avoid competition is to promote product innovations. Within this approach, the main objective of innovating firms is not to increase competition but to escape it by transforming an existing market-interface into another (different) one. By proposing or seeking different goods, they are able to alleviate the competitive pressure, at least temporarily. Innovation strategies are simultaneously a consequence of competition, as they enable agents to withdraw from it, and an impediment to its deployment, as they hinder it by making it imperfect. Innovation that affects products transforms the structures of markets, yet without challenging the fact that they are and remain interface-markets. The variable on which innovation plays is the platform-good. By modifying it, it redefines the blocs that it articulates, and thereby the population of agents concerned. The characterization of market structures is enriched when the dynamic dimension introduced by product innovations is taken into account. It is no longer enough to describe them with the usual criteria, that is, the number of competing agents and the height of the barriers to entry (a height which moreover has an impact on entries and exits, and thus on the number of agents). A new variable has to be added: the degree of product differentiation. The more products resemble one another, the greater the intensity of the competition. Product differentiation, on the other hand, reduces this intensity, to the point of eliminating it when products are no longer comparable and a new market has been created. Product innovation, which is an effect of market structures, impacts on them in turn and leads to their transformation. Thus, innovation concerns the functioning of interface-markets only to the exact extent that it is correlated to their structural transformations. Innovation is one of the drivers of interface-market dynamics – a type that is conceived of as a sequence of changing structures (Malerba 2007). Envisaged from the agents’ point of view, it is a regulator of competition, which gives them leverage to influence its intensity. Goods are taken into consideration only from the point of view of the articulation they provide between supply and demand. It is not essential to know how the good is maintained as a platform, through superficial or profound changes, and how as it changes it creates attachments, that is, how it constantly reproduces its function as a platform articulating supply and demand. The design and qualification of goods and consequently the innovation process are considered to be outside the market per se. Yet it is this design and qualification that explain how a market is created and evolves, how supply and demand are formed, and why, as they change, they (sometimes) adjust to one another. This work, which is at the heart of the market dynamics, is nowhere to be found in the description and analysis of interface-markets. It is mentioned only with regard to its cost or chances of success. Malerba (2002, 2007) provides a comprehensive review of these studies that inter-relate market structures (especially the imperfection of competition) and innovation strategies. He points out the lack of interest in innovation as a collective and conflictual process of product design. The only perspective of these analyses, he shows, is that of the relations between market structures and innovations (with notably more attention granted to patenting strategies). Design and articulation activities are not endogenized. Innovation derives from competition instead of defining it.

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INNOVATION IS COMPETITION TO ESTABLISH BILATERAL TRANSACTIONS A radically different view of market activities has come to prevail in the past two decades as a result of empirical research on design activities and more broadly on innovation processes. The idea that innovation strategies aim to reduce competitive constraints and make competition imperfect is not consistent with empirical findings. Additionally, the hypotheses underpinning the interface-market concept have been challenged. Goods cannot be equated to platforms. The distribution of agents in various blocs external to one another is unrealistic. Competition does not demand the disappearance of bilateral transactions; on the contrary, it tends to establish them. Innovation is moreover not a strategy intended to attenuate competitive pressure; it is an essential driver of that pressure. To account for this reversal of perspective, I suggest moving from the notion of interface-market to that of market-agencement. From Platform-Goods to Process-Goods: The Proliferation of Interconnected Agents The description of markets cannot be based on the hypothesis of two independent blocs. Activities concerning the design, qualification and circulation of goods simultaneously involve agents of both supply and demand. Networks are woven and through them adjustments between supply and demand are made in various ways. Throughout the design, production and commercialization processes goods change, along with the related supply and demand. Thus, goods, supplies and demands do not constitute separate entities; they are closely inter-related, caught in webs of evolving relations, which ensure adaptations and adjustments that would otherwise be incomprehensible. These activities are not adjacent to markets; they are their core. Describing the functioning of a market means describing this continuously renewed process. As research on this co-production advanced, it became clear that it was not limited, as von Hippel (1988) had initially believed, to certain high-tech sectors where lead users – who are just as competent as producers and who know what they need better than the latter – play a strategic role in the definition and qualification of the products intended for them. Extending his research to other sectors, von Hippel (2004) himself showed that similar mechanisms could be observed in the fields of sport, leisure, information and communication technology (ICT) and health (particularly with patient organizations). To characterize this phenomenon, he coined the term “democratization of innovation” while noting that from one sector to another the intensity, modalities and extension of these collaborative activities vary. Chesbrough (2003) generalized these findings with the notion of openness, thus emphasizing the multifarious agents likely to be involved (from design to consumption), as well as the proliferation of collaboration in which they engage. However, talking of open innovation or of democratization is probably excessive (Dahlander and Gann 2010; West et al. 2014). Collaboration and networking do not prevent certain agents from being excluded, nor certain questions from being censored. Moreover, the notion of collaboration conceals the fierce struggles and the exacerbation of interests found in this collective work (see Amin 2012 and his notion of a collaboration between strangers). Irrespective of these reservations, what matters here is that it is impossible to consider blocs as independent, and to design goods as platforms articulating them. The sociology

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of innovation and uses as well as the anthropology of commercial activities (e.g. Araujo, Finch et al. 2010; Akrich, Callon et al. 2002; Amin and Cohendet 2004; Callon, Muniesa et al. 2007; Cochoy 2004b, 2011; Garud, Tuertscher et al. 2013; Oudshoom and Pinch 2003; Stark 2009) have highlighted the variety of agents who are collectively involved in the creation, production, sale and use of these products (firms, universities, financial institutions, government agencies, sales professionals, consumer groups), as well as the diversity of relations formed between them (hierarchical or heterarchical relations, trade, bartering, gifts). The collective and dynamic nature of the design, production, commercialization and consumption of goods which, at some stage of their life, are subject to market transactions, stems from the fact that their shape and characteristics at the time and place they are bought or sold are simply one step in a series of transformations which take place before and after the transaction. This process is the qualification of goods, whereby their characteristics and the profiles of demanders and suppliers are co-produced (Callon, Méadel et al. 2002). Consider an example illustrating the generality of this process which is now unexceptional in sectors such as health, e-commerce and services but which has actually constantly characterized market activities: a good corresponding to a mass market whose architecture looks very similar to that of interface-markets, such as a particular model of a car of which several hundred thousand are sold. The model in question starts its existence on a drawing board, or rather in the form of a 3D digital representation, and then moves on to become a list of specifications, a series of diagrams and maps in a design department, a model on a platform, a still vague form of a concept car, a prototype, an image in glossy catalogues with technical attachments, a demonstration model in showrooms, described by the salesperson’s explanations and rhetoric, and then an object of tests and comparative evaluations in magazines. Once the transaction has been concluded, the car continues to be requalified, to live a life that was not necessarily planned: it turns into an object of social distinction, it is lent, (re)sold as a used car (which its previous owner had anticipated by taking care of it and/or choosing a model with a high second-hand value), reduced to a wreck whose components are recycled in the form of scrap or spare parts, or re-manufactured as in certain developing countries so that it may have a second life on the market. The notion of an object’s career (Appadurai 1986; Kopytoff 1986) applies perfectly, provided it is associated with all the material changes affecting the vehicle as it goes from hand to hand, and without which it would be immobilized. The transformations of the product before and after the transaction, that is, its successive qualifications, can start very early on, what we could call high upstream, for instance in the design or research department, in the standardization or certification services, and so on. They may then carry on significantly once the transaction has been concluded, and finally mobilize and involve a large number of heterogeneous agents. Or they may take place mainly in a small number of places, for example high upstream and then in the organization of commercial activities, in which case the number of agents involved may be small. In any case, the qualification of goods results from a collaborative activity, as the form it takes at the time of the market transaction is only one of the many it will have throughout its career (Akrich, Callon et al. 2002). We can thus see why we need to talk of the product as a process. Like the platform-good, the process-good articulates supply and demand (and if it was unable to do so it would

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not warrant being called a market good), but this articulation is an action resulting from a series of transformations and adaptations between design, production, circulation and consumption. An instantaneous and lateral approach to the market is completed by a temporal and longitudinal one (Garud and Karnoe 2004). The Logic of Competition Is to Establish Bilateral Monopoly In view of the existence and upsurge of collaboration and inter-relations that cause the various agents to cooperate and that ensure the co-profiling of supply, demand and goods, it is interesting to reconsider the signification of the bilateral transaction. The aim of market organization is not to eliminate bilateral transactions, but to establish them, to enable them to exist and to last through their successive metamorphoses, on an ever greater scale. Whereas bilateral transactions act as a foil in the market-interface conception, in concrete markets they are the configuration that should be sought after. Any good – such as a car sold on such-and-such a day to such-and-such a buyer – that has found a customer, is a good that has been singularized. A mass market is a juxtaposition of bilateral transactions. Making the establishment of the bilateral transaction the ultimate goal of market organization and not what must be avoided at all costs, leads to a complete reversal of the role attributed to competition. This is confirmed by empirical studies. Research on industrial dynamics shows for example that, in a given industry, rival firms have different characteristics (see Malerba 2007 for a synthesis). Even if they are supposed to participate in the same markets, firms and products are never identical. As Grandclément (2006) has shown, even when the commercial strategies of suppliers seek explicitly to suggest and impose similarities between the products that are offered (“metoo” products), the material organization of interactions between sellers still retains sufficient leeway to restore differences. As Cochoy (2004a) so rightly says: imitation and differentiation go hand in hand, for the best way of highlighting difference (which in certain cases may simply be a different price) is to make products equal in other respects. This diversity persists irrespective of the level of disaggregation (Griliches and Mairesse 1997), that is, it constitutes a structural characteristic of markets. Competition feeds on the differences and not on the similarities between rivals and between the goods they propose. To compete, to want to continue to exist, is to differ. In a market there are only singular supplies and demands, articulated by goods which are themselves singularized. With the interface-market, monopoly is defined by the absence of competition, and the main virtue of market organization is to put an end to monopoly situations, generally by increasing the (actual and potential) number of suppliers or demanders of identical or similar products. In contrast, in concrete markets bilateral monopoly is the most perfect form of competition. The more capable a firm is of excluding the agents that could threaten the bilateral monopoly, the greater its competitive strength will be. We could say that the intensity of the competition rises proportionally to the decline in the number of agents. This is nothing new. It was established by authors such as Robinson (1965) and Chamberlin (1933) in the early 20th century. In his masterly history of economic thought, Schumpeter commented on these two authors’ contribution: “[They proposed] to reconstruct the theory of value by allowing monopoly to ‘swallow up the competitive analysis’ – every firm being a monopolist, that is, a single seller of its own product” (Schumpeter 1996: 1155). Whereas in the traditional conception, monopoly and competition are

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opposites, in this perspective monopoly is, on the contrary, the strategy that expresses the logic of competition in the purest terms. A firm designs, produces and sells a product that is singular and comparable to no other, to escape the influence of other firms and capture its customers without leaving them the possibility to turn to another supplier. The result, which is any firm’s objective, is the constitution of a demand that is made impermeable to the influences of other firms on the behaviour of the firm under study. Supply and demand are singularized, that is, they are related to a product which has been designed to be different from all other imaginable products, and the singular characteristics of which are provided by, and provide for, its (exclusive) attachment to the beneficiaries, that is, to those for whom it is intended and who agree to pay to make the attachment last. In one case, competition takes place through the increase in the number of suppliers of a given product; in the other, its intensity culminates with the reduction of the number of firms that offer the same product. This led Schumpeter (1996: 985) to show the lack of realism of Cournot’s analysis of paucilateral situations (with few agents), and particularly duopolies. He argued – and we cannot but agree – that these situations do not correspond to intermediate (or imperfect) configurations since monopoly and competition are not two opposite ends of a continuum (this critique could also be extended to the use of game theory). Despite all the partial interpretations that have been made of his work, Chamberlin (1933) showed insight when he spoke of monopolistic competition and not imperfect competition. We could moreover choose the notion of competitive monopoly to highlight the fact that monopoly constitutes the standard configuration. The only weakness in Chamberlin’s thesis is that he failed to integrate the establishment of bilateral transactions into his formal analysis of the functioning of markets. By maintaining the notion of bloc, he starts his analysis when the articulation between supply and demand is (successfully) completed. The reasoning should not stop at considering the supply alone. As we suggested above, it is not realistic to draw a line that cannot be crossed between supply and demand (as platform-goods do in interface-markets, for example). The qualification of products is a continuous process in both space and time; it does not stop with the transaction. The product pursues its career because the agents are as active on the demand side as they are on the supply side (whereas in the interface-market the consumer consumes but is not involved in the qualification of the good). This means that there is no reason to confine Robinson’s and Chamberlin’s thesis to the supply only. It must be extended, by paraphrasing Schumpeter, to the demand as well: “Reconstruct the theory of value by allowing the monopson to swallow up the analysis of competition: any consumer would be considered as a monopson, that is, as the only buyer of its product.” Market economic activity tends towards the joint constitution of monopolies and monopsons, that is, the constantly renewed establishment of bilateral transactions. This struggle is endless as the result is continuously threatened by certain agents’ desire to do away with or deny differences. The mastery and outcome of this battle for the legitimate perception of differences and resemblances are fragile and always likely to be called into question. In market-agencements, competition can also be imperfect, but as we will see, in a different way to that of interface-markets.

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Innovation as the Driver of Competition It follows that any successful transaction implies a particular formatting of the traded good, and that it can therefore be analysed as the result of product innovation. There is no transaction and therefore no market activity without innovation. Product innovation, which is another name for singularization, can have differing degrees of depth (drastic or incremental), mobilize few or many actors and competences, and concern mainly technical and productive phases or commercial phases, or both at once. The classifications and analyses devoted to the different modalities of innovation apply perfectly (Abernathy and Clark 1985). Innovation is not a strategy that agents develop to escape competition or alleviate its severity. Innovation is inseparable from market activity, since the latter consists in establishing bilateral transactions and since any successful transaction implies its singularization, that is, a specific qualification of the traded good, even if only subtle (and able in some cases to be reduced to a price difference alone). A firm that does not innovate is a firm that progressively excludes itself from market activities. Innovation is the essence of competition rather than being considered as a strategy to avoid competition, and the intensity of the latter is indexed on the depth and scope of the innovation proposed. The greater the singularization, in other words, the more drastic the innovation, the more the competition can be qualified as intense. Conversely, the weaker the singularization, meaning the more innovation is incremental, the more the competition can be qualified as imperfect. It can go so far as to be cancelled out when singularization tends to become imperceptible. This assertion, to which it is difficult to subscribe when one remains confined to the representation of supply and demand prevailing in the interface-market model, applies to a particular firm – even if this is even more counter-intuitive. A firm’s commercial activity must be analysed as an aggregation of a varying number of bilateral transactions, each of which link the firm to each of its customers. Every new customer, every new sale, implies an innovation, which can be minimal or even insignificant but the existence of which must be acknowledged and recognized, and without which the very existence of market competition is denied. Marketing specialists clearly see this when they speak of a firm’s cannibalization of its own products. A firm struggles against itself – and it can choose to minimize the effects of this struggle by striving to contain the singularization of its products (one should say of each copy of each of its products) – as much as it struggles against rival firms.

MARKETIZATION AND ITS IMPLICATIONS: FROM INTERFACE-MARKETS TO MARKET-AGENCEMENTS The above findings show the limits of the interface-market concept to account for forms of market competition. Markets cannot be described as compound structures comprised of platform-goods that articulate blocs, which are themselves independent. Recent research shows the multiplicity of agents involved, the heterogeneity and specificity of their profiles, and the diversity of relationships that form not only among them but also between them and the material entities that circulate (Callon 1991). Markets, as we see

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them developing around us, call for new analytical tools, as the activities that they organize include design activities, sometimes way upstream (for example in academic R&D laboratories), as well as all the downstream practices related to what we call consumption and which continue the work of reconfiguring goods and uses. Competition brings together vast networks of heterogeneous agents which are mobilized through a wide range of relations and activities to establish bilateral transactions. For example, in the health field, scientific research, public agencies, hospitals, pharmaceutical laboratories and patient organizations, to name but a few, collaborate to singularize treatment and devise combinations of molecules and therapies, all of which participate in defining and consolidating the profiles of the patients concerned (Keating and Cambrosio 2012). That which recent developments in health and services have brought to the fore equally applies, in various forms, to the sectors usually associated with mass production. To distinguish this description of market activity from the one proposed by the interface-market conception, I have suggested talking in terms of market-agencement. Market-agencement refers to the collective action structured by socio-technical devices and intended to establish successful bilateral commercial transactions and to promote their proliferation. In market-agencements, innovations that produce bilateral monopoly, without which markets collapse, drive competition and at the same time ensure the sustainability and the extension of market activities. I first show how market-agencement, unlike interface-markets, calls into question the distinction between market and marketization. I then suggest some directions for further reflection, in order to identify the implications of this form of marketization. For Market-Agencements, the Market Is Marketization As pointed out above, in interface-markets the rationale for competition is the elimination of bilateral transactions and the power relations they imply. The design and qualification of goods are secondary. Interface-markets make up a world in which the concern is for agents, their autonomy, and their ability to take decisions and make choices, but in which the definition of goods is considered as purely instrumental and unproblematical. One of the reasons for this casting aside of activities qualifying goods relates to the problems they pose in accounting for price levels (Callon 2013). Innovations are indeed produced and new goods offered, but that is not where the main interest of the market as a form of organization lies. In market agencing this view no longer holds. Activities are organized around a single rationale: the establishment, maintenance and proliferation of bilateral transactions. The innovation process becomes central since, through progressive adaptations and transformations of goods (which become process-goods) and agents (which become identity trajectories), it allows singularization to take place. Product innovation, which is the prevailing form of competition, drives the development of market activities. Every new (individual) transaction can be seen as an extension of the market or more precisely as the construction of a new market. The analysis of the functioning of markets merges with that of the innovation process; that is, with that of the mechanisms resulting in the co-profiling of goods and agents, and in the property transfers in exchange for monetary compensation. For this result to be reproducible, repeatedly and regularly, and for coordination to exist between the different entities involved, a specific organization is necessary. To ensure that

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the actors likely to be engaged in the innovation process – for example researchers, financiers, salespersons, logisticians, production engineers, workers, jurists and consumers with their formal and informal organizations, to mention but a few – do not move off in opposite directions and fail to produce the singularization required, their activities have to be coordinated. But coordination is not enough. It must be oriented towards a specific result: having a customer pay to acquire a singular good. It is the reason why the coordination needs to be organized. But it is not even enough to refer to the general notion of organization. One must explain how and why the collective action ends up with the establishment of commercial bilateral transactions. It was to meet this requirement that I put forward the notion of market-agencement. The term agencement denotes a form of arrangement that acts and at the same time imposes a certain format on the action. Saying that an agencement is a market-agencement (as opposed to agencements that can be for example qualified as altruistic, political or scientific) means specifying that it is structured to direct the collective action towards the establishment of bilateral commercial transactions. This structuring of collective action is achieved through a series of specific framings, which contribute to giving collective action the specific format that it should have. In the case of market-agencements, I have identified five framings, briefly and (very) incompletely presented hereunder (Callon 2013). For a market transaction to take place, it is necessary first to have a divide between agencies capable of valuing the goods offered to them, and the goods to be valued. This divide, which appears fully during the transaction, is the result of a process starting in the first moments of design and consisting of two series of framings. The first is intended to passivate the entities that will be traded. It would moreover be preferable to talk of passiva(ct)ion to denote a process which a) detaches the good (or disentangles it, to use Thomas’ (1991) terminology) from all those that have participated in its elaboration and profiling; b) makes it able to instigate certain courses of action (which correspond to what is usually called its uses) and to contribute to their performance; and c) ensures that its behaviour is to some extent controllable and predictable. This neologism (passivaction) is intended to emphasize the fact that passivity is a modality of action. Goods are passive (they are controllable and predictable) and active (they instigate, and take part in, courses of action). The investments required to achieve this passivaction become more costly and complex when the entities to transform into market goods are close to the living world or include living beings. If certain agents are to agree to pay to acquire them, the goods have to have a value for them, one that is related to the courses of action that they make conceivable and implementable. The process of creating value, a result of the transformation of goods throughout the passivaction, therefore implies a second series of framings which activate agents that are formatted for operations of valuation. These agencies (which can be individuals or collective entities, such as a firm) must have the adequate equipment (e.g. accounting tools, management tools, cost–benefit analysis methods) to be able to carry out their activities (Chapman, Cooper et al. 2009). By assuming that the passivactivated goods and the valuing agencies are unproblematically available, the interface-market does not take their formatting into account. Conversely in concrete markets, passivactivating goods, making them valuable, and populating markets in all their upstream and downstream compartments with valuing agencies, participate directly in their functioning. They require the mobilization of con-

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siderable material and discursive means which, throughout the design, production and commercialization process, ensure that the goods in their successive shapes do what they are supposed to do, and that the various agents engaged in the process of qualification have the tools and instruments to value them and to decide on the changes to make. It is not enough for goods to be passivactivated and for agencies to be empowered with valuating capacities. The operations of valuation must lead to certain agencies agreeing to pay to acquire the goods offered to them. Two additional series of framings contribute towards this result. The one series orchestrates the encounters between goods in all their (successive) forms and the many agencies involved in the qualification-singularization process. The other one organizes goods’ attachment to the consumers so that they agree to pay. The organization of market encounters requires devices that combine components of various kinds: technical-material (such as matchmaking algorithms, a supermarket, a mall, a shop, a website, shop windows, directories, supermarket stalls, R&D and design platforms, clinical trials), textual and audiovisual (advertising messages and clips, brochures) and human (salespersons, customer services, after-sales services, conferences, etc.). These devices are designed to progressively capture the potential customer’s attention and to arouse their desires, cravings and passions (Barrey et al. 2000; Cochoy 2004a). But they are not enough. We could imagine a framing of the encounters that arouses curiosity (Cochoy 2011), amplifies it and transforms it into interest, but that finally fails to obtain the agreement to pay. This consent can be secured only if the attachment of the good to the customer, and the customer to the good, is achieved; that is, if the process of singularization has gone so far as to make the good and the agent constituent of each other and to affect consumers to the point they consider the possibility of paying in order to actualize this attachment (Trompette 2007). That is the purpose of the second series of framings. To carry out this work of incorporation and habituation, which prepares the consent to pay and which is better described in terms of passions, affects and emotions, rather than of interests, usefulness or needs, significant devices are once again required. We cannot account for market competition if we disregard this essential component of market agencing (as interface-markets do when, without denying the reality of these activities, they consider them as having no impact on the logic of the functioning of markets). Once this agreement to pay has been obtained, a fifth and last framing remains to be performed, that of fixing the price of the good in the transaction. Setting the price, which is not automatically determined by the encounter of two blocs, that of the supply and that of the demand as in the interface-markets, stems from particular activities that I have proposed to call price formulation (Callon 2013). On the basis of qualitative–quantitative operations, price formulation connects the particular conditions of the (bilateral) transaction to more general (e)valuations. It thus contributes to the process of singularization in which the price becomes a variable that qualifies the good and contributes to its coprofiling (Muniesa 2007). It is enough to say here that these activities, in turn, necessitate specific devices that mobilize the competencies and know-how of a large number of actors and organize the application of sophisticated information search and calculation tools. These five framings, with the commitments, investments and implementation of devices that they imply, structure the various activities leading to the establishment of bilateral transactions. All together, they put in motion and organize the various activities that shape product innovations and contribute to feed the singularization process. They can

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be summed up in a series of actions (passivactivating goods, activating agencies capable of valuing these goods, organizing their encounter, ensuring the attachment of goods to agencies and obtaining their consent to pay, setting a price and ensuring that payment is made), which are combined, in some cases with backtracking and iterations. With market agencing, marketization is confused with market functioning. Any market marketizes, because the competitive mechanisms that they organize aim constantly at producing innovations and consequently at extending the sphere of bilateral commercial transactions. Indeed, the modalities of this marketization, as well as its effects, depend partially on the form taken by these framings and consequently on the conception of the many possible devices that enact them. Marketization and its Implications With market-agencements and the view of marketization that they impose, new concerns arise. Unlike interface-markets, market-agencements draw no distinction or separation between static and dynamic, between market and marketization. The idea that markets may be contained or confined falls away, since they have no purpose other than to extend and thereby develop bilateral transactions and singularization of goods and identities. This change of perspective raises new questions. The first question that market agencing raises concerns precisely the priority that it gives to innovation and to its role, that is, the relentless exploration of individual identities and their transformation, through the establishment of bilateral transactions. Markets do not simply broaden choices for agents that are already there; they seek to transform those agents to varying degrees. Should this race forward be continued? Would it not be wiser, as some are advocating, to devote most available resources to stabilizing existing identities and to keeping to tried-and-tested platform-goods corresponding to consolidated expectations, thereby introducing more justice into the access to these goods? To answer these questions, it seems that we have to recognize the ambivalence of market-agencements. They appear to be devices that make the possession and fulfilment of desires the main motivation of human existence. For some, they tend to reduce human beings, their identity, what they are, to what they buy, that is, what they own, what they have. This critical view is not wrong, but it does not fully do justice to markets. Market-agencements constitute a highly efficient machine for exploring identities and demands, and never considering this quest as over. Why not take advantage of this force of investigation and renewal, while taming it and framing it? Market-agencements, correctly channelled by state action, could contribute to enhance the attention granted to individual trajectories and promote the consideration given to specific and singular problems, without bracketing off the issue of each one’s access to this collective process. This is notably called for in the health sector, where such demands are starting to emerge (Rabeharisoa et al. 2014). More generally, it is the state’s role that is concerned. The main aim of public policy could be to interfere with the mechanisms of definition of goods and of those they are intended for; in short, to intervene in innovation networks, rather than being limited to the regulation and surveillance of markets which are entrusted with allocating resources as efficiently as possible. The second question, on the modalities of qualification of goods, derives from the first one. In market-agencements, innovation and competition are closely connected.

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Competition frequently results in an accumulation of resources on the supply side, which does indeed collaborate with the demand but which simultaneously facilitates the development of profitable framings and asymmetries. These are accentuated by the alliances that the suppliers conclude between themselves, and which lead to the pooling of whole sections of the networks they mobilize (R&D, platforms to develop spare parts) in order to establish and preserve bilateral transactions. These asymmetries generally result in the ascendancy of the agents who, through their available means and the knowledge and knowhow, or even infrastructures, they control, can frame, limit and direct the other agents’ behaviours, and are thus a driving force in the qualification of goods. The production of these asymmetries and the relations of domination that they establish raise the question of the modalities of participation and expression of the various agents, and especially of users in the establishment of bilateral transactions; in other words, more generally, in the process of qualification of goods and in the dynamics of innovation. Significant analysis and reflection is necessary to imagine what procedures and devices would be capable of countering these asymmetries. Imagining solutions is becoming inevitable as attested for example by the controversies on firms’ unilateral use of databases containing users’ personal information (in the case of e-commerce or personalized medicine), on the addictive nature of certain goods (such as mobile phones or cigarettes) or on the environmental or sanitary impacts of certain innovations, not to mention the denunciation of their futility. Arising from these debates there is a new conception of public policy and of the role it should play in the organization of balanced participation in the conception of goods. Creative destruction is not bad per se, but it needs to be organized! As competition intensifies within market-agencements, the pace of innovation accelerates. The notion of an intensive innovation regime has been proposed to characterize this growing movement (Le Masson, Weil et al. 2006). This acceleration is accompanied by an upheaval in the forms of organization of the processes of design and exploration of supply and demand that can make difficult the opening up that some call for. In these conditions the solution is to slow down, to take the time for exploration, experimentation, consultation and expression. The so decried precautionary principle can be used to reduce the pace – even though I prefer the notion of measured action consistently attentive to the effects it produces (Callon, Lascoumes et al. 2009). The competition associated with market-agencements implies and has the effect of generating, developing and consolidating individual and singular trajectories. In the structuring of economic activities, interface-markets also rely on individuals (whether they are considered to be autonomous or caught in networks of interdependence), but on individuals whose identities are already there and who remain external to the goods proposed. Interface-markets do not act on identities whereas the logic of market-agencements is to reconfigure identities through the design of goods. In the former case, market activity takes from society what it needs to function; in the latter, it participates in producing the social. In the one, the question raised is on the reweaving of the social link: how can solidarity be rebuilt if not through non-market activities as antidotes? In the other, it is not relations that are lacking; on the contrary, as we have seen, singularization and bilateral transactions cause them to proliferate and abound, through innovation networks. But the problem, constantly revisited, is that of the right to difference and to the collective composition of singularities. The example of health, with the upsurge of translational medicine and personalized

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medicine, is a fine illustration of this. Treatment is targeted and matches profiles (e.g. genetic) which are themselves partially defined by available molecules. The design and implementation of treatment mobilizes a wide diversity of actors, from fundamental research to patient organizations. This type of dynamics, associated to the emergence of biomedicine, enhances the costs of treatment and demands that choices be made: which profiles to favour, in the name of which criteria? It also leaves in the cold patients whose profile does not correspond to the targets attained by the molecules tested. What investigations and experiments should be prioritized? It is not so much a question of solidarity as one of equity that is raised, and through which the organization of collective life is at stake. This is no longer a matter of counterbalancing the market by the non-market. What is at stake here is the very structuring of market-agencements, their political engineering, that is, the way in which they organize the design of goods, profile innovations, shape identities and format bilateral transactions. Politics plays out within markets and not outside of them, because markets produce the social rather than undoing it (Geiger et al. 2015; Cochoy 2012).

CONCLUSION The marketization of society usually denotes a form of economization that expands the sphere of influence of markets and the reign of merchandise. It triggers endless debates on the implications of this trend. Some argue that marketization allows individuals to escape the tyranny and liberticidal constraints of social life. Others maintain that by extending the reign of merchandise, marketization leads to, on the contrary, the programmed disappearance of community life and solidarity behaviour. On the one hand, freedom; on the other, injustice and inequalities. These contrasting standpoints are based on a common and seemingly obvious conception of the market. Yet this assumption does not hold for a second. The notion of a market is caught in language games, which compound its polysemy and thereby obscure the meaning of marketization. In this chapter, my reflection on the implications of marketization has not been intended to devise a (new) definition of a supposedly more precise and objective definition of market activities. The market concept is both shared by a large number of actors, whether specialists or practitioners, and used in diverse and irreconcilable ways. Rather than venturing onto the minefield of definitions and general classifications, I preferred to start out from the notion of competition, which is frequently associated with the idea of markets and which allows to go deeper in the exploration of pressing questions arising from the extension of markets. How can market competition be described? Of all the possible approaches, I considered it relevant to compare two contrasting models of competition. In the first, that of interface-markets, competition makes innovation an avoidance strategy that leaves unanswered the question of the qualification of goods. In the second, that of marketagencement, product innovation is the very nature of competition. The challenge is then to articulate the collective process of design and qualification of goods, with the establishment of bilateral commercial transactions. If we adopt the interface-market conception, marketization denotes a set of processes leading to an extension of market activities that exclude the question of goods and of

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mechanisms of mutual attachment of goods to people. Why these goods rather than others? This question is not dealt with by interface-markets. It is relegated to the political or moral spheres, which are supposed to (sometimes) debate and decide (or rather dictate) what can be bought and sold, as well as the (re)distribution of income. In these conditions, we can understand why the question of marketization is becoming so critical and so difficult to deal with, for it implies a powerful organization, outside of markets, capable of counterbalancing the market machinery. In the market-agencement conception there is no difference between market and marketization. The functioning of markets directly and simultaneously raises the question of goods and identities, which are co-produced through processes of attachment between the two. Thinking about marketization amounts to thinking about the architecture of markets and the organization of competition, that is, the mechanisms whereby goods are designed, the formatting of bilateral transactions, and the singularization that they imply. Political and moral reflection is at the heart of markets and not pushed out to their fringes. Acknowledgements Although also published as ‘Revisiting marketization: From interface-markets to marketagencements’ in Consumption, Market and Culture, the chapter was originally conceptualized for this Companion. Permission to reprint the manuscript was acquired from Routledge.

REFERENCES Abernathy, W. and K. Clark (1985). ‘Innovation: mapping the winds of creative destruction’. Research Policy, 14: 3–22. Akrich, M., M. Callon, et al. (2002). ‘The key to success in innovation’. International Journal of Innovation Management, 6(2): 187–225. Amin, A. (2012). Lands of Strangers. Cambridge, Polity. Amin, A. and P. Cohendet (2004). Architectures of Knowledge: Firms, Capabilities and Communities. Oxford, Oxford University Press. Appadurai, A. (1986). The Social Life of Things: Commodities in Cultural Perspective. Cambridge, Cambridge University Press. Araujo, L., J. Finch, et al., Eds. (2010). Reconnecting Marketing to Markets. Oxford, Oxford University Press. Barrey, S., F. Cochoy, et al. (2000). ‘Designer, packager et merchandiser: trois professionnels pour une même scène marchande’. Sociologie du Travail, 42(3): 457–482. Baumol, W., J. Panzar, et al. (1982). Contestable Markets and the Theory of Industrial Structure. San Diego, Harcourt Brace Jovanovitch. Braudel, F. (1985). La dynamique du capitalisme. Paris, Arthaud. Caliskan, K. and M. Callon (2009). ‘Economization, part 1: shifting attention from the economy towards processes of economization’. Economy and Society, 38(3): 369–398. Caliskan, K. and M. Callon (2010). ‘Economization, part 2: a research programme for the study of markets’. Economy and Society, 39(1): 1–32. Callon, M. (1991). Techno-economic Networks and Irreversibility. A Sociology of Monsters. J. Law. London, Routledge and Kegan. 38: 132–164. Callon, M. (2013). ‘Qu’est-ce qu’un agencement marchand?’ In M. Callon et al., Eds., Sociologie des agencements marchands. Paris, Presses des Mines: 325–440. Callon, M., P. Lascoumes, et al. (2009). Acting in an Uncertain World: An Essay on Technical Democracy. Cambridge MA, MIT Press. Callon, M., C. Méadel, et al. (2002). ‘The economy of qualities’. Economy and Society, 31(2): 194–217. Callon, M., F. Muniesa, et al., Eds. (2007). Market Devices. London, Blackwell. Case, K. E., R. C. Fair, et al. (2009). Principles of Economics. Upper Saddle River, Pearson Education.

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Chamberlin, E. (1933). The Theory of Monopolistic Competition. Cambridge MA, Harvard University Press. Chapman, C., D. Cooper, et al., Eds. (2009). Accounting, Organizations, and Institutions: Essays in Honour of Anthony Hopwood. Oxford, Oxford University Press. Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Boston MA, Harvard Business School Press. Cochoy, F. (2004a). ‘Is the modern consumer a Buridan’s Donkey? Product packaging and consumer choice’. In K. M. Ekström and H. Brembeck, Eds., Elusive Consumption. Oxford, New York, Berg: 205–228. Cochoy, F., Ed. (2004b). La captation des publics: C’est pour mieux te séduire mon client. . .. Toulouse, Presses Universitaires du Mirail. Cochoy, F. (2011). De la curiosité: l’art de la séduction marchande. Paris, Armand Colin. Cochoy, F., Ed. (2012). Du lien marchand: comment le marché fait société. Toulouse, Presses Universitaires du Mirail. Dahlander, L. and D. M. Gann (2010). ‘How open is innovation?’ Research Policy, 39(6): 699–709. Depeyre, C. and H. Dumez (2010). Qu’est-ce qu’un marché? Un exercice wittgensteinien. In A. Hatchuel, O. Favereau and F. Aggeri, Eds., L’activité marchande sans le marché? Paris, Presses des Mines. Fligstein, N. (2001). The Architecture of Markets: An Economic Sociology of Twenty-first-century Capitalist Societies. Princeton, Princeton University Press. Fourcade, M. and K. Healy (2007). ‘Moral views of market society’. Annual Review of Sociology, 33(August): 285–311. Garud, R. and P. Karnoe (2004). Distributed agency and interactive emergence. In S. Floyd, J. Ross, C. Jacobs and F. Kellermanns, Eds., Innovating Strategy Process. Oxford, Blackwell Publishers: 88–96. Garud, R., P. Tuertscher, et al. (2013). ‘Perspectives on innovation processes’. Academy of Management Annals, 7(1): 775–819. Geiger, S., D. Harrison, et al., Eds. (2015). Concerned Markets, Cheltenham and Northampton MA, Edward Elgar Publishing. Grandclément, C. (2006). ‘Le marketing des similarités. Les produits à marque de distributeur’. Réseaux, 24(135–136): 221–252. Griliches, Z. and J. Mairesse (1997). ‘Production function: the search for identification’. In S. Strom, Ed., Econometrics and Economic Theory in the 20th Century. Cambridge, Cambridge University Press: 169–203. Harvey, D. (2003). The New Imperialism, Oxford, Oxford University Press. Hirschman, A. O. (1982). ‘Rival Interpretations of market society: civilising, destructive of feeble?’ Journal of Economic Literature, 20(4): 1463–1484. Keating, P. and A. Cambrosio (2012). Cancer on Trial: Oncology as a New Style of Practice. Chicago, University of Chicago Press. Kopytoff, I. (1986) ‘The cultural biography of things: commoditisation as process’. In A. Appadurai, Ed., The Social Life of Things: Commodities in Social Perspective. Cambridge, Cambridge University Press: 64–91. Le Masson, P., B. Weil, et al. (2006). Les processus d’innovation: Conception innovante et croissance des entreprises. Paris, Hermes. Malerba, F. (2002). ‘Sectoral systems of innovation and production’. Research Policy, 31: 247–264. Malerba, F. (2007). ‘Innovation and the dynamics and evolution of industries: progress and challenges’. International Journal of Industrial Organization, 25: 675–699. McNulty, P. J. (1967). ‘A note on the history of perfect competition’. Journal of Political Economy, 75(4): 395–399. Mirowski, P. (2009). ‘Postface. Defining neoliberalism’. In P. Mirowski and D. Plehwe, Eds., The Road from Mont Pèlerin: The Making of the Neoliberal Thought Collective. Cambridge MA, Harvard University Press: 417–455. Muniesa, F. (2007). ‘Market technologies and the pragmatics of prices’. Economy and Society, 36(3): 377–395. Oudshoom, N. and T. Pinch, Eds. (2003). How Users Matter: The Co-construction of Users and Technology. Cambridge MA, MIT Press. Polanyi, K. (1944). The Great Transformation. New York, Toronto, Farrar & Rinehart. Rabeharisoa, V., M. Callon, et al. (2014). ‘From “politics of numbers” to “politics of singularisation”: patients’ activism and engagement in research on rare diseases in France and Portugal’. BioSocieties: 194–217. Robinson, J. (1965). The Economics of Imperfect Competition. London, Macmillan. Rochet, J.-C. and J. Tirole (2002). ‘Cooperation among competitors: some economics of payment card associations’. Rand Journal of Economics, 33(4): 549–570. Rochet, J.-C. and J. Tirole (2003). ‘Platform competition in two-sided markets’. Journal of the European Economic Association, 1(4): 990–1029. Rodrik, D. (2006). ‘Goodbye Washington Consensus, Hello Washington Confusion? A review of the World Bank’s “Economic Growth in the 1990s: Learning from a Decade of Reform”’. Journal of Economic Literature, 44(4): 973–987. Schumpeter, J. (1996). History of Economic Analysis (revised edition). Oxford, Oxford University Press.

Markets, marketization and innovation

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Stark, D. (2009). The Sense of Dissonance: Accounts of Worth in Economic Life. Princeton, Princeton University Press. Steiner, P. and M. Trespeuch, Eds. (2014). Marchés contestés. Quand le marche rencontre la morale. Toulouse, Presses Universitaires Mirail-Toulouse. Thomas, N. (1991). Entangled Objects: Exchange, Material Culture and Colonialism in the Pacific. Cambridge MA, Harvard University Press. Trompette, P. (2007). ‘Customer channeling arrangements in market organization: competition dynamics in the funeral business in France’. Revue française de sociologie, 48(5): 3–33. von Hippel, E. (1988). The Sources of Innovation. Oxford, Oxford University Press. von Hippel, E. (2004). Democratizing Innovation. Cambridge MA, MIT Press. West, J., A. Salter, et al. (2014). ‘Open innovation: the next decade’. Research Policy, 43(5): 805–811.

37. Market formation and innovation systems Ulrich Dewald and Bernhard Truffer

INTRODUCTION The increasing threat of global climate change has led to the adoption of far reaching policy programs in many countries aimed at the promotion of more sustainable technologies in sectors such as energy, transport, agriculture and urban water management (Markard et al. 2012). To accelerate technological change and the establishment of new cleantech industries, market pull policies have been introduced in many countries since the 1990s. Regulatory interventions such as emission caps and emissions charges or feed-in tariffs were aimed at stimulating the business sector in the respective fields (Grubb 2004). As a starting point, we ask whether this move from technology push to market pull policies is associated with specific patterns that characterize the spatial relationship between technological knowledge production and societal acceptance in the respective technological areas. A closer look into the trajectories of environmental technologies implies that their initial development draws on specific assets that are not evenly distributed geographically. Instead, highly variegated patterns of technology generation and diffusion across regions have been observed in markets such as photovoltaic technology (Dewald and Truffer 2012), car-sharing (Truffer 2003), wind energy (Garud and Karnøe 2003) or solar thermal technology (Ornetzeder and Rohracher 2006). The literature on regional or national innovation systems delivers mostly insights about aggregate innovation dynamics. It is however much less suited to explaining patterns and trajectories for specific technologies. Regional innovation systems (RIS) approaches for instance fail to integrate the market dimension, which is treated as the a-spatial selection environment for technological path creation (Lagendijk 2002). National innovation systems (NIS) fail to address the processes of early industry formation (Bunnell and Coe 2001). They provide explanations for technological developments at distinct points in time, and at distinct spatial levels (Dewald and Fromhold-Eisebith 2015). They provide less explanatory power when engaging with multi-scalar configurations and shifts in the spatial organization of technologies or production systems over time. In contrast to RIS and NIS, emerging markets have been discussed in the recent literature on technological innovation systems (TIS), which analyzes industry emergence and maturation by the co-evolutionary alignment of strategies of different actors, by means of the construction of specific networks and institutional arrangements (Bergek et al. 2008). A large number of empirical studies on emerging cleantech industries have been published in recent years (Markard et al. 2012; Jacobsson and Johnson 2000; Negro et al. 2007; Musiolik et al. 2012). These have emphasized the core role of processes such as entrepreneurial activities, knowledge generation and regulatory support for research and development (R&D). Processes of market formation have either been neglected or considered in aggregate terms only (Dewald and Truffer 2011). There has also hitherto been little concern about a spatial differentiation of these processes (Coenen et al. 2012). 610

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In this context, this chapter presents a conceptual framework to capture processes of market formation in TIS for renewable energies. With a view to developing a more differentiated concept of market formation, we shall introduce three interrelated sub-processes. First, the “formation of market segments” encompasses the establishment of supply structures for specific sub-markets including different sets of actors, networks and institutional arrangements. Second, the “formation of market transactions” is conceptualized as a bundle of processes which mediate the transaction between suppliers and end-users. The third sub-process deals with the “formation of user profiles”, that is, the social construction of preferences, use patterns and user identities that develop alongside specific product variants. The three sub-processes have to develop in an interdependent way as early exchange relations between pioneer suppliers and consumers gradually turn into conventional consumer markets. To speak in Callon’s (1998) terms, we shall reconstruct the transformation process from hot to cold socio-technical configurations. We shall also discuss market formation in the broader context of overall maturation of a technological innovation system and show how the different processes interact and form mature sectoral structures (Dewald and Truffer 2012). Emphasis is also placed on the fact that the interaction of these processes plays out differently in distinct development phases and shows distinct spatial development patterns. To illustrate major insights from this research, we shall first present a brief review on spatial relations in innovation systems. We will then elaborate on how market formation can be conceptualized in a TIS framework. This is followed by an analysis of how market formation dynamics relate to overall TIS dynamics. The conceptual considerations are illustrated with reference to the case of photovoltaic technology development in Germany. The chapter finishes with some concluding remarks.

INNOVATION SYSTEMS IN THEIR GEOGRAPHICAL CONTEXT In order to develop a spatially differentiated account of market formation in innovation system approaches, we wish to focus first on the TIS concept and its main pillars. This concept has developed in parallel with a broader range of national, regional and sectoral approaches on innovation systems (see also Truffer and Coenen 2012). Common to all of these systemic approaches is the fact that innovation is conceived as a non-linear process where technologies and social structures mutually align across the value chain (Edquist 2005). Moreover, learning and interaction between diverse types of actors is central to their analysis. In recent years, TIS have become the conceptual reference framework of a range of contributions, especially in the field of new environmental technologies (Markard et al. 2012; Murphy 2015). Carlsson and Stankiewicz (1991, p. 111) originally defined a technological system as “a network of agents interacting in a specific economic/industrial area under a particular institutional infrastructure and involved in the generation, diffusion, and utilization of technology”. Spatial innovation system concepts (Bathelt and Henn, Chapter 28, this volume) were initially developed in order to inform national and regional innovation policies. Both variants emphasized the historical alignments between actors, industry structures and institutional arrangements in specific territorial units which enable or restrain specific industrial development trajectories. NIS approaches focused on strategies of dominant

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industry sectors, institutional support structures and specific national industrial and innovation policy designs (Lundvall 1992; Freeman 1995). RIS emphasized processes of collective learning, socially embedded information exchange networks, labor markets and shared objectives, routines or cultures that benefit from the spatial proximity between organizations and people (Cooke 2001; Asheim and Gertler 2005). While RIS and NIS approaches have been instructive for national and regional industrial policy, they have been criticized repeatedly for being overly introspective and lacking an explicit consideration of local–global dynamics of innovation processes (Bunnell and Coe 2001; Carlsson 2006). Moreover, RIS have been challenged for an overly pragmatic conceptualization of systems (Bathelt 2003). Due to their limited scope and their dependence on multiple external linkages, it is reasoned that RIS cannot yield self-sustained dynamics of, for example, learning and innovating and therefore cannot form true systems. Delimiting innovation systems on the basis of specific technologies potentially offers a more flexible approach for assessing the interplay between local and global resources (Oinas and Malecki 2002). Carlsson and Stankiewicz (1991, p. 111) already emphasized the importance of variable spatial boundaries of technological systems: “Where the boundaries [of technological systems] are drawn depends on the circumstances, e.g. the technological and market requirements, the capabilities of various agents, the degree of interdependence among agents, etc.” Despite this statement, most of the ensuing TIS literature only looks to the national level and “conceptual and empirical work [remains] primarily focused on describing TIS as spatially undifferentiated entities” (Coenen et al. 2012, p. 970). Contributions typically investigate TIS structures within national boundaries (e.g. Jacobsson and Bergek 2004; Negro et al. 2007), without investigating sub- or transnational dynamics. By referring explicitly to a technological delineation of innovation dynamics, Oinas and Malecki (2002, p. 110) observe that technologies have their specific, path-dependent time geographies: technologies emerge somewhere, in a place – or sometimes similar technological solutions are invented in more than one place simultaneously . . . where possibly new qualities are added to them. Technological development is the result of the intermingling of such technological paths, overlapping in content and possibly also in space.

Indeed, much of the literature now applies geographically differentiated TIS concepts (Coenen et al. 2012; Coenen and Truffer 2012; Dewald and Fromhold-Eisebith 2015; Truffer et al. 2015; Binz et al. 2016). This work emphasizes specific relationships of market formation, technology provision, institutional support and patterns of competence building. It provides a spatially differentiated understanding of the interrelations between the generation, diffusion and use of new products and technologies. There are several reasons as to why the TIS approach is preferable when it comes to analyzing environmental technologies. First, the TIS framework allows the analysis of co-evolutionary dynamics between different kinds of actors and institutions which are crucial for the formative dynamics of new industries and technologies. By contrast, national or sectoral innovation system approaches focus mostly on established sectors. The second reason relates to the capacity to address technological change and industry formation from a socio-technical perspective within TIS analysis, including the role of users, preference formation and societal dynamics. The third point, finally, refers to the

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spatial dimension in innovation system concepts and the capability of the TIS approach to address spatial relationships beyond regional or national delineations.

DIFFERENT FORMS OF MARKET FORMATION WITHIN RENEWABLE ENERGY TIS Apart from the prioritization of distinct spatial levels, the RIS and NIS literature focuses on upstream processes in technology development. Empirical work almost exclusively investigates R&D- and industry-related structures and processes as well as the respective indicators (Grabher et al. 2008). However, use-related concerns often remain focused on direct interactions between users and technology development, as in concepts of leadusers (von Hippel 1986) or user–producer interaction (Lundvall 1988). The formation of end-user markets as a process in its own right with its place-specific, technological, political and institutional preconditions has received little attention in both the RIS and the NIS literature (Lagendijk 2002; Moulaert and Mehmood 2010). Markets are perceived as exogenously defined exchange places for mature end products whose geographical differentiation does not matter much and is mostly attributed to the global scale. Especially RIS approaches implicitly advocate a binary view: dynamic, interactive processes of knowledge generation are attributed to the regional scale, whereas “the processes and forces engendering competition [are] manifested in the global market environment” (Lagendijk 2002, p. 38) and remain separated. It is fair to say that the TIS literature has also favored upstream activities such as knowledge generation, entrepreneurial activities or standard formation. The formation of markets appears to be reduced mainly to accounting for increases in sales numbers (Jacobsson and Bergek 2004). In response to such shortcomings, we propose a more differentiated conceptualization of how markets for new technologies come into existence and analyze the dynamic interrelation between upstream and downstream parts of a TIS (Dewald and Truffer 2011). This is performed along two dimensions. The first dimension covers processes to enable supply, exchange and use (Dewald and Truffer 2012): (i) market segment formation as supply structures, (ii) market transaction formation as exchange structures and (iii) the establishment of distinct user profiles to engage with patterns of use and articulation of demand. The mutual interplay of these three processes then enables us to deduce characteristics of distinct phases of market formation as a second dimension. This adds a temporal perspective of market development to the analytical framework. Product markets for consumer goods in particular often follow a more linear development as introduced in the product-life-cycle literature (Jacobsson and Bergek 2004). For the emergence of technological systems in the field of renewable energy technologies, such ideal-type maturation processes are often disturbed: markets are controlled by incumbents, legislation favors specific technologies, or else technical requirements close the market to a new technology (Jacobsson and Johnson 2000). The specific stages of a technological trajectory therefore only provide aggregated representations of characteristic constellations of the aforementioned sub-processes. The balanced interplay of the sub-processes might lead to a steady growth-path in one case while in others interruptions occur, for example, due to stop-and-go sequences of market promotion policies. The mutual alignment of the sub-processes leads to different spatial outcomes.

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In order to identify core processes in emerging technology markets, we draw on the concept recently put forward by Möllering (2009). He outlines six key processes: innovating, associating, institutionalizing, commodifying, communicating and competing. Based on this work we then go on to demonstrate how these processes co-determine supply, exchange and use in an emerging TIS. Innovating, associating and institutionalizing are especially (though not exclusively) relevant for establishing supply structures. The other set of processes, namely commodifying, communicating and competing, shed light on the conditions of exchange between supply and demand. We subsequently emphasize processes of user profile formation to deal with the social construction of different types of users and preferences. The preconditions for the emergence of supply structures are explored under the heading of “market segment formation”. When it comes to the process of innovating, absorptive capacities of actors to attract external knowledge or to develop knowledge from scratch are different ways of initiating or joining a technological path at a specific location. This depends on the interplay of actors and networks (“associating”) and the formation of informal and formal institutional support structures (“institutionalizing”). Due to the historically grown socio-cultural alignment in a region, preconditions for successful market formation are likely to show a large degree of variation across space. Insights from economic geography are especially instructive for the processes of market segment formation as they often focus on the emergence of these supply structures. By fostering interaction and cooperation (Asheim and Gertler 2005), the role of trust-based relationships (Malmberg and Maskell 2002) or advantages in sharing information, risks and costs (Sunley 2000), spatial proximity between developers, producers and users might contribute to very distinct patterns of market segment formation. Apart from hosting specific actors and their networks, formal and informal institutional settings have also been identified as important resources for innovation success in regions (Martin 2000). Institutions specific to a locality may offer benefits by reducing uncertainties as they regularize activities through routines, conventions and habits (Morgan 2004). Formal institutional arrangements, such as laws or subsidy schemes, are often preconditions for markets to emerge. These institutions stabilize at varying territorial levels – federal states, regions or municipalities. Yet informal institutions may also strongly affect behavior, attitudes and perceptions related to new technologies, leading to a high spatial variety of conditions for innovations to succeed (Martin 2000). “Market transaction formation” encompasses commodifying, communicating and competing, processes which have attracted much less attention in innovation studies (Grabher et al. 2008) and economic geography (Berndt and Boeckler 2009). These processes focus on the establishment of more or less stable exchange relationships between suppliers and customers and have been the subject of extensive literature on the economic sociology of markets (Fligstein and Dauter 2007; Callon 2007; Callon, Chapter 36, this volume). To enable exchange, specific product profiles need to be established and price–performance characteristics have to be negotiated. For new products commodification is initially characterized by high levels of uncertainty about core user groups, their preferences and use patterns and thus requires high levels of interaction and constant feedback. Over time, more standardized product channels may be established that show distinct organizational and spatial patterns. Commodification very much depends on and interacts with communication. An adequate and transparent provision of information

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on products, prices and suppliers is essential for markets to become established. Limited information is often a barrier for market success (Jacobsson and Johnson 2000), especially when it comes to emerging technologies. Users can also provide feedback through communication channels in order that product improvements may be implemented. Products that require a high level of knowledge about their operation, maintenance or financing call for particularly intensive communication strategies (Garud and Karnøe 2003). It can be assumed that barriers for communication may be lowered if suppliers and customers share a similar background and are able to draw on different types of proximities. This may entail not only spatial but also social, institutional or cognitive proximity (Boschma 2005). Competition finally draws on the provision of alternative product variants. Market maturation depends on the co-existence of a variety of suppliers with a range of competing business models. In a sociological approach to market formation, competition is “a highly context-specific, and political process, that must continuously be enacted and governed in order to keep performing. It requires an extensive physical and institutional infrastructure to transmit information on products, qualities, prices and ordering, and to secure a certain degree of stability and order” (Lagendijk 2002, p. 40). A wide range of business models and suppliers is likely to co-evolve across the different phases of market maturation, as markets are impacted by different sources of instability. Initially firms do not know how best to serve users, while dominant business models only manifest themselves at later stages. Different types of knowledge transfer are crucial at different stages of maturation to build up stabilized market transactions. As long as critical knowledge is mostly tacit, frequent interaction and face-to-face contact will be essential. Over time, information exchange arenas can emerge, for example in temporary settings like conventions or trade fairs which provide new platforms to share information (Maskell et al. 2006). Combined with ongoing codification of knowledge, face-to-face communication might then become less important while new and more remote spatial relations are easier to establish. The “formation of user profiles” as a third sub-process emphasizes the ways users develop preference structures when exposed to new products, how they “domesticate” new technologies, the flexibility with which they interpret the use conditions of a new artifact (Pinch and Bijker 1987) and what sort of social identities they develop when using these products in public. Niche markets (Kemp et al. 1998) in which these communicative acts take place often develop in limited geographical contexts, where short-term meetings, feedback between user groups and other reflexive practices are facilitated. Pioneering users, for instance, collect experiences that are then shared with users whom they trust. This may ultimately result in patterns of neighborhood diffusion, as already described in the early work on diffusion dynamics (Hägerstrand 1966) and recently exemplified with reference to photovoltaics (Graziano and Gillingham 2015). In general, user profiles will differ substantially in the different phases of a diffusion process (Rogers 2003). As markets grow and products mature, the local anchoring of these processes can become weaker as communication gets codified and market actors relocate and transfer established practices to other markets (Truffer 2003). Moreover, related variety in capabilities and knowledge might not only play out for technological innovations (Boschma and Frenken 2006) but also in user profile formation and related dynamics. The knowledge and experiences gained by a specific user group in the context of related product classes may well provide lower entry barriers for buying a product. For instance, home owners who have invested

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in thermal insulation might be more expert at estimating financial returns when deciding whether to have solar panels installed on their roofs. A specific region may host a substantial number of proficient users if former support programs are in place or if the region hosts a platform for corresponding communication and education programs.

MARKET FORMATION IN THE CONTEXT OF OVERALL TIS MATURATION Before illustrating this framework empirically, a note of caution is appropriate: markets are often perceived as an external selection environment for technological choices (Lagendijk 2002). This corresponds to a linear understanding of innovation which suggests a largely distinct sequence of research, development and marketing. However, literature on innovation studies has repeatedly shown that innovation processes are often reflexive and interdependent (Kline and Rosenberg 1986). In the same vein, the model suggested here emphasizes that market formation dynamics continuously interact with other core processes of a TIS. As a first example supporting this type of interaction, we can use the relationship between knowledge and market formation which changes during different stages of TIS maturation. In early stages, technology providers might be deeply involved in building up proto-markets. Over time, if markets stabilize and become independent from this kind of support, more regular types of transaction develop, alongside the maturation and differentiation of supply structures and value chains. At this point, the roles of actors become more specialized and the spaces of technology production and consumption may start to diverge. In the context of wind energy, Garud and Karnøe (2003) investigate patterns of industry formation for wind turbines in Denmark and the US. In Denmark, some kind of co-construction of early users in close cooperation with pioneering technology providers enabled a gradual upscaling of markets and a concomitant upscaling of technological designs from low-tech to high-tech. In the US, by contrast, a policy approach was preferred that aimed for breakthrough science. Essentially this failed to provide learning opportunities and the gradual alignment of technologies and markets. A second example to illustrate the above argument relates to the fact that different market segments provide different functional resources to the overall TIS development (Dewald and Truffer 2011). Decentralized renewable energy technologies allow broad participation and legitimation by involving home-owners or citizen cooperatives as operators of solar and wind parks. This can stabilize legitimacy and political support for a technological path. Different, more centralized types of renewable energies may therefore provide other sets of resources to an overall technological path: they could enable fast upscale of power production capacities and an incentive for large-scale capital investors to enter a market. Specific actor groups may operate in different market segments and this, in turn, may influence the preferred technological trajectory (Garud and Karnøe 2003). These segments even compete for resources, access to pre-products, consumers and political support. However, the existence of different parallel market segments might also foster a more robust overall TIS development and prevent lock-in. A third possible link relates to cases where early markets and technology development are actively co-constructed by pioneering end-users (Truffer and Dürrenberger 1997;

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Oudshoorn and Pinch 2005). Solar thermal applications in Austria (Ornetzeder and Rohracher 2006) and car-sharing initiatives in Switzerland (Truffer 2003) were used as examples. Grabher et al. (2008) identified very distinct ways in which users are involved in the provision of technologies, from more remote consumers of technologies to active co-designers, as in the case of Web 2.0 technologies. All these examples highlight the intimate relationship of market formation to upstream dynamics of knowledge generation and technology provision.

SPATIAL DYNAMICS OF MARKET FORMATION: THE CASE OF PHOTOVOLTAIC TECHNOLOGY This section applies the preceding framework to market formation and its impact on the overall development of the German photovoltaic TIS. As suggested above, the different market formation processes have very distinct spatial characteristics: they depend on spatially co-located resources and may, as a consequence, give rise to widely varying spatial patterns of market expansion. The notion of resources draws on a relational understanding (Bathelt and Glückler 2005) and emphasizes the social context in which different kinds of resources such as knowledge and power are mediated. The different sub-processes which we have outlined with reference to a new technological system in sustainable/renewable energy technologies, interact and reinforce each other, as is illustrated below using the example of the German photovoltaic technology and its maturation process. This adds a temporal perspective to the analysis as shown in Figure 37.1. Market growth as measured by yearly photovoltaic installations shows specific dynamics in a range of national core markets. Accordingly, the formation of the German market is characterized by three phases: a nursing stage with low and even declining yearly installations until 1999, a bridging stage following the introduction of a nationwide feed-in tariff in 2000, and a maturation stage with the establishment of a mass market after 2007. The socio-spatial dynamics behind these data reflect changing market segments, changing types of user groups involved and changing relations between end-users and technology providers. I. Nurturing Phase: Designing Local Proto-Markets The emergence of the first local pioneering markets in Germany occurred in the early 1990s, predominantly in Southern Germany and North-Rhine Westphalia. The initial implementation of these markets depended on parallel developments at different levels and was owed to the dedication of early activists. Establishing supply structures to provide technology for interested users was a main coordinative activity in the nurturing phase. Formal networks like the Green party and green cooperatives emerged based on the green movement in Germany. A fundamental technological and political goal advocated by this movement was the shift from fossil to renewable energy sources. They lobbied for support schemes at the local level to foster installations. To this end, legal requirements had to be established in line with the needs of the specific technology: so-called local cost-covering tariffs in at least 40 German cities were implemented in the early 1990s (Dewald 2012).

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10000 Germany Spain Japan

1000

USA

100

10

12

11

20

10

20

09

20

08

20

07

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06

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05

20

04

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01

20

00

20

99

20

98

19

97

19

96

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95

19

94

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93

1

Source: IEA-PVPS (2013, p. 76).

Figure 37.1 Yearly photovoltaic installations in major markets worldwide, 1993–2012 (megawatts, log. scale) Parallel to this, an initial national research and test program to investigate grid integration of decentralized small-scale systems was rolled out on a national scale. Referred to as the “1000-roofs program”, this was initiated by the Ministry of Education and Research. The local remuneration models and the state-funded “1000-roofs program” marked a more fundamental shift from technology support to market pull policies (Jacobsson et al. 2004), set out to solve problems at the interface between technology provision and use. Both developments are examples of institutional support and network formation for early market segment formation. Their emergence encouraged first installers to enter the photovoltaic market and to solve technical issues related to the specific requirements of home-owner systems. This adaptation to user needs is a typical problem of early market formation (Jacobsson and Bergek 2004). These installers faced low competition in their local markets. Intense and face-to face communication efforts to spread knowledge among potential users were initiated. As another empirical example of early market transaction formation, local utilities gained experience with the legal and administrative handling of billing and remuneration. This led to growth in decentralized and small-scale systems as the dominant market segment (Dewald and Truffer 2011). To coincide with the decentralized market segment, another segment focused on large-scale systems. This market segment was supported by early manufacturing firms and large-scale utilities but did not manage to overcome initial barriers to market formation. When it came to technology production, solar modules were already imported from the US and Japan to cover the demand that exceeded the low capacities of the few German producers of solar modules (Dewald 2012).

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The market dynamics illustrate the key role of intermediary actors, akin to change agents in diffusion theory (Rogers 2003). The action groups, which were often organized in the form of so-called local solar civic action groups (Solarbürgerinitiativen), addressed a broad range of tasks in all of the sub-processes introduced in the framework. They acted on market segment formation by providing supportive regulative conditions. The launch of demonstration projects and the provision of technical information for interested installers and users contributed to market transaction formation. Members of these action groups were often pioneering users. Sales numbers remained at a low level, though, and the 1990s saw slow growth mainly in evidence in a few regional, but separated markets. Characteristic features of a nurturing phase can be identified: a high level of uncertainty, especially with regard to financing and the eminent role of networking activities for aligning use and provision of technology. The local social context became a major arena for achieving technological alignment based on interaction between early users and technology providers. These insights are in line with other studies on decentralized renewable energy technologies which were also characterized by a high degree of personal commitment by early users in collecting information on financial, technical and planning problems (Garud and Karnøe 2003; Ornetzeder and Rohracher 2006). A particularly strong dependence on local community structures, regional institutional conditions and word-of-mouth-based communication were decisive in the launch of these proto-markets. Intermediary actors like action groups and initiatives set up by early users were important during this phase. This illustrates the significance of local settings for a successful alignment between users, technological variants and institutional conditions for emerging technologies. II. Bridging Phase: Stabilization and Spatial Expansion The implementation of uniform support measures at the national level was the decisive factor for strong market growth since the late 1990s. The “100 000-roofs program” and a national feed-in tariff (established by the Renewable Energy Sources Act of 2000) were prerequisites for this growth. The introduction of this market pull policy at the national level should not be interpreted solely as an isolated top-down intervention. Instead it relied strongly on experience of support policies from the pioneering phase: protagonists of the early market formation and community initiatives gained political influence at the federal level. Some started political careers and, following the victory of the red-green coalition in the 1998 federal elections, large-scale subsidies for renewables became a national priority (Lauber and Jacobsson 2016). Additional market segments started to emerge and new user groups were attracted (Dewald and Truffer 2011). Thanks to lobbying efforts by the photovoltaic industry and efforts to vastly reduce installation costs, market formation was diversified to include large-scale greenfield applications. Apart from the well-established homeowner roof-top-mounted systems, this new market segment widened the range of actors involved in the overall market trajectory. While the early homeowner segment drew heavily on community action groups or initiatives and committed pioneer users, the large-scale market was much more driven by technology providers such as installers, producers and new actors such as large-scale capital investors. This highlights the increasing diversification and impact of actors engaged with market transaction activities over time. This diversification was accompanied by an increasing professionalization and expansion

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within processes of market transaction formation: professional installers and specialized system providers emerged as new actor groups in the downstream parts of the photovoltaic value chain. They explored the new market segments and had an interest in widening the customer base within and around the proto-markets of the nurturing stage. Rooftop installations for farmers and subsequently on commercial buildings were explored (Dewald and Truffer 2011). Other new actors also entered the photovoltaic value chain. Traditional purchasing pools set up by the farming community to source machinery and other devices for farmers entered the photovoltaic market and served as intermediaries for this technology among farmers. As a further market segment, greenfield installations attracted large-scale capital investors and specialized project firms. As a consequence, the increased market growth in these new segments led to the foundation of start-ups in the upstream parts of the photovoltaic value chain (Dewald 2012). This exemplifies how during a bridging stage regularized processes of commodification and competition alter the market formation process. New user segments and product variations emerge, leading to an expansion of the customer base beyond the pioneering segment. Growing competition in the early proto-markets led installers to explore new business models and to move to new geographical markets. During this process many of the pioneering installers turned from direct sellers into wholesalers. A more standardized and specialized value chain for different market segments became established as a result. The spatial expansion to other regional markets in Germany occurred with a delay: the first years after the introduction of the national feed-in tariff saw a striking boom of installations in those regions close to the early hubs of proto-market formation. These showed increases in per-capita installations that were 2–3 times higher than in other regions (Dewald and Truffer 2012). Based on experiences and pre-existing support and supply structures during the nurturing stage, these regions were better equipped for fast upscaling of installations. It took several years for market formation to spread to other regions, based on new market segments such as commercial and greenfield installations. Bridging markets were characterized by an expansion of market volume, a geographical spread and a diversification of market segments. These built on resources established in the early market segments, but also required adaptation to the specific conditions of these new markets. III. Mass Market Phase: Shaping the Geography of Diffusion The mass market phase is characterized by the increasing integration into transnational relations of both supply and demand. Triggered by global oversupply of photovoltaic modules, a massive reduction in installation costs enabled rapid market growth in Germany across all market segments. While the institutionalization of the market formation process based on feed-in tariffs was a precondition for the bridging market to stabilize, the broad political support coalition was now called into question due mainly to a cost debate. As market support policies were introduced in a number of other European countries as well as overseas, German project developers took advantage of these new opportunities. Project developers with their experience in diverse end-user markets actively contributed to the formation of new national markets, as in the case of the Spanish market (Figure 37.1), which experienced significantly faster growth than Germany between 2006 and 2008. This illustrates the emerging local–global division of knowledge production and

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integration in global technology markets which became predominant at this more mature stage of technology development. As the market matured, the impact of actors on market segment formation and market transaction changed. Initial mechanisms for diffusion, such as intense communication by community actors as provided by local solar initiatives, developed into standard marketing processes, involving actors such as professional traders and installers. This example emphasizes ongoing realignments of supply and market transaction structures as the market evolves. The transition to the mass market phase occurred under new context conditions for market formation. The massive expansion of photovoltaic generation capacity started to impact fundamental characteristics of the entire electricity market. As a consequence, heated public debates about the role of photovoltaic technology in future energy systems developed (Lauber and Jacobsson 2016). Reductions of feed-in tariffs, more flexible market mechanisms and the support of technological solutions for self-consumption activities are the most recent attempts to mitigate impacts from further expanding photovoltaic electricity generation. In terms of installations, these redirections led to a sharp decline in annual installations, with numerous market exits at all stages of the value chain. In the mature stage, photovoltaic technology is thus characterized by permanent exploration of new market segments rather than resulting in a single dominant market segment. Direct marketing or solutions for own consumption of electricity based on new storage systems serve as examples for this ongoing transformation. At this stage, the specialization and professionalization of actors in one of the sub-processes of market formation becomes apparent. Based on this characterization of three development stages, the case of photovoltaic technology illustrates the co-construction of users, markets and technology in the geographical context. The case sheds light on the essential but changing role of intermediaries and the different social and spatial contexts in which the actors involved in the proliferation of such a technology interact. The example of the German market exemplifies a bottom-up pattern of market formation that supports the formation of the overall TIS. Many other countries have introduced market pull policies as well (Stenzel and Frenzel 2008). Due to different institutional contexts and actor configurations in user markets, these are likely to experience very different formation trajectories. The formation in a bottom-up fashion, as in this case, is one of several possible diffusion patterns which emerge from the interplay of alignment processes between provision, exchange and use of such new technologies.

CONCLUSIONS: IDENTIFYING GEOGRAPHIES OF MARKET FORMATION In this chapter, we have aimed to conceptualize market formation processes in the context of industrial dynamics, focusing on the case of sustainable/renewable energy technologies. Some general insights may be deduced from this analysis. The description of the development stages highlights the multi-layered and complex interaction and co-determination of different sub-processes over the course of market development and its impact on the creation of industrial development paths. The chapter argues against the predominant view in innovation systems research that markets be treated as largely passive targets of

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ready-to-use technology solutions. Tight and frequent interaction between users and technology development suggest that market formation is an integral dimension of innovation system dynamics. We suggest market segment formation, market transaction formation and user profile formation as analytical categories to reveal these dynamics in more detail. Especially in early phases of technological development, market formation is able to shape technological trajectories. We also argue that market formation requires a geographically sensitive conceptualization. The specific form of early market formation may crucially depend on local sociospatial conditions. As in our case of a renewable energy technology, these encompass experiments with new support schemes, the mobilization of actors from the business sector to explore market opportunities, the development of different market segments and the provision of legitimacy for the later introduction of a national support scheme to sustain market growth. These locally produced assets remain influential even when the regulatory environment is rescaled at the national level. Over time, the spatial organization of different market segments changes and gives rise to divergent locational dynamics of production and use. As illustrated in our example, integration into a globalized market has impacted this formation process only in later, more mature stages of development. This is in line with other contributions in the field of emerging cleantech technologies (Garud and Karnøe 2003; Ornetzeder and Rohracher 2006). However, it does not rule out the possibility that technological paths may emerge that follow a different spatial pattern, as in the context of leapfrogging or anchoring of technologies (Binz et al. 2016). A crucial contribution of our perspective is to contextualize early processes of path creation and stabilization, and to provide additional perspectives to the question of how and where windows of opportunities for such technology markets open up. The usual assumptions in the RIS literature about the geographical disconnect between the dynamics of market structures and productive capacity tend to overlook fundamental coupling dynamics with respect to market formation, as demonstrated for the German photovoltaic innovation system.

REFERENCES Asheim, B.T. and Gertler, M. (2005) ‘Regional innovation systems and the geographical foundations of innovation’, in J. Fagerberg, D. Mowery and R. Nelson (eds) The Oxford Handbook of Innovation, Oxford: Oxford University Press, 291–317. Bathelt, H. (2003) ‘Geographies of production: growth regimes in spatial perspective 1 – innovation, institutions and social systems’, Progress in Human Geography, 27(6): 91–111. Bathelt, H. and Glückler, J. (2005) ‘Resources in economic geography: from substantive concepts towards a relational perspective’, Environment and Planning A, 37(9): 1545–1563. Bathelt, H. and Henn, S. (2017) ‘National and regional innovation systems’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 457–471. Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S. and Rickne, A. (2008) ‘Analyzing the functional dynamics of technological innovation systems: a scheme of analysis’, Research Policy, 37(3): 407–429. Berndt, C. and Boeckler, B. (2009) ‘Geographies of circulation and exchange: constructions of markets’, Progress in Human Geography, 33(4): 535–551. Binz, C., Truffer, B. and Coenen, L. (2016) ‘Systemic anchoring of global innovation processes and new industry formation – the emergence of on-site water recycling in China’, Economic Geography, 92(2): 172–200. Boschma, R.A. (2005) ‘Proximity and innovation: a critical assessment’, Regional Studies, 39(1): 61–74. Boschma, R.A. and Frenken, K. (2006) ‘Why is economic geography not an evolutionary science? Towards an evolutionary economic geography’, Journal of Economic Geography, 6(3): 273–302.

Market formation and innovation systems

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Bunnell, T. and Coe, N. (2001) ‘Spaces and scales of innovation’, Progress in Human Geography, 25(4): 569–589. Callon, M. (1998) ‘An essay on framing and overflowing: economic externalities revisited by sociology’, Sociological Review, 46(S1): 244–269. Callon, M. (2007) ‘An essay on the growing contribution of economic markets to the proliferation of the social’, Theory, Culture and Society, 24(7): 139–163. Callon, M. (2017) ‘Markets, marketization and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 589–609. Carlsson, B. (2006) ‘Internationalization of innovation systems: a survey of the literature’, Research Policy, 35: 56–67. Carlsson, B. and Stankiewicz, R. (1991) ‘On the nature, function and composition of technological systems’, Journal of Evolutionary Economics, 1: 93–118. Coenen, L., Benneworth, P. and Truffer, B. (2012) ‘Towards a spatial perspective on sustainability transitions’, Research Policy, 41: 968–979. Coenen, L. and Truffer, B. (2012) ‘Places and spaces of sustainability transitions: geographical contributions to an emerging research and policy field’, European Planning Studies, 20(3): 367–374. Cooke, P. (2001) ‘Regional innovation systems, clusters, and the knowledge economy’, Industrial and Corporate Change, 10(4): 945–974. Dewald, U. (2012) Energieversorgung im Wandel – Marktformierung im deutschen Photovoltaik-Innovationssystem, Münster: LIT. Dewald, U. and Fromhold-Eisebith, M. (2015) ‘Trajectories of sustainability transitions in scale-transcending innovation systems: the case of photovoltaics’, Environmental Innovations and Societal Transitions, 17: 110–125. Dewald, U. and Truffer, B. (2011) ‘Market formation in technological innovation systems – diffusion of photovoltaic applications in Germany’, Industry and Innovation, 18(3): 285–300. Dewald, U. and Truffer, B. (2012) ‘The local sources of market formation: explaining regional growth differentials in German photovoltaic markets’, European Planning Studies, 20(3): 397–420. Edquist, C. (2005) ‘Systems of innovations: perspectives and challenges’, in J. Fagerberg, D. Mowery and R. Nelson (eds) The Oxford Handbook of Innovation, Oxford: Oxford University Press, 181–208. Fligstein, N. and Dauter, L. (2007) ‘The sociology of markets’, Annual Review of Sociology, 33: 105–128. Freeman, C. (1995) ‘The “National System of Innovation” in historical perspective’, Cambridge Journal of Economics, 19: 5–24. Garud, R. and Karnøe, P. (2003) ‘Bricolage versus breakthrough: distributed and embedded agency in technology entrepreneurship’, Research Policy, 32(2): 277–300. Grabher, G., Ibert, O. and Flohr, S. (2008) ‘The neglected king: the customer in the knowledge economy of innovation’, Economic Geography, 84(3): 253–280. Graziano, M. and Gillingham, K. (2015) ‘Spatial patterns of solar photovoltaic system adoption: the influence of neighbors and the built environment’, Journal of Economic Geography, 15(4): 815–839. Grubb, M. (2004) ‘Technology innovation and climate change policy: an overview of issues and options’, Keio Economic Studies, 41(2): 103–132. Hägerstrand, T. (1966) ‘Aspects of the spatial structure of social communication and the diffusion of innovation’, Papers of the Regional Science Association, 16(1): 27–42. IEA-PVPS (International Energy Agency-Photovoltaic Power Systems Program) (2013) Trends 2013 in Photovoltaic Applications. Online. Available HTTP: (15 January 2014). Jacobsson, S. and Bergek, A. (2004) ‘Transforming the energy sector: the evolution of technological systems in renewable energy technology’, Industrial and Corporate Change, 13(5): 815–849. Jacobsson, S. and Johnson, A. (2000) ‘The diffusion of renewable energy technology: an analytical framework and key issues for research’, Energy Policy, 28: 625–640. Jacobsson, S., Sandén, A. and Bangens, L. (2004) ‘Transforming the energy system – the evolution of the German technological system for solar cells’, Technology Analysis and Strategic Management, 16(1): 3–30. Kemp, R., Schot, J. and Hoogma, R. (1998) ‘Regime shifts to sustainability through processes of niche formation: the approach of strategic niche management’, Technology Analysis and Strategic Management, 10(2): 175–198. Kline, S.J. and Rosenberg, N. (1986) ‘An overview of innovation’, in R. Landau and N. Rosenberg (eds) The Positive Sum Strategy: Harnessing Technology for Economic Growth, Washington, DC: National Academy Press, 275–305. Lagendijk, A. (2002) ‘Beyond the regional life world, against the global system world: towards a relational-scalar perspective on spatial-economic development’, Geografski Annaler B, 84(2): 35–50. Lauber, V. and Jacobsson, S. (2016) ‘The politics and economics of constructing, contesting and restricting socio-political space for renewables – The German Renewable Energy Act’, Environmental Innovations and Societal Transitions, 18: 147–163.

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Lundvall, B.-Å. (1988) ‘Innovation as an interactive process – from user–producer interaction to national systems of innovation’, in G. Dosi, C. Freeman, R. Nelson, G. Silverberg, and L.G. Soete (eds) Technical Change and Economic Theory, London: Pinter, 349–367. Lundvall, B.-Å. (1992) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, London: Pinter. Malmberg, A. and Maskell, P. (2002) ‘The elusive concept of localization economies: towards a knowledgebased theory of spatial clustering’, Environment and Planning A, 34(3): 429–449. Markard, J., Raven, R. and Truffer, B. (2012) ‘Sustainability transitions: an emerging field of research and its prospects’, Research Policy, 41: 955–967. Martin, R. (2000) ‘Institutional approaches to economic geography’, in T. Barnes and M. Sheppard (eds) A Companion to Economic Geography, Oxford: Blackwell, 77–94. Maskell, A., Bathelt, H. and Malmberg, A. (2006) ‘Building global knowledge pipelines: the role of temporary clusters’, European Planning Studies, 14: 997–1013. Möllering, G. (2009) Market Constitution Analysis: A New Framework Applied to Solar Power Technology Markets, MPlfG Working Paper 09/7. Online. Available HTTP: (14 March 2013). Morgan, K. (2004) ‘The exaggerated death of geography: learning, proximity and territorial innovation systems’, Journal of Economic Geography, 4: 3–21. Moulaert, F and Mehmood, A. (2010) ‘Analyzing regional development and policy: a structural realist approach’, Regional Studies, 44: 103–118. Murphy, J. (2015) ‘Human geography and socio-technical transition studies: promising intersections’, Environmental Innovations and Societal Transitions, 17: 73–91. Musiolik, J., Markard, J. and Hekkert, M. (2012) ‘Networks and network resources in technological innovation systems: towards a conceptual framework for system building’, Technological Forecasting and Social Change, 79(6): 1032–1048. Negro, S., Hekkert, M. and Smits, R. (2007) ‘Explaining the failure of the Dutch innovation system for biomass digestion – a functional analysis’, Energy Policy, 35(2): 925–938. Oinas, P. and Malecki, E.J. (2002) ‘The evolution of technologies in time and space: from national and regional to spatial innovation systems’, International Regional Science Review, 25(1): 102–131. Ornetzeder, M. and Rohracher, H. (2006) ‘User-led innovation processes: lessons from sustainable energy technologies’, Energy Policy, 34: 138–150. Oudshoorn, N. and Pinch, T. (2005) ‘Introduction: how users and non-users matter’, in N. Oudshoorn and T.  Pinch (eds) How Users Matter: The Co-Construction of Users and Technology, Cambridge, MA: MIT Press, 1–25. Pinch, T. and Bijker, W. (1987) ‘The social construction of facts and artifacts: or how the sociology of science and the sociology of technology might benefit each other’, in W. Bijker, T. Hughes and T. Pinch (eds) The Social Construction of Technological Systems, Cambridge, MA: MIT Press, 17–50. Rogers, E.M. (2003) Diffusion of Innovations, 5th edn (1st edn 1962), New York: Free Press. Stenzel, T. and Frenzel, A. (2008) ‘Regulating technological change – the strategic reactions of utility companies towards subsidy policies in the German, Spanish and UK electricity markets’, Energy Policy, 36(7): 2645–2657. Sunley, P. (2000) ‘Urban and regional growth’, in E. Sheppard and T. Barnes (eds) A Companion to Economic Geography, Oxford: Blackwell, 187–201. Truffer, B. (2003) ‘User-led innovation processes: the development of professional car sharing by environmentally concerned citizens’, Innovation, 16(2): 139–153. Truffer, B. and Coenen, L. (2012) ‘Environmental innovation and sustainable transitions in regional studies’, Regional Studies, 46 (1): 1–21. Truffer, B. and Dürrenberger, G. (1997) ‘Outsider initiatives in the reconstruction of the car: the case of lightweight vehicle milieus in Switzerland’, Science, Technology, and Human Values, 22(2): 207–234. Truffer, B., Murphy, J. and Raven, R. (2015) ‘The geography of sustainability transitions: contours of an emerging research field’, Environmental Innovations and Societal Transitions, 17: 63–70. von Hippel, E. (1986) ‘Lead users: a source of novel product concepts’, Management Science, 32(7): 791–805.

38. Innovation and entrepreneurship Edward J. Malecki and Ben Spigel

INTRODUCTION The connection between innovation and entrepreneurship originated with Schumpeter’s (1934) emphasis on new combinations – new goods, new methods or processes, new markets, or the new organization of an industry – introduced by entrepreneurs. “New combinations are, as a rule, embodied, as it were, in new firms” (Schumpeter 1934: 66). As Landström et al. (2012: 1155) observe, “Schumpeter’s idea was . . . that economic growth resulted not from capital accumulation, but from innovations or ‘new combinations’ that create a disequilibrium on the market.” Despite the common origins of research on both innovation and entrepreneurship in the work of Schumpeter, they have “evolved over time as two largely separate research fields” and remain “surprisingly disconnected” (Landström et al. 2012: 1171–1172). Drucker (1985) combines innovation and entrepreneurship and stresses that entrepreneurs must be innovative, which requires systematic search and analysis of innovative opportunities. The definition of entrepreneurship has evolved over time to emphasize the importance of innovation and recombination of existing knowledge in the creation of new ventures, with a special emphasis in the research literature on new technology-based firms (Braunerhjelm 2011; Hébert and Link 2006). New technology-based firms acquire, synthesize and introduce new technology, and contribute to local or regional economic development (Fontes and Coombs 2001). Small, innovative firms contribute to economic heterogeneity by creating new products and new industries. Innovation and entrepreneurship are highlighted in recent research examining how knowledge sparks innovation and lubricates entrepreneurship (Bae and Koo 2009; Iammarino and McCann 2006; Qian et al. 2013). While the conventional wisdom and standard models focus on cities as the locus of innovation, innovation occurs in small and isolated places as well (Shearmur 2012). In this review of entrepreneurship and innovation in regions, we stress that this chapter is not about the topic of spillovers and agglomeration (Feldman and Kogler 2010; van Oort and Bosma 2013). We identify four topics in this chapter that have been central to the study of innovation and entrepreneurship and which have influenced the development of both the research literature and public policy. First is the importance of particular innovative industries such as biotech and information and communication technology (ICT). Second, the phenomenon of firms spinning out of university research puts a spotlight on academic and basic research as the starting point of innovative entrepreneurship. Third, the local or regional innovation system has been identified as a nexus of social and economic links and support for entrepreneurship. Fourth, underlying these topics are the regional and organizational cultures that influence the willingness of actors to engage with the risks of the entrepreneurship process. While these topics do not encompass the entire field of innovation and entrepreneurship research, they establish the contours 625

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of contemporary thinking regarding the geography of innovative and entrepreneurial activity and its connections with economic development and growth.

THE HIGH-TECHNOLOGY CONTEXT Empirical observation of the link between innovation and entrepreneurship largely grew out of the experience in California’s Silicon Valley and Route 128 outside Boston beginning in the 1960s, and set the tone for understanding the links between high-technology innovation and entrepreneurship. By the mid-1980s, it was well established that places where high-technology, R&D-intensive firms were active and abundant generated spinoffs and grew into (what would later be called) clusters (Cooper 1986; Cooper and Folta 2000). Silicon Valley was not the product only of entrepreneurs: large multilocational firms also played a key role in the development of the region and its universities, and they continue to be a lure (Adams 2011). Moreover, globalization of R&D has spread the potential for high technology to new places (Malecki 2010). Large firms, together with entrepreneurs, co-create regional high-technology clusters (Smilor et al. 2007). A vibrant global competition for high-technology success continues (Anttiroiko 2004; Castells and Hall 1994). Technology firms are of particular importance to researchers and policy makers for a number of reasons. Their reason for existence is innovation: to succeed and grow in the market they must respond to changing market and consumer demands by developing new incremental or radical innovations. This requires them to draw on knowledge outside the organization to identify new technological and market developments and recombine this knowledge with their own propriety knowledge through their internal capabilities and competences. New and innovative firms depend on their ability to incorporate outside knowledge and transform it into new innovation. A key aspect of research into economic geography and regional science over the past two decades has been the influence of a firm’s location(s) on its ability to perform these absorption and recombination activities (Feldman 2000; Malmberg and Maskell 2002). A large deal of this research is tied into the role of technology clusters in propelling innovation and discovery (Frenken et al. 2015). Technology clusters differ significantly from other industrial clusters in that they are tied to the early stages of industry life cycles, and resources at the regional level support growth and innovation (St. John and Pouder 2006). What is considered “high technology” changes over time, encompassing innovative sectors that present new opportunities. The principal activity of technology-based sectors is research, and their main input as well as output is knowledge. For firms, locating near sources of knowledge (such as universities and research centers) and clustering in specialized labor markets maximizes opportunities for collective learning and exploitation of entrepreneurial opportunities (Audretsch et al. 2006). We may be able to identify tendencies such as those above, but diversity and heterogeneity continue to operate, so that we are unable to predict with accuracy where innovative clusters will emerge (Iammarino and McCann 2006; Storper 2013).

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UNIVERSITY SPINOFFS Universities are frequently among the “incubator organizations” that act as the foundation of innovative entrepreneurial regions (Mayer 2007). Universities offer the potential for creating cutting-edge technology and radical innovations that can be commercialized by academic entrepreneurs or nearby startups with close connections to the university’s laboratories. But, of course, the successful spinoff processes and their consequences in the Boston area from the Massachusetts Institute of Technology (MIT) and in Silicon Valley from Stanford University are difficult for other regions to imitate since the numbers of spinoff firms from top research institutions can hardly be matched in other settings (Degroof and Roberts 2004). Shane (2004) summarizes the findings for the US, updated by Grimaldi et al. (2011). Many researchers have attributed the commercialization success of a few universities to a specific organizational culture that develops within some universities that normalizes entrepreneurship as part of a larger academic career (Bramwell et al. 2008). Both the US and European experiences have created an expectation regarding the regional role of a university: to contribute to the regional economy through spinoffs of new firms based on innovative technologies flowing from university research (Lerner 2005; Wright et al. (2007). Etzkowitz (1983) has documented the role of entrepreneurial scientists and entrepreneurial universities in American academic science. His picture of entrepreneurial researchers and science parks has had broad influence, which has grown further with the development of the “triple helix” model (Etzkowitz and Leydesdorff 2000). Through spinoffs – and profiting from spinoffs – universities have broadened the scope of their activities beyond teaching and research. The entrepreneurial university model includes patenting, commercialization, and technology transfer as a “third stream” of revenue and/or as a way to contribute to the local economy (Clark 1998). European universities are learning to compete and becoming multidimensional in their entrepreneurialism and other aspects of the “knowledge business” (McKelvey and Holmén 2009). The allure of profit has been especially strong in biomedical fields. Åstebro and Bazzazian (2011), Rothaermel et al. (2007), and Siegel (2011) review the burgeoning literature on academic entrepreneurship and university technology transfer. European countries have attempted to imitate the favorable conditions found for spinoff formation in US high-technology regions (Mustar et al. 2008). The variability among national academic cultures is overwhelmed by the “imitation effect” that has “led to a convergence of policies toward the same goal: to foster a larger number of academic spin-offs” (Wright et al. 2007: 63). Imitation also leads nearly all imitators to target biomedical and nanotechnology fields. Such policies, however, have not imitated faculty mobility, university autonomy, and generous support of basic research found in the US (Franzoni and Lissoni 2009; Howells et al. 2012). Spinoffs started by professors, researchers, and other university employees are relatively easy to track. However, former students (including alumni) also start firms, utilizing the knowledge and contacts they develop during their studies. Many spinoffs are by graduates (Åstebro et al. 2012), but such firms are not readily identified in available data sets (Bathelt et al. 2011). Thus, research reviewed by Grimaldi et al. (2011) and Mustar et al. (2006) moves beyond university faculty and research staff to identify a broader cadre

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of university-related entrepreneurs. One of the key roles of universities is producing or pulling in future entrepreneurs and skilled startup employees who develop their own firms and innovations or who attract outside firms into the region that want to take advantage of the high levels of human capital (Kasabov and Sundaram 2016). The principal challenge of understanding university startups is that far more knowledge transfer occurs than is imagined in the linear technology transfer model. Research “spills over” and informal transfer of knowledge takes place continually as research is conducted and as student entrepreneurship is encouraged in other ways that are underestimated and understudied (Grimaldi et al. 2011; Nelson 2012). Many spinoffs are spontaneous rather than planned, growing out of informal activities in a “grey zone” where tacit knowledge is shared and transfused into society (Bathelt et al. 2010; Nilsson et al. 2010). Most university-related startups arise from a process of decentralized idea development that may have originated in a classroom or lab or from social ties that are informal and related to the university only indirectly. Such tacit knowledge is basically unknown to and uncontrollable by a university, yet it may well represent the majority of knowledge transferred (Karnani 2013). Whether patentable or not, knowledge-related collaboration by academic researchers with non-academic organizations is innovative (Perkmann et al. 2013). Universities produce many outputs, including new knowledge and human capital. They transfer existing know-how and produce technological innovation. They also provide regional leadership, co-producing the regional knowledge infrastructure. Together, the mechanisms by which university R&D activity stimulates economic development are both broader and more diverse than spinoffs, patenting, and licensing activity (Benneworth and Charles 2005; Goldstein 2009; Lendel 2010). As the discussion above suggests, research on academic spinoffs has been empirically rich but “mainly atheoretical,” providing examples of entrepreneurial (or not so entrepreneurial) universities but missing important aspects of the role of universities in regional economic activity (Autio 2000: 332). Spinoffs are clearly connected with the culture of entrepreneurial universities but they can hardly be planned or predicted. Entrepreneurs penetrate the “knowledge filter” between research and economic use (Carlsson et al. 2009). They are able to do so when an entrepreneur has the ability “to understand new knowledge, recognize its value, and subsequently commercialize it by creating a firm” (Qian and Acs 2013: 191). This ability, which Qian and Acs call entrepreneurial absorptive capacity, involves two dimensions: scientific knowledge as well as market or business knowledge. A pool of academic researchers and their new firms adds to the regional knowledge base for further innovative entrepreneurship. University spinoffs are innovative, applying cutting-edge research to new purposes. They also are an unusual type of entrepreneurship, because academic entrepreneurship is counter to long-standing scholarly models (Martin 2012). Traditional scholarly goals include advanced fundamental research into new fields and open publication of novel results, whereas academic entrepreneurship requires building focused products that solve immediate customer needs and protecting newly developed intellectual property through patents and non-disclosure agreements. This can lead to cultural conflicts between researchers who are used to traditional academic norms and those who want to promote academic entrepreneurship (Murray 2010). New skills must be acquired, and new networks formed, to assemble a new spinoff firm and for it to succeed (Clarysse et al.

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2005; Vohora et al. 2004). Without question, university spinoffs combine innovation and entrepreneurship and are the archetypes of new combinations.

REGIONAL/LOCAL SYSTEMS OF ENTREPRENEURSHIP AND INNOVATION Urban areas offer favorable conditions as incubators for innovative entrepreneurship, as a result of their economies of density and the opportunities created by cities as nuclei of broader networks, both local and global (Nijkamp 2003). Much research has aimed to understand just what is present in the most innovative regions. Once the conditions are known, can they be created, grown, or transferred to other places? The synergy necessary for a self-sustaining area is found in an active local or regional “entrepreneurial ecosystem” (Bahrami and Evans 1995). Some environments can be argued to act as “regional incubators” (Clarysse et al. 2005: 213). Entrepreneurial ecosystems represent the collection of cultural outlooks, resources such as financial capital, skilled workers, and advisors, and networks connecting entrepreneurs, customers, suppliers, and knowledge producers such as universities together (Spigel 2017). These systems create an environment that supports the survival and competitiveness of innovative new ventures. In some ways, regional entrepreneurial ecosystems can be conceptualized as a particularly fruitful type of regional innovation system. Although there are significant differences between a fully formed RIS and an entrepreneurial ecosystem, such as the importance of knowledge about entrepreneurship and successful entrepreneurial mentors and role models in ecosystems, they share a common vision of innovation emerging from interdependencies between firms, universities, and state policies. Silicon Valley’s innovative ecosystem represents the archetypical entrepreneurial regional innovation system (ERIS) (Cooke 2004). ERISs represent the amalgamation of entrepreneurs, networks, cultural outlooks, state policies and institutions, and specific actors such as venture capitalists, that create an environment supportive of radical entrepreneurial innovation (Cooke 2007). “There is no such thing as an innovation system without entrepreneurs. Entrepreneurs are essential for a well functioning innovation system” (Hekkert et al. 2007: 421). Indeed, entrepreneurial activities, together with knowledge development, knowledge diffusion through networks, and market formation, are among the key functions of innovations. Recent research emphasizes the systemic nature of entrepreneurship, whether within national and regional innovation systems (Qian et al. 2013; Radosevic and Yoruk 2013) or as a national system of entrepreneurship (Szerb et al. 2013). Knowledge-intensive entrepreneurship is a systemic feature of innovation systems, and new knowledge, innovation, and entrepreneurship are inseparable elements of a dynamic innovation system (Radosevic and Yoruk 2013: 1015). However, a strong ERIS propelled by a strong local university or highly innovative anchor firm is not a sufficient precondition to create a thriving entrepreneurial community (Cooke 2007). Such regions require other attributes such as entrepreneurial training, early stage financing from angel investors and venture capitalists, and a culture or milieu that normalizes activities such as starting high-risk ventures, knowledge sharing between firms, and organizations supporting spinouts rather than trying to prevent them (Julien 2007).

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RISs can be seen to embrace other concepts at the subnational scale, including clusters, territorial production complexes, productive systems, territorial systems, milieus, and local systems (Asheim et al. 2011; Moulaert and Sekia 2005). An RIS also must be linked to knowledge and innovation sources in other regions. These “pipelines” serve as conduits for an interregional spatial innovation system (Bathelt et al. 2004; Oinas and Malecki 2002). However, the structure and extent of spatial innovation systems differs among high-technology industries. Biotechnology is characterized by an economic geography that is less concentrated (and more dispersed among global biotech centers) than that of semiconductors (Kenney and Patton 2005). Key ingredients in ERISs are venture capital and angel investments that facilitate product development and market expansion efforts by new startups (Cooke 2007). Venture capitalists contribute more than money to entrepreneurs; they also provide advice and guidance and links to other networks (De Clerq et al. 2006; Zider 1998). Without this investment, firms will not possess the financial resources and connections to potential clients necessary to grow beyond small niche markets. Other intermediaries, such as attorneys, lawyers, and consultants, also provide key knowledge and act as gatekeepers for entrepreneurs (Cooke 2008; Howells 2006). Entrepreneurs exist in networks – social and personal as well as business- and innovation-oriented (Johannisson et al. 1994). A network mix – weak ties and strong ties, local and nonlocal networks, and internal and external networks within the regional environment – is needed to provide social ties, knowledge, technology, marketing, and reputation for entrepreneurial success (Lechner and Dowling 2003). Networking among firms promotes innovation in both medium- and high-technology clusters (Cappellin and Wink 2009). Dense networks of relations and multifaceted links between academic and non-academic actors are also common (Martinelli et al. 2008). In network-intensive regions, knowledge is shared and a more favorable entrepreneurial climate results (Malecki 1997). For high-technology firms, localized linkages are an important source of innovation (Lawton Smith 2008).

ENTREPRENEURIAL CULTURE AND IMPACT Innovative entrepreneurship is not a disembodied economic activity. While the popular view of entrepreneurship may be of an isolated technologist who disrupts existing markets through novel innovations and insights, this is not the case. Entrepreneurs are deeply embedded in networks of social and cultural outlooks and beliefs. Researchers have long argued that the social character of a place, industry, or organization has a profound influence on innovation and entrepreneurship processes (Huggins and Thompson 2014). This realization has, in turn, led to an interest among policy makers on how to foster a particular culture within a region or organization that supports risk taking, cooperation, and entrepreneurship (e.g. Acs et al. 2008; Bercovitz and Feldman 2008). The goal of such efforts is the creation of a community that encourages the free exchange of knowledge and support, with the expectation that this contributes to innovative entrepreneurship and therefore higher levels of economic growth. While the importance of culture is well understood, its complex and ephemeral nature has made it difficult to study its role in the entrepreneurship process. Many authors fail to

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define how they use culture or use only individual attributes of a person, such as nationality or ethnicity, to identify their “culture” (see Raghuram and Strange 2001). The lack of attention to defining culture is problematic but not surprising: culture is a difficult word to define, with meanings ranging from the agricultural to the artistic. A major focus within the entrepreneurship literature is how cultural attributes influence decisions relating to starting, financing, and growing a new firm. This began with very broad based investigations into differences in entrepreneurial rates and performance between nations (Tan 2002; Morris and Schindehutte 2005). Such research frequently draws on the work of Hofstede (2001), who identifies five core cultural attributes and measurements of these attributes for over seventy countries, allowing researchers to investigate relationships between variations in national cultural attributes and aggregate levels of innovation and entrepreneurship. The presence of low levels of uncertainty avoidance (the desire to avoid risk and uncertainty) and high levels of individualism (focus on individual over collective needs) within a culture are correlated with higher levels of innovation-based entrepreneurship (Lee and Peterson 2000). However, critics question the usefulness of Hofstede’s methods for cross-cultural research, pointing out that it ignores variation in cultural outlooks within nations (McSweeney 2002) and relegates culture to a deterministic variable (Baskerville 2003). In light of these criticisms, there has been a renewed interest in interpretive approaches to understanding the relationship between culture and innovative entrepreneurship. These studies employ qualitative, ethnographic, or historical analysis to identify salient cultural attributes within a region, organization, or industry and show how these encourage or discourage innovative entrepreneurial practices. Saxenian (1994) and Schoenberger (1997) demonstrate not only the existence of cultures that are beneficial or detrimental to innovation, but also reveal the causal relations between these outlooks and innovative activities. However, individual case studies provide few generalizable findings that are useful in other contexts. Policies that are effective in one context often fail in other regions, because they fail to connect with their new cultural environment. Organizational and regional cultures have become important tools in the study of academic entrepreneurship and university spinoffs (Jenson et al. 2003; Bathelt et al. 2011). The culture of a university plays an important role in the creation of formal policies and informal beliefs within the university that promote successful spinoff and technology transfer activities or, conversely, that discourage non-research activities like patenting and commercialization (Colyvas 2007). It is difficult for top-down policies by administrators to promote the formation of spinoffs because they clash with preexisting informal cultural outlooks that dismiss entrepreneurship as a violation of academic norms (Bathelt and Spigel 2011). This points to the role of culture as a social system underlying more formal programs such as tenure, promotion, and hiring: universities such as Stanford or MIT, which have had an industry-oriented culture since their founding, have had greater success in seeding innovative startups than other universities whose culture is focused on basic science. Qualitative studies of entrepreneurial cultures are part of a larger movement within entrepreneurship studies to highlight the discursive and social aspects of entrepreneurship (Drakopolou Dodd 2002; Steyaert and Katz 2004). As Licht and Siegel (2006: 516) argue: “culture bears a profound impact on all facets of entrepreneurship in society.” The development of these perspectives in management science has been mirrored by other allied

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disciplines such as economic geography, where a growing body of work has investigated the origins and influence of regional cultures on entrepreneurship (Feldman et al. 2005; James 2005). Culture is seen as a key component of entrepreneurial ecosystems (Spigel 2017). As with innovation systems, a supportive culture within entrepreneurial ecosystems fosters the constant circulation of knowledge and resources through local networks and encourages successful entrepreneurs to act as role models and investors for fast-growing startups. This creates what Malecki (1997: 82) terms an “entrepreneurial climate” that normalizes the high risk, long hours, and absence of immediate rewards that characterize innovative entrepreneurship. While the importance of culture in entrepreneurial ecosystems is well accepted, the processes through which cultural outlooks catalyze and reproduce ecosystems remain unclear (Nijkamp 2003; Julien 2007). Entrepreneurs are to some extent culturally embedded in their home region, meaning that they internalize the collective understandings and social “scripts” within the community (Zukin and DiMaggio 1990). A supportive entrepreneurial culture might normalize certain activities such as knowledge sharing and cooperation among knowledge workers, mentoring relationships between entrepreneurs, or a tendency for angel investors and successful businesspeople to actively network with startup founders. However, while embeddedness provides some clues to how culture contributes to entrepreneurial ecosystems, it does not explain how these cultures initially develop within a region or how they change over time (James 2007). Further work is necessary to reveal the connections between cultural outlooks and innovative and entrepreneurial activities with regions and organizations (Drakopoulou Dodd et al. 2013). Innovative entrepreneurship depends on more than the presence of financial and knowledge endowments: it also requires a supportive set of cultural outlooks to normalize the risks it entails. While researchers have long acknowledged the importance of culture to innovation and entrepreneurship, our understanding of how such culture develops within regions or organizations and actually influences the actions of entrepreneurs remains underdeveloped.

CONCLUSIONS Building innovative, entrepreneurial regions remains an imperfect art. There are no simple policy prescriptions that will quickly enhance entrepreneurial innovation within a region or community, nor can successful policies from one place be copied and implemented in another with any hope of success. Entrepreneurs and innovations can arise in any industry and from universities and other institutions. Much of what we know is based on a few success stories and on case studies. Less is known about regions that fail to create innovation-led economies or those which passively wait for innovative entrepreneurs to appear. The presence of individual ingredients, such as research universities, large innovative firms, or science parks, does not guarantee success. Nearly all regions aspire to high-technology success, targeting biotechnology and nanotechnology; few will succeed in the short term, but many will improve their development capacity and future innovative potential. However, innovation policy does not exist in a vacuum. Policies that fail to account

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for the cultural outlooks they exist in are to likely flounder. Instead, policy makers must craft new programs taking into account the local cultural outlooks, for instance considering their tolerance for the risks of leaving stable employment to lead a startup. At the same time, policy makers must keep in mind that culture is not static: the presence of successful, innovative entrepreneurs can spur others to follow in their footsteps while a period of economic decline can lead to a retrenchment where the risks of innovation and entrepreneurship seem increasingly untenable. Acknowledgements Thanks to Harald Bathelt and Breandan O’hUallachain for comments on earlier drafts of this chapter.

REFERENCES Acs, Z., Glaeser, E., Litan, R., Fleming, L., Goetz, S.J., Kerr, W., Klepper, S., Sorenson, O. and Strange, W.C. (2008) Entrepreneurship and Urban Success: Toward a Policy Consensus, Kansas City, MO: The Kauffman Foundation. Adams, S.B. (2011) ‘Growing where you are planted: exogenous firms and the seeding of Silicon Valley’, Research Policy, 40: 368–379. Anttiroiko, A.-V. (2004) ‘Editorial: global competition of high-tech centres’, International Journal of Technology Management, 28: 289–323. Asheim, B.T., Lawton Smith, H. and Oughton, C. (2011) ‘Regional innovation systems: theory, empirics and policy’, Regional Studies, 45: 875–891. Åstebro, T. and Bazzazian, N. (2011) ‘Universities, entrepreneurship, and local economic development’, in M. Fritsch (ed.) Handbook of Research on Entrepreneurship and Regional Development: National and Regional Perspectives, Cheltenham and Northampton, MA: Edward Elgar Publishing, 252–333. Åstebro, T., Bazzazian, N. and Braguinsky, S. (2012) ‘Startups by recent university graduates and their faculty: implications for university entrepreneurship policy’, Research Policy, 41: 663–677. Audretsch, D.B., Keilbach, M.C. and Lehmann, E.E. (2006) Entrepreneurship and Economic Growth, Oxford: Oxford University Press. Autio, E. (2000) ‘Growth of technology-based new firms’, in D.L. Sexton and H. Landström (eds) The Blackwell Handbook of Entrepreneurship, Oxford: Blackwell, 329–347. Bae, J. and Koo, J. (2009) ‘The nature of local knowledge and new firm formation’, Industrial and Corporate Change, 18: 473–496. Bahrami, H. and Evans, S. (1995) ‘Flexible re-cycling and high-technology entrepreneurship’, California Management Review, 37 (3): 62–89. Baskerville, R.F. (2003) ‘Hofstede never studied culture’, Accounting, Organizations and Society, 28: 1–14. Bathelt, H., Kogler, D.F. and Munro, A.K. (2010) ‘A knowledge-based typology of university spin-offs in the context of regional economic development’, Technovation, 30: 519–532. Bathelt, H., Kogler, D.F. and Munro, A.K. (2011) ‘Social foundations of regional innovation and the role of university spin-offs: the case of Canada’s Technology Triangle’, Industry and Innovation, 18: 461–486. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28: 31–56. Bathelt, H. and Spigel, B. (2011) ‘University spin-offs and regional policy in comparative perspective: the cases of Columbus (Ohio) and Toronto and Waterloo (Ontario)’, International Journal of Knowledge-Based Design, 3: 202–219. Benneworth, P. and Charles, D. (2005) ‘University spin-off policies and economic development in less successful regions: learning from two decades of policy practice’, European Planning Studies, 13: 537–557. Bercovitz, J. and Feldman, M. (2008) ‘Academic entrepreneurs: organizational change at the individual level’, Organization Science, 19: 69–89. Bramwell, A., Nelles, J. and Wolfe, D.A. (2008) ‘Knowledge, innovation and regional culture in Waterloo’s ICT cluster’, Regional Studies, 42: 101–116. Braunerhjelm, P. (2011) ‘Entrepreneurship, innovation and economic growth’, in D.B. Audretsch, O. Falck,

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S. Heblich and A. Lederer (eds) Handbook of Research on Innovation and Entrepreneurship, Cheltenham and Northampton, MA: Edward Elgar Publishing, 161–209. Cappellin, R. and Wink, R. (2009) International Knowledge and Innovation Networks: Knowledge Creation and  Innovation in Medium-technology Clusters, Cheltenham and Northampton, MA: Edward Elgar Publishing. Carlsson, B., Acs, Z.J., Audretsch, D.B. and Braunerhjelm, P. (2009) ‘Knowledge creation, entrepreneurship, and economic growth: a historical review’, Industrial and Corporate Change, 18: 1193–1229. Castells, M. and Hall, P. (1994) Technopoles of the World: The Making of 21st Century Industrial Complexes, London: Routledge. Clark, B. (1998) Creating Entrepreneurial Universities: Organizational Pathways of Transformation, Oxford: Pergamon. Clarysse, B., Wright, M., Lockett, A., Van de Velde, E. and Vohora, A. (2005) ‘Spinning out new ventures: a typology of incubation strategies from European research institutions’, Journal of Business Venturing, 20: 183–216. Colyvas, J. (2007) ‘From divergent meanings to common practices: the early institutionalization of technology transfer in the life sciences at Stanford University’, Research Policy, 36: 456–476. Cooke, P. (2004) ‘Introduction: regional innovation systems – an evolutionary approach’, in P. Cooke, M. Heidenreich and H.-J. Braczyk (eds) Regional Innovation Systems, second ed., London: Routledge, 1–18. Cooke, P. (2007) ‘Regional innovation, entrepreneurship and talent systems’, International Journal of Entrepreneurship and Innovation Management, 7: 117–139. Cooke, P. (2008) ‘Regional innovation systems, clean technology and Jacobian cluster-platform policies’, Regional Science Policy and Practice, 1: 23–45. Cooper, A.C. (1986) ‘Entrepreneurship and high technology’, in D.L. Sexton and R.W. Smilor (eds) The Art and Science of Entrepreneurship, Cambridge, MA: Ballinger, 153–168. Cooper, A.C. and Folta, T. (2000) ‘Entrepreneurship and high-technology clusters’, in D.L. Sexton and H. Landström (eds) The Blackwell Handbook of Entrepreneurship, Oxford: Blackwell, 348–367. De Clerq, D., Fried, V.H., Lehtonen, O. and Sapienza, H.J. (2006) ‘An entrepreneur’s guide to the venture capital galaxy’, Academy of Management Perspectives, 20 (3): 90–112. Degroof, J.-J. and Roberts, E.B. (2004) ‘Overcoming weak entrepreneurial infrastructures for academic spin-off ventures’, Journal of Technology Transfer, 29: 327–352. Drakopoulou Dodd, S. (2002) ‘Metaphors and meaning: a grounded cultural model of US entrepreneurship’, Journal of Business Venturing, 17: 519–535. Drakopoulou Dodd, S., Jack, S. and Anderson, A.R. (2013) ‘From admiration to abhorrence: the contentious appeal of entrepreneurship across Europe’, Entrepreneurship and Regional Development, 35: 68–89. Drucker, P.F. (1985) Innovation and Entrepreneurship, New York: Harper & Row. Etzkowitz, H. (1983) ‘Entrepreneurial scientists and entrepreneurial universities in American academic science’, Minerva, 21: 198–233. Etzkowitz, H. and Leydesdorff, L. (2000) ‘The dynamics of innovation: from national systems and “Mode 2” to a triple helix of university–industry–government relations’, Research Policy, 29: 109–123. Feldman, M. (2000) ‘Location and innovation: the new economic geography of innovation, spillovers and agglomeration’, in G. Clark, M. Feldman and M. Gertler (eds) Oxford Handbook of Economic Geography, Oxford: Oxford University Press, 373–394. Feldman, M.P., Francis, J. and Bercovitz, J. (2005) ‘Creating a cluster while building a firm: entrepreneurs and the formation of industrial clusters’, Regional Studies, 39: 129–141. Feldman, M.P. and Kogler, D.F. (2010) ‘Stylized facts in the geography of innovation’, in B.H. Hall and N. Rosenberg (eds) Handbook of the Economics of Innovation, vol. 1, Amsterdam: Elsevier, 381–410. Fontes, M. and Coombs, R. (2001) ‘Contribution of new technology-based firms to the strengthening of technological capabilities in intermediate economies’, Research Policy, 30: 79–97. Franzoni, C. and Lissoni, F. (2009) ‘Academic entrepreneurs: critical issues and lessons for Europe’, in A. Varga (ed.) Universities, Knowledge Transfer and Regional Development, Cheltenham and Northampton, MA: Edward Elgar Publishing, 163–190. Frenken, K., Cefis, E. and Stam, E. (2015) ‘Industrial dynamics and clusters: a survey’, Regional Studies, 49: 10–27. Goldstein, H.A. (2009) ‘What we know and what we don’t know about the regional economic impacts of universities’, in A. Varga (ed.) Universities, Knowledge Transfer and Regional Development, Cheltenham and Northampton, MA: Edward Elgar Publishing, 11–35. Grimaldi, R., Kenney, M., Siegel, D.S. and Wright, M. (2011) ‘30 years after Bayh–Dole: reassessing academic entrepreneurship’, Research Policy, 40: 1045–1057. Hébert, R.F. and Link, A.N. (2006) ‘Historical perspectives on the entrepreneur’, Foundations and Trends in Entrepreneurship, 2 (4): 261–408. Hekkert, M.P., Suurs, R.A.A., Negro, S.O., Kuhlmann, S. and Smits, R.E.H.M. (2007) ‘Functions of innovation

Innovation and entrepreneurship

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systems: a new approach for analysing technological change’, Technological Forecasting and Social Change, 74: 413–432. Hofstede, G. (2001) Culture’s Consequences: International Differences in Work-Related Values, Beverly Hills, CA: Sage. Howells, J. (2006) ‘Intermediation and the role of intermediaries in innovation’, Research Policy, 35: 715–728. Howells, J., Ramlogan, R. and Cheng, S.-L. (2012) ‘Innovation and university collaboration: paradox and complexity within the knowledge economy’, Cambridge Journal of Economics, 36: 703–721. Huggins, R. and Thompson, P. (2014) ‘Culture, entrepreneurship and uneven development: a spatial analysis’, Entrepreneurship and Regional Development, 26: 726–752. Iammarino, S. and McCann, P. (2006) ‘The structure and evolution of industrial clusters: transactions, technology and knowledge spillovers’, Research Policy, 35: 1018–1036. James, A. (2005) ‘Demystifying the role of culture in innovative regional economies’, Regional Studies, 39: 1197–1216. James, A. (2007) ‘Everyday effects, practices and causal mechanisms of “cultural embeddedness”: learning from Utah’s high tech regional economy’, Geoforum, 38: 393–413. Jensen, R., Thursby, J. and Thursby, M. (2003) ‘Disclosure and licensing of university innovations: “the best we can do with the s**t we get to work with”’, International Journal of Industrial Organization, 21: 1271–1300. Johannisson, B., Alexanderson, O., Nowicki, K. and Senneseth, K. (1994) ‘Beyond anarchy and organization: entrepreneurs in contextual networks’, Entrepreneurship and Regional Development, 6: 329–356. Julien, P.-A. (2007) A Theory of Local Entrepreneurship in the Knowledge Economy, Cheltenham and Northampton, MA: Edward Elgar Publishing. Karnani, F. (2013) ‘The university’s unknown knowledge: tacit knowledge, technology transfer and university spin-offs findings from an empirical study based on the theory of knowledge’, Journal of Technology Transfer, 38: 235–250. Kasabov, E. and Sundaram, U. (2016) ‘Conceptualizing clusters as dynamic and path-dependent pools of skills’, Regional Studies, 50(9): 1520–1536. Kenney, M. and Patton, D. (2005) ‘Entrepreneurial geographies: support networks in three high-technology industries’, Economic Geography, 81: 201–228. Landström, H., Harirchi, G. and Astrom, F. (2012) ‘Entrepreneurship: exploring the knowledge base’, Research Policy, 41: 1154–1181. Lawton Smith, H. (2008) ‘Inter-firm networks in high-tech clusters’, in C. Karlsson (ed.) Handbook of Research on Innovation and Clusters, Cheltenham and Northampton, MA: Edward Elgar Publishing, 107–123. Lechner, C. and Dowling, M. (2003) ‘Firm networks: external relationships as sources for the growth and competitiveness of entrepreneurial firms’, Entrepreneurship and Regional Development, 15: 1–26. Lee, S.M. and Peterson, S. (2000) ‘Culture, entrepreneurial orientation, and global competitiveness’, Journal of World Business, 35: 401–416. Lendel, I. (2010) ‘The impact of research universities on regional economies: the concept of university products’, Economic Development Quarterly, 24: 210–230. Lerner, J. (2005) ‘The university and the start-up: lessons from the past two decades’, Journal of Technology Transfer, 30: 49–56. Licht, A.N. and Siegel, J.I. (2006) ‘The social dimension of entrepreneurship’, in A. Basu, M. Casson, N. Wadeson and B. Yeung (eds) The Oxford Handbook of Entrepreneurship, Oxford: Oxford University Press, 511–539. Malecki, E.J. (1997) ‘Entrepreneurs, networks, and economic development: a review of recent research’, in J.A. Katz (ed.) Advances in Entrepreneurship, Firm Emergence and Growth, vol. 3, Greenwich, CT: JAI Press, 57–118. Malecki, E.J. (2010) ‘Global knowledge and creativity: new challenges for firms and regions’, Regional Studies, 44: 1033–1052. Malmberg, A. and Maskell, P. (2002) ‘The elusive concept of localization economies: towards a knowledgebased theory of spatial clustering’, Environment and Planning A, 34: 429–449. Martin, B.R. (2012) ‘Are universities and university research under threat? Towards an evolutionary model of university speciation’, Cambridge Journal of Economics, 36: 543–565. Martinelli, A., Meyer, M. and von Tunzelmann, N. (2008) ‘Becoming an entrepreneurial university? A case study of knowledge exchange relationships and faculty attitudes in a medium-sized, research-oriented university’, Journal of Technology Transfer, 33: 259–283. Mayer, H. (2007) ‘What is the role of the university in creating a high-technology region?’, Journal of Urban Technology, 14 (3): 33–58. McKelvey, M. and Holmén, M. (2009) Learning to Compete in European Universities: From Social Institution to Knowledge Business, Cheltenham and Northampton, MA: Edward Elgar Publishing. McSweeney, B. (2002) ‘Hofstede’s model of national cultural differences and their consequences: a triumph of faith – a failure of analysis’, Human Relations, 55: 89–118.

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Morris, M. and Schindehutte, M. (2005) ‘Entrepreneurial values and the ethnic enterprise: an examination of six subcultures’, Journal of Small Business Management, 43: 453–479. Moulaert, F. and Sekia, F. (2005) ‘Territorial innovation models: a critical survey’, Regional Studies, 37: 289–302. Murray, F. (2010) ‘The OncoMouse that roared: hybrid exchange strategies as a source of distinction at the boundary of overlapping institutions’, American Journal of Sociology, 116 (2): 341–388. Mustar, P., Renault, M., Colombo, M.G., Piva, E., Fontes, M., Lockett, A., Wright, M., Clarysse, B. and Moray, N. (2006) ‘Conceptualising the heterogeneity of research-based spin-offs: a multi-dimensional taxonomy’, Research Policy, 35: 293–308. Mustar, P., Wright, M. and Clarysse, B. (2008) ‘University spin-off firms: lessons from ten years of experience in Europe’, Science and Public Policy, 35 (2): 67–80. Nelson, A.J. (2012) ‘Putting university research in context: assessing alternative measures of production and diffusion at Stanford’, Research Policy, 41: 678–691. Nijkamp, P. (2003) ‘Entrepreneurship in a modern network economy’, Regional Studies, 37: 395–405. Nilsson, A.S., Rickne, A. and Bengtsson, L. (2010) ‘Transfer of academic research: uncovering the grey zone’, Journal of Technology Transfer, 35: 617–636. Oinas, P. and Malecki, E.J. (2002) ‘The evolution of technologies in time and space: from national and regional to spatial innovation systems’, International Regional Science Review, 25: 102–131. Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., Fini, R., Geunae, A., Grimaldi, R., Hughes, A., Krabel, S., Kitson, M., Llerena, P., Lissoni, F., Salter, A. and Sobrero, M. (2013) ‘Academic engagement and commercialisation: a review of the literature on university–industry relations’, Research Policy, 42: 423–442. Qian, H. and Acs, Z.J. (2013) ‘An absorptive capacity theory of knowledge spillover entrepreneurship’, Small Business Economics, 40: 185–197. Qian, H., Acs, Z.J. and Stough, R.R. (2013) ‘Regional systems of entrepreneurship: the nexus of human capital, knowledge and new firm formation’, Journal of Economic Geography, 13: 559–587. Radosevic, S. and Yoruk, E. (2013) ‘Entrepreneurial propensity of innovation systems: theory, methodology and evidence’, Research Policy, 42: 1015–1038. Raghuram, P. and Strange, A. (2001) ‘Studying economic institutions, placing cultural politics: methodological musings from a study of ethnic minority enterprise’, Geoforum, 32: 377–388. Rothaermel, F.T., Agung, S.D. and Jian, L. (2007) ‘University entrepreneurship: a taxonomy of the literature’, Industrial and Corporate Change, 16: 691–791. Saxenian, A. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128, Cambridge, MA: Harvard University Press. Schoenberger, E. (1997) The Cultural Crisis of the Firm, Cambridge, MA: Blackwell. Schumpeter, J.A. (1934) The Theory of Economic Development, Oxford: Oxford University Press. Shane, S. (2004) Academic Entrepreneurship: University Spinoffs and Wealth Creation, Cheltenham and Northampton, MA: Edward Elgar Publishing. Shearmur, R. (2012) ‘The geography of intrametropolitan KIBS innovation: distinguishing agglomeration economies from innovation dynamics’, Urban Studies, 49: 2331–2356. Siegel, D.S. (2011) ‘The rise of university technology transfer and academic entrepreneurship: managerial and policy implications’, in D.B. Audretsch, O. Falck, S. Heblich and A. Lederer (eds) Handbook of Research on Innovation and Entrepreneurship, Cheltenham and Northampton, MA: Edward Elgar Publishing, 300–314. Smilor, R., O’Donnell, N., Stein, G. and Wellborn, R.S. (2007) ‘The research university and the development of high-technology centers in the United States’, Economic Development Quarterly, 21: 202–222. Spigel, B. (2017) ‘The relational organization of entrepreneurial ecosystems’, Entrepreneurship Theory and Practice, 41(1): 49–72. St. John, C.H. and Pouder, R.W. (2006) ‘Technology clusters versus industry clusters: resources, networks, and regional advantages’, Growth and Change, 37: 141–171. Steyaert, C. and Katz, J. (2004) ‘Reclaiming the space of entrepreneurship in society: geographical, discursive and social dimensions’, Entrepreneurship and Regional Development, 16: 179–196. Storper, M. (2013) Keys to the City: How Economics, Institutions, Social Interaction, and Politics Shape Development, Princeton, NJ: Princeton University Press. Szerb, L.A., Acs, Z. and Autio, E. (2013) ‘Entrepreneurship and policy: the national system of entrepreneurship in the European Union and in its member countries’, Entrepreneurship Research Journal, 3: 9–34. Tan, J. (2002) ‘Culture, nation, and entrepreneurial strategic orientations: implications for an emerging economy’, Entrepreneurship Theory and Practice, 26: 96–111. van Oort, F.G. and Bosma, N.S. (2013) ‘Agglomeration economies, inventors and entrepreneurs as engines of European regional economic development’, Annals of Regional Science, 51: 213–244. Vohora, A., Wright, M. and Lockett, A. (2004) ‘Critical junctures in the development of university high-tech spinout companies’, Research Policy, 33: 147–175.

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Wright, M., Clarysse, B., Mustar, P. and Lockett, A. (2007) Academic Entrepreneurship in Europe, Cheltenham and Northampton, MA: Edward Elgar Publishing. Zider, B. (1998) ‘How venture capital works’, Harvard Business Review, 76 (6): 131–139. Zukin, S. and DiMaggio, P. (1990) ‘Introduction’, in S. Zukin and P. DiMaggio (eds), Structures of Capital: The Social Organization of the Economy, Cambridge: Cambridge University Press, 1–38.

39. Transnational entrepreneurs and global knowledge transfer Sebastian Henn and Harald Bathelt

INTRODUCTION Economic geographers have long been interested in understanding how face-to-face contacts between co-localized actors affect corporate innovation processes. Research into related issues has generated debates about concepts such as clusters (e.g. Bathelt et al. 2004), regional innovation systems (e.g. Cooke 2001) and innovative milieus (e.g. Crevoisier and Maillat 1991). Increasing evidence has emerged over the past decade, however, which suggests that knowledge cannot be created solely in local and regional contexts but that firms have to constantly exchange knowledge with actors located in other regions in order to gain or maintain competitiveness (Bathelt and Henn 2014; Giuliani, Chapter 22, this volume). Two major strands of literature have particularly developed around such debates. The first discusses the role of temporary spaces – such as international trade fairs (e.g. Bathelt and Schuldt 2008) and conferences (e.g. Henn and Bathelt 2015) – where firm representatives meet for a short time period to exchange business-related knowledge. After returning home, these actors transfer newly acquired knowledge to their corporate contexts, for instance by initiating new product developments or improvements. The second strand of literature deals with permanent cross-border linkages or ‘pipelines’ (Bathelt et al. 2004) that allow for continuous flows of knowledge between firms over sometimes large distances. While research on pipelines originally focused on connections between firms (e.g. Bathelt et al. 2004; Lorenzen and Mudambi 2013), recent studies suggest that transnational kinship or ethnic relations between transnational entrepreneurs – that is, self-employed diaspora migrants (Portes et al. 2002; Saxenian 2006a; Drori et al. 2009; Henn 2012) – lead to global transfers of knowledge between locations that can also be conceptualized as pipelines (Saxenian 2006b; Henn 2012). This body of work links up with an older literature on global value chains and production networks that focuses on international linkages, although not usually on knowledge linkages (Gereffi and Korzeniewicz 1990; Henderson et al. 2002; Humphrey and Schmitz 2002; Van Assche, Chapter 45, this volume; Herod et al., Chapter 46, this volume). This chapter aims to explore how transnational entrepreneurs contribute to knowledge transfers over geographical distance. Since transnational entrepreneurs can be regarded as a specific type of diaspora entrepreneurs, we will start by discussing the relationship between diasporas and entrepreneurship, before elaborating the concept of transnational entrepreneurship in particular. This will be followed by two case studies that show how transnational entrepreneurs contribute to knowledge transfers over geographical distance. A conclusion will sum up the most important results and identify goals for future 638

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research. The chapter ends by discussing the increasing importance of transnational entrepreneurs in public policy.

DIASPORA ENTREPRENEURSHIP Diasporas have existed throughout history and across different parts of the world. Nevertheless, they were a relatively rare phenomenon in academic studies until recently, since, among other reasons, most migrants assimilated into their host societies within a period of three generations (Light 2010, 87), thereby contributing to a gradual dissolution of diasporic structures. Since the 1970s, however, a multiplication of global diasporas has been observed. This development has been paralleled not only by an increasing number of studies about diasporas, primarily in history, social anthropology, political science, geography and economics, but also by a changing understanding of what the notion of the ‘diaspora’ actually refers to (Cohen 2008). Originally, the term was applied to the Jewish people who spread to different countries after the destruction of their Temple. Later, it was also used to refer to other peoples that were forced to emigrate because of persecution or genocide. With growing interest in the subject of diasporas, the term was also applied to social groups that were, for other reasons, dispersed widely across space. In the broadest sense, diasporas even include ‘expatriates, expellees, political refugees, alien residents, immigrants and ethnic and racial minorities tout court’ (Safran 1991, 83). Because of these developments, there are now many different co-existing conceptualizations of diasporas (Brubaker 2005). While some authors (e.g. Bruneau 2010) stick to a narrow traditional conceptualization of diasporas as those groups subject to forced dispersal of peoples, others argue that diasporas refer to any type of cross-border distribution of social groups that has either resulted from traumatic events or evolved voluntarily. Additional characteristics are often included in delineating diasporas, such as the members’ orientation to a real or putative homeland or the maintenance of social boundaries that help to mobilize and retain group solidarity (Brubaker 2005). Academic discussions of diasporas have covered a wide range of topics. Researchers have developed classification schemes and have investigated themes including the evolution and stabilization of diasporic and related structures over time and the interactions between the members of diasporas (e.g. Cohen 2008). Especially in recent years, an increasing number of studies have analyzed economic aspects of diasporas with a particular focus on entrepreneurship. This is unsurprising, given that many diasporas are ‘structured around an entrepreneurial pole’ with everything else being subordinated to it (Bruneau 2010, 39). The literature on entrepreneurship and diasporas has paid particular attention to migrant entrepreneurs in host societies. These entrepreneurs are often located in neighborhoods with a high concentration of co-ethnics, which, when urban, have been referred to as ‘ethnic enclaves’ (Wilson and Portes 1980; Waldinger 1993) and, when suburban, as ‘ethnoburbs’ (Li 2009). Even though some authors (e.g. Chaganti and Greene 2000) explicitly distinguish between immigrant entrepreneurs, migrant entrepreneurs and minority entrepreneurs, these terms are often used synonymously. Migrants typically run relatively small businesses that supply co-ethnics and other customers. One focus of the literature on ethnic enclaves and related concepts has been to understand the reasons for

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the emergence of ethnic businesses and their spatial concentration (Light and Gold 2000; Zhou 2004). Discussions on innovation or knowledge creation have not played a major role in this work, since ethnic firms have been found to often engage in the trade of basic goods (e.g. food products) and to rarely rely on crucial external knowledge flows or the creation of new goods and services in their business models. While most research has focused on the experience of diasporas in a host country, there are also investigations suggesting that ethnic entrepreneurs can sometimes provide short-term benefits to their countries of origin. Studies that do not explicitly focus on ethnic entrepreneurs have paid attention to the role of remittances in supporting family members or friends at home. Related work has investigated the causes of remittance flows (Lianos 1997) and their contribution to income growth in the migrants’ home countries (Taylor 1999), as well as to the reduction of poverty in developing countries (Adams and Page 2005). Apart from remittances, access to resources other than money has also been found to be beneficial to the development of the migrants’ home countries. In a study on ethnic scientific communities, for example, Kerr (2008) shows that the manufacturing output in certain countries has increased due to ethnic linkages with research and entrepreneurial communities in the United States. Such studies point to the existence of knowledge transfers and thereby challenge other work, which argues that outmigration inevitably affects a country in negative ways due to the loss of human capital (so-called brain drain effect) (Portes 2009). The precise transfer mechanisms involved, however, still remain underresearched (Saxenian and Sabel 2008). Another group of studies about diaspora entrepreneurship focuses on the question of how self-employed migrants retain and make use of transnational social capital to organize trans-border business activities in particular fields (Landolt et al. 1999; Yeung 2009). Since knowledge flows have played an important role in studies on transnational entrepreneurship, such research will be discussed in the next section in more detail.

TRANSNATIONAL ENTREPRENEURS Main Features of the Concept Academic work on transnational entrepreneurship has, over the last decade, developed into a new interdisciplinary research field (Drori et al. 2009) at the interface of economic geography, economics, international business, management, sociology and anthropology. As shown in Figure 39.1, article publications about transnational entrepreneurship have increased continuously since 2000, even though the total number of publications up to now is relatively low. While research on transnational entrepreneurs is still limited at this point, some observers believe that ‘such dispersed peoples – and their worldwide business and cultural networks – will increasingly shape the economic destiny of mankind’ (Kotkin 1992, 4) for at least three reasons. First, the number of migrants that form new global diasporas will likely increase in the future because of significant improvements in transportation systems and new mobile communication technologies such as online messaging systems, which allow migrants to constantly keep in touch with their peers in remote locations. In fact, it has been suggested that electronically coordinated ethnic diasporas engage in more and

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35 29

Number of Contributions

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25 21 20 17 15 10 7 4

5

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2 0 2000–2001 2002–2003 2004–2005 2006–2007 2008–2009 2010–2011 2012–2013 2014–2015

Note: Displayed are the numbers of Google Scholar hits for the search strings ‘transnational entrepreneurship’, ‘transnational entrepreneur’ and ‘transnational entrepreneurs’ (accessed 1 May 2016). The data include all article publications per year recorded by Google Scholar (without patents and citations), containing at least one of the search strings in their title. Source: Based on Google Scholar counts.

Figure 39.1

Number of article publications on transnational entrepreneurship, 2000–2015

superior economic activities than any other types of migrants before them (Leicht et al. 2012, 23), by bridging increasingly large distances. Second, economic growth as well as deregulation and liberalization in developing economies allow an increasing number of actors to participate in economic exchanges, from which they were previously excluded. As a consequence, new business communities evolve that sooner or later start to engage in cross-border transactions (Kotkin 1992; Damodaran 2008). Third, globalization is often seen to be associated with the weakening of nation states and the increasing importance of subnational entities that compete for capital and talent. Diaspora migrants can play an important role in such processes since they transfer knowledge about new technologies, capabilities and capital to remote regions, thus contributing to the development of those areas (Kotkin 1992). Research on transnational entrepreneurship has covered numerous topics, ranging from the socio-cultural influences on start-up processes (Urbano et al. 2011) to analyses of internationalization strategies (Terjesen and Elam 2009) and regional case studies (e.g. Alvarez 2015). Related research finds its roots in microsociological analyses conducted since the 1980s to investigate quantitative and qualitative changes in worldwide migration flows. Such studies stressed that migration flows can no longer be understood as unidirectional phenomena, according to which migrants fully integrate and assimilate with their host country’s population. It has been shown, instead, that changes in transportation costs and in immigration policies (e.g. with respect to naturalization, residence permits or green card regulations), as well as advances in communication systems, lead to situations in which migrants increasingly commute between their home and host locations. Such oscillating migration patterns result in the emergence of social fields or ‘de-territorialized

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transnational social spaces’ that allow ‘transmigrants’ to link developments between distant regions and locations (Glick Schiller et al. 1995). Related linkages permit these individuals to establish social capital, generating access to additional resources and enabling entrepreneurs to use these resources flexibly. Especially since the late 1990s, selfemployed transmigrants and transnational migrant entrepreneurs (Landolt et al. 1999) have played a crucial role in this literature. In a broad critique of conceptualizations in entrepreneurship and international business studies, Yeung (2009) advocates a general framework similar to that expressed in research on transnational social fields and argues for a specific role for geographical research. He criticizes the strong focus of the international business literature on large corporations and its concomitant neglect of individual entrepreneurs. His criticism of entrepreneurship studies points to the fact that related research highlighting the importance of local and regional determinants of entrepreneurship neglects that entrepreneurs actively shape those spaces of economic interaction where they become active. Transnational entrepreneurs engage in both their home country and their host country (or in several host countries), thus creating specific entrepreneurial spaces. These spaces are characterized by varying locational configurations of enduring relations and transactions that depend on the organizational ability and global reach of the entrepreneurs. In this chapter, transnational entrepreneurs are viewed as businesspeople that engage in economic activities in different regions, or in the words of Portes et al. (2002, 287), as ‘self-employed immigrants whose business activities require frequent travel abroad and who depend for the success of their firms on their contacts and associates in another country, primarily their country of origin’. Because of their transnational presence, these entrepreneurs benefit from competitive advantages not available to those firms that engage in only one region or country. They are ‘in a unique position to identify and exploit opportunities that might not be otherwise recognised . . . By virtue of their unique geographical affiliations, they may be in a position to exploit opportunities either unobserved, or unavailable, to other entrepreneurs located in a single geographical location’ (Drori et al. 2009, 1002). According to Yeung (2009), these entrepreneurs share two main characteristics. First, since cross-border business activities typically involve substantial uncertainties, one of their most important characteristics is the willingness to accept risks. This, of course, is related to prior experiences made when living and working abroad. The entrepreneurs’ social networks provide crucial support and access to informal information and insights into the host countries’ structures (Bathelt and Henn 2014; Malecki and Spigel, Chapter 38, this volume). Second, transnational entrepreneurs have a strong vision according to which they position their firms in the global marketplace. They are also able to identify sales opportunities abroad and act accordingly.

KNOWLEDGE TRANSFER AND INNOVATION THROUGH TRANSNATIONAL ENTREPRENEURS Transnational entrepreneurs typically belong to closely knit communities such as families, clans or ethnically defined groups that are characterized by a shared institutional context, for instance defined by the same religion or belief system. Kinship relations typically

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support transnational entrepreneurs by providing emotional and financial support, as well as access to resources such as family labor, pooled savings or trusted networks. In the context of innovation and knowledge creation, it is important that these networks are ‘long-lasting and do not appear to need frequent contact in order to be activated’ (Mustafa and Chen 2010, 104). This means that transnational entrepreneurs are able to establish trust within their networks (Coleman 1990) and exchange specific artifacts and fine-grained knowledge (Hsing 1996). Unlike global pipelines between large corporations, such entrepreneurial networks do not require large investments but exist related to primordial trust between the agents involved. This allows even small firms to exchange knowledge at low cost and to establish or reconfigure production linkages internationally (Saxenian 2002). If transnational entrepreneurs succeed in integrating previously peripheral locations into supranational production networks (in a way that goes against the logic of center– periphery technology transfers), they can stimulate development processes in their original home regions (Saxenian and Hsu 2001). Such processes are especially likely to develop in emerging Asian economies (Riddle et al. 2010). First, countries such as China and India are still characterized by strong family networks and traditional trading communities that extend their geographical reach by taking advantage of new technologies and economic and political liberalization (Damodaran 2008). These families and communities are relevant in the newly emerging value chains of transnational migrants because the high level of trust enables effective knowledge transfers between scattered family members. Second, an increasing number of younger people from Asian countries have moved to developed economies in North America or western Europe in order to study, do their PhD and/or work as post-docs. These migrants gain experience in a different institutional context, thereby accumulating innovation-related knowledge, which – if they return – can be applied to the innovation systems of their home countries (Saxenian 2006a; Sternberg and Müller 2010). Third, Asian economies are characterized by comparatively low labor costs which generate incentives to relocate elements of the value chain to the migrants’ home countries, thereby increasing the competitiveness of firms while, at the same time, contributing to regional development (Saxenian 2006a, 41). To illustrate how transnational entrepreneurs affect innovation processes, two case studies are discussed below. Case Study 1: The New Argonauts A pioneering study that illustrates how transnational migrants positively shape economic structures in both their home country and host country by transferring knowledge over great distances was provided by Saxenian (2006a). In her analysis of the development of Silicon Valley, she discovered that migrant entrepreneurs played a significant role in the evolution of the region. One group of migrants of particular importance were Taiwanese engineers who had come to the United States in the post-World War II period to receive a graduate education. While the exodus of highly talented young people at this time suggested that a brain drain process took place from Taiwan to the United States, things started to change in the late 1980s when, after completing their education and gaining work experience in the local high-technology sector, many engineers started to return to Taiwan. This movement was caused in part by successful government recruitment

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strategies, but was also related to the promising economic climate and growth potential in their home country. To illustrate this process, Saxenian (1999; 2006a, 98ff; 2007, 191) uses the case of K. Y. Han, whom she considers to be a typical example of a ‘New Argonaut’ in Silicon Valley. In the early 1970s, Han graduated from National Taiwan University and then went on as a post-graduate to complete a Master’s degree in solid-state physics at the University of California in Santa Barbara. In the 1980s, he moved to Silicon Valley, where he acquired specialized knowledge by working for different semiconductor firms for almost a decade. In 1998, Han used his experience and expertise in the sector to establish the firm Integrated Silicon Solutions, Inc. (ISSI) together with his friend Jimmy Lee, who had a similar curriculum vitae. During the early 2000s, Han and Lee were very successful and expanded the firm by activating and exploiting their professional networks in both the United States and in Taiwan. In the United States, they recruited former colleagues to their R&D center to design high-performance SRAM chips for the personal computer market. This worked well and they achieved a strong market position, with many of their initial customers being located in Taiwan. The firm also received assistance from the Taiwanese government and established a partnership with the newly founded Taiwan Semiconductor Manufacturing Corporation, a semiconductor foundry. Han and Lee went on to establish a firm in the Hsinchu Science and Industrial Park, which was related to their business activities in the United States focusing on assembly, packaging and testing functions. Han initially commuted between the United States and Taiwan to monitor operations. In the mid-1990s, when he realized that more presence was necessary in Taiwan, he decided to move back with his family, while maintaining strong links to the United States. Being on site not only allowed him to expand his customer base in Taiwan but also to apply his knowledge within the local production system and to expand business relations with the firm’s main foundry. In developing their transnational business, Han and Lee benefited from being able to communicate in Chinese and from the fact that many of their former fellow students from National Taiwan University now had senior management positions in the local industry. In the 2000s, Lee and Han founded another independent firm – Integrated Circuit Solutions, Inc. (ICSI) – which was also very successful. The exploitation of the United States–Taiwan connection allowed the firms to expand to other countries as well (Saxenian 2007, 191). In 2001, for instance, ISSI invested $40 million in a semiconductor foundry in Shanghai in order to extend low-cost wafer production and gain access to the growing Chinese market. The case of Han is only one of many examples of highly qualified migrants, who, after having worked and lived in Silicon Valley, returned home to apply knowledge gained in the United States to set up new technology-based firms and thus support the development of a new high-technology cluster in Taiwan. By describing these transnational entrepreneurs as ‘Argonauts’, Saxenian (2006a; 2006b) draws parallels with the Greek epic hero Jason and his companions who travelled on their ship Argo in search for the Golden Fleece and took on risky but ultimately rewarding challenges. The ‘New Argonauts’ are ‘US-educated but foreign-born entrepreneurs . . . embarking on risky foreign adventures in pursuit of wealth’ (Saxenian 2006b, 101). In her research on these entrepreneurs in Silicon Valley, Saxenian found that the majority were first-generation emigrants who had no difficulties working in the business environment of their home country, as they benefited from

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existing friends, family and professional networks (Saxenian 2006a). Second- or thirdgeneration migrants, by contrast, although still being able to speak the language of their ancestors, often lacked personal connections and knowledge about institutions in their (grand)parents’ home country, thus facing more obstacles to entrepreneurship than their first-generation counterparts. In general terms, a precondition for the establishment of such Argonaut networks is the actors’ willingness to return to their home countries. Further, the remigrants must be in a position to make connections with professional communities at both ends. This implies that the countries, which can benefit from the New Argonauts, are those that have invested in education (especially in technical fields) and are politically and economically stable enough for emigrants to consider remigration. Such conditions are, of course, not always in place, as can be seen in the case of refugees from Vietnam or Iran who are in most cases not inclined to return to their home countries later on. While research on the New Argonauts is still at an early stage, it can be expected that migration patterns of highly skilled professionals not only play a role in Silicon Valley but also occur in other contexts – for example Turkey – that have received little attention in academic studies thus far (Pusch and Aydin 2012). Case Study 2: Transnational Family Businesses in the Diamond Sector While the concept of the New Argonauts refers to remigrants in high-technology industries who nurture intense relations with their former host country, transnational entrepreneurs can also stimulate knowledge transfers in traditional industries. This is demonstrated through a case study of families from Palanpur, a city located in the western Indian State of Gujarat, who specialized in jewelry trade (Henn 2012). At the beginning of the 20th century, members of these families made their way to Bombay where they worked as jewelers supplying the ruling class with jewelry. In Bombay, they made contact with Jewish diamond dealers from Antwerp, which was already a significant diamond trading and processing hub at that time. These Belgian dealers had opened up offices in the city of Bombay to sell polished diamonds, earning substantial premiums from their Indian customers. The Indian jewelers soon realized that they could increase their margins by cutting out pricy middlemen and sourcing diamonds directly in Europe. Some dealers ventured to Antwerp where they opened up buying offices in the 1920s. Being on site, they managed to get in touch with Belgian polished diamond dealers and, in some cases, Indo-Belgian partnerships were established. After their forced retreat during World War II, some Indians returned to Antwerp in the late 1940s and early 1950s with the goal of pursuing pre-war business. Following the Indian Independence Act of 1947, however, the import of polished diamonds was forbidden. This not only resulted in substantial smuggling but also in a shift of the dealers’ commercial focus. An increasing number of the Palanpuris now focused on buying rough stones and having them processed in India. This situation generated enormous challenges. First, the market for polished diamonds was very different from the one for rough diamonds, especially in terms of assessing the value of traded goods. Second, even though India had a rich history in trading and manufacturing diamonds, the manufacturing techniques applied in India were outdated. In order to succeed in the world market, Indian businesspeople had to acquire knowledge about the market of rough diamonds

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as well as about cutting and polishing techniques. The presence at both locations helped the Palanpuri community to transfer important knowledge between the two countries. The community members in Antwerp, for example, recruited a master cutter who taught them about new manufacturing techniques. Being on site in Antwerp also allowed more Indian businesspeople to get in touch with Belgian traders and manufacturers. Once they returned to India, these individuals were able to provide other community members with relevant knowledge from Belgium. Later, the Palanpuris extended their transnational network by applying similar mobile strategies as those used in Antwerp. More specifically, to be closer to the world’s most important market for jewelry, family members migrated to New York and engaged in the local jewelry business. The involvement at three different locations allowed them to quickly exchange knowledge about market conditions on a global scale (Henn 2013). The Palanpuri family network increasingly began to resemble that of a global value chain: rough diamonds were sourced in Belgium, manufactured in India and sold in the United States. As a consequence of these developments, and because of favorable market conditions, new diamond manufacturing firms mushroomed in India. Over the course of time, and facilitated by various external influences, new industrial structures emerged. Today, more than one million people work in this business in western India, making the region the world’s largest diamond cluster (Henn 2010). The fact that it was families that were able to succeed in building up a global value chain and linking specialized locations around the world is quite remarkable, even more so since the diamond business was previously highly concentrated in only a few locations. Crucial enabling factors for this development were the specific features of the Indian community, which facilitated knowledge sharing and the exchange of goods between peers. Transfers of knowledge over large geographical distance usually involve actors who speak different languages and act on the basis of different norms, traditions and practices. In such a situation, it is necessary to develop a common institutional basis that enables processes of knowledge generation. This can take considerable amounts of time and money (Bathelt et al. 2004). In the case of the Palanpuris, however, a common institutional background already existed from the start related to their shared belief system and language. Another striking feature of the Palanpuri network, which facilitated knowledge sharing and goods exchange, was that professional activities were not detached from the private sphere. Many diamond dealers had been exposed to trading activities since their childhood, which created close links between their personal and business lives over time. Under these circumstances, it would be difficult if not impossible for an individual to cut ties with the diamond sector without also leaving the community. This embeddedness of professional life in transnational family networks helped to facilitate fine-grained knowledge flows about the industry. Furthermore, the networks of the Palanpuris were characterized by mutual and enforceable trust, leading to strong support between members of the community. This was relevant with respect to knowledge circulation as trust networks also included competing peers. Altogether, these practices contributed to the development of a highly integrated localized production system among the Palanpuri community with elements of both competition and cooperation. The regional implications of the knowledge transfers from Antwerp to India were significant. A new diamond cluster began to grow in India with the emerging industrial

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structures being complementary to those that existed in Antwerp. Over time, however, the Indian manufacturers also learned to operate in higher-value-added segments of the diamond market and received increasing amounts of higher-quality stones from the rough diamond supplier De Beers. Due to low production costs, they were able to pay more for rough diamonds and still offer finished products at a lower price than their competitors. Over time, this created rivalry and tensions between Indian and Belgium firms and resulted in a gradual relocation of the diamond sector from Antwerp to India, as well as to other low-cost locations in countries such as China and Vietnam. The Belgian cluster began to stagnate and today only a few diamond market segments are still being produced in Antwerp. The two case studies discussed above illustrate the complexity of transnational entrepreneurship and show that this phenomenon relates to a broad spectrum of relevant actors and industries. It includes particularly migrants who return to their home country to make use of their transnational relations. The New Argonauts in the first case study belong to this group. In contrast, the second case study shows how transnational entrepreneurs develop transnational business activities by moving from their home country to a new location. The two cases also illustrate that transnational entrepreneurship can be analyzed at different spatial and organizational levels: in the first case study, the focus is on individual actors, while the second puts emphasis on larger communities (Roberts, Chapter 21, this volume). Both examples also demonstrate that transnational entrepreneurship can play a role in different national and industrial contexts. Last but not least, the cases clearly illustrate the crucial role of family and kinship relations for global knowledge transfers between firms and related regional development processes.

CONCLUSION This chapter demonstrates that transnational entrepreneurs in their roles as migrants ‘maintaining business-related linkages with their former country of origin, and currently adopted countries and communities’ (Drori et al. 2009, 1001f.) play an important role in today’s knowledge-based economy, due to their ability to link different localities, sometimes over large distances, and transfer knowledge between them. Transnational migrants often have dense social networks across multiple regions and countries that help them access social capital and resources not available to other entrepreneurs. As a consequence, transnational entrepreneurs can support peripheral regions in the development of innovation systems based on knowledge that has been developed elsewhere and is subsequently being adapted to the local institutional framework. Such processes emerge around traditional trading communities, extended family structures and low-cost labor contexts and seem to be particularly important in South/Southeast Asia. The examples described in this chapter show that transnational entrepreneurs can have a significant impact on – and, in fact, sometimes trigger – regional development in a variety of ways. In the case of the New Argonauts, returnee entrepreneurs who used to live and work in Silicon Valley were responsible for the emergence of new cluster structures in Taiwan that are complementary to those in the United States. Both regions benefited from these close complementary linkages. The case of the Indian diamond traders is one

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in which a newly emerging cluster competed with an existing one in Europe, ultimately leading to relocations in the industry. The case studies suggest that the way in which transnational entrepreneurs influence regional development depends on at least three influences (Henn 2012, 505): 1.

2.

3.

Type of industry. In industries with strong price competition like the diamond industry, relocations may occur from developed to developing countries. Such relocations are less likely in high-technology industries that depend on the ability to constantly develop new knowledge and generate strong ties with the national and regional science base. Time horizon. While numerous studies have shown that the activities of transnational entrepreneurs can be quite beneficial for regional development at both ends, it cannot be taken for granted that this is always true in the long run. In the case of Antwerp, for example, the diamond traders from India were initially not regarded as a threat to the local industry, but as an opportunity to develop a new market. This view has, however, changed over time. Openness of the transnational entrepreneurial community. For firms in the host country to benefit from transnational entrepreneurs, the respective industrial communities, while being part of tightly knit internal networks, must have a minimum level of openness. A community that is secluded, for example, may gain from operating in different locations, but benefits would unlikely travel to other agents beyond the localized community.

POLICY IMPLICATIONS The tremendous success stories of New Argonauts have encouraged policy makers to establish programs and initiatives to support similar processes in their respective territories. Saxenian and Sabel (2008, 389) state that ‘the new Argonauts have influenced policy in other developing nations, using best practices and models from Silicon Valley to lever open and animate discussion of institutional reform in their home countries’. Despite potential benefits, there are also problems with such approaches. First, not many regions have conditions in place that are supportive of processes similar to Silicon Valley (Ferrary and Granovetter, Chapter 20, this volume), such as specific property rights, flexible labor markets, transparent capital markets and so on (Saxenian and Sabel 2008). There is a risk therefore that public policies may not render the desired outcome. Second, it may be unwise to provide incentives for young talents to emigrate to other countries with the idea in mind that they could later return in the form of New Argonauts and trigger economic development processes back home. Such policies risk causing substantial brain drain. The most productive parts of the labor force may emigrate and the home country government may have limited control over whether they will indeed return at some later point. More careful regional policies would instead focus on existing transnational entrepreneurs and aim to exploit their economic potential. In fact, it appears that ‘nations increasingly view technology transfer as primarily a people-oriented phenomenon . . . Immigration is thus becoming increasingly an inseparable segment of national technology

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policies’ (Mahroum 1999, 189). In recent years, ‘brain competition policies’ (Reiner 2010; cf. Solimano 2008) have been accompanied by the rise of new private, semi-public and public support structures that aim to nurture transnational entrepreneurship. Business incubators can play an important role in overcoming existing institutional constraints on such developments, as suggested by Riddle et al. (2010). However, as long as potential transnational entrepreneurs have difficulties finding local business partners, such approaches will have a limited impact, especially since establishing an appropriate institutional context can be a long process. This is best illustrated by the fact that it took two decades in the case of Taiwan to develop the requisite technical infrastructure, skills and growing venture capital industry. Finally, it should be noted that the potential for and impact of transnational entrepreneurship is always open-ended. A crucial precondition is that both the home regions and the host regions of such developments are characterized by an open and tolerant culture, since the ‘“othering” of migrants, particularly ethnic minorities, generally obstructs knowledge sharing’ (Williams 2007, 42). This may well be the first and foremost task of public policies that aim to initiate transnational entrepreneurship. Acknowledgements We would like to thank Vanessa Hünnemeyer, Daniel Hutton Ferris, Susann Schäfer and Patrick Werner for constructive criticism on an earlier draft of this chapter.

REFERENCES Adams, R. H. and Page, J. (2005) ‘Do international migration and remittances reduce poverty in developing countries?’, World Development, 33: 1645–1669. Alvarez, S. M. S. (2015) ‘Migrant transnational entrepreneurship in Colombia’, in W. Newburry and M. A. Gonzalez-Perez (eds): International Business in Latin America: Innovation, Geography and Internationalization, New York: Palgrave Macmillan, 83–100. Bathelt, H. and Henn, S. (2014) ‘The geographies of knowledge transfers over distance: Toward a typology’, Environment and Planning A, 46: 1403–1424. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: Local buzz, global pipelines and the process of knowledge generation’, Progress in Human Geography, 28: 31–56. Bathelt, H. and Schuldt, N. (2008) ‘Between luminaires and meat grinders: International trade fairs as temporary clusters’, Regional Studies, 42: 853–868. Brubaker, R. (2005) ‘The “diaspora” diaspora’, Ethnic and Racial Studies, 28: 1–19. Bruneau, M. (2010) ‘Diasporas, transnational spaces and communities’, in R. Bauböck and T. Faist (eds): Diaspora and Transnationalism: Concepts, Theories and Methods, Amsterdam: Amsterdam University Press, 35–50. Chaganti, R. and Greene, P. G. (2000) ‘Who are migrant entrepreneurs? A study of entrepreneurs’ migrant involvement and business characteristics’, Journal of Small Business Management, 40: 126–143. Cohen, R. (2008) Global Diasporas: An Introduction. London: Routledge. Coleman, J. S. (1990) Foundations of Social Theory, Cambridge, MA: Harvard University Press. Cooke, P. (2001) ‘Regional innovation systems, clusters, and the knowledge economy’, Industrial and Corporate Change, 10: 945–974. Crevoisier, O. and Maillat, D. (1991) ‘Milieu, industrial organization and territorial production system: Towards a new theory of spatial development’, in R. Camagni (ed.): Innovation Networks, London: Belhaven Press, 13–34. Damodaran, H. (2008) India’s New Capitalists: Caste, Business, and Industry in a Modern Nation, New York: Palgrave Macmillan. Drori, I., Honig, B. and Wright, M. (2009) ‘Transnational entrepreneurship: An emergent field of study’, Entrepreneurship Theory and Practice, 33: 1001–1022.

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Ferrary, M. and Granovetter, M. (2017) ‘Social networks and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 327–341. Gereffi, G. and Korzeniewicz, M. (1990) ‘Commodity chains and footwear exports in the semiperiphery’, in W. G. Martin (ed.): Semiperipheral States in the World-Economy, Westport, CT: Greenwood Press, 45–68. Giuliani, E. (2017) ‘Industrial clusters in global networks’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 360–371. Glick Schiller, N., Basch, L. and Szanton Blanc, C. (1995) ‘From immigrant to transmigrant: Theorizing transnational migration’, Anthropological Quarterly, 68: 48–63. Henderson, J., Dicken, P., Hess, M., Coe, N. and Yeung, H. W.-C. (2002) ‘Global production networks and the analysis of economic development’, Review of International Political Economy, 9: 436–464. Henn, S. (2010) ‘Transnational communities and regional cluster dynamics. The case of the Palanpuris in the Antwerp diamond district’, Die Erde, 141: 127–147. Henn, S. (2012) ‘Transnational entrepreneurs, global pipelines and shifting production patterns. The example of the Palanpuris in the diamond sector’, Geoforum, 43: 497–506. Henn, S. (2013) ‘Transnational entrepreneurs and the emergence of clusters in peripheral regions: The case of the diamond cutting cluster in Gujarat (India)’, European Planning Studies, 21: 1779–1795. Henn, S. and Bathelt, H. (2015) ‘Knowledge generation and field reproduction in temporary clusters and the role of business conferences’, Geoforum, 58: 104–113. Herod, A., Pickren, G., Rainnie, A. and McGrath-Champ, S. (2017) ‘Innovation, development and global destruction networks’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 752–770. Hsing, Y. (1996) ‘Blood thicker than water: Interpersonal relations and Taiwanese investments in Southern China’, Environment and Planning A, 28: 2241–2261. Humphrey, J. and Schmitz, H. (2002) ‘How does insertion in global value chains affect upgrading in industrial clusters?’, Regional Studies, 36: 1017–1027. Kerr, W. R. (2008) ‘Ethnic scientific communities and international technology diffusion’, Review of Economics and Statistics, 90: 518–537. Kotkin, J. (1992) Tribes: How Race, Religion and Identity Determine Success in the New Global Economy, New York: Random House. Landolt, P., Autler, I. and Baires, S. (1999) ‘From Hermano Lejano to Hermano Mayor: The dialectics of Salvadoran transnationalism’, Ethnic and Racial Studies, 22: 290–315. Leicht, R., Di Bella, J., Langhauser, M., Leiß, M., Philipp, R., Volkert, M. and Werner, L. (2012) Schöpferische Kraft der Vielfalt. Bedeutung, Triebkräfte und Leistungen von Migrantenunternehmen in Baden-Württemberg (und Deutschland) [Creative Power of Diversity. Importance, Impetus and Performance of Migrant Firms in Baden-Württemberg (and Germany)], Mannheim: Institut für Mittelstandsforschung, University of Mannheim. Li, W. (2009) Ethnoburb: The New Ethnic Community in Urban America, Honolulu: University of Hawai’i Press. Lianos, T. P. (1997) ‘Factors determining migrant remittances’, International Migration Review, 31: 72–87. Light, I. (2010) ‘Transnational entrepreneurs in an English-speaking world’, Die Erde, 141: 87–102. Light, I. and Gold, S. (2000) Ethnic Economies, San Diego: Academic Press. Lorenzen, M. and Mudambi, R. (2013) ‘Clusters, connectivity and catch-up: Bollywood and Bangalore in the global economy’, Journal of Economic Geography, 13: 501–534. Mahroum, S. (1999), ‘Highly-skilled globetrotters’, in OECD (ed.): Mobilising Human Resources for Innovation: Proceedings of the OECD Workshop on Science and Technology Labour Markets, Paris: OECD. Malecki, E. J. and Spigel, B. (2017) ‘Innovation and entrepreneurship’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 625–637. Mustafa, M. and Chen, S. (2010) ‘The strength of family networks in transnational immigrant entrepreneurship’, Thunderbird International Business Review, 52: 97–106. Portes, A. (2009) ‘Migration and development: Reconciling opposite views’, Ethnic and Racial Studies, 32: 5–22. Portes, A., Guarnizo, L. E. and Haller, W. (2002) ‘Transnational entrepreneurs: An alternative form of immigrant economic adaptation’, American Sociological Review, 67: 278–298. Pusch, B. and Aydin, Y. (2012) ‘Migration of highly qualified German citizens with Turkish background from Germany to Turkey: Socio-political factors and individual motives’, International Journal of Business and Globalisation, 8: 471–490. Reiner, C. (2010) ‘Brain competition policy as a new paradigm of regional policy: A European perspective’, Papers in Regional Science, 89: 449–462. Riddle, L., Hrivnak, G. A. and Nielsen, T. M. (2010) ‘Transnational diaspora entrepreneurship in emerging markets: Bridging institutional divides’, Journal of International Management, 16: 398–411.

Transnational entrepreneurs and global knowledge transfer

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Roberts, J. (2017) ‘Community, creativity and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 342–359. Safran, W. (1991) ‘Diasporas in modern societies: Myths of homeland and return’, Diaspora: A Journal of Transnational Studies, 1: 83–99. Saxenian, A. (1999) Silicon Valley’s New Immigrant Entrepreneurs, San Francisco: Public Policy Institute of California. Saxenian, A. (2002) ‘Transnational communities and the evolution of global production networks: The case of Taiwan, China and India’, Industry and Innovation, 9: 183–202. Saxenian, A. (2006a) The New Argonauts: Regional Advantage in a Global Economy, Cambridge, MA: Harvard University Press. Saxenian, A. (2006b) ‘The New Argonauts’, Words into Action, 99–110. Saxenian, A. (2007) ‘Brain circulation and regional innovation: The Silicon Valley–Hsinchu–Shanghai triangle’, in K. Polenske (ed.): The Economic Geography of Innovation, Cambridge: Cambridge University Press, 190–210. Saxenian, A. and Hsu, J.-Y. (2001) ‘The Silicon Valley–Hsinchu connection: Technical communities and industrial upgrading’, Industrial and Corporate Change, 10: 893–920. Saxenian, A. and Sabel, C. (2008) ‘Roepke lecture in economic geography. Venture capital in the “periphery”: The New Argonauts, global search, and local institution building’, Economic Geography, 84: 379–394. Solimano, A. (ed.) (2008) The International Mobility of Talent: Types, Causes, and Development Impact, Oxford: Oxford University Press. Sternberg, R. and Müller, C. (2010) ‘“New Argonauts” in China – Return migrants, transnational entrepreneurship and economic growth in a regional innovation system’, Die Erde, 141: 103–124. Taylor, E. J. (1999) ‘The new economics of labour migration and the role of remittances in the migration process’, International Migration, 37: 63–88. Terjesen, S. and Elam, A. (2009) ‘Transnational entrepreneurs’ venture internationalization strategies: A practice theory approach’, Entrepreneurship Theory and Practice, 33: 1093–1120. Urbano, D., Toledano, N. and Ribeiro-Soriano, D. (2011) ‘Socio-cultural factors and transnational entrepreneurship: A multiple case study in Spain’, International Small Business Journal, 20: 1–16. Van Assche, A. (2017) ‘Global value chains and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds), The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, 739–751. Waldinger, R. (1993) ‘The ethnic enclave debate revisited’, International Journal of Urban and Regional Research, 17: 428–436. Williams, A. M. (2007) ‘International labour migration and tacit knowledge transactions: A multi-level perspective’, Global Networks, 7: 29–50. Wilson, K. L. and Portes, A. (1980) ‘Immigrant enclaves: An analysis of the labor market experiences of Cubans in Miami’, American Journal of Sociology, 86: 295–319. Yeung, H.-W. (2009) ‘Transnationalizing entrepreneurship: A critical agenda for economic geography’, Progress in Human Geography, 33: 210–235. Zhou, M. (2004) ‘Revisiting ethnic entrepreneurship: Convergencies, controversies, and conceptual advancements’, International Migration Review, 38: 1040–1074.

40. Institutional entrepreneurship in Alzheimer’s disease treatment Nina Geilinger, Stefan Haefliger, Georg von Krogh and Fotini Pachidou

INTRODUCTION New and changing treatments, techniques and practices are frequently examined topics in healthcare (Reay et al. 2013), in particular through the lens of institutional theory (see Currie et al. 2012 a, b; e.g., Allen 2014; Goldner 2004; Nigam 2013; Yang et al. 2007; Glückler and Bathelt, Chapter 8, this volume). For example, recent studies have analyzed the introduction of innovative practices by institutional entrepreneurs such as new clinical guidelines (Nigam 2013) or new patient delivery services (Lockett et al. 2012). We explore an organizational field comprising various actors engaged in the treatment of patients suffering from Alzheimer’s disease (AD). This field is characterized by high degrees of complexity, regulation and institutionalization, a long tradition of medical practice in need of effective treatments, and little consensus among stakeholders on which treatment solutions may be superior. It has undergone a shift in institutional logics that has begun to allow for and endorse non-pharmacological treatments, indicating a consequent institutional change rooted in the treatment of patients. Our study asks about innovation in practices and the types of change in the field of AD treatment in Switzerland, thereby answering the recent call for more research into the specific practices of institutional entrepreneurs in healthcare (Lockett et al. 2012; Nigam 2013) and, more specifically, engaging with AD as a more varied and diverse set of conditions and treatments than often portrayed (MacRae 2008). Institutional entrepreneurship in the field of AD treatment is relevant because of the often stereotypical and inaccurate perceptions that pervade the complex set of actors involved (Beard and Neary 2013; MacRae 2008), which can lead to negative consequences for caregivers as well as patients (Lloyd and Stirling 2011).

INSTITUTIONAL LOGICS Institutions and institutionalized practices undergo changes that affect the organizations and actors involved. In the institutionalist tradition, rules impact organizations and actors as logics spanning organizational fields, defined as “socially constructed, historical patterns of cultural symbols and material practices, including assumptions, values, and beliefs by which individuals and organizations provide meaning to their daily activity, organize time and space, and reproduce their lives and experience” (Thornton and Ocasio 1999, p. 804; Thornton et al. 2012, p. 2). Rationalizing and legitimizing these logics is part of the ongoing effort to maintain stability and guide individual behavior in organizations (Meyer 652

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and Rowan 1977; Powell and DiMaggio 1991). If institutions influence individual behavior and preferences, how can actors change logics and adopt new ways of doing things? A series of authors have addressed this problem of embedded agency under the heading of institutional logics (Greenwood et al. 2010; Nigam and Ocasio 2010; Thornton and Ocasio 1999, 2008; Thornton et al. 2012). Institutional logics theory conceptualizes society as an inter-institutional system, in which logics are culturally differentiated, fragmented and contradictory and therefore allow change to occur. Existing studies have focused on competition among institutional logics due to geographical differences (Lounsbury 2007), differing responses to environmental concerns (Herremans et al. 2009), resistance to new healthcare guidelines (Reay and Hinings 2009; Nigam and Ocasio 2010) or resistance to the consolidation of an industrial sector (Marquis and Lounsbury 2007). A subject that perhaps remains less explored is whether and how change is permitted in highly regulated and complex contexts, in which, despite the discrepant needs and wants of multiple actors, all actors must obey rigorous rules and regulations. Institutional Logics in Healthcare In the healthcare field, previous studies have identified at least two rather dominant institutional logics: medical professionalism and business-like healthcare (Harris and Holt 2013; Kitchener and Mertz 2012; McDonald et al. 2013; Reay and Hinings 2009). The organizing principles of medical professionalism include physician authority, autonomy in delivering patient care and a focus on a trusting physician–patient relationship. In contrast, the business-like healthcare logic is associated with cost-efficient care and services and a “do more with less” logic (Reay and Hinings 2009, p. 630). Care providers may be obliged to follow explicit governmental guidelines, in which patients are considered units of a population and not individual cases. Table 40.1 shows examples of studies that have described such multiple institutional logics in the field of healthcare and have discussed the consequences of logic multiplicity in the field. Multiple logics can co-exist, or a new logic can be adopted as the dominant one. For example, Reay and Hinings (2009) studied the conflict between government and medical professionals in the Alberta health system, which drove radical change. The government introduced a business-like healthcare logic, using its authority, legislative power and control over financial resources to regulate healthcare better and to reduce costs. By altering regulations and rules, the government shifted the prior logic of medical professionalism to one of business-like healthcare. Institutional Entrepreneurship A second perspective addressing the problem of embedded agency is institutional entrepreneurship (Battilana et al. 2009; Garud et al. 2007; DiMaggio 1988). Institutional entrepreneurs are defined as “change agents who initiate divergent changes, that is, changes that break the institutional status quo in a field of activity and thereby possibly contribute to transforming existing institutions or creating new ones” (Battilana et al. 2009, p. 67). Institutional entrepreneurs initiate divergent change, involve themselves and implement change through interactional, technical and cultural projects, for which various skills are needed (political, analytical and cultural; Perkmann and Spicer 2007).

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Table 40.1 Studies of multiple institutional logics in healthcare Reference Reay and Hinings 2009

Harris and Holt 2013

Nigam and Ocasio 2010

Institutional logics Medical professionalism − Physician–patient relationship guides all service provision − Physicians determine appropriate care using their professional knowledge − Government provides funds to meet the need determined by physicians Two sub-logics: − Ownership responsibility: Autonomy and sustainability of business enterprise − Professionalism: Commitment to clinical expertise and independence in delivering patient care Physician authority − Equal to Reay and Hinings’ “Medical professionalism” (2009)

Consequences of logic multiplicity

Business-like healthcare (or population-based medicine) − Cost-efficiency and customer satisfaction guide all service provision − Physicians are identified as cost drivers

Conflicting logics co-exist and separately guide the behavior of different field actors

Two sub-logics: − Population health managerialism: Accountability and procedural diligence − Entrepreneurial commercialism: Commercial opportunities (expansion, marketing)

Field of general dental practice: conflicting logics co-exist and are combined in different manners

Managed competition model − Equal to Reay and Hinings’ “Business-like healthcare” (2009) Managed care logic − Cost-effective, quality care to insured populations through market mechanisms Care logic Dunn and Science logic − Quality healthcare achieved Jones 2010 − Quality healthcare by physicians providing achieved by innovative compassionate, preventive diagnostic and therapeutic care procedures to alleviate − Focus is placed on human suffering and to physicians’ clinical skills to help eradicate disease improve the health of the − Focus on knowledge of community diseases is built through research New logic Traditional logic Macfarlane, − Focus on needs and − Focus on diseases Bartonpriorities of patients − Fragmented, mainly acute Sweeney, − Coordinated whole-pathway care Woodard, and care (prevention, follow-up, − Episodic audit Greenhalgh rehab, etc.) 2013 − Continuous quality improvement

Adoption of a new dominant logic

Field of medical education: conflicting logics co-exist, and their dominance fluctuates over time

Shift from traditional to new emerging logic

Note: The intention is to show a variety of studies of multiple institutional logics in healthcare and not to present a complete review of the literature.

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Institutions change when actors equipped with sufficient resources seize opportunities to realize interests of high perceived value (DiMaggio 1988). What remains less understood, however, is whether it is possible for institutional entrepreneurs “to change their emphasis from one type of project toward another one, and to acquire the necessary skills” (Perkmann and Spicer 2007, p. 1118) and under what conditions this is possible. Two of the most recent and comprehensive reviews of institutional entrepreneurship offer different approaches to the concept (Hardy and Maguire 2008; Battilana et al. 2009). First, there is a research stream suggesting that field-specific conditions enable individuals to take action and initiate change. Emerging fields with low institutionalization and greater uncertainty (Maguire et al. 2004; Lawrence and Phillips 2004; David et al. 2013; Kiss et al. 2012; Garud et al. 2002), for example, and mature organizational fields experiencing decline and destabilization (Greenwood and Suddaby 2006; Holm 1995; Child et al. 2007) comprise the most favorable fields for institutional entrepreneurship. Second, other scholars highlight the social position (status, power, authority, multiple embeddedness, network memberships) and individual characteristics (reflexivity, visionary abilities, political skills and rhetoric) of institutional entrepreneurs as their source and means of enacting change (Mutch 2007; Dorado 2005; Rao et al. 2003; 2000; Battilana et al. 2009). Both approaches have been criticized for failing to jointly address embedded agency and institutional pressures that affect agents’ behavior, as well as for considering institutional entrepreneurs to be “superheroes” or “Deus Ex Machina” (Battilana et al. 2009; Cooper et al. 2008). To address these issues, Battilana and co-authors developed a process model of institutional entrepreneurship. Their model exhibits a dynamic relationship between field characteristics and the social positions of the actors enabling them, “despite institutional pressures towards stasis, to engage in the implementation of divergent change that involves the creation of a vision and the mobilization of allies” (Battilana et al. 2009, pp. 86–87). These agents initiate and implement divergent changes that are hostile or in opposition to other actors’ embeddedness. Their success is subject both to field characteristics (context or situation) and actors’ social positions (the actor as an individual or organization). Success would imply the diffusion of divergent change and, as a result, institutional change, which impacts anew the two enabling factors of institutional entrepreneurship (field characteristics and actors’ social position). As the authors suggest, further research is necessary to provide “a more fine-grained understanding of the process”. A complementary theoretical paper on the relationship between institutions and practices by von Krogh et al. (2012b) offers an explanation of institutional change, which is induced when the institutions insufficiently protect or adhere to the ethical foundations and standards of excellence of a social practice.

STUDY CONTEXT: THE FIELD OF AD TREATMENT AD is a degenerative cerebral disease with neuropathological and neurochemical features, and is the most common type of dementia among the elderly. This chronic and progressive mental disorder affects cognitive abilities (memory, thinking, comprehension, learning, reasoning, judging), functional abilities (dressing, hygiene, shopping, managing

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money) and behavior (agitation, aggressiveness, difficulty communicating), and has other symptoms such as depression, delusions and hallucinations (Ballard et al. 2011). AD is recognized as one of the most severe health and social threats worldwide. It is the sixth leading cause of death, with more than forty million persons affected worldwide (Alzheimer’s Association 2013). Studies have estimated that these numbers will nearly double by 2030, and more than 135 million individuals of all ages will suffer from some type of dementia worldwide by 2050 (Alzheimer’s Disease International 2013). In Switzerland, more than 110,000 individuals suffered from dementia in 2014, of which approximately 60 percent had AD. The annual cost of care related to dementia reached CHF 7 billion (USD 7.9 billion; Swiss Alzheimer’s Association 2014) in 2013. Governments and health organizations have intensively discussed public health priorities, new policies, and more effective health and social care systems informed and responsive to this impending threat (Swiss Alzheimer’s Association 2014). There are two types of drugs for treating AD pharmacologically: acetylcholinesterase (AChE) inhibitors (donepezil, rivastigmine and galantamine) and a voltage-dependent NMDA receptor antagonist (memantine). The efficacy and safety of all four drugs has been demonstrated. While these drugs do not per se cure AD, they slow its progression and palliate the symptoms. The lack of a proven cure and the high complexity of patient requirements have created challenges for drug development. The “AD market” has shown to be a difficult area for innovation, with several of the most promising new drug candidates failing phase III clinical trials (Mangialasche et al. 2010; The Lancet 2010). Pharmaceutical firms, such as Pfizer, Eisai, Novartis, Johnson & Johnson, Forest, Merz and Lundbeck, seek to address this challenge by improving the efficiency and convenience of their products and to understand better the needs of stakeholders. Physicians and other actors involved in AD treatment have been developing alternative solutions that either complement or seek to substitute existing options to reduce the burden of the disease. The observed effects of existing treatments, failures of new clinical studies, uncertainty over future treatments, a shift toward early diagnosis and prevention, and a more humanitarian perspective on the disease have redirected attention from the disease itself to the patients and caregivers (Chaufan et al. 2012). As a result, a new institutional logic may be emerging in AD treatment around complementary non-pharmacological treatments. Such treatments are developed and promoted by professionals and communities, who potentially represent institutional entrepreneurs and participate in institutional change. Groups of researchers and physicians are exploring novel treatments, sometimes combined with existing medications. Non-pharmacological treatments or social interventions have been recognized as helpful and potentially promising solutions for AD patients and their caregivers; however, the number and size of clinical studies have been insufficient to secure scientific evidence on efficacy, and safeguard official approval of a nonpharmacological treatment. Examples of new treatment practices include dance classes or music therapy, doll therapy, sensor aprons, warm drinks or soothing baths (Maurer et al. 2006). Some authors have argued that such interventions could precede, and if necessary be combined with, pharmacologic interventions to maximize benefits (Power 2010).

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DATA AND METHODS We employed a qualitative case study method (Yin 2003) to gain insights into individuals’ specific practices and visions. Data collection and analysis were performed between 2011 and 2014. Data included semi-structured interviews, clinical trial records, observations and field notes. Three “change initiators” of non-pharmacological therapies were interviewed during a seminar organized by the Swiss Alzheimer’s Association for educating and supporting patients, professional caregivers and family members; a symposium on dementia and neurodegeneration at the University Hospital of Zurich; and a meeting organized for investors at the Gerontology and Rehabilitation Unit of the University of Bern. The interviews were recorded when allowed and transcribed. Below, we discuss the findings from these three cases (Table 40.2). The cases are located in three different cities within Switzerland and thus embedded in the same national healthcare system and policy context. Because most studies on institutional entrepreneurship in healthcare thus far have been carried out either in the USA or the UK (Currie et al. 2012a), Switzerland provides an unusual geopolitical context. We classified the change initiators’ activities of institutional entrepreneurship according to the categories “create a vision”, “mobilize allies” and “motivate others to sustain and achieve the vision” (see Battilana et al. 2009). The first activity is to create a vision for divergent change or for framing the change. The vision of institutional change states the problem, demonstrates how the proposed solution may be superior, and shows how it must be supported with convincing arguments. The second activity of institutional entrepreneurship is to mobilize allies. New practices should gain support and acceptance among others (von Krogh et al. 2012b). Mobilizing allies includes using discourse and mobilizing resources, such as social or financial capital or formal authority. As a third activity, institutional entrepreneurs must motivate others to sustain and achieve the vision, which defines all of the following activities necessary to implement and sustain change. Dancing in Basel: Established Non-Pharmacological Intervention The City of Basel runs its own division of the Alzheimer’s association and operates under the umbrella of the Swiss Alzheimer’s Association. The individual initiating change in the first case is the head physician and director of a university center for geriatric medicine in Basel. Demented individuals, apart from cognitive impairment, exhibit symptoms of physical frailty and mobility dysfunctions, such as reduced gait speed and functional mobility, loss of balance and others. As a result, they frequently experience falls with consequent bone fractures. Scientists have discovered that physical activity interventions in elders with dementia improve their physical performance (Alzheimer’s Disease International 2011). In particular, clinicians in Switzerland have reported a significant reduction in the fall rates of elderly persons after their participation in a multi-tasking, rhythmic movement intervention set to music (Gschwind et al. 2011; Trombetti et al. 2011). As a result, the geriatric clinic in Basel organizes dancing-based training classes for patients with dementia. Scientists and clinicians found that cognitive skills such as memory, planning activities or information processing decline in parallel with simple mobility activities such as

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New approach Combination of pharmacological and to AD non-pharmacological treatment in-patient treatments Vision for new Find a cure for AD and AD treatment provide high-quality healthcare through the continuation of established in-patient methods Practices for − Use scientifically proven and established implementing practices for AD new AD treatment applied treatment clinically − Study AD patients in clinical trials

Dancing in Basel

Create vision: − Participate in initiative by the city aimed at finding solutions for new challenges in healthcare and integrating services; jointly lead by medical service of Zurich and care centers in Zurich − Compile the project plan and documentation − Show need to share expertise among actors in the field of AD treatment Mobilize allies: − Engage primary care physicians, memory clinic and care services − Receive formal commitment by the city for a pilot project − Recommend new referral processes for primary care physicians; have costs paid by insurers

Development of a virtual reality gaming application for early AD diagnosis and to delay symptoms and memory loss

Establishment of an expert team to conduct early diagnosis at patient’s home and to connect and coordinate participants in formal and informal care systems Delay institutionalization through at-home early diagnosis and consultation and improve quality of life for patients and informal caregivers

Mobilize allies: − Win fellowship − Receive funding from new participants − Engage with external collaborators (nursing homes) for pilot studies

“Help doctors identify the earliest signs of Alzheimer’s disease (2–3 years in advance) and increase dementia detection rates without the need for extra resources” (quote from company Web site) Create vision: − Conduct research and clinical studies to prove the superiority and cost-efficiency of a virtual reality game to the previous solutions − Participate in fellowship competition; create new firms

Virtual reality in Bern

Home visits in Zurich

Table 40.2 Cross-comparison of AD treatment cases

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Type of change

Minimal: − No considerable change in established practices and logics − Knowledge and tasks remain centralized within hospitals − No new external relationships

Motivate others to achieve and sustain change: − Demonstrate success of pilot in final report − Hire consultants to promote program among primary physicians after the end of the initiative − Continue sharing knowledge among experts and nurses, caregivers and relatives of AD patients Decoupled: − Decisive power and knowledge distributed among program experts and memory clinics − New referral process formally recommended but not always followed by primary care physicians, leading to the exclusion of program experts from early diagnosis and consultation of patients

Divergent: − Change in field composition: entrance of new actors − Change in patient care logic: introduction of new practice of virtual reality game application, radically shifting expertise and power from physicians to software

Motivate others to achieve and sustain change: − Develop solution further − Pilot test solution with collaborators − Secure additional funding through philanthropic initiatives

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walking. This finding can be used (and it is used in Switzerland) as a tool to forecast, if not diagnose, cognitive impairment. The team at the clinic has performed clinical trials (Clinical trials identifiers: NCT01745263, NCT01539200, NCT01607736 and NCT01046292) and published scientific papers (Bridenbaugh et al. 2013; Granacher et al. 2012) focusing on mild cognitive impairment, mobility dysfunctions and healthy aging. These results, in conjunction with those of similar studies, were presented at the Alzheimer’s Association International Conference in Vancouver in 2012. The clinic holds salsa classes and uses Dalcroze Eurhythmics training to improve dual tasking and to reduce falls and mobility dysfunction (the method is termed “the Basel motor-cognition dual-task paradigm”; e.g., Gschwind et al. 2013; Granacher et al. 2012). The goal in this case was not to achieve disruptive institutional change because the approach neither challenges existing institutions nor introduces radically new solutions. When the change initiator was asked about the purpose of dancing courses and conducting these studies, and about whether he had considered commercializing the results, he answered: I am not a businessman in a way. Of course I receive patents and make money from them, but I always think, as soon as you want money for something, it’s a barrier – a barrier for implementation – and my main goal as a university researcher is if I have a good result, I want to see it afterwards in reality. Most important is implementation of what I do in research. The university will take care of selling this to a company but only as long as these are interventions like doing sports like doing whatever. I mean there is no money in there.

The change initiator and his team conducted studies geared toward the early diagnosis and ultimate curing of AD. Clinical excellence, a vision of better care and a close patient– doctor relationship guide the practices. The team seeks to legitimize its work through scientific publications and community acceptance and recognition. Therefore, the team did not involve new actors in the field, and it kept the expertise and decisive power on AD therapy within its hospital and achieved minimal change in AD treatment practices. Home Visits in Zurich: Integrated Consultation and Care Services In the case of Zurich, the change initiator is the head neurologist of a gerontological center in the city of Zurich, who initiated a new program for at-home care. He summarized his view on treatment as follows: We are not as enthusiastic (authors’ note: about medication) as we used to be; we say it is indicated to try this medication, and then we also tell them [the patients] that, if they want, we can write the prescription. If the patients cannot live at home anymore and come to the nursing home, we stop the medications (AChEI) – too many side effects and no real effect. We try to keep the neuroleptics as low as possible, and we prefer the non-anticholinergic antidepressants. I have never prescribed memantine. Sometimes, if somebody has a very severe behavioral disorder, we might try it. However, I am not convinced. I don’t see it.

The change initiator proposed a non-pharmacological intervention called SiL (“Sozialmedizinische individuelle Lösungen” in German), which translates to socialmedical individual solutions. SiL was established as a sub-project of an initiative by the Department of Health and Environment of the city of Zurich (“Gesundheitsnetz 2025”).

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The initiative’s goals are to integrate different existing healthcare services and to develop new services through collaborative projects. SiL aims to empower all participants to achieve the best possible handling of various situations involving AD, such as help with handling living or finding the right contact points for financial, social or legal questions. The change initiator asserted: AD patients don’t suffer themselves; they remain human beings; they remain very interesting human beings . . . they change, but they have the same personality, like you: you were not the same 10 years ago, and you won’t be the same in 10 years from now.

SiL’s approach is to care for AD patients in their own environments. Employees of the program have a first consultation at home and then consult with the memory clinic and jointly discuss recommendations for patients. The patients continue their lives as before their diagnoses and care for themselves with assistance from their families and SiL employees. SiL employees are qualified caregivers, specially trained to treat individuals with dementia, and they visit the patients regularly to assess the progression of the disease by evaluating their cognitive abilities and providing other necessary services. They are in close contact with the attending primary care physician and the care organization. The goal of this intervention, as the head of the SiL program reports, is to offer the best possible at-home care to AD patients and to delay patients’ institutionalization as long as possible to reduce healthcare costs while providing quality of life for patients and caregivers. The SiL program is partly covered by the patient’s health insurance and partly by the city of Zurich. The SiL program integrates the existing resources and services of different organizations to improve care further for AD patients, without using any new treatment methods or involving new organizations in the field of AD treatment. It does, however, depart from previous practice by recommending a new referral process, by sharing expertise among physicians, caregivers and SiL employees and by increasing collaboration among the actors. Although the Center for Gerontology at the University of Zurich evaluated the SiL program as effective overall (Angst Fuchs et al. 2011), it achieved only limited awareness among primary care physicians in Zurich, who should ideally be the people referring patients to the SiL program. The results of a follow-up survey to the SiL program revealed that primary care physicians acknowledged the need for the program but had referred patients only infrequently (Angst Fuchs et al. 2011). The final report of the program suggested increasing advocacy for the SiL program among primary care physicians. We therefore labeled the achieved change “decoupled”, in agreement with the decoupling of organizations described by Meyer and Rowan (1977). Organizations “decouple” a practice when they symbolically endorse the practice according to an institutional logic, while implementing the practice according to another logic (Meyer and Rowan 1977; Pache and Santos 2013). The change initiator targeted a transformation of patient care by combining existing resources and care services in novel ways, but failed to motivate primary physicians sufficiently to apply the new process in their work. The institutional change in formally recommended procedures was therefore decoupled from the actual adherence in practice.

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Virtual Reality in Bern: Innovative Technological Solution for Early Diagnosis and Consultation In the third case, the change initiator participated in a fellowship contest on “healthy living”, jointly organized by the social innovation lab and business incubator The Hub and the pharmaceutical firm Novartis. The contest offered prize money of CHF 34,000, a working place, and business coaching to the winner. The change initiator was a scientist, who developed a new virtual reality game for AD diagnosis and therapy, and he won the fellowship and consequently founded a firm to further develop and commercialize the game. He describes the game as follows: Alterniity sells virtual reality serious gaming software for patients suffering from cognitive decline, particularly dementia and Alzheimer’s disease. Our product is the first non-pharmaceutical, risk-free solution that has been scientifically proved to improve several aspects of cognition. Our value proposition is to offer a non-invasive and risk-free solution fulfilling the need for healthy aging, with a focus on improving and maintaining the brain fitness of both healthy and dementia-affected elderly people.

The game is the first of its type developed in Switzerland. The change initiator and his team are located in a research center at the University of Bern. The virtual reality game places patients in a virtual environment, such as a kitchen, a living room, or ancient Athens or Rome. Patients must perform basic cognitive tasks that are transformed into imaginative and engaging 3D virtual exercises, which include physical interaction, interactive storytelling and social gaming aspects. Similar activities are then performed in the real world. The change initiator cites an example of a virtual activity in the game: Mrs. “Virtual”, aged 55, burned her food many times recently, although she was at home. She believes it is too early to be experiencing a memory loss, so she starts training with us. We place her in a realistic 3D virtual environment where we ask her to actually prepare a virtual meal. We monitor her moves with our 3D sensor and realistically assess her cooking skills, which is the first ability affected in early dementia. We then train her by repeating the correct process, following the cognitive stimulation training strategies.

The software is based on 12 years of in-house research and 3 years of clinical studies (e.g., Giotakos et al. 2007; Laskaris et al. 2013) and has been shown to be useful in very early AD diagnosis, early intervention and delaying the progression of dementia. The change initiator turned into an institutional entrepreneur, because he aimed for divergent change by radically shifting expertise for diagnosing AD and recommending therapies from physicians to software (which indicates the stage of disease development, makes a forecast about the expected disease progress, and suggests treatments). The institutional entrepreneur created a vision by introducing the product as a personalized, patient-centered solution that is safe, enjoyable (no side effects while being entertaining) and cost efficient. The institutional entrepreneur also sought to demonstrate his product’s efficiency and effectiveness compared with existing solutions for AD treatment. Moreover, the institutional entrepreneur mobilized allies from business and philanthropic fields as financiers, which opened up new opportunities to secure further funding, gain publicity and increase the legitimization of his software through more organized advocacy for the

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treatment (such as scientific studies, publications, patent, awards, preparing for Food and Drug Administration (FDA) approval). After creating a vision and mobilizing the initial allies, the institutional entrepreneur developed the solution further, secured more funding and started pilot tests with collaborators in the healthcare field. The entrepreneur’s team further aimed to demonstrate not only technical and clinical efficacy but also cost-efficiency and profitability to attract support from institutional actors, such as the state, regulators and other communities. The team reported that the firm turned profitable after the second year, with its products attracting an increasing user base. The entrepreneur particularly emphasizes the role of Novartis as an ally in sustaining the innovative practice: We are in a favorable position to cultivate a strategic partnership with Novartis International, a company we believe is interested in increasing its exposure to the emerging healthcare gaming industry and that would benefit from adding Alterniity’s suite of products to their portfolio in the future.

DISCUSSION Conditions in the field of AD treatment, such as a lack of novel solutions to meet the growing needs of the elderly in society, public dissatisfaction with existing treatments, and the growing concern of public authorities about the rising costs of healthcare, all comprise enabling conditions for institutional entrepreneurship. In this study, we studied the practices of change initiators in three cases. The practices are variously disruptive and build on Nigam’s (2013) work demonstrating the path toward institutional change in a highly complex and regulated, yet comparable and narrow, environment of AD treatments in Switzerland. Such changes could potentially assist early-stage AD patients by supporting innovative forms of treatment (MacRae 2008). Treatments for AD represent a highly regulated context and might not easily allow actors to operate differently. However, the conditions mentioned above may challenge previous assumptions (“treatments can be only pharmacological”, “a cure is needed”) and may over time contribute to drift in the beliefs and values of actors (shift in institutional logics). Simultaneously, in this setting institutional entrepreneurs must act vigorously to obtain legitimacy (scientific studies of alternative solutions, increase awareness), and mobilize additional actors to participate in the change process (such as getting pharmaceutical firms to invest in their solutions and possibly integrate these into their productand service portfolios). Active participation in vision creation and advocacy, the acquisition of necessary skills and allies, and resource mobilization are crucial to the success of institutional entrepreneurship, as the cases of Zurich and Bern demonstrate. In highly regulated institutions with multiple actors engaging in the treatment of a complex disease affecting an increasingly large population of patients and caregivers, professions, governments, the market and many other stakeholders, a new or alternative institutional logic has inevitably combined with an existing logic. Institutional logics are hybrids, complementing one another to allow divergent change to diffuse throughout the field (Besharov and Smith 2014).

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CONCLUSIONS Healthcare and, in particular, the treatment of AD represent regulated and complex institutional contexts and markets with high relevance for both policy and business. This study is exploratory, revealing the innovative practices of change initiators. We expand on the creation of vision, the mobilization of allies and the motivation of others to achieve and sustain change. Two of the cases, Zurich and Bern, document contemporary treatments that impact institutional logics in varied manners and that inform a future research agenda in healthcare, as well as policy and management. We follow Goldner’s (2004, p. 728) recommendation to recognize geographical differences, and build on her work on activists and agents of institutional change in healthcare by specifying the role that change initiators play in our cases given different institutional logics. Institutional logics are not only dominant or competing, but also complementary. Our study therefore supports and extends the position that institutional logics need not necessarily conflict to bring about change (Reay and Hinings 2009). Institutional change can occur even if logics are complementary (Harris and Holt 2013; McDonald et al. 2013), a condition that might in fact be essential in cases of highly regulated and complex fields. This form of institutional change has been categorized as “transformational” in the literature (Thornton et al. 2012), in opposition to “developmental” change, and the current study contributes some understanding on how this form of change occurs in terms of specific practices. We observe that actors and practices are very diverse. The communication of innovative practices played an important role in Zurich, and venture-financing efforts were likely key to the success in Bern. Institutional entrepreneurs must acquire broader skills than actors who remain within an institutional logic, even when updating and changing the practices, as in the case of Basel. The type of knowledge and skills (Goldberg et al. 2013) required by healthcare practitioners who become institutional entrepreneurs is an important subject of future research. Further, the role of the geographical context remains to be explored in greater detail, as well as the long-term consequences of the mobilization of allies within their logics. Implications for Practice Technological progress and the combination of innovative technologies (e.g., gamification in healthcare) appear to be key enabling factors for institutional entrepreneurship (Li et al. 2010). Corporate venture activities are known to occur for reasons of knowledge acquisition beyond a motivation of pure financial returns (von Krogh et al. 2012a). We observe that the role of Novartis in liaising with a potentially disruptive start-up is telling for the role that institutional entrepreneurs can play. Established pharmaceutical firms as well as policy makers may need to observe closely entrepreneurial activities that experiment with new practices of medical treatment, as these may harbor attractive business opportunities. Finally, the case of AD treatment could pave the way for other treatment areas in healthcare. We observe transformations in the business models of pharmaceutical firms toward stronger service components (gamification, education, training). Future research in management should focus more intensely on business models in healthcare and examine ways in which healthcare can be provided affordably for more individuals (Christensen

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et al. 2000). A broader context of caregivers and family members shares a vital interest in delayed hospitalization of the patient (Allan et al. 2014), and services that are targeted and aligned with medical professionalism can do much good (Weinberg et al. 2007). In addition, technology, such as virtual reality, serious gaming, and mobile applications, can lead to institutional entrepreneurship and far-reaching changes in patient care and the healthcare system.

REFERENCES Allan, H. T., Brearley, S., Byng, R., Christian, S., Clayton, J., Mackintosh, M., Price, L., Smith, P., and Ross, F. (2014). People and teams matter in organizational change: professionals’ and managers’ experiences of changing governance and incentives in primary care. Health Services Research, 49(1), 93–112. Allen, D. (2014). Lost in translation? “Evidence” and the articulation of institutional logics in integrated care pathways: from positive to negative boundary object? Sociology of Health and Illness, 36(6), 807–822. Alzheimer’s Association. (2013). Alzheimer’s disease facts and figures. Alzheimer’s and Dementia, 9. Chicago, IL: Alzheimer’s Association. Retrieved from http://www.alz.org/downloads/facts_figures_2013.pdf, accessed March 11, 2016. Alzheimer’s Disease International. (2011). World Alzheimer Report 2011: The benefits of early diagnosis and intervention. London, UK: Alzheimer’s Disease International. Retrieved from http://www.alz.co.uk/research/ world-report-2011, accessed March 11, 2016. Alzheimer’s Disease International. (2013). Dementia Statistics. London, UK: Alzheimer’s Disease International. Retrieved from http://www.alz.co.uk/research/statistics, accessed March 11, 2016. Angst Fuchs, S., Hediger, H., and Oppikofer, S. (2011). Begleitevaluation des Pilotprojektes SiL Schlussbericht. University of Zurich, Center for Gerontology. Retrieved from http://www.gn2025.ch/fileadmin/files/docs/ Projekte/SiL_Evaluation_12_04_2011.pdf, accessed March 11, 2016. Ballard, C., Gauthier, S., Corbett, A., Brayne, C., Aarsland, D., and Jones, E. (2011). Alzheimer’s disease. The Lancet, 377, 1019–1031. Battilana, J., Leca, B., and Boxenbaum, E. (2009). How actors change institutions: towards a theory of institutional entrepreneurship. Academy of Management Annals, 3, 65–107. Beard, R. L., and Neary, T. M. (2013). Making sense of nonsense: experiences of mild cognitive impairment. Sociology of Health and Illness, 35(1), 130–146. Besharov, M., and Smith, W. (2014). Multiple logics in organizations: explaining their varied nature and implications. Academy of Management Review, 39, 364–381. Bridenbaugh, S. A., Beauchet, O., Annweiler, C., Allali, G., Herrmann, F., and Kressig, R.W. (2013). Association between dual task-related decrease in walking speed and real versus imagined timed up and go test performance. Aging Clinical and Experimental Research, 25, 283–289. Chaufan, C., Hollister, B., Nazareno, J., and Fox, P. (2012). Medical ideology as a double-edged sword: the politics of cure and care in the making of Alzheimer’s disease. Social Science and Medicine, 74, 788–795. Child, J., Lu, Y., and Tsai, T. (2007). Institutional entrepreneurship in building an environmental protection system for the People’s Republic of China. Organization Studies, 28(7), 1013–1034. Christensen, C. M., Bohmer, R., and Kenagy, J. (2000). Will disruptive innovations cure health care? Harvard Business Review, 78(5), 102–112. Cooper, D. J., Ezzamel, M., and Willmott, H. (2008). Examining “institutionalization”: a critical theoretic perspective. In R. Greenwood, C. Oliver, and R. Suddaby (Eds.), The SAGE handbook of organizational institutionalism (pp. 673–701). London, UK: Sage Publications Ltd. Currie, G., Dingwall, R., Kitchener, M., and Waring, J. (2012a). Let’s dance: organization studies, medical sociology and health policy. Social Science and Medicine, 74, 273–280. Currie, G., Lockett, A., Finn, R., Martin, G., and Waring, J. (2012b). Institutional work to maintain professional power: recreating the model of medical professionalism. Organization Studies, 33, 937–962. David, R. J., Sine, W. D., and Haveman, H. A. (2013). Seizing opportunity in emerging fields: How institutional entrepreneurs legitimated the professional form of management consulting. Organization Science, 24(2), 356–377. DiMaggio, P. J. (1988). Interest and agency in institutional theory. In L. Zucker (Ed.), Institutional patterns and organizations: Culture and environment (pp. 3–22). Cambridge, MA: Ballinger. Dorado, S. (2005). Institutional entrepreneurship, partaking, and convening. Organization Studies, 26(3), 385–414.

666

The Elgar companion to innovation and knowledge creation

Dunn, M. B., and Jones, C. (2010). Institutional logics and institutional pluralism: the contestation of care and science logics in medical education 1967–2005. Administrative Science Quarterly, 55, 114–149. Garud, R., Hardy, C., and Maguire, S. (2007). Institutional entrepreneurship as embedded agency: an introduction to the special issue. Organization Studies, 28, 957–969. Garud, R., Jain, S., and Kumaraswamy, A. (2002). Institutional entrepreneurship in the sponsorship of common technological standards: the case of Sun Microsystems and Java. Academy of Management Journal, 45(1), 196–214. Giotakos, O., Tsirgogianni, K., and Tarnanas, I. (2007). A virtual reality exposure therapy (VRET) scenario for the reduction of fear of falling and balance rehabilitation training of elder adults with hip fracture history. New York, NY: IEEE. Glückler, J., and Bathelt, H. (2017). Institutional context and innovation. In H. Bathelt, P. Cohendet, S. Henn, and L. Simon (Eds.), The Elgar companion to innovation and knowledge creation (pp. 121–137). Cheltenham, Northampton, MA: Edward Elgar Publishing. Goldberg, D. G., Mick, S. S., Kuzel, A. J., Feng, L. B., and Love, L. (2013). Why do some primary care practices engage in practice improvement efforts whereas others do not? Health Services Research, 48(2pt1), 398–416. Goldner, M. (2004). The dynamic interplay between Western medicine and the complementary and alternative medicine movement: how activists perceive a range of responses from physicians and hospitals. Sociology of Health and Illness, 26(6), 710–736. Granacher, U., Muehlbauer, T., Bridenbaugh, S. A., Wolf, M., Roth, R., Gschwind, Y., . . . and Kressig, R. W. (2012). Effects of a salsa dance training on balance and strength performance in older adults. Gerontology, 58(4), 305–312. Greenwood, R., Díaz, A. M., Li, S. X., and Lorente, J. C. (2010). The multiplicity of institutional logics and the heterogeneity of organizational responses. Organization Science, 21(2), 521–539. Greenwood, R., and Suddaby, R. (2006). Institutional entrepreneurship in mature fields: the big five accounting firms. Academy of Management Journal, 49(1), 27–48. Gschwind, Y. J., Kressig, R. W., Lacroix, A., Muehlbauer, T., Pfenninger, B., and Granacher, U. (2013). A best practice fall prevention exercise program to improve balance, strength/power, and psychosocial health in older adults: study protocol for a randomized controlled trial. BMC Geriatrics, 13, 105. Gschwind, Y. J., Wolf, I., Bridenbaugh, S. A., and Kressig, R. W. (2011). Basis for a Swiss perspective on fall prevention in vulnerable older people. Swiss Medical Weekly, 141, w13305. Hardy, C., and Maguire, S. (2008). Institutional entrepreneurship. In R. Greenwood, C. Oliver, and R. Suddaby (Eds.), The SAGE handbook of organizational institutionalism (pp. 198–217). London, UK: SAGE Publications Ltd. Harris, R., and Holt, R. (2013). Interacting institutional logics in general dental practice. Social Science and Medicine, 94, 63–70. Herremans, I. M., Herschovis, M. S., and Bertels, S. (2009). Leaders and laggards: the influence of competing logics on corporate environmental action. Journal of Business Ethics, 89(3), 449–472. Holm, P. (1995). The dynamics of institutionalization: transformation processes in Norwegian fisheries. Administrative Science Quarterly, 40(3), 398-422. Kiss, A. N., Danis, W. M., and Cavusgil, S. T. (2012). International entrepreneurship research in emerging economies: a critical review and research agenda. Journal of Business Venturing, 27(2), 266–290. Kitchener, M., and Mertz, E. (2012). Professional projects and institutional change in healthcare: the case of American dentistry. Social Science and Medicine, 74, 372–380. The Lancet. (2010). Why are drug trials in Alzheimer’s disease failing? The Lancet, 376, 658. Laskaris, N. A., Tarnanas, I., Tsolaki, M. N., Vlaikidis, N., and Karlovasitou, A. K. (2013). Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses. Journal of Neuroscience Methods, 212, 344–354. Lawrence, T. B., and Phillips, N. (2004). From Moby Dick to Free Willy: macro-cultural discourse and institutional entrepreneurship in emerging institutional fields. Organization, 11(5), 689–711. Li, F., Papagiannidis, S., and Boulakis, M. (2010). Living in “multiple spaces”: extending our socio-economic environment through virtual worlds. Environment and Planning D: Society and Space, 28, 425-446. Lloyd, B. T., and Stirling, C. (2011). Ambiguous gain: uncertain benefits of service use for dementia carers. Sociology of Health and Illness, 33(6), 899–913. Lockett, A., Currie, G., Waring, J., Finn, R., and Martin, G. (2012). The role of institutional entrepreneurs in reforming healthcare. Social Science and Medicine, 74, 356–363. Lounsbury, M. (2007). A tale of two cities: competing logics and practice variation in the professionalizing of mutual funds. Academy of Management Journal, 50(2), 289–307. Macfarlane, F., Barton-Sweeney, C., Woodard, F., and Greenhalgh, T. (2013). Achieving and sustaining profound institutional change in healthcare: case study using neo-institutional theory. Social Science and Medicine, 80, 10-18.

Institutional entrepreneurship in Alzheimer’s disease treatment

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MacRae, H. (2008). “Making the best you can of it”: living with early-stage Alzheimer’s disease. Sociology of Health and Illness, 30(3), 396–412. Maguire, S., Hardy, C., and Lawrence, T. B. (2004). Institutional entrepreneurship in emerging fields: HIV/AIDS treatment advocacy in Canada. Academy of Management Journal, 47(5), 657–679. Mangialasche, F., Solomon, A., Winblad, B., Mecocci, P., and Kivipelto, M. (2010). Alzheimer’s disease: clinical trials and drug development. The Lancet Neurology, 9, 702–716. Marquis, C., and Lounsbury, M. (2007). Vive la résistance: competing logics and the consolidation of US community banking. Academy of Management Journal, 50(4), 799–820. Maurer, K., McKeith, I., Cummings, J., Ames, D., and Burns, A. (2006). Has the management of Alzheimer’s disease changed over the past 100 years? The Lancet, 368, 1619–1621. McDonald, R., Cheraghi-Sohi, S., Bayes, S., Morriss, R., and Kai, J. (2013). Competing and coexisting logics in the changing field of English general medical practice. Social Science and Medicine, 93, 47–54. Meyer, J. W., and Rowan, B. (1977). Institutionalized organizations: formal structure as myth and ceremony. American Journal of Sociology, 8, 340–363. Mutch, A. (2007). Reflexivity and the institutional entrepreneur: a historical exploration. Organization Studies, 28(7), 1123–1140. Nigam, A. (2013). How institutional change and individual researchers helped advance clinical guidelines in American health care. Social Science and Medicine, 87, 16-22. Nigam, A., and Ocasio, W. (2010). Event attention, environmental sensemaking, and change in institutional logics: an inductive analysis of the effects of public attention to Clinton’s health care reform initiative. Organization Science, 21, 823–841. Pache, A.-C., and Santos, F. (2013). Inside the hybrid organization: selective coupling as a response to competing institutional logics. Academy of Management Journal, 56, 972–1001. Perkmann, M., and Spicer, A. (2007). “Healing the scars of history”: projects, skills and field strategies in institutional entrepreneurship. Organization Studies, 28, 1101–1122. Powell, W. W., and DiMaggio, P. J. (1991). The new institutionalism in organizational analysis. Chicago, IL: University of Chicago Press. Power, G. A. (2010). Dementia beyond drugs: Changing the culture of care (1st ed.). Baltimore, MD: Health Professions Press. Rao, H., Monin, P., and Durand, R. (2003). Institutional change in Toque Ville: Nouvelle Cuisine as an identity movement in French gastronomy. American Journal of Sociology, 108(4), 795–843. Rao, H., Morrill, C., and Zald, M. N. (2000). Power plays: how social movements and collective action create new organizational forms. Research in Organizational Behavior, 22, 237–281. Reay, T., Goodrick, E., Casebeer, A., and Hinings, C. R. (2013). Legitimizing new practices in primary health care. Health Care Management Review, 38(1), 9–19. Reay, T., and Hinings, C. R. (2009). Managing the rivalry of competing institutional logics. Organization Studies, 30, 629–652. Swiss Alzheimer’s Association. (2014). Zahlen und Fakten zur Demenz. Switzerland: Swiss Alzheimer’s Association. Retrieved from http://www.alz.ch/index.php/zahlen-zur-demenz.html, accessed March 11, 2016. Thornton, P. H., and Ocasio, W. (1999). Institutional logics and the historical contingency of power in organizations: executive succession in the higher education publishing industry 1958–1990. American Journal of Sociology, 105, 801–843. Thornton, P. H., and Ocasio, W. (2008). Institutional logics. In R. Greenwood, C. Oliver, and R. Suddaby (Eds.), The SAGE handbook of organizational institutionalism (pp. 99–128). London, UK: SAGE Publications Ltd. Thornton, P. H., Ocasio, W., and Lounsbury, M. (2012). The institutional logics perspective: A new approach to culture, structure, and process. Oxford, UK: Oxford University Press. Trombetti, A., Hars, M., Herrmann, F. R., Kressig, R. W., Ferrari, S., and Rizzoli, R. (2011). Effect of musicbased multitask training on gait, balance, and fall risk in elderly people: a randomized controlled trial. Archives of Internal Medicine, 171, 525–533. von Krogh, G., Battistini, B., Pachidou, F., and Baschera, P. (2012a). The changing face of corporate venturing in biotechnology. Nature Biotechnology, 30, 911–915. von Krogh, G., Haefliger, S., Spaeth, S., and Wallin, M. W. (2012b). Carrots and rainbows: motivation and social practice in open source software development. MIS Quarterly, 36(2), 649–676. Weinberg, D., Lusenhop, R. W., Gittell, J. H., and Kautz, C. M. (2007). Coordination between formal providers and informal caregivers. Health Care Management Review, 32(2) 140–149. Yang, C.-W., Fang, S.-C., and Huang, W.-M. (2007). Isomorphic pressures, institutional strategies, and knowledge creation in the health care sector. Health Care Management Review, 32(3), 263–270. Yin, R. K. (2003). Case study research: Design and methods. Thousand Oaks, CA: Sage Publications.

PART VIII GOVERNANCE AND MANAGEMENT OF INNOVATION

41. Relational geographies of knowledge and innovation James R. Faulconbridge

INTRODUCTION This chapter explores how economic geographers have developed a relational perspective on knowledge and innovation, and the implications of this perspective for management in organizations and for public policy. For economic geographers, interest in knowledge and innovation is mediated through two spatial lenses. First, an interest in sites of knowledge and innovation, this being seeded in particular through studies of regional agglomeration and localization economies (Storper and Christopherson 1987; Henry and Pinch 2000). Most recently this lens has been deployed to analyse clusters as geographical nodes of knowledge and innovation (Pinch et al. 2004; Giuliani, Chapter 22, this volume). Second, questions about firms have drawn attention to the spatiality of the communities in which knowledge and innovation ‘happen’ (Amin and Cohendet 2004; Faulconbridge 2010). Both of these lenses have contributed to the development of relational perspectives in which focus falls on ‘economic and social relations, processes of organizing, problem solving, and innovation, as well as on the creation of informal and formal institutions’ (Bathelt and Glückler 2005, 1546). The relational perspective offers a number of unique insights into knowledge and innovation. In particular, it allows both the social and the spatial dimensions of knowledge and innovation to be simultaneously addressed through a perspective that shifts focus ‘from the macro-level . . . to the micro-level (i.e. agents and their interactions)’ (Boggs and Rantisi 2003, 111). This involves analysing through fine-grained and often qualitative studies the way knowledge and innovation are innately shaped by the contexts in which they are produced; context in this sense being both a social and spatial dimension. As Bathelt and Glückler (2005) note though, this micro-scale focus is not restricted to phenomenological accounts of action. It also seeks to reveal how analysis of the micro-scale can contribute to understanding of the macro-scale in the sense of institutional effects. The remainder of this chapter seeks to unpack the way that the relational approach can, therefore, provide a distinctive means of understanding knowledge and innovation as processes. It also reflects on the implications of a relational perspective for how knowledge and innovation might be managed in organizations and through policy. As such, the main contention of the chapter is that the relational approach offers a unique perspective that can bring both people and places to the forefront of studies, this being beneficial because of the significance of socio-spatial collaboration to effective knowledge and innovation production. The chapter proceeds as follows. The next two sections outline the origins of the relational approach and its key principles. This is followed by analysis of the implications of the relational perspective for understandings of knowledge and innovation. Two sections 671

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then in turn consider how relational insights can be used to better manage knowledge and innovation in organizations and through policy, before the concluding section considers the future directions for relational research on knowledge and innovation.

RELATIONAL PERSPECTIVES The relational turn in economic geography has been inspired by and has contributed to similar turns across the social sciences, including in other parts of geography (Jones 2009), in sociology (Emirbayer 1997) and in management (Uzzi and Lancaster 2003). As Boggs and Rantisi (2003) note, motivations for a relational turn are multiple. First, corporate organization has become increasingly relational. As part of processes of vertical disintegration and the externalization of activities to sub-contractors, something being especially significant in generating growth in the service economy (see Goe 1991), relationships between firms have become increasingly significant in determining innovation capacity. Miles (2001) argues that innovation now increasingly involves collaboration between firms as, in particular, knowledge-intensive business services (but also sub-contractors providing components in manufacturing; see Dicken 2011) collaborate in a co-production process. As a result, understanding the nature of these relationships is crucial. Second, and in line with a shift to the micro-scale, focus has fallen upon the way agency at the level of individual actors and groups matters in economic activity. As part of efforts to understand firms as social communities (Cohendet and Llerena 2003; Morgan 2001) it has been emphasized that understanding the dynamics of social action, and the way it is constrained by, but also has the potential to exert influence over, corporate structures and societal institutions (Archer 2000), is an essential part of understanding economic competitiveness. Relational perspectives take this agency seriously and examine what constitutes and constrains it. Third, the emergence of a global space economy has refocused attention on the importance of analysing the spatiality of corporate practice (Jones 2008). Of course, spatiality has always mattered. Marshall’s (1890) observation that the secrets of regional economies were ‘in the air’ set the scene for research that considered the importance of local relationships for knowledge and innovation. However, with globalization came a new imperative to understand internationally stretched relationships. As Amin and Cohendet (1999) noted, in the global era it was no longer tenable to understand firms as ‘islands’ of innovation. Rather, decentralized business networks, between subsidiaries of global firms but also involving traded and untraded relations with other firms and individuals, became of interest. Hence relational approaches took on a new spatial significance, being a crucial way of understanding the extent to which processes of knowledge and innovation found at the local scale (intra-firm or locality) could be reproduced across space (Gertler 2003).

PRINCIPLES OF THE RELATIONAL APPROACH A number of core principles of analysis have developed which are significant for how knowledge and innovation might be studied and conceived of relationally. First, in developing the micro-scale perspective, the socio-cultural constituents of economic practice are

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prioritized (Jones and Murphy 2011). This has taken a number of forms. For Ettlinger (2003) the cultural turn within economic geography (Barnes 2001) and the prioritization of how culture shapes the ‘relational microspaces’ of interaction within firms provides a way to make sense of how issues such as trust and reciprocity influence economic practice. Gertler (2003) offers a parallel but subtly different perspective, arguing that culture needs to be understood as being related to institutions, this helping reveal how all interactions are geographically situated. In addition, a growing body of work has drawn attention to how power relations are fundamental to economic practice. This has involved conceptualizing power itself as relational (Allen 2003); the social interactions that define innovation thus being means through which power is both exercised and constructed (Faulconbridge 2012). The major advantage of this focus on the socio-cultural constituents of practice is that it allows the contingency of relational action to be accounted for in analyses of the determinants of successful innovation practice. A second core principle of relational approaches relates to understandings of spatiality. In an effort to move beyond regional science perspectives that understand spatiality as a pre-existing container, relational approaches conceive of space as continually produced by economic action (Bathelt and Glückler 2003; Yeung 2005). This socially constructed view conceives of spatiality as defined by social agency; actions and interactions constitute the space in which knowledge and innovation occur. For instance, Amin and Cohendet (2004) in their communitarian approach highlight the architectures and infrastructures which generate relational spaces of knowledge, these being both social (collaborations and relationships) and material (informational technology networks and circulating documents). Crucial for Amin and Cohendet (2004) is the topological nature of relational spaces, being as they are defined by the complex criss-crossing of different social and material relations. This means knowledge and innovation is associated not with pre-defined spaces, such as the city, region, country or organization, but by spaces that are constantly being produced and reproduced through social agency. Using the case of global architecture firms, Faulconbridge (2010) shows that this can result in communities and constellations of practice emerging that generate spaces of knowledge and innovation unhindered by geographical distance (see also, Grabher and Ibert, Chapter 33, this volume; Henn and Bathelt, Chapter 39, this volume). Underlying such spaces are complex assemblages of social collaboration (e.g. project meetings, corporate away-days) and non-human connectivity (e.g. video conferences, circulating building models). Ultimately, the socially constructed understanding of spatiality adopted in the relational perspective reconfigures conceptions of the sites of knowledge and innovation. It frees analyses from the shackles of a container view of spatiality which tends to lead to assumptions about the localness of knowledge and innovation practice (Bathelt et al. 2004; Faulconbridge 2006). In particular, the relational perspective emphasizes the need for spaces of knowledge and innovation to be created, and that this creative process has the potential to connect together into communities of individuals located both in proximity and at a distance (Faulconbridge 2007a; Vallance 2011; Rallet and Torre, Chapter 26, this volume). The third key principle of the relational approach relates to research methodology. As a result of the focus on culture, power and social action, research has emphasized the importance of fine-grained qualitative studies. Whilst accepting that as far back as

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Granovetter’s (1985) work relationality has been studied through quantitative means, economic geographers have emphasized the value gained from studying the intricacies of situated economic action. Yeung (2003) thus called for a tracing of actor networks through the triangulation of various data sources, including documents, interviews and participant observation. Each method should be deployed at each of the different sites connected together by relational action and should seek to reveal the contingencies shaping outcomes. As Faulconbridge (2012) notes, such an approach requires the researcher to operationalize and triangulate qualitative methods effectively, something economic geographers have not always been good at. It also requires attention to be paid to the outcomes of economic action and not just the contingencies of it. Bathelt and Glückler (2005) remind us that a focus on the micro-scale should not be at the expense of an understanding of the macro-scale implications. When such factors are taken account of, fine-grained qualitative research has the potential to reveal much about the practice and spatiality of knowledge and innovation.

KNOWLEDGE AND INNOVATION VIEWED RELATIONALLY What, then, are the implications of the principles of the relational approach outlined above for perspectives on knowledge and innovation? To address this requires consideration of three important questions: what is knowledge, how are knowledge and innovation produced, and what determines the spatiality of knowledge and innovation? In terms of the nature of knowledge, relational perspectives complement the practice turn in approaches to understanding knowledge and innovation. Heavily influenced by the seminal work of Lave and Wenger (1991) and latterly Wenger (1998), the practice approach has been extended beyond its communities of practice origins into a broader practice-theoretical framing which sees knowledge and learning as constituted through embodied social action (Brown and Duguid 2000; Gherardi and Nicolini 2000, 2006). In such a perspective, knowledge is viewed not as a static commodity or resource, but as a dynamic social accomplishment that is always in the making. As Amin and Cohendet (2004) describe, both relational and practice perspectives dismiss the idea that knowledge exists as an economic artefact which can, for instance, be ‘held’ by an individual, company or in a database. Instead, they understand knowledge to be collectively produced and shared by social communities in an ongoing manner. This means transcending common distinctions between tacit and explicit knowledge and recognizing that because of its social nature knowledge always has both tacit and explicit dimensions. For Brown and Duguid (2000), the practice-informed understanding is best captured by a shift from the use of the term knowledge to the use of knowing. By adopting the verb form the intention is to highlight the significance of social action for creating, maintaining and reproducing knowledgeability. This view of knowledge, which departs in significant ways from perspectives adopted in many knowledge management literatures in which knowledge is viewed as an asset to be captured, stored and disseminated, has important implications for perspectives on how knowledge and innovation are produced. Specifically, social practice is seen as the means by which knowledgeability emerges and innovations are generated.

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As already noted, work on communities of practice provides the basis for much of the practice-based thinking. In Wenger’s (1998) development of the communities of practice idea, he highlights how interactions between community members allow learning and the production of knowledge (see also Roberts, Chapter 21, this volume). Three contingencies are said to affect these interactions and make knowledge production possible. First, those interacting must have a mutual engagement, this referring to involvement in a similar activity. Second, joint enterprise is important, this being a common way of understanding and completing the activity in question. Third, shared repertoires are needed, these being constituted by common languages, tools or procedures. Whether an individual becomes a member of a community of practice, and in turn helps generate new knowledge, depends on the extent to which each of the three contingencies is fulfilled. As such, the three contingencies, because they shape crucial social interactions, determine the success of the production of knowledge and innovation. Others have since added a number of further dimensions to Wenger’s (1998) analysis of communities of practice. Fox (2000) points out that power relations need to be accounted for, with interactions in communities being shaped in important ways by both resource inequalities and discursive efforts at control that can lead to exclusion from communities. Amin and Roberts (2008) highlight the subtly different nature of interactions depending on the characteristics of the knowledge being produced in a community. By contrasting craft, professional, creative and virtual communities and their different forms of knowledge and means of social interaction, Amin and Roberts show that learning in practice can take radically different forms, from intensive master-apprentice embodied interactions in craft industries to fleeting virtual interactions in online communities. Such refinements of understanding of the social interactions that allow knowledge and innovations to be produced confirm the significance of the emphasis placed in the relational approach on studying the contingencies of social engagement. Relational approaches have also developed the practice-inspired approach outlined above in terms of understandings of the spatial dimensions of communities. As part of an agenda to move beyond an initial fetish with the local scale in which knowledge and innovation was assumed to require the co-location of interacting parties, studies have examined the way that the social interactions which produce community spaces of knowledge and innovation can be stretched (for summaries of this agenda see Bathelt et al. 2004; Faulconbridge 2006; Vallance 2011). Focus has fallen on two fundamental questions about such stretching. First, what is the spatiality of the means of interaction that allows knowledge and innovation to be produced? In work which emphasized the localness of knowledge and innovation, face-to-face contact and embodied encounter was emphasized as crucial for allowing the social action that creates, maintains and reproduces knowledge (Storper and Venables 2004). The relational approach has questioned such assumptions, and demonstrated that interaction need not occur solely between co-located individuals. Studies have shown that the architecture of knowledge and innovation ‘includes, yes, face-to-face meetings, sociality, and casual contact . . . but it also draws on distant objects such as drawings faxed between offices around the world, global travel to form temporary project teams, and daily internet/telephone/video conversations’ (Amin and Cohendet 2004, 110). In highlighting this, and in line with the relational perspective’s understanding of space as socially constructed, research encourages the tracing of practice to identify the

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geographies of knowledge and innovation, which can be both local and global (Bathelt et al. 2004; Faulconbridge 2007a). The second fundamental question about spatiality relates to the contingencies that underlie more or less local knowledge production and innovation. Reflecting the ideas outlined above about the range of factors that can affect interactions, research has sought to clarify whether physical proximity renders knowledge and innovation production more effective. For instance, reflecting ideas in work on communities of practice, the idea of relational proximity has been developed (Blanc and Sierra 1999) to take account of the way mutual engagement, joint enterprise and shared repertoire can exist between both spatially proximate and distanciated individuals. In particular, various means of creating relational proximity have been noted. Gertler (2008) argues that proximity can emerge as a result of shared social affinities such as common educational background, work experience or occupation. Faulconbridge (2006, 2010) suggests that in multinational corporations relational proximity can be manufactured through strategies such as common corporate languages, globally stretched project teams, and the circulation within the firm of handbooks and other objects which create a common reference point. In terms of questions about culture and power, literatures suggest that a key priority is managing the effects of these on social interactions. For example, Gertler (2004) shows that culture determines whether differently situated individuals and communities can learn from one-another and whether opportunities for innovation are spotted. Faulconbridge (2008) shows that cultural difference requires negotiated compromises between actors in different subsidiaries of multinational firms, these compromises facilitating collective learning and innovation in some cases but in others preventing the sharing of ideas and expertise. Boussebaa (2009) points out that in global teams innovation can be impeded by power relations and politics that render collaboration more difficult than it might be, these power relations often being geographical in form as they relate to neo-colonial core–periphery relationships (such as between Western and Eastern European team members). In sum, as explored further below, the relational approach suggests that it is crucial to study and manage the social interactions and their contingencies that are fundamental to knowledge and innovation. Of course, not all of these interactions will be at a distance – the relational approach has tended to focus on distanciated interactions because of a desire to better understand the spatiality of knowledge and learning. As Asheim et al. (2007) point out, depending on the nature of the knowledge being produced and informing innovation, interactions and the resultant spatiality may be more or less local. Asheim and colleagues differentiate between analytic, synthetic and symbolic knowledge bases, each they suggest being associated with different kinds of industry (for instance the life sciences, furniture manufacturing, and film production industries respectively). Analytical knowledge bases tend to be composed of more formalized knowledge, for example captured in patents, whilst synthetic knowledge bases involve applying formal knowledge in creative way. Symbolic bases are reliant more on the craft skill of individuals. Asheim et al. (2007) argue that an industry with an analytical knowledge base is more likely to be able to engage in spatially stretched processes of knowledge and innovation production as embodied encounter is less important for learning than in industries reliant on synthetic and symbolic knowledges. It is debatable whether any industry fits perfectly into the typology proposed by Asheim et al. (2007), but the important point to emerge

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from their analysis is that the relational geographies of knowledge and innovation are potentially both local and global depending on both the contingencies of social interaction described above and the nature of the knowledge being produced and informing innovations.

MANAGING KNOWLEDGE AND INNOVATION IN ORGANIZATIONS: A RELATIONAL VIEWPOINT The analysis above reveals a number of distinctive features of the relational perspective that have implications for managing knowledge and innovation in organizations. As already noted, most fundamentally the approach suggests knowledge itself cannot/should not be managed. Rather it is the social interactions between individuals that need attention. In particular, it is the occurrence and effectiveness of these interactions that should be of concern. For researchers, this means adopting a methodology capable of unpicking the forms and contingencies of social interaction. Two examples from the literature that adopt a relational perspective usefully illustrate these points. Wood and Reynolds (2012) consider the case of store location decisions in retail firms and the way knowledge that informs decision making is developed. They observe that ‘Clearly the management of interaction between different communities within the firm presents significant challenges’ (page 554), power relations between groups within the same organization being the most important consideration when seeking to generate the collaboration needed to produce knowledge that will inform effective decision making. Wood and Reynolds (2012) conclude that organizations must, therefore, put in place strategies that ensure individuals from different departments are able to interact effectively. A focus on developing supportive corporate cultures can be one means of achieving such interactions; Cohen and Levinthal (1990) long ago identifying this as being key to the ‘absorptive capacity’ of an organization. The boundaries of the community of interacting agents must also be fluid, allowing different people to contribute as and when useful and ensuring participation is not exclusive. Wood and Reynolds (2012, 558) thus conclude by agreeing with Amin and Cohendet’s (2004, 12) suggestion that a ‘common anthropology of socialization, social interaction, interest alignment, and community maintenance’ are fundamental to effective knowledge production. Faulconbridge (2010) offers a different set of insights by examining global communities of practice in architecture firms. By examining the way that firms seek to generate collaborations between professionals employed in offices around the world, the case reveals the role of combinations of the social affinities that Gertler (2008) outlines, deliberate corporate strategies to generate relational proximity, and crucially non-human objects that act as boundary spanners between distributed agents. In particular, the case reveals that in multinational organizations there is likely to be some of the social infrastructure needed for effective innovation, such as individuals with shared educational backgrounds and professional memberships, but that efforts also need to be made to bolster relational proximity by generating the mutual engagement, joint enterprise and shared repertoire crucial to the success of communities of practice. These efforts can take various forms. For instance, allowing business travel and organizing firm-wide conferences provides an opportunity for individuals to interact in person as well as virtually, this helping overcome

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some of the cultural and power-related contingencies discussed previously. In addition, Faulconbridge (2010) highlights the role of non-humans, in the case of architecture firms these being combinations of documents (corporate newsletter, building blueprints etc.) and models of buildings which circulate between offices and help build relational proximity. The two cases outlined above are significant in that they draw attention to three key implications of the relational view. First, they show the value of a communitarian perspective which recognizes that firms, and equally the cities and regions documented in the cluster literatures, are actual social communities rather than hollow structural or spatial containers. When viewed as communities, attention shifts to the identities of, but most crucially to the relationships between, individuals who can produce knowledge and innovation through interactions. As a result, there is an emerging literature in human resource management that argues that knowledge management is a task not for dedicated departments (often staffed by computer experts) but for human resource managers who manage the culture of the firm and the development of its employees (Sparrow 2012). Second, the two cases reveal that there is always the potential for frictions in the interactions that produce knowledge and innovation, but these can be a productive part of the process. One of the strengths of the relational approach is its ability to understand the contingencies of these interactions, with the role of culture, power and forms of relational proximity all becoming clear thanks to the micro-scale approach adopted. This reemphasizes the point that in managing knowledge and innovation the social conditions that facilitate the social processes of their production should be at the top of agendas. Third, the cases reveal the potential of the relational approach to explore knowledge and innovation production as processes that are similar regardless of whether community members are co-located or not. By de-emphasizing face-to-face and embodied interactions, and emphasizing the social conditions discussed in the previous points, the relational approach helps reveal the potential for criss-crossing local–global geographies of innovation, and challenges presumptions that local, face-to-face mediated encounters are always most productive (Faulconbridge 2007a). The relational view offers, therefore, a means of de-centring the firm, region and knowledge itself as part of efforts to recognize the ‘social life’ of knowledge and innovation (cf. Brown and Duguid 2000).

RELATIONAL POLICY The insights of the relational approach outlined above present two significant challenges to ways of thinking about public policy designed to encourage innovation (see also Lagendijk, Chapter 30, this volume). First, they reconfigure the priorities of regional innovation policies by emphasizing the importance of social interaction. Second, they give parity to extra-regional networking as the connection of individuals and groups in one region to those in others emerge as crucially important. These two challenges are explored further in the next two sub-sections of the chapter.

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Managing Corporate Relationality The relational perspective outlined in this chapter suggests that building material infrastructures, such as science parks, and providing tax relief for innovating firms, might be usefully complemented by strategies designed to seed and manage the kinds of social interactions that produce knowledge and innovations. This means enabling interactions that transcend the boundaries of firms and produce a regional ecology of knowledge. For instance, studies have shown that supporting the formation of effective regional communities of practice that allow those in related industries to collectively address economic and societal challenges is a productive use of policy resource (Benner 2003; Faulconbridge 2007b). In such communities there needs to be suitable cultures and power relations to facilitate open collaboration, something that can be strategically managed. The benefit likely to be gained from policies designed to seed interaction and collaboration is knowledge production and innovation that is more path-breaking than that constrained by the boundaries of a single organization. As such, there is an imperative for policy to ‘get inside’ the corporations operating in any locality and dissolve boundaries between the organizations and the local community. Clearly questions of competitive advantage and intellectual property pose some hurdles to such work. But, using policy to ensure the engendering of a sense of membership of a community in those managing and working in firms, to celebrate the successes of interfirm collaboration, and to develop an agenda for the locality that all firms can somehow shape, own and ensure the success of, can provide a means of ensuring the inner walls of corporations are penetrated and intra-regional relationality constructed. This in turn would allow the spurring of the inter-firm social interactions that the relational view tells us is so important. For corporations themselves, this means recognizing that success is reliant as much on interactions beyond the boundaries of the firm as those within. Too often in relation to questions about knowledge and innovation the focus falls on intra-organizational issues of knowledge management. Whilst not unimportant, the relational approach draws attention to the need to manage interactions not only between spatially separated entities in the same organizations, such as subsidiaries in different parts of the world, but also between the firm and its suppliers, competitors and customers. As such, dissolving the boundaries of the firm to ensure that employees interact and learn with other firms in the locality, in the same industry and in different industries, is crucial. This would support the kinds of regional policy outlined above, and involve firms enabling, incentivizing and rewarding collaborations, whether through conference activities, working group membership, or virtual or face-to-face forums. Only by developing the kinds of relational interactions described here as at the centre of processes of learning and innovation can corporations hope to prosper in the knowledge-based economy. Extra-Territorial Relational Policy The discussion above of managing interactions within a locality is not, however, to suggest that policy should solely focus on the local scale. Indeed, an important insight of the relational approach is that local fetishes potentially lead to missed opportunities in terms of stretched relational spaces of innovation. There is, then, a need to recognize the

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limitations of the cluster-inspired approach to local/regional development (Martin and Sunley 2003). For firms and regions to avoid local lock-in it is necessary to explore policy that effectively builds stretched relational spaces of knowledge and innovation which connect firms and localities into global networks of related organizations. In many ways there is nothing new in recognizing this; Amin and Thrift (1992) long ago documented that Marshallian nodes of innovation are reliant on global networks for their competitiveness. What is new, however, is to suggest policy should invest in such global networks as well as local infrastructures. What might extra-territorial relational policy look like? Tactics might include networking activities for individuals and firms, such as trade conventions that create moments of temporary proximity and allow immediate but also more sustained longer-term interactions between individuals within firms from around the world (Bathelt and Schuldt 2008). It may also mean local development agencies stimulating investments by multinational firms. For the multinationals, presence in a locality provides access to ‘listening posts’ which can inform innovations. For other firms in the locality, the multinational provides a connection into global knowledge pipelines that help generate novel innovations (Maskell 2014). The aim of policy initiatives should, then, be to spur effective forms of local–global interaction that help form a community of practice that can be maintained virtually over time. Policy must, of course, therefore also avoid multinationals simply exploiting the locality, as tax incentives or forms of labour arbitrage tend to encourage (Faulconbridge 2017), the basis for this being the encouragement of investments by multinationals seeking to contribute to the development of a critical mass of industry specialization in a region, and not simply to extract cost savings or resource access. This implies, then, that territorial policy itself needs to be conceived of through a relational lens. On the one hand, this means, as outlined above, thinking about how policy can envisage a locality’s place in wider national and international networks, and then support the development of a relational architecture which allows interactions that enable learning and innovation. On the other hand, it implies thinking relationally about the process of producing policy itself. As the growing body of work on policy mobilities attests (for a synopsis see McCann 2011), it is increasingly difficult to envisage policy being produced in a local vacuum given the influence of various circuits of policy knowledge that lead to ideas and practice moving around the world. It thus seems important to stay alert to the influences on the policy production itself of relational learning, this requiring policy makers to interact with their counterparts elsewhere as part of a process of both shaping the characteristics of policy and ensuring the relational networks discussed above are realized. In addition, there also needs to be recognition of the potential to generate significant tangible effects through relational policy making on the local economy and/or global institutional infrastructure (Bathelt and Glückler 2005). Locally this might mean new rules of the game as far as collaboration, investment and skill development are concerned, as firms respond to insights gained from interactions by reconfiguring priorities and practices. Ultimately this might lead to global competitive advantage for a region in a particular industry. Globally the result might be the emergence from communities of new transnational product standards, modes of market governance, or industry protocols. The outcomes will differ from case to case, but the important point is that relational interactions always have effects that should not be underestimated. Indeed, one of the critiques developed by work on policy mobilities is that relational policy making can lead to the

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importing of ideas unsuited to a locale or which replicate undesirable social and economic effects. This reminds us, then, that whilst the discussion in this chapter has been about the benefits that relationality can bring for learning and innovation, there is a darker side in terms of the potential for the spread of ideas and limited critical reflection on their merits and local impacts.

CONCLUSIONS This chapter has considered the insights that a relational perspective on knowledge and innovation provide. It has drawn on the key tenets of the relational approach, namely that research should focus on social interaction, the way spatiality is constructed through this social interaction, and therefore adopt a micro-scale methodology, to argue that rather than studying knowledge and innovation themselves, the social interactions that produce them should be the primary concern of researchers. There is, of course, still much to do to leverage the relational perspective outlined here. As noted, the methods needed to effectively study the interactions underlying knowledge and innovation production are in need of further development, something that requires more longitudinal and in-depth case study based research (Faulconbridge 2012). There is also a need to better compare and contrast proximate and distanciated interactions. The question is no longer whether one is more valuable than the other, but whether it is possible to more effectively differentiate the two in terms of both means and ends. The different roles and means of interaction of actors, from individuals to professional associations, firms to online communities, in local and global knowledge production and innovation also warrant further unpacking so as to better inform policy interventions that target and leverage these agents. In effect, future research needs to further unpack the three fundamental principles of the relational approach to add more fine-grained understanding of how knowledge and innovation are produced through social interaction. From a policy perspective, the relational approach provides two substantive challenges. First, the unit of analysis in terms of policy effects is transformed by the emphasis in the relational approach on social interactions. This implies that focus should fall not just upon hard infrastructure, investment in knowledge assets such as R&D centres, and attracting talented workers. In addition, it is necessary to consider the opportunities, incentives and rewards for collaboration within the region and the kinds of social exchanges that enable learning. This means seeding collaboration and exchange, and perhaps most importantly developing the institutional thickness that allows rivalries to be put aside when individuals from competing firms come together. Such softer infrastructures necessitate a different kind of investment that, whilst hard to render tangible and measure, is according to the relational approach fundamental to innovation. Second, the relational approach requires the re-scaling of policy foci, away from cities and regions and towards relational topologies of collaboration. This does not mean ‘local’ development agencies and agendas need to disappear. Rather, it means the success of these local agencies and agendas must be recognized as intimately tied to the national and global relational ties of the locality. Developing such a perspective on and approach to policy is innately tricky given the territorialized nature of states, but is crucial and at the heart of the success of innovation sites in the global economy.

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The relational approach provides, then, a theoretical–conceptual–methodological mode of analysis in relation to issues of knowledge and learning, and also an orientation to policy that unsettles some of the more static and localized visions that exist in some literatures. As such, it provides a powerful way of reimagining questions about the knowledge and innovation so central to contemporary economies.

REFERENCES Allen, J. (2003) Lost geographies of power. Oxford: Blackwell. Amin, A. and Cohendet, P. (1999) ‘Learning and adaptation in decentralised business networks’, Environment and Planning D: Society and Space, 17: 87–104. Amin, A. and Cohendet, P. (2004) Architectures of knowledge: firms capabilities and communities. Oxford: Oxford University Press. Amin, A. and Roberts, J. (2008) ‘Knowing in action: beyond communities of practice’, Research Policy, 37: 353–369. Amin, A. and Thrift, N. (1992) ‘Neo-Marshallian nodes in global networks’, International Journal of Urban and Regional Research, 16: 571–587. Archer, M. S. (2000) Being human: the problem of agency. Cambridge: Cambridge University Press. Asheim, B., Coenen, L. and Vang, J. (2007) ‘Face-to-face, buzz, and knowledge bases: sociospatial implications for learning, innovation, and innovation policy’, Environment and Planning C: Government and Policy, 25: 655–670. Barnes, T. J. (2001) ‘Retheorizing economic geography: from the quantitative revolution to the “cultural turn”’, Annals of the Association of American Geographers, 91: 546–565. Bathelt, H. and Glückler, J. (2003) ‘Toward a relational economic geography’, Journal of Economic Geography, 3: 117–144. Bathelt, H. and Glücker, J. (2005) ‘Resources in economic geography: from substantive concepts towards a relational perspective’, Environment and Planning A, 37: 1545–1563. Bathelt, H., Malmberg, A. and Maskell, P. (2004) ‘Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation’, Progress in Human Geography, 28: 31–56. Bathelt, H. and Schuldt, N. (2008) ‘Between luminaires and meat grinders: international trade fairs as temporary clusters’, Regional Studies, 42: 853–868. Benner, C. (2003) ‘Learning communities in a learning region: the soft infrastructure of cross-firm learning networks in Silicon Valley’, Environment and Planning A, 35: 1809–1830. Blanc, H. and Sierra, C. (1999) ‘The internationalisation of R&D by multinationals: a trade-off between external and internal proximity’, Cambridge Journal of Economics, 23: 187–206. Boggs, J. S. and Rantisi, N. (2003) ‘The “relational turn” in economic geography’, Journal of Economic Geography, 3: 109–116. Boussebaa, M. (2009) ‘Struggling to organize across national borders: the case of global resource management in professional service firms’, Human Relations, 62: 829–850. Brown, J. S. and Duguid, P. (2000) The social life of information. Boston, MA: Harvard Business School Press. Cohen, W. M. and Levinthal, D. A. (1990) ‘Absorptive capacity: a new perspective on learning and innovation’, Administrative Science Quarterly, 35: 128–152. Cohendet, P. and Llerena, P. (2003) ‘Routines and incentives: the role of communities in the firm’, Industrial and Corporate Change, 12: 271–297. Dicken, P. (2011) Global shift, 6th edition. London: Sage. Emirbayer, M. (1997) ‘Manifesto for a relational sociology’, American Journal of Sociology, 103: 281–317. Ettlinger, N. (2003) ‘Cultural economic geography and a relational and microspace approach to trusts, rationalities, networks and change in collaborative workplaces’, Journal of Economic Geography, 3: 145–171. Faulconbridge, J. R. (2006) ‘Stretching tacit knowledge beyond a local fix? Global spaces of learning in advertising professional service firms’, Journal of Economic Geography, 6: 517–540. Faulconbridge, J. R. (2007a) ‘Exploring the role of professional associations in collective learning in London and New York’s advertising and law professional service firm clusters’, Environment and Planning A, 39: 965–984. Faulconbridge, J. R. (2007b) ‘London and New York’s advertising and law clusters and their networks of learning: relational methodologies and scalar reflections’, Urban Studies, 44: 1635–1656. Faulconbridge, J. R. (2008) ‘Negotiating cultures of work in transnational law firms’, Journal of Economic Geography, 8: 497–517.

Relational geographies of knowledge and innovation

683

Faulconbridge, J. R. (2010) ‘Global architects: learning and innovation through communities and constellations of practice’, Environment and Planning A, 42: 2842–2858. Faulconbridge, J. R. (2012) ‘Economic geographies of power: methodological challenges and interdisciplinary analytical possibilities’, Progress in Human Geography, 36: 734–756. Faulconbridge, J. R. (2017) ‘Communities inside and outside the corporation: control, power and interests’. In G. Baars and A. Spicer (Eds) The corporation: a critical, multi-disciplinary handbook. Cambridge: Cambridge University Press, 457–469. Fox, S. (2000) ‘Communities of practice, Foucault and actor-network theory’, Journal of Management Studies, 37: 853–866. Gertler, M. (2003) ‘Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there)’, Journal of Economic Geography, 3: 75–99. Gertler, M. (2004) Manufacturing culture. Oxford: Oxford University Press. Gertler, M. (2008) ‘Buzz without being there? Communities of practice in context’. In A. Amin and J. Roberts (Eds) Community, economic creativity and organization.Oxford: Oxford University Press, 203–226. Gherardi, S. and Nicolini, D. (2000) ‘To tranfer is to transform: the circulation of safety knowledge’, Organization, 7: 329–348. Gherardi, S. and Nicolini, D. (2006) Organizational knowledge: the texture of workplace learning. Oxford: Blackwell. Giuliani, E. (2017) ‘Industrial clusters in global networks’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 360–371. Goe, W. (1991) ‘The growth of producer services industries: sorting through the externalization debate’, Growth and Change, 22: 118–140. Grabher, G. and Ibert, O. (2017) ‘Knowledge collaboration in virtual communities’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 537–555. Granovetter, M. (1985) ‘Economic action and social structure: the problem of embeddedness’, American Journal of Sociology, 91: 481–510. Henn, S. and Bathelt, H. (2017) ‘Transnational entrepreneurs and global knowledge transfer’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 638–651. Henry, N. and Pinch, S. (2000) ‘Spatialising knowledge: placing the knowledge community of Motor Sport Valley’, Geoforum, 31: 191–208. Jones, A. (2008) ‘Beyond embeddedness: economic practices and the invisible dimensions of transnational business activity’, Progress in Human Geography, 32: 71–88. Jones, A. and Murphy, J. T. (2011) ‘Theorizing practice in economic geography: foundations, challenges and possibilities’, Progress in Human Geography, 35: 366–392. Jones, M. (2009) ‘Phase space: geography, relational thinking, and beyond’, Progress in Human Geography, 33: 487–506. Lagendijk, A. (2017) ‘Innovation, regional development and relationality’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 490–505. Lave, J. and Wenger, E. (1991) Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press. Marshall, A. (1890) Principles of economics London: Macmillan. Martin, R. and Sunley, P. (2003) ‘Deconstructing clusters: chaotic concept or policy panacea’, Journal of Economic Geography, 3: 5–35. Maskell, P. (2014) ‘Accessing remote knowledge – the roles of trade fairs, pipelines, crowdsourcing and listening posts’, Journal of Economic Geography, 14 (5): 883–902. McCann, E. J. (2011) ‘Urban policy mobilities and global circuits of knowledge: toward a research agenda’, Annals of the Association of American Geographers, 101: 107–130. Miles, I. (2001) ‘Services innovation: a reconfiguring of innovation studies’. PREST discussion paper series. ZAvailable from author at https://www.researchgate.net/publication/235407868_Services_Innovation_A_ Reconfiguration_of_Innovation_Studies. Accessed June 13, 2017. Morgan, G. (2001) ‘Transnational communities and business systems’, Global Networks, 1: 113–130. Pinch, S., Henry, N., Jenkins, M. and Stephen, T. (2004) ‘From “industrial districts” to knowledge clusters: a model of knowledge dissemination and competitive advantage in industrial agglomerations’, Journal of Economic Geography, 3: 373–388. Rallet, A. and Torre, A. (2017) ‘Geography of innovation, proximity and beyond’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 421–439.

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Roberts, J. (2017) ‘Community, creativity and innovation’. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (Eds), The Elgar companion to innovation and knowledge creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 342–359. Sparrow, P. (2012) ‘Global knowledge management and international HRM’. In G. Stahl, I. Bjorkman and S.  Morris (Eds) Handbook of research in international human resource management. Cheltenham, Northampton, MA: Edward Elgar Publishing, 117–141. Storper, M. and Christopherson, S. (1987) ‘Flexible specialization and regional industrial agglomerations: the case of the US motion picture industry’, Annals of the Association of American Geographers, 77: 104–117. Storper, M. and Venables, A. J. (2004) ‘Buzz: face-to-face contact and the urban economy’, Journal of Economic Geography, 4: 351–370. Uzzi, B. and Lancaster, R. (2003) ‘Relational embeddedness and learning: the case of bank loan managers and their clients’, Management Science, 49: 383–399. Vallance, P. (2011) ‘Relational and dialectical spaces of knowing: knowledge, practice, and work in economic geography’, Environment and Planning A, 43: 1098–1117. Wenger, E. (1998) Communities of practice: learning meaning and identity. Cambridge: Cambridge University Press. Wood, S. and Reynolds, J. (2012) ‘Managing communities and managing knowledge: strategic decision making and store network investment within retail multinationals’, Journal of Economic Geography, 12: 539–565. Yeung, H. W.-C. (2003) ‘Practicing new economic geographies: a methodological examination’, Annals of the Association of American Geographers, 93: 442–462. Yeung, H. W.-C. (2005) ‘Organizational space: a new frontier in international business strategy?’, Critical Perspectives on International Business, 1: 219–240.

42. Innovation, governance and place Maryann Feldman and Nichola Lowe

INTRODUCTION Policy makers and planners seek place-specific advantage to capture the benefits of innovative industries. The conventional wisdom argues that a favorable business climate is needed to secure future economic prosperity. However, often a favorable business climate is construed as low taxes, a docile labor force and lax regulation. The results from this strategy have often been disappointing as when places compete on these dimensions they continue to try to outbid one another, leading to a reduced tax base, declining wages, deteriorating work conditions and environmental degradation – a race to the bottom rather than an economic development strategy. Moreover, this is counterproductive as technology-intensive industries rely on amenities, and an engaged, skilled and creative workforce and high quality of life. Yet, there is little work that considers the effect of regulation or the larger issue of how to create the conditions conducive to establishing an appropriate business climate that provides an increased likelihood of safety guarantees for citizens. In this chapter, we draw on prior work (Feldman and Lowe 2008 and 2011; Lowe and Feldman 2008) which examines the role of governance in creating place-specific advantages. We define governance as the interactive process of building consensus to solve a collective problem. Thus, governance creates social norms and institutions that may responsibly advance place-specific advantage. Through this process, conditions are created that generate interest and expertise around a local industry, putting the “secrets” of the industry into the air of public conversation (see also Bradford and Wolfe, Chapter 44, this volume). While most innovation is incremental, the most interesting type of innovation, which holds the greatest promise for the development of new industries, is breakthrough innovation that creates something new, previously unrealized and risky. When new technologies are first discovered, it is not clear whether they even have any potential to reach commercial value, much less become a platform for the development of a new industry. Only a few people – insiders familiar with the technology, who realize the limitations of an existing technology – may recognize the importance of a new idea. Taking innovation forward requires a process of building consensus about the technology’s potential, how it may be used and how to move from simply having an idea to realizing the idea’s potential as a new industry. Moreover, these activities are not the purview of the lone inventor but require the involvement of larger communities and the building of institutions that come together to overlap, intersect and interact as an enabling ecosystem. These elements are the essence of governance. Conventional wisdom advocates that scientific resources and know-how matter for place-specific industrial development, that innovative activities in a region are selforganizing and that ecosystems create the conditions for innovation. We argue that ecosystems are themselves the result of a temporal governance process that reflects the social 685

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dynamics within a place. Rather than purely being a technological issue, innovation is socially defined and constructed. This implies that innovation may not only shape a place but rather is shaped by the conversations within a place. To be successfully anchored in a location requires building a community of practice around the idea, agreement on the use of critical resources and institutions that enable collective problem solving. Vibrant regional economies are not simply the result of luck or randomness: our thesis is that regional advantage is socially constructed via public conversation and adaptive governance processes over time. Engagement with a technology generates conversational spaces that guide and promote the technologies’ future development. Clusters emerge when these conversations are local and engaged. Over time a shared narrative emerges that promotes what has been termed “buzz” (Bathelt et al. 2004). In this chapter, we draw on our prior work to synthesize the role of local improvisation and governance. The first case we examine is the Cambridge biosafety ordinance (Feldman and Lowe 2008). The active debate and strong public engagement created a conversational space that was important to increasing local understanding of the industry. This example is a good contrast to other biosafety ordinances adopted in other places in the United States, including the City of Berkeley (Lowe and Feldman 2008). The evidence suggests that simply adopting an ordinance, as in the case of Berkeley, is not sufficient to assist industrial development; municipalities that engage the public, including critics, during the policy development process are more likely to succeed in passing a locally supported and well-understood policy. Our final case examines organizational design and considers the implementation of two initiatives adopted in the same place at roughly the same time. This case considers how organizational design and policy implementation affect the ability of organizations to effectively guide long-term economic development plans (Feldman and Lowe 2011).

CAMBRIDGE CASE: AN UNLIKELY ADVANTAGE FOR THE BIOTECH INDUSTRY Cambridge, Massachusetts, has the largest concentration of biotechnology firms in the world. This is rather ironic since during the late 1970s there was great community resistance to the biotechnology industry. While private firms reap the rewards of new and emerging technologies in the biotechnology field, the local communities where experimental research activities occur are exposed to various risks associated with such research. In an effort to ensure public safety, Cambridge instituted the earliest – and one of the most onerous – biosafety ordinances. One could easily assume the Cambridge ordinance was planned and easily implemented. Yet, as detailed in Feldman and Lowe (2008), and retold here, the ordinance was instead the result of a heated and contentious public debate that balanced concerns over public safety with the need for scientific progress. Background of Controversy over Recombinant DNA Research The modern commercial biotechnology or life sciences industry emerged when scientific discoveries were made in “recombinant DNA” (rDNA), which was also referred to as “genetic engineering”. These early terms that described what is now known as

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biotechnology, conjured up a sinister and technical image that further provoked the public’s concern about the safety of those living near scientific laboratories conducting such research. The early geographic epicenter of research and commercial activity was near the San Francisco Bay area (Feldman and Yoon 2012). However, Cambridge and the larger Boston area was one of the first places where the research diffused geographically (Feldman et al. 2015). As genetic engineering research became more prevalent in the early 1970s, scientists and the public grew concerned about the potential health and safety risks of these new, unproven technologies. In particular, there was concern about the repercussions of accidental release of, or exposure to, rDNA. Professor Paul Berg and graduate student Janet Mertz, both from Stanford University, were at the forefront of genetic engineering research. Presentations on their research caused scientists in the field to become concerned about the risk of such research since they believed this was radically different from existing technologies. In response to these concerns, Berg organized two conferences (the “Asilomar conferences” in 1973 and 1975) which provided a space to discuss the concerns about, and risks of, this biomedical research. Additionally, Berg submitted a public statement (referred to as “the Berg letter”) to the National Academy of Sciences which was later published in the journals Nature (19 July 1974) and Science (26 July 1974). The statement asked scientists to defer on specific types of biomedical experiments until the potential hazards were better understood (Berg et al. 1974). During the second conference a panel of expert scientists advised the National Institutes of Health (NIH) as they worked to develop federal guidelines for rDNA research. These guidelines were issued in June of 1976 and covered the activities of all research laboratories funded by NIH (Frederickson 1976). Developing the First Municipal-Level Biosafety Ordinance In 1976, Harvard University wanted to retrofit a laboratory to meet biosafety level 3 standards in order to support rDNA research. The lab was located in the City of Cambridge, which had jurisdiction over approval of building permits. This rather mundane administrative function created the opportunity for local government to initiate a conversational space for public debate. The local community held a common interest in this laboratory and the research conducted within it since any adverse outcomes had the potential to negatively impact those within close geographic proximity. When the new facility was announced at an internal meeting on 14 April 1976, Harvard biologists raised several concerns about safety. After all, the scientists and their families lived near the university. These responses caused Harvard’s Committee on Research Policy to hold a university-wide meeting on 28 May 1976. Harvard faculty member Ruth Hubbard Wald expanded the discussion to bring in Cambridge City councilperson Barbara Ackerman. Reporters also attended the meeting and later published an article in the Boston Phoenix titled, “Biohazards at Harvard: Scientists will create new life forms – but how safe will they be?”, which helped attract additional public attention to the issue, including the attention of Cambridge Mayor Al Vellucci. It is also worth noting that at the time of this building permit application, the thriller film Andromeda Strain was popular and further sparked the community’s concern about the potential adverse effects of genetic engineering and cloning.

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At the insistence of Mayor Vellucci, the Cambridge City Council voted unanimously to hold several public hearings to gather citizens’ opinions on the laboratory and research, and to provide an opportunity for the local community to better understand Harvard’s research plans. There were serious concerns about potential environmental and health issues, particularly because of the recent attention to this issue at the national level as the NIH worked to develop federal guidelines for such research. This was the first instance of concern about a local facility. Scientists at the university, as residents of the City of Cambridge, were equally concerned as they too did not want to be exposed to health or safety risks. After two public hearings in which the university presented its research plans, the city council passed a resolution to establish the Cambridge Experimentation Review Board (CERB), which was charged with providing recommendations to the city council. The CERB was composed of nine members who were Cambridge residents intentionally selected to represent diverse interests in the community and who did not have backgrounds related to biotechnology. The board approved by the city council consisted of a nun, a nurse, a community activist, an engineer, a physician, two former city councilors and a professor of environmental policy and planning. Through over a hundred hours of meetings and reviews, members of the board learned about the technology, both its scientific potential as well as the need for high-quality laboratory procedures and public safeguards. Before the CERB’s first meeting, Cambridge held a public science fair in Harvard Yard in the summer of 1976 to improve citizens’ understanding of molecular biology. Similar to the well-known model of a high school science fair, researchers had posters and props. They presented their research and discussed their work with citizens. Though critics initially mocked the event as reducing Cambridge scientists to “peddlers selling their wares in the street trying to get people to buy their project – DNA” (Vellucci 1977), participating scientists themselves enjoyed this unique opportunity to speak openly with the public about their work. One of the CERB’s first actions was to request a three-month moratorium on rDNA research to deliberate the issue, though they later requested an additional three-month extension to the moratorium. As a result, all rDNA research in Cambridge was stopped for a total six months – a period that created great anxiety for Harvard faculty members worried about their standing in the research community. During the review period, CERB actively spent over a hundred hours meeting and interviewing research faculty members – both opponents and proponents of this emerging technology. They also solicited advice from scientists at the NIH and engaged in developing rDNA safety guidelines. An open public scientific debate by CERB was held in November 1976 that involved two Nobel Prize winners in biology who presented opposing views on this topic. This five-hour long debate carried out “a type of mock courtroom affair” with advocates from both sides of the controversy arguing their views against one another (CERB 1976/1977, pp. 10–11). CERB members were able to hear responses to some of the most important questions on biotechnology from expert scientists on both sides of the issue. On 5 January 1977, CERB presented the final recommendations to the city council. Based on CERB’s recommendations, the Cambridge City Council agreed on enacting the nation’s first municipal-level biosafety ordinance on 7 February 1977. In spite of the fact that the ordinance still banned biosafety level 4, any rDNA research at or below biosafety

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level 3 was permitted after the passage of the ordinance. In addition to this, any research carried out within the Cambridge city limit would be under local regulatory oversight. To do research involving rDNA, the ordinance required scientists to follow guidelines, including scientists’ submission of an application for external review to five members of the Cambridge Biohazards Committee (CBC); participation in a public hearing; agreeing to regular site inspections conducted by local public health officials; and, if necessary, finishing a pre-approved biosafety training course. Failure to comply with these guidelines could result in the loss of permission for carrying out rDNA research. In some cases, a laboratory could be shut down. Some Harvard faculty initially regarded the proposed ordinance as a potential barrier to their research. However, once it was well established it became a positive signal to many research scientists, entrepreneurs and investors who wished to conduct or support rDNA research and commercialization. Biogen, the first biotechnology company to locate in Cambridge, cited the ordinance as a reason Cambridge was their first choice for their research center (Biogen 1980). Since Cambridge had already addressed the citizens’ concern about biosafety and reached a solution that appeased those on both sides of the issue, companies like Biogen believed the ordinance could play a role in mitigating social conflicts among citizens and scientists, and in reducing any potential risks from research. The city’s ordinance clearly outlined a biosafety research regulatory process that demonstrated its “more mature understanding of the field” (Lipson 2003), which appealed to Biogen’s financiers who were greatly interested in avoiding negative publicity in response to this new field of biotechnology. Through the open and transparent process of enacting the ordinance, citizens gained a better understanding of and appreciation for rDNA research. In turn, this increased public knowledge may have helped make it easier to induce citizens to engage in activities related to rDNA research, such as pretesting of new products or capability building for research and innovation. As research was actively conducted, and entrepreneurs were engaged in finding a new source of communalization, the city could become more innovative. Innovation can occur anywhere, but it is these social processes that play a key role in helping a community take advantage of and anchor innovative activities. While CERB was disbanded after the two-year process, it continued to shape policy implementation as members were invited to join national-level discussions around biosafety initiated by the NIH. Equally, the CERB model of citizen engagement informed the permitting process in Cambridge which required bioscience firms to have at least one non-scientific Cambridge resident on their internal biosafety review board.

HOW IDEAS GET INTO THE AIR: CONVERSATIONS ABOUT NEW TECHNOLOGIES In the nascent stage, technology is often uncertain, so there is considerable risk in terms of whether the technology will create new business opportunities. Of course, entrepreneurs can play a critical role in discovering technology and contributing to building regional capabilities (Feldman 2001). However, our argument is that as more individuals – including entrepreneurs, social agents, government and everyday citizens – understand technology and become involved in the process of building capabilities, there will be

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greater potential for success in the community. Through a vibrant participatory process, a common language can be developed, so that the technology and its applications can be better described and better understood. In addition, based on shared technology terminology, the diverse actors can reinterpret existing information and more easily explain tacit knowledge (Gertler 2004; Sabel 2001). Furthermore, through public discussion, individuals in the community will be able to learn about opportunities for employment and potential investment. A conversational space can be a starting point for building consensus and community capability around a technology and innovation (Lester and Piore 2004). By creating shared conversational space, research becomes discussed more widely – creating a type of Marshallian buzz as “the secrets of the industry were in the air”. It may be easily assumed that shared conversational space occurs among people who are within the scientific or engineering profession. However, shared conversational space may be open to anyone in the community. Non-technologists can learn more about the technology if they find that there is a gap they can help fill, or if they realize the technology could be useful for their business. Political actors or non-governmental organizations (NGOs) can join the conversation as either technology advocates or opponents. By participating in the conversation, these people will become a part of a shared community, closely linking with one another because of common interests and, in turn, playing a pivotal role in the community and industrial development. It is important to note, however, that participation in this conversation is not a magical solution for eliminating all social conflict, in particular in the case of technology related to environmental or public safety issues. While new technology can offer a great opportunity for industrial development and innovation in a given region, it can also create safety and security issues. To mitigate these potential adverse outcomes, government often uses regulation, which is typically believed to cause unfavorable business climates. Rather than creating a barrier to potential business owners, however, our argument is that an effective regulatory process actually induces more people to participate in the conversational space. A participatory process can help increase understanding about the technology – including its benefits and economic potential – as well as provide greater belief in the community’s risk mitigation. New technologies often cause environmental, health, and safety issues that can immediately affect the communities where the research is conducted. These risks attract public attention and create pressure for regulation. Traditional views presume that local regulation negatively affects regional industrial development, arguing that business-friendly climates can be created through minimal regulation, limited public oversight and low taxes. Unlike the traditional view, however, our alternative view is that local regulation can in fact bring about outcomes that are socially and economically desirable for a local community by allowing for publicly open debate and discussion, sharing information among networks of involved individuals, and thus promoting understanding of the significance of a new technology. Jurisdictions that compromise public safety, or public preferences more generally, in response to immediate pressure to create jobs, lose the opportunity for public discussion and debate that educates the public, provides an opportunity for compromise and creates democratic consensus. Moreover, local regulation may help establish industry standards, reducing risks and thus making the location attractive to potential investors, entrepreneurs and venture capitalists. During the process of public discussion on regulating local industry, citizens and local

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officials are made aware of what is required to build the capabilities that support that industry. Thus, the process of regulating industry may create conversations that have the unintended consequence of increasing understanding about how to foster a favorable business climate for these technologies. Rather than deterring entrepreneurs and venture capitalists from establishing their firms and investing in the location, the public conversation provides an opportunity to share community identity that enables the location to take off through cluster development. The Upside of Regulation The conventional wisdom is that regulations tend to restrict the behavior of economic actors (North 1984). For instance, environmental standards, workplace safety rules and medical testing requirements can be widely regarded as distortions that “limit the operation of markets” (Macgregor et al. 2000, p. 2). Therefore, regulations should be limited so that market distortions may be mitigated. On the other hand, some institutional economists have challenged the traditional view, arguing that regulation does not have to be seen unfavorably. For instance, the assignment of property rights facilitates market transactions and bolsters economies (Chang and Evans 2005; de Soto 2000; Hodgson, 2005). In a nascent stage where technology development is uncertain and thus risky, regulation can improve legal clarity about liability for adverse events. For instance, interventionist regulations such as medical testing, research protocol and environmental and safety standards can mitigate consumer fears and concerns in terms of products and public safety issues around the area, thus boosting market expansion. Regulations can also catch the attention of entrepreneurs, potentially providing new business opportunities. When companies decide to develop new technologies, one of their overriding concerns is about legitimacy issues as well as investment returns. Since local and state regulators tend to lack expertise in science and engine engineering, it is not always easy to effectively deal with companies’ concerns. For this reason, many scholars have advocated national self-regulation based on peer-review processes (Miller and Conko 2000; Wright 2001). However, this view of national self-regulation may not clearly handle conflicts between scientific experts and a community. To be specific, this view ignores the importance of mediating steps that may be essential to satisfying the needs of technologists and increasing scientific accountability, which a community strongly demands. Furthermore, residents within a community may not have enough of a chance to become involved in the technology development process, and are thus little aware of the potential for innovation; as a result, a top-down approach may increase conflict. In other words, the community may have a great opportunity for strengthening technological innovation and regional development once the gap of conflicts between scientific experts and the local community is bridged. Legal scholars have focused on the relationship between economic actors and regulation. They are especially interested in “law-in-action”, and in particular “extralegal social processes [that] continuously construct and reconstitute the meaning and impact of legal norms” (Suchman and Edelman 1996, p. 907), while many scholars have found regulations to be one of the means of restricting behavior of economic actors. Unlike the traditional view of regulation, the socio-legal approach is focused on the social and political processes

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that greatly influence agents’ response to formal legal rules and structures (Suchman and Edelman 1996). In this regard, the primary thing is not the strength and enforceability of regulatory rules but the degree to which extralegal social processes draw community consensus (Rodríguez-Pose and Storper 2006). Therefore, community consensus and responses to regulations greatly depend on how these processes move forward at a given location and time. Regulations can also play a critical role in building trust and setting up moral order within current or emerging markets in a community. Public and private entities are likely to arrive at consensus regulations when actively engaged in expressing their interests and concerns. In addition, this process enables the remediation of social tension and conflict due to businesses’ self-regulation, in turn inducing non-market actors to become involved in technological development and innovation. This simple participatory democracy, with all of its transparency and accountability, enables society to make good decisions.

IT’S NOT SIMPLY REGULATION: PROCESS TOWARDS CONSENSUS MATTERS The wrong lesson to take from this case is that local regulation, in and of itself, facilitates industrial development. Local industry and startups may benefit if regulations turn uncertainty stemming from emerging technology into calculable risk by providing industry standards. Yet, it is the process of debating and designing regulation that enables a better understanding of the potential of the technology. From these conversations, new opportunities for employment, investments, or new startups emerge that can play a critical role in regional development. In this way, the activities of the industry can become more generally discussed and understood by local citizens, rather than only being understood by a small group of highly involved experts. In 1976, city councilors in Cambridge, Massachusetts, and Berkeley, California, both faced citizens’ concerns about the environmental and health risks of rDNA research. City Council members in Berkeley quickly adopted Cambridge’s lead and enacted identical ordinances in late 1977. However, the act of simply copying the regulation did not yield the expected result. It became clear that what was most important to the success of the regulation was the process of community engagement during the establishment of the regulation, and not just the regulation itself. Like Cambridge, Berkeley also had great potential for biotechnology innovation and new firm formation. Berkeley had a top-notch research university, and qualified scientists, and moreover had ready geographic proximity to Silicon Valley venture capital. But local government failed to engage the public and change perception when adopting the biosafety regulation. In 1982, Leon Wofsy, an immunologist at the University of California at Berkeley, noted in a public lecture, “there has been a striking lack of discussion on the Berkeley campus about the new world in which biology finds itself ” (Wade 1984, p. 20). While many startups and existing firms in Cambridge had embraced the city’s ordinance and used it to justify their location decisions, the lack of startup activity in Berkeley suggests that entrepreneurs were discouraged by the regulation. Cetus Corporation, founded in Berkeley in 1971 by Ronald E. Cape, Peter Farley and Nobel Laureate Donald A. Glaser, relocated its production facilities to nearby Emeryville, which

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did not have an ordinance. Other biotechnology firms located scattershot throughout the region, without a specific jurisdictional concentration (Feldman and Yoon 2012). One could easily assume that these distinct differences in reactions to the regulation may have stemmed from differing levels of industrial support, or resources, such as financing, technological know-how and/or qualified labor. By the mid-1970s, however, the universities in both Berkeley and Cambridge shared similar biotechnology-related research strengths, and also provided crucial sources of local talent to contribute to the biotechnology industry’s pioneers (Jong 2006; Vettel 2006). Additionally, University of California, Berkeley provided high-quality labor. Well-established venture capital markets were near both Cambridge and Berkeley and eager to support research and any biotechnology startups (Owen-Smith and Powell 2006). Given this evidence, Berkeley was also a very attractive location for biotechnology research. In a comparative study, Lowe and Feldman (2008) propose three key differences between Cambridge and Berkeley. First, institutionalizing a set of procedures and regulation with diverse participants may need time. Cambridge benefitted from a four-year lag time between the implementation of the rDNA research ordinance in 1977 and the local establishment of Biogen, the city’s first biotech firm, which relocated from Europe. Berkeley, however, did not benefit from such a grace period. When Biogen was searching for a commercial research site in the United States in 1980, they found Cambridge. They noted that Cambridge was the first choice because of the proximity to Harvard and MIT. They also cited the city’s ordinance as a reason for selecting Cambridge over neighboring municipalities, stating: We are also attracted by the fact that the City (of Cambridge), as a result of the initial work of the Cambridge Biohazards Committee, has made the political and scientific decision to permit the use of rDNA techniques within the framework of the City’s Ordinance and the NIH Guidelines; the City, through the Committee, has had approximately four years of experience in monitoring such activities; and the City appears receptive to Biogen Inc. (Biogen 1980)

When Biogen asked to establish an rDNA research and development facilities in the City of Cambridge in December 1980, the city reconvened the CERB to advise them in response to Biogen’s request. According to the recommendation of the CERB, the city council approved the extension of the original biosafety ordinance to permit large-scale production processes and commercial uses of rDNA (Lipson 2003). Unlike the Cambridge case, Cetus (a medical diagnostics firm originally based in Berkeley) began taking steps to diversify into biotechnology before the City of Berkeley had started discussions to develop a local biosafety regulation. In fact, it was Cetus’ initial request to establish an rDNA laboratory facility in 1977 in the City of Berkeley that first caused city officials to begin to think about adopting a policy to regulate biotechnology research (Krimsky et al. 1982). This uncertainty about what the policy would look like and how it would be implemented may have contributed to Cetus’ negative response. Second, the governance difference from legal lines of authority that have long patterned town–gown relations may have played a critical role. The University of California, Berkeley, as a public university, remains exempt from most locally enforced zoning and environmental laws. This pattern, in which public universities are accountable to state agencies and a state-appointed board of regents, removes them from local oversight. Ironically these laws may contribute to town–gown conflicts as universities are able to

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bypass local government, and are not required to engage with local communities on issues that may require local regulation for non-public entities. In Cambridge, however, Harvard and MIT – as private institutions – had to deal directly with local authorities and obtain their permission for all of the following: proposed research activities, workplace practices and laboratory siting. This governance required local officials, faculties with rDNA, and even citizens to engage in – and maintain – an ongoing dialogue about biosafety. This dialogue, in turn, encouraged most participants (including both university researchers and local citizens) to better understand the importance of the ordinance and the impact of the ordinance on university research and the city. Additionally, citizens became better educated about the biotechnology research and better understood the perspectives and needs of the scientists. In other words, these events contributed to a “shared conversational space” that helped people from different backgrounds – citizens, scientists, existing firms and startups – overcome initial conflicts of the regulation (Lester and Piore 2004, p. 51). Finally, during this process in Cambridge, conversational coordination by those enforcing the regulation helped mitigate conflicts. A very important difference between Berkeley and Cambridge is the fact that the City of Cambridge hired biosafety officers to help uphold this regulation and act as technology translators between the scientists and the public. These officers were not, however, only acting in the capacity of technology translators. During the process of technology translation, officers both educated the citizens about the complex emerging technologies, and communicated the concerns of citizens back to the university scientists in a clear yet professional manner. This coordination further contributed to the transparency of university research, and provided a mechanism for public concerns to be effectively relayed back to university scientists. In 1977, Harvard and MIT were the first universities in the United States to hire biosafety officers. In addition to the responsibilities described above, the officers were responsible for educating bench scientists on federal and local biosafety procedures. Also, the officers represented Harvard and MIT at monthly meetings of the CBC and answered questions about university safety and reporting procedures. MIT’s first biosafety officer helped bioscience faculty write and edit research grants to make sure there was “no hidden bio hazardous components in the project” (Minutes of the CBC, 26 September 1977). Ultimately his work focused on eliminating potential conflicts with the local regulatory processes and rising scientific awareness of public safety and concerns. Interestingly, the City of Berkeley and the University of California, Berkeley, had a similar mediation process in the early days of the biotech industry. In 1978, the City of Berkeley hired an environmental scientist to help biological research firms and inspect private rDNA facilities. Due to budgetary constraints, the position was subsequently eliminated. Even though the individual was subsequently hired by the University of California, Berkeley, to continue work in biosafety and to assist with implementation of federal-level biosafety guidelines, there was no formal requirement to engage with either city officials or local industry. Thus university officers were unable to maintain a technology governance conversation. It is important to note that scientists and local entrepreneurs viewed these government interventions in dramatically different ways in Berkeley and Cambridge. While the East Bay biotechnology community perceived adoption of the ordinance as proof of the city’s “technology ignorance” and “political arrogance”, local entities in Cambridge recognized that the ordinance would play a key role in reducing environmental risks and uncertainty,

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and ultimately as a source of regional development that could help bring about innovation in the community. Early support for the regulation in Cambridge by the first biotechnology startup, Biogen, had a great and positive effect on the location decision of local entities. As a result, a sizeable number of biotechnology firms were established by the mid-1980s, and Cambridge became one of the United States’ most vibrant and innovative life sciences regions. Today the City of Cambridge has earned a reputation as the home to many biotechnology firms including industry giants Biogen, Genzyme and Vertex. The outcomes in Berkeley unfortunately were very different from those in Cambridge. Cetus Cooperation (one of the first biotechnology firms, originally located in Berkeley), chose to relocate its rDNA facilities to Emeryville (just outside the purview of the Berkeley ordinance) in response to the City of Berkeley’s biosafety regulation. This decision greatly influenced the perceptions of other biotechnology firms, which opted to locate their research facilities to less regulated nearby municipalities. To our knowledge, no other biotechnology firms have ever located in the City of Berkeley. As these two cases illustrate, the ways in which existing firms and startups (that can play key roles in regional development and, in turn, innovation) embrace government intervention can make a huge difference. In other words, local governments should strongly consider how to shift any negative perceptions of proposed regulation during the process of establishing and adopting that regulation. It is important for local businesses to begin to understand the potential benefits of proposed regulations to their businesses and, more broadly, to their industry within the local community. If local governments are unsuccessful in shifting cynical views of regulation through publicly open debates, they may lose not only potential startups and existing firms (to relocation), but also the potential for innovation in their community and the resulting advantages of such innovation to their local economy.

ORGANIZATIONAL DESIGN MATTERS A frequent industrial development strategy is to establish a dedicated organization as a vehicle for technology-based economic development. Often with colorful names like Pennsylvania’ Ben Franklin Technology Partners, Ohio’s Third Frontier or Connecticut Innovations, these organizations have a specific mission of developing technology clusters (Feldman and Lowe 2008). These organizations are frequently quasi-public entities, which are a hybrid organizational type that maintains some of the functions of government, yet have greater flexibility than state agencies. These organizations focus on some specific targeted activities such as helping startups or developing a new industry in a region. One advantage is that these public organizations are less likely to be affected by the vagaries of political election cycles, so they may be better suited for adapting policies over a longer time horizon. This characteristic of public organizations is especially important since rapidly changing technology areas may require greater policy patience and improvisation. From our perspective these organizations can perpetuate and maintain a shared conversational space. But towards this end, it is not enough to simply have a dedicated industrial development organization – the design and incentives matter greatly. This section derives from the case study of two of the earliest quasi-public organizations: the North Carolina Biotechnology Center and Microelectronics Center of North

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Carolina (MCNC) (Feldman and Lowe 2011). These organizations were established by the same legislative mandate, in the same jurisdiction, at the same time, but with radically divergent results. While the North Carolina Biotechnology Center flourished, and continues to provide effective support to biotechnology research and partnering in the state, the MCNC, despite many important outcomes, was disbanded, creating a void in representation of this industry in the state of North Carolina. Establishing these two entities had a theoretical grounding in balanced growth theory, which claimed economic development was the outcome of simultaneous investments into more than one economic sector, in this case both microelectronics and biotechnology. The North Carolina State Governor of the time, Jim Hunt, was interested in developing policy to support innovation in science and technology. Hunt believed such research and innovation could improve economic development in the state. In pursuit of these goals, Hunt elevated the pre-existing North Carolina Board of Science and Technology (established by legislative act in 1963) from a committee within the Department of Commerce to a cabinet-level function. Hunt also hired Quentin Lindsey (a trusted advisor to, and former professor of, Hunt) to help design new science-based economic development policies, and to direct the North Carolina Board of Science and Technology. A 1980 report by the board, known as the “Lindsay Report”, advocated for concurrent sector-specific policies, recognizing that these initiatives should be calibrated and coordinated: “A major component of ‘Balanced Growth Policy’ in North Carolina is the provision of more and better jobs through . . . high technology industry requiring highly skilled workers and paying high wages” (North Carolina Board of Science and Technology 1980, pp. 16–19). The goal of this work was to create a vehicle that supported the development and deepening of scientific infrastructure in the state with the ultimate objective being high-wage employment opportunities for the state’s residents. In the early 1980s, microelectronics was an established and fast-growing technology industry while the prospects for biotechnology were less certain. Microelectronics firms were expanding and seeking new locations; there was very strong national support for this field due, in part, to concerns about increasing competition in the technology field from Japan. There was strong support for microelectronics research creating opportunities for entrepreneurs; additionally, these microelectronic firms were able to provide high-wage jobs. As stated by one technology expert, microelectronics were perceived as the “highest of high technology because the underlying technology is changing more rapidly than in other high-technology industries” (Rodgers 1986). For these reasons, microelectronics seemed to be a fairly strong and secure field in which to invest, while pursing technologybased economic development. In contrast, there were only a handful of dedicated biotechnology establishments in the United States (e.g., Genentech, Biogen, Amgen, Cetus). These were small companies that were not in a position to move away from their university-anchored research bases in Northern California and Cambridge, Massachusetts. In 1980, only one firm, Burroughs Wellcome, was conducting biotechnology research in the state of North Carolina. Additionally, the local universities did not have faculty working on these topics. The Feldman and Lowe (2011) case study reveals that organizations should institutionalize deliberative and reflective processes that help easily guide ongoing modifications in technology development, and identify effective problem-solving tactics during challenging circumstances. The process for establishing the MCNC was expedited (taking only

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18 months) because of interest from General Electric (GE) in setting up a $100 million semiconductor research and development facility in the state, which was expected to create approximately 500 high-paying jobs within its first five years. When negotiating with Governor Hunt, GE asked that North Carolina leaders support silicon-related research at the state’s universities so as to attract additional highly qualified engineers and technicians. Governor Hunt emphatically believed in the economic growth potential of establishing the Center, stating, “the microelectronics industry may be North Carolina’s only chance to make a dramatic improvement in the state’s wage and income rankings in the nation . . . I believe that microelectronics is our chance, perhaps the only chance that will come along in our lifetime . . .We must seize the moment” (Governor’s Office 1980). As a result, MCNC received $24.4 million of public funding and opened with considerable fanfare and media coverage. There were great expectations for what it would be able to accomplish with the significant resources provided. The North Carolina Biotechnology Center was more controversial and faced greater resistance and greater public scrutiny, and received only $843,000 (a mere 3.5 percent of that received by MCNC) of startup money from the state. Yet the unexpected outcome was that this greater scrutiny created a need to engage in public conversations and to create consensus about the organization’s objectives. This engagement with the public and consensus building greatly contributed to the later success of the North Carolina Biotechnology Center. The Center’s mission was “creating economic development through the support of biotechnology research”. Their catalytic role was clear from the start. They engaged in a process of building goodwill with all the stakeholders: the public, the legislature and the local universities. This process was slow-moving and deliberate, such that the North Carolina Biotechnology Center did not open until three years after MCNC (despite their both being initiated at the same time). This lag time allowed the North Carolina Biotechnology Center to observe the beginnings of MCNC and learn from some of the challenges MCNC faced. The differences between the board memberships of the two organizations suggest that a larger board with active board members who have diverse backgrounds and express diverse opinions, while more difficult to manage and coordinate, is important to a successful and effective conversational space and creating consensus. In other words, banal details like the composition of an organizational board could be a key factor for success in supporting technology development that contributes to a strong innovation infrastructure and helps generate regional wealth. By contrast, MCNC had a small board of 16 members with 11 voting members. Moreover, the governor appointed 7 of the 11 voting members. The other four members served as chancellors, representing both public and private universities in the state. A problem was that the governor’s appointments lacked diversity of expertise and interest while the choice of chancellors did not generate active board members who had adequate time and expertise in microelectronics. The result was an organizational board that looked great on paper but was merely window dressing, with little active engagement and little controversy. On the other hand, the North Carolina Biotechnology Center started with 32 members and ultimately grew to 36 members, with every industrial sector, geographic region and constituency represented. While this move might, at first, seem to make the board more difficult to manage, the composition of this board (from a range of industries, including animal husbandry, plant agriculture, the marines trade, pharmaceuticals, medicine and

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the local universities) guaranteed broad representation and diverse opinions. Unlike the MCNC board, the authority to appoint members to the North Carolina Biotechnology Center’s board was equally shared among the state governor, the lieutenant governor and the speaker of the state house of representatives. As a result of this diverse composition, board meetings were vibrant. Topics were debated and decisions were postponed until a clear consensus had been reached. The two organizations also had very different strategies for engaging the general public. Executives from the North Carolina Biotechnology Center maintained a strong commitment to citizen engagement and outreach, with the goals of both being transparent, and making the technology accessible and easy for the general public to understand. The North Carolina Biotechnology Center’s third paid employee was an educational expert who held a liberal arts degree. His primary responsibility was to translate biotechnology into accessible language for non-experts. Because of this focus on transparency, annual reports and public communications from the Center were broadly accessible and very informative. By contrast, communications from the more insular and academic MCNC provided dense descriptions of microelectronics research activities that were incomprehensible to all but the most informed experts. MCNC’s annual reports were not designed to educate the public or to be used to build broad-based public support. Rather, they tended to feature the scientific research activities and publication record of affiliated MCNC scientists. This outcome is perhaps best captured by a quote from a state legislator at a public hearing: “I understand wood chips,” a direct reference to the North Carolina Biotechnology Center’s discussion of applying technology to the state’s forestry industry, “but I don’t get microchips” (Holt Anderson interview, July 2010 and Steven Burke interview, March 2010). Putting this history together, we begin to see the influence of organizational design on each organization’s problem-solving abilities. Both centers would face a series of organizational crises and challenges. The North Carolina Biotechnology Center better weathered challenges, with the quick identification of potential sources of conflict and adaptive responses. Deliberative and reflective processes had become institutionalized and resulted in effective problem solving and resilience. The North Carolina Biotechnology Center built up considerable good will and was able to call on numerous allies representing broad segments of society to defend their continued existence and ongoing state support. By contrast, executives at MCNC tended to ignore or dismiss signs of crisis, and continued to alienate important institutional allies. Ultimately MCNC failed to make their substantial technology contribution clear and well understood by the general public, particularly the elected state representatives. The lack of public understanding about MCNC was perhaps more problematic given the large investment of $24.4 million that the state provided at the outset of the Center, and the large annual operating budget that became an easy target for adversaries. It is not surprising then, that with a deepening financial and organizational crisis at hand, the state government decided in 1995 to end its financial commitment to MCNC. Unable to sustain itself, MCNC was forced to sell off most of its assets – including its research facilities and equipment – and significantly trim down its staff. While the hollowing out of MCNC in the mid-1990s did ultimately benefit the state by releasing top-quality researchers into the local labor market, a talent pool that was quickly absorbed by industry and by universities

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in North Carolina, it still represented a major policy loss for the state. As a shadow of its former self, MCNC is now a privately funded non-profit that specializes in rural broadband. MCNC now has limited economic and industrial influence in North Carolina, and functions as little more than a specialized utility.

REFLECTIVE CONCLUSIONS: REGIONAL CONVERSATION SPACE AND IMPROVISATION It is commonly accepted that regional innovative activities are self-organizing and develop through the actions of institutions and entrepreneurs. The role of government, as a vehicle for collective action, or governance as a process for collective action, is less often examined. There is no shortage of advice for policy makers about how to engage in technology-based economic development; however, most of this advice is at odds with the premise of self-organization and adaptability that underlies cluster development. Perhaps the lesson from these examples presented here, and the best advice for policy makers, is to allow public discussion to become educated about the industry, while following a process that is inclusive, transparent and open to criticism and debate. There is a need to appreciate the local context and concerns. In the process of accommodating the concerns of the local community, unintended consequences create local advantage. Additionally, rather than a linear and predictable process, engagement with an emerging technology requires continuous adaptation. From the case studies of Cambridge (Feldman and Lowe 2008), and its comparison to Berkeley (Lowe and Feldman 2008), it is clear that the success of a local regulation does not solely depend on the regulation, in and of itself. The process of crafting and implementing a policy is far more important and should, as in the successful case of Cambridge, include educating the public, selecting decision makers with diverse backgrounds and opinions, and ultimately building consensus in the local community. As exemplified by the case of Berkeley, the adoption of this same biosafety ordinance had a strikingly different result, due to the lack of transparency and community engagement and limited transparency. The conversational space was critically important in creating consensus. In the third case, Feldman and Lowe (2011) document how the state of North Carolina was able to create a quasi-public entity that was able to catalyze a biotech industry cluster. The state currently has a large concentration of bioscience companies even though it started with no discernable advantage and was not a likely location for the industry. Building up slowly and studying the biotech industry provided an opportunity to achieve public consensus about the potential of the developing industry. The full story requires more time to tell, but our claim is that good organizational design allowed for this success. The main point of this case study is that good ideas require good implementation. Though starting from the same analysis and set of recommendations and implemented in the same state, the MCNC and the Biotechnology Center of North Carolina evolved along different trajectories. While the concept of dedicated sector-specified technology development agencies was appropriate, this case study reveals several lessons about organizational design; the importance of both understanding and adapting to local context; and the need for transparency, community building efforts and inclusiveness. The North Carolina Biotechnology Center has continued to expand and flourish, and ensures life

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sciences remains a strong industry in the region (Lowe 2014; Lowe and Feldman 2014). While it would be difficult to pronounce that MCNC was a complete failure, because its many investments have in fact proven important to the state and helped establish North Carolina as a site for the industry, we are left to speculate about how the microelectronics and information and telecommunications sectors might have progressed with stronger, continuous state advocacy and shared resources. The fact that MCNC’s facilities and assets were sold, the research group dismantled, and its mission changed to the provision of statewide broadband access suggests that the original vision of technology leadership has been compromised. The counterfactual example of how well the state’s microelectronics industry would be doing if MCNC had continued to be strong and to advocate for innovation and entrepreneurship is an open topic.

REFERENCES Bathelt, H., Malmberg, A. and Maskell, P. (2004) “Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation”, Progress in Human Geography, 28: 31–56. Berg, P. et al. (1974) “Potential biohazards of recombinant DNA molecules”, Science, 185(4148): 303. Biogen (1980) Letter sent to Robert Neer, Chairman Cambridge Experimentation Review Board, 30 December. Bradford, N. and Wolfe, D. A. (2017) “Learning through governance”. In: Bathelt, H., Cohendet, P., Henn, S., Simon, L. (eds). The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 723–751. Cambridge Experimentation Review Board (CERB) (1976/1977) Guidelines for the Use of Recombinant DNA Molecule Technology in the City of Cambridge. Cambridge, MA: Recombinant DNA History Collection, Archives and Special Collections, MIT Libraries. Chang, H. and Evans, P. (2005) “The role of institutions in economic change”. In: De Paula, S., Dymski, G. (eds). Reimagining Growth: Towards a Renewal of Development Theory. London: Zed Books, pp. 99–129. de Soto, H. (2000) The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books. Feldman, M.P. (2001) “The entrepreneurial event revisited: Firm formation in a regional context”, Industrial and Corporate Change, 10(4): 861–891. Feldman, M.P., Kogler, D.F. and Rigby, D.L. (2015) “rKnowledge: The spatial diffusion and adoption of rDNA methods’, Regional Studies, 45(5): 798–817. Feldman, M.P. and Lowe, N. (2008) “Consensus from controversy: Cambridge’s biosafety ordinance and the anchoring of the biotech industry”, European Planning Studies, 16(3): 395–410. Feldman, M.P. and Lowe, N. (2011) ‘Restructuring for resilience’, Innovations: Technology, Governance, Globalization, 6(1): 129–146. Feldman, M.P. and Yoon, J.W. (2012) ‘An empirical test for general purpose technology: An examination of the Cohen–Boyer rDNA technology’, Industrial and Corporate Change, 21(2): 249–275. Frederickson, D.S. (1976) Press Release for NIH’s recombinant DNA Guidelines, 23 June. The Donald S. Frederickson Papers, National Library of Medicine. Available at http://profiles.nlm.nih.gov/FF/B/B/H/S/ (accessed 22 February 2008). Gertler, M. (2004) Manufacturing Culture: The Institutional Geography of Industrial Practice. Oxford: Oxford University Press. Governor’s Office (1980) “Hunt says microelectronics a chance in a lifetime”, press release, Governor’s Office, State of North Carolina, 5 December. Hodgson, G. (2005) “Institutions and economic development: Constraining, enabling and reconstituting”. In: de Paula S., Dymski, G. (eds). Reimagining Growth: Towards a Renewal of Development Theory. London: Zed Books, pp. 85–98. Jong, S. (2006) “How organizational structures in science shape spin-off firms: The biotechnology departments of Berkeley, Stanford and UCSF and the birth of the biotech industry”, Industrial and Corporate Change, 15: 251–283. Krimsky, S., Baeck, A. and Bolduc, J. (1982) Municipal and State Recombinant DNA Laws: History and Assessment. Medford, MA: Tufts University. Lester, R. and Piore, M. (2004) Innovation: The Missing Dimension. Cambridge, MA: Harvard University Press.

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Lipson, S. (2003) “The Cambridge model of biotech oversight”, GeneWatch, 16(5): 7–10. Available at http:// www.imakenews.com/bfoster/e_article000323366.cfm (accessed 30 May 2017). Lowe, N. (2014) “Beyond the deal: Using industrial recruitment as a strategic tool for manufacturing development”, Economic Development Quarterly, 28(4): 287–299. Lowe, N. and Feldman, M.P. (2008) “Constructing entrepreneurial advantage: Consensus building, technological uncertainty and emerging industries,” Cambridge Journal of Regions, Economy and Society, 1(2): 265–284. Lowe, N. and Feldman, M.P. (2014) Breaking the Waves: Innovation at the Intersections of Economic Development Policy. Working paper. Chapel Hill: University of North Carolina. Macgregor, L., Prosser, T. and Villiers, C. (2000) “Introduction”. In: Macgregor, L., Prosser, T., Villiers, C. (eds). Regulation and Markets Beyond 2000. Dartmouth/Aldershot: Ashgate, pp. 1–13. Miller, H. and Conko, G. (2000) ‘The science of biotechnology meets the politics of global regulation’, Issues in Science and Technology, 17: 47–54. North, D. (1984) “Transaction costs, institutions and economic history”, Journal of Institutional and Theoretical Economics, 140: 34–49. North Carolina Board of Science and Technology (1980) “The FY 1980 Report and FY 1981 Plan”, pp. 16–19. Owen-Smith, J. and Powell, W. (2006) “Accounting for emergence and novelty in Boston and Bay Area biotechnology”. In: Braunerhjelm, P., Feldman, M.P. (eds). Cluster Genesis: Technology-Based Industrial Development. Oxford: Oxford University Press, pp. 61–84. Rodgers, E. (1986) “High-technology companies that are university spinoffs”, Entrepreneurship and Technology: World Experiences and Policies. Harlow, UK: Longman. Rodríguez-Pose, A. and Storper, M. (2006) “Better rules or stronger communities? On the social foundations of institutional change and its economic effects”, Economic Geography, 82(1): 1–25. Sabel, C. (2001) Diversity, Not Specialization: The Ties that Bind the (New) Industrial District. Paper presented to Complexity and Industrial Clusters: Dynamics and Models in Theory and Practice Conference, Milan, Italy. Suchman, M. and Edelman, L. (1996) “Legal rational myths: The new institutionalism and the law and society tradition”, Law and Social Inquiry, 21: 903–941. Vellucci, A. (1977) Audio recording of interview with Alfred Vellucci by Rae Goodell, 9 May. Cambridge, MA: Recombinant DNA History Collection, Archives and Special Collections, MIT Libraries. Vettel, E. (2006) Biotech: The Countercultural Origins of an Industry. Philadelphia, PA: University of Pennsylvania Press. Wade, N. (1984) The Science Business. New York: Priority Press. Wright, S. (2001) ‘Legitimating genetic engineering’, Perspectives in Biology and Medicine, 44(2): 235–247.

43. The dynamics of organizational structures and performances Giovanni Dosi and Luigi Marengo

INTRODUCTION In this work we analyze the characteristics and dynamics of organizations wherein members diverge in terms of the capabilities and visions they hold, and the interests which they pursue (see also Cohendet et al., Chapter 13, this volume). How do organizations and society as a whole put together such distributed and possibly conflicting pieces of knowledge? The question is one of the most fundamental in economics and has often been analyzed through the “Hayekian” lens of the superiority of decentralization in promoting the development and coordination of the dispersed pieces of knowledge in society. However, in the real world such coordination occurs only to a very limited extent via decentralized market transactions. As emphasized by Simon (1991), most human activities take place in social structures other than markets. In his famous metaphor of the visitor from Mars approaching Earth and able to spot activities within firms (and other institutions) marked in green and market transactions marked in blue, the visitor would see green as the dominant color with a few blue lines connecting green masses of different sizes (Simon 1991, p. 27). Moreover, the problem is not only, and perhaps not so much, one of coordinating given pieces of dispersed knowledge, but one of developing and modifying such knowledge, and indeed there no dispute that hierarchical organizations are key actors in these processes. The view which claims the superiority of perfect decentralization and coordination via market interaction implicitly assumes that knowledge is divided and somehow crystallized into separate pieces (modules, we could more properly say) which can be coordinated through the standardized interfaces provided by impersonal market transactions. In Simonian terms the implicit hypothesis is one of full decomposability of knowledge into quasi-independent modules (Simon 1981). If instead the quasi independence hypothesis does not hold, or if the boundaries among the different pieces of knowledge should be often redrawn, perfect decentralization may no longer coordinate efficiently. In this chapter we start from an opposite assumption of ill-defined knowledge. Our assumption is not only that knowledge is distributed, with strong and widespread interdependencies among the various pieces of it, but also that there is a high degree of uncertainty as to where the relevant knowledge is located. Our artificial agents have different representations of the problem and all these representations, in principle, are partly correct and partly wrong. Similarly, the principal of our organization also has an incorrect representation of the problem and therefore is uncertain on how to allocate decisions. Our main result is that, in this world of high uncertainty about the location of the relevant knowledge, authority and power have indeed an important role. 702

The dynamics of organizational structures and performances 703 We consider three types of power: the power to divide the decision task into subtasks and allocate them to different agents; the power to overrule decisions autonomously taken by the agents; and a subtler power to induce the agents to modify their representations and preferences to increasingly align them to those of the principal. We analyze the role of these different types of power and show that, in general, power may not only increase coordination and control (by the principal), but also learning. More specifically, we could summarize the main results of our model with the maxim “divide, rule and learn”. By combining the power to impose a fine division of decisions with the power to overrule, the principal can not only achieve coordination, control and exploitation of the available knowledge but also increase exploration and acquire relevant knowledge from agents. It is interesting to note that our model suggests that this principle of dividing, ruling and learning prescribes that agents should be always only given decision rights on a small number of policies, even if their knowledge is broader. There is an advantage, within our model, to subdividing decision rights into small separate modules because this gives the principal more opportunities to increase both control and learning. Remarkably, the advantages of finely partitioning decision rights are such that it may pay dividends to assign a limit to an agent’s decision rights even when this agent has valuable knowledge over a broader set of decision items. Decision rights should therefore be more finely partitioned than knowledge, against the standard principle that decision rights should be co-located with the knowledge that is relevant to that decision (Hayek 1945; Jensen and Meckling 1992). The chapter is organized as follows: in the next section we discuss the various facets of power, a crucial but often neglected feature of organizations. The third section presents the simulation model and the fourth discusses the main results. Finally, in the last section we draw some conclusions and implications.

POWER, AUTHORITY AND HIERARCHIES A major “foundational” dimension of organizations concerns their hierarchical authorityridden nature and the associated notion of power. In social sciences, in this respect, one finds two alternative archetypes. According to the first one, which we could call the exchange view, power is not an autonomous dimension of social interaction and its notion does not have any clear analytical status. Indeed, market transactions are the normal and efficient coordination mode (Williamson states that “in the beginning there were markets” (1975, p. 20)) and different forms of monitoring, authority or specific types of power emerge only as possible efficient solutions to problems of market failure. Broadly speaking, and with important differences, this view is shared by theories which locate the raison d’être of the firm in transaction costs (Williamson 1995), in shirking and monitoring costs (Alchian and Demsetz 1972), in non-contractible residual rights of control (Grossman and Hart 1986), in non-contractible access to critical resources (Rajan and Zingales 1998) and so on. The problem of interdependencies among pieces of knowledge or resources is indeed present in most of these theories. In particular, Alchian and Demsetz (1972) consider the complementarities among members of a production team the main source of monitoring costs. Demsetz (1995) pushes this idea further and argues that the stronger the interdependencies the larger is the scope for authority (giving of direction, in his terminology):

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The productive giving of directions requires confidence that these directions are carried out. . . . Reliability becomes more important to an organization as the productivities of its various parts become more interdependent. The military organization, at least during a war, is an outstanding example of interdependence. . . . This interdependency creates a demand for discipline that is stronger than in organizations in which spillover effects like this are not important. (Demsetz 1995, p. 33)

The second archetype, which we shall (improperly) call the political view, holds, on the contrary, that (i) an essential, although not unique, feature of organizations is their authoritative structure; (ii) authority relations are inherently different from exchange relations; and (iii) power must be considered an autonomous interpretative dimension. In the following we shall explore the implications of the latter perspective for coordination and learning. The political view, of course, does not claim to be exhaustive: command and exchange coexist in different forms within and outside organizations. But it claims – at least as we interpret it – that the sole consideration of exchange relations prevents any first-order understanding of what goes on within the “organizational black box” of the boundaries between organizations and of organizational dynamics. Here we shall adopt a quite broad definition of power. First, power entails the ability of some agent (the “ruler”, the authority) to determine the set of actions available to the other agents (the “ruled”). Second, it involves the possibility of the authority to veto the decisions or intentions of the ruled. Third, power relates to the ability of the authority to influence or command the choice within the “allowed” choice set (i.e. the span of control of the “ruled”), according to the deliberations of the ruler himself (this definition echoes in some ways the analysis contained in Luhmann 1979). Here, in these respects, the units of analysis are the dimensionality and boundaries of the choice sets and the mechanisms by which authority is enforced. As Herbert Simon puts it: “Authority in organizations is not used exclusively, or even mainly, to command specific actions. Most often, the command takes the form of a result to be produced (‘repair this hinge’), or a principle to be applied (‘all purchases must be made through the purchasing department’) or goal constraints (‘manufacture as cheaply as possible consistent with quality’)” (Simon 1991, p. 31). These aspects of command are part of what in the following we shall call “policies”. Fourth, the most subtle exercise of power concerns the influence of the authority upon the preferences of the ruled themselves, so that, in Max Weber’s words, the conduct of the ruled is such that it is “as if the rules had made the content of the command the maxim of their conduct for its own sake” (Weber 1978, p. 946). That easily accounts for the fact that “organizations can be highly productive even though the relation between their goals and the material rewards received by employees, if it exists at all, is extremely indirect and tenuous” (Simon 1991, p. 38). Obedience, docility and identification in the role and in the organization are central elements of such processes of adaptive learning and coordination (classic discussions of these processes can be found in Milgram 1974; Simon 1976, 1981, 1993; Lindblom 1977; Lukes 2005; Moore 1958). Docility offers the inclination to “depend on suggestions, recommendation, persuasion and information obtained through social channels as a major basis for choice” (Simon 1993, p. 156). And, emphatically, such inputs are not inputs to an inferential (let alone Bayesian) decision process. Both cognitive frames

The dynamics of organizational structures and performances 705 and preferences are endogenous to the very process of social adaptation and social learning. It is crucial to note that the social endogeneity of identity building is exactly the opposite to any type of decision-theoretic model: one learns socially not only what one can do, but, more fundamentally, what one wants, the very interpretation of the natural and social environment one lives in, and, ultimately, the very self-perception and identity of the agents. The conjecture we shall explore in the following is that in many circumstances such processes of cognitive and behavioral adaptation yield much more efficient and quicker coordination patterns. In the next section, we introduce a simple model, which tries to formalize the abovementioned notions of power within an organizational decision-making framework characterized by high degrees of interdependence between the individual decisions.

THE MODEL Policies, Preferences and Delegation We model an organization that combines together dispersed pieces of knowledge in order to accomplish an organizational task; the model is an extension and generalization of the one contained in Marengo and Pasquali (2012). Our model also has some points in common with Siggelkow and Rivkin (2005), who also study delegation of decisions in a complex search problem where decisions are interdependent. However, in relation to the latter paper, we introduce some important new elements: our organization is made up of agents who have a subjective representation of the problem the organization is facing and this representation involves, for each agent, all the organizational decisions and not only the decisions which are delegated to him/her. In other words, we want to model an organization in which agents have a different representation of the entire organizational problem and not simply a local knowledge of the subset of policies allocated to them, like in Siggelkow and Rivkin (2005). In our model agents are delegated a subset of decisions, but they take such decisions according to their subjective representation (subjective landscape) of the entire organizational problem. In this way we study the interplay between the extent of delegation and heterogeneity of knowledge, representations, visions, in a model in which delegation does not only create a problem of coordination, but may also allow to allocate decisions to those who have a more powerful representation or better knowledge. In our model we suppose that the organization has to take a set of interrelated decisions (or implement a set of policies, or perform a set task). Policy vectors can be ranked, with a complete and transitive order relation, from best to worst. We suppose that there is a “true”, objective and exogenously given ranking which is in principle unknown to the members of the organization. The latter is composed by a principal and a set of agents. The principal and each agent have their own subjective and heterogenous rankings of the policy vectors, which reflect their heterogeneous knowledge. The principal does not take any decision directly but only allocates them to the agents and chooses the agenda, that is, the sequence with which decisions have to be taken. In addition she can also overrule the agents’ decisions by exerting authority. We will introduce the notion of organizational landscape, which, for each possible

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policy vector, gives the set of vectors that can be reached from it. Such a set is determined by the individual preferences (rankings), the allocation of decisions and the agenda (that together we call organizational structure), and the frequency and mode of authority interventions. We will study the properties of such an organizational landscape as a function of both the organizational structure and, especially, authority. In particular, we will analyze the ruggedness of the resulting landscape, that is, the number and locations of local and global optima, and therefore the set of possible outcomes and the likelihood and expected time to achieve one of them. Since the principal and the agents have, in principle, a subjective ranking of the policy vectors which bears no similarity to the objective one, the resulting organizational landscape bears no relation to the true one. However, in a second set of simulations we will introduce some simple learning mechanisms through which the principal and/or agents will try and learn the real ranking of policy vectors and consequently adapt their subjective ranking and the resulting organizational landscape. We will use a very simple learning algorithm based on actual trial and error: only by implementing and experimenting with different policy vectors can our artificial agents learn their true relative performance and consequently update their own subjective rankings. Thus, rugged organizational landscapes, with their multiplicity of organizational equilibria, have a learning advantage as they allow higher rates of experimentation. Our model has some elements in common with NK fitness landscapes models (Kauffman 1993; Levinthal 1997), but also some important differences. First, instead of attributing arbitrary fitness values to policy vectors we only rank them. Second, we do not limit search to one bit mutation algorithms: our agents may mutate up to all the policies under their control. In this respect our model belongs more to the literature on modularity in complex systems (Ethiraj and Levinthal 2004; Brusoni et al. 2007), and can be considered as a model in which a complex decision task is decomposed into modules and the resulting modules are delegated to diverse agents. To be more precise, we suppose that the organization is fully characterized by n (binary, for simplicity) policies P 5 {p1,p2,. . .,pn}. Policies are interrelated, in the sense that the value of, say, switching policy i from 0 to 1 depends on the current value of other policies (possibly of all the other n – 1 policies). We suppose that policy vectors can be ranked from best to worst according to their “objective” performance in the given environment. We call “policy landscape” the mapping of the 2n binary vectors of policies to their ranks, that is, to the set of integers in the interval [1, 2n], where rank 1 is attributed to the best policy vector and rank 2n to the worst one. Since policies are interrelated, the policy landscape is, in general, “rugged”, meaning that small changes in a policy vector (e.g. changing only one policy) may result in large changes of rank. Thus searching the policy landscape is a complex task. The organization is formed by one principal and a set of agents A 5 {a1,a2,. . .,ah}, with 1 ≤ h ≤ n. Members of the organization are heterogeneous in their beliefs or views of the organizational landscape. The principal and each agent hold their own subjective ranking of the policy environment which is, in general, different from other agents’ and different from the “true” one. These differences reflect the heterogeneity of knowledge among individuals who hold different representations of the world in which they operate. Agents can therefore be characterized by their competence, that is, the extent to which their individual landscape is correlated to the true one. The degree of competence of an

The dynamics of organizational structures and performances 707 agent can be measured by Spearman’s rank correlation between his own ordering and the true one. Such competence may be subject to adaptive change through a learning process by which an agent tries to adjust his own landscape either to the true one or to the principal’s. The agent’s propensity to adapt his own landscape to the principal’s can be considered as an indicator of the agent’s docility, as mentioned above (Simon 1993). We assume that the principal does not perform directly any task but simply allocates them to the different agents. Let di # P be a generic non-empty subset of the set of policies. We define an allocation of decision rights as a partition of the set of policies, that is, a set of non-empty subsets D 5 {d1,d2,. . .,dk} such that: k

di51 di 5 P with di t dj 5[, 4i 2 j We call organizational structure O a mapping from the domain set D to the codomain set A of agents, that is, a mapping that assigns each subset of policies to one and only one agent (i.e. O: D A A). The image of O is a subset of the set of agents containing as many agents as the number of subsets into which P is partitioned (i.e. the cardinality k of D). Finally, the organizational structure may also be characterized by an agenda a 5 ai1,ai2,. . .,aik, that is a permutation of the subset of agents which form the image of O. This permutation states the sequence in which agents are called to perform their tasks. As already mentioned, we suppose that the principal does not choose any policy directly, but can: ●

● ●

freely choose and modify the organizational structure (that is, the partition of policies and their allocation to agents); exert direct power by vetoing or overruling the agents’ decisions; exert indirect power through influence by making the agents’ landscapes progressively more and more aligned with her own.

Organizational Decision Making and Organizational Landscape We assume that agents, although they are assigned only a subset of policies, take their decisions by considering their preference profiles over the entire vector of policies. In particular, when asked to decide, an agent will choose the policies under his control that, given the current state of the other policy items that are not under his control, produce the overall vector of policies which ranks higher in his own ordering. To give an example, assume that the set of policies is made of four binary policies, that agent i is allocated the first policy, and that the current policy vector is 0101. Then agent i will choose to implement policy 0 if 0101 s 1101 in his own ordering, and policy 1 if 1101 s 0101. Of course, because of interdependencies among policies, his preference between 0 and 1 might well be reversed when the three policies not under his control have current values which differ from 101. We assume that at the outset an initial “status quo” policy vector is (randomly) given. In the simulations we present in this chapter we start from all the 2n possible initial policy vectors and find all possible equilibria and cycles the decision process can end up in. Then the first (according to the agenda) agent may modify the policies under his control. He generates all the sub-vectors for the policies under his control and chooses the one that,

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together with the status quo policies that are not under his control, will produce the vector he prefers. With some probability πauth the principal may exert authority and overrule the agent’s decision. We consider two possible kinds of authority: a simple veto power and a fiat. In the former case the principal can simply veto a policy change proposed by an agent, in which case the current status quo is preserved, even if the agent preferred a change. In the case of fiat the principal does not only have the choice between the status quo and the changes proposed by the agent but can impose on the agent, within his subset of policies, the one she prefers, that is, the agent is de facto replaced by the principal for the current decision. When the first agent on the agenda has taken a decision (and possibly the principal has overruled it), the value he (or the principal on his behalf) has chosen for the policies under his control becomes part of the new status quo. Then the second (according to the agenda) agent operates on this new status quo in the same way: he evaluates all the alternatives for the set of policies under his control and chooses those that inserted in the new status quo produces the overall policy vector which ranks higher for him. Then again the principal may veto or overrule such a choice. The same procedure is then repeated for the third, fourth, . . ., h – th agents on the agenda. Once all the agents have acted on the policies under their control, we consider two alternative options. The first alternative is that the agenda may be repeated over and over again until an equilibrium or a cycle is reached. An organizational equilibrium is a policy vector whereby no agent (nor the principal, in the case of fiat) wants to modify items under his (her) control according to the procedure outlined above. An organizational cycle is a sequence of policy vectors that keep being repeated in the same order, without ever reaching an equilibrium. Some of the simulations we present below follow deterministically the procedure outlined so far, therefore the outcome is deterministic, and if a cycle is encountered it will last forever. Other simulations have instead a random component, such as a probabilistic intervention by the principal, and therefore may originate different types of outcome and cycles may be exited. In this latter case the results we present must also be interpreted in probabilistic terms. A second alternative instead does not allow the agenda to be repeated after all the h agents have acted once (and the principal has possibly vetoed or overruled their decisions). The resulting policy vector will not be in general an equilibrium, in the sense that some agents would still like to revise their decisions after observing the new status quo, but they are not allowed to do it. We will call this resulting policy vector an organizational outcome, to distinguish it from an organizational equilibrium which may be reached with agenda repetition. Of course, without agenda repetition, cycles are not possible and an organizational outcome is always reached. Finally, the procedure is repeated starting from all the possible 2n policy vectors as initial conditions. The initial conditions (individual rankings of the policy vectors held by the principal and the agents), the organizational structure and the mode and likelihood of the exercise of authority determine, together, an organizational landscape, that is, a neighborhood structure which, for every policy vector, indicates which are the vectors that can be reached, given the organizational structure, the decision-making procedure and the individual rankings of the policy vectors. Later in the chapter we will study some proper-

The dynamics of organizational structures and performances 709 ties of the organizational landscape in relation to the organizational structure and the decision-making procedure. We will especially analyze the determinants of the ruggedness or smoothness of the landscape along with the location of local and global optima. But before that, in the next subsection, we describe three learning mechanisms that we will use in a second family of simulations. As already mentioned, the organizational landscape is built as an aggregation of the individual landscapes. The latter are subjective rankings of the policy vectors which we suppose are initially randomly generated and are subject to learning and adaptation. Learning and Adaptation of Preferences We implement three types of learning and adaptation: the first two concern, respectively, the principal and the agents that adapt their own rankings to the “true” one, while the third one implies that agents adapt their own rankings to the principal’s. In all the three cases we use a very simple procedure for adaptive learning based exclusively on actually experienced feedbacks. Let us first describe adaptations to the “true” ranking of policy vectors. They take place only when a new organizational decision is reached (either an organizational equilibrium, in the case of agenda repetition, or an organizational outcome, in the case without agenda repetition), the corresponding policy vector is implemented, and a feedback from the environment is received. When a cycle is reached we suppose that no decision is taken and implemented and therefore no feedback for learning is received from the environment. In other words, we implicitly suppose that there is a deliberation phase in which an equilibrium decision is reached and an action phase in which the latter is implemented. If the former phase enters a cycle, then the latter phase cannot happen. In particular, we suppose that principal and agents can observe only whether the new policy vector is better or worse than the previous one in terms of the “true” ranking. j i If the previous policy p vector ranked worse (better) than the new one p , with some probability  πpadapt for the principal and πaadapt for the agents, those who ranked pi better  (worse) than p j will swap the positions of the two vectors in their rankings. j j If, instead, either the individual preference is in accordance with the environment’s, or the organizational decision process has produced a cycle and no equilibrium has been implemented, no adaptation occurs. j Moreover, agents can kalso adapt their rankings to the principal’s with probability πdocil, d which is a measure of the agents’ propensity to conform to the principal, that d is, of their docility. Also, in this case we suppose that adaptation can only occur through a very simple mechanism based on k actual observation. We suppose that whenever the principal j overrules an agent decision d (either by veto or by fiat) and imposes policy vector p over i j i p chosen by the agent, the overruled agent learns that for the principal p s p and with probability πdocil he or she will swap the positions of the two vectors in his own ranking. If,j instead, the principal does not overrule the agent’s decision, either because she does k j not exert authority or because she shares with the agent the same preference,d no such k adaptation occurs. d j

j

k

k

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Simulations We will simulate the organizational decision-making process described so far, comparing different organizational structures and analyzing the role of authority (in the three versions of veto power, fiat and influence), with learning by the principal and/or by the agents, each of them controlled by the corresponding probabilities. We simulate randomly generated policy landscapes with n 5 8 policy items and up to eight agents with randomly generated preferences. We test the following organizational structures with 1, 2, 4 and 8 agents: ● ● ●

●

O1: a1 ←{1, 2, 3, 4, 5, 6, 7, 8} O2: a1 ←{1, 2, 3, 4}, a2 ←{5, 6, 7, 8} with agenda a 5 a1, a2 O4: a1 ← {1, 2}, a2 ← {3, 4}, a3 ← {5, 6}, a4 ← {7, 8} with agenda a 5 a1, a2, a3, a4 O8: a1 ← {1}, a2 ← {2}, a3 ← {3}, a4 ← {4}, a5 ← {5}, a6 ← {6}, a7 ←{7}, a8 ←{8} with agenda a 5 a1, a2,. . ., a8

In what follows, we study the properties of decision making in randomly generated policy landscapes (that is, the true ordering of policy vectors). In each simulation we study the outcome for every initial status quo and we repeat the exercise for 1000 different randomly generated problems.

RESULTS We will concentrate on the role of organizational structure, authority, learning and docility. We will consider the performance of organizations in randomly generated policy landscapes. As indicators of performance we shall use the following: ●

●

●

●

●

average performance, that is, the average true ranking of all the attainable organizational equilibria; best performance, that is, the true ranking of the best attainable organizational equilibrium; average control, that is, the average ranking according to the principal’s preferences of all the attainable organizational equilibria; best control, that is, the ranking according to the principal’s preferences of the best attainable organizational equilibrium; principal’s competence, that is, the final correlation between the principal’s ranking and the true one.

Organizational Structure The role of organizational structure when neither authority nor learning are in place has been already analyzed in Marengo and Pasquali (2012). Since these results are also the points of departure for the simulations we develop here, we report them here again. Table  43.1 shows that the decision process with agenda repetition in O8 ends up in a

The dynamics of organizational structures and performances 711 Table 43.1 Number of organizational equilibria for different organizations Org. structure

No. of equilibria

Share of cycles

2.78 (1.22) 1.89 (0.98) 1.03 (0.45) 1.00 (0.00)

0.78

O8 O4 O2 O1

Notes:

0.74 0.58 0.00

n58, 1000 simulations, standard deviation in brackets.

Source: Marengo and Pasquali (2012, p. 1305).

Table 43.2 Number of organizational outcomes for different organizations Org. structure

No. of outcomes 41.93 (3.14) 27.73 (2.45) 10.30 (1.22) 1 (0.0)

O8 O4 O2 O1

Notes:

n58, 1000 simulations, standard deviation in brackets.

Source: Marengo and Pasquali (2012, p. 1306).

cycle in 78 percent of cases. If it does not lead to a cycle, it stops in about three different organizational equilibria. On the contrary, in O1 all decisions are delegated to one agent, therefore only one organizational equilibrium is possible (the policy vector preferred by the agent) and no cycles may occur (because all agents have transitive preferences). This however is the extreme case whereby there is no coordination problem because all the knowledge is embodied in one autocratic ruler, who is in every respect both principal and agent. In some respect, this case resembles central planning: the coordination problem is solved by definition and the performance at equilibrium fully depends on the quality of the knowledge of the central planner itself. Table 43.2 reports instead the number of organizational outcomes when the agenda is not repeated. In this case cycles cannot occur and therefore the number of different outcomes is much greater than the number of different equilibria reported in Table 43.1, except when all policies are delegated to a single agent. In the latter case there is a unique outcome which is also the unique equilibrium. Each equilibrium or outcome has a basin of attraction, that is, a set of initial policy

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vectors starting from which that equilibrium or outcome may be attained. The size of such basin of attraction does not seem to bear any significant relation to the rank of the equilibrium or outcome itself. In particular, higher ranked equilibria or outcomes do not have in general larger basins of attraction than lower ranked ones. Also the distribution of equilibria and outcomes is random. Since there is in general no relation whatsoever between the “true”, the principal’s and the agents’ rankings, equilibria and outcomes are also randomly distributed with respect to their position in the true and the principal’s rankings. However, the higher the number of equilibria or outcomes, the higher the likelihood that some of them rank high in the true or in the principal’s ordering. Thus with organizational structures in which delegation is finely partitioned (and especially when agenda repetition is not allowed), there exists the possibility for the principal to obtain high levels of control (i.e. an organizational equilibrium or outcome close to her preferred policy vector) and/or higher performance (i.e. an organizational equilibrium or outcome close to the policy vector which ranks higher in the environment) only by appropriately choosing the initial status quo, that is, an initial condition within the basin of attraction of the preferred equilibrium and outcome. In other words, finely partitioned organizational structures give the principal a higher possibility to manipulate organizational decisions, even without using authority. Similar manipulability results are more rigorously discussed within a standard social choice framework in Marengo and Pasquali (2011) and Marengo and Settepanella (2010). It is important to stress that what we propose here are a kind of “possibility” results: a higher number of organizational equilibria increases the possibility for the principal to select, among the many outcomes, the one which is closer to her preferred policy vector. Of course, this does not imply that the principal will be actually able to select this most preferred equilibrium. However, if the principal starts from a status quo which is equal or close to her preferred policy vector she will likely obtain an equilibrium close to this policy vector. Actually, since the larger the number of local optima the smaller is their basin of attraction, if there are many local optima the principal will almost certainly obtain a policy vector close to the preferred one, by starting from nearby initial conditions. On the contrary, if there exists only one organizational equilibrium this will always be reached from any initial status quo. It is worth stressing that finely partitioned structures only give the possibility to attain these preferred equilibria or outcomes, and that this possibility can be exploited only if the principal can choose (by locating in its basin of attraction), among the many equilibria or outcomes, one which ranks higher in terms of control or performance. Moreover, since in principle there is no correlation between the objective and the principal’s rankings, there is also no relation between control and performance. Later in the chapter we will introduce some learning mechanisms and analyze under which conditions the location of good equilibria and outcomes can be learned and control and performance can be aligned. But first, in the following simulation exercises, we will show that the use of authority greatly increases the scope for manipulability. Authority We just mentioned the advantages of highly partitioned structures, but we also reminded that, if agenda repetition is allowed, they tend to produce higher numbers of cycles. Authority can indeed prevent the latter.

The dynamics of organizational structures and performances 713 Table 43.3 The effect of veto in O8 P(veto) 0.0 0.3 0.5 0.8 1.0

No. optima

No. cycles

Best control loss

2.78 13.88 27.60 46.67 56.65

200.12 146.99 86.45 14.46 0.00

−161.20 −71.88 −65.82 −65.74 −64.61

Best performance loss −159.51 −14.45 −6.90 −3.93 −3.16

Table 43.4 The effect of fiat in O8 P(fiat) 0.0 0.3 0.5 0.8 1.0

No. optima

No. cycles

Best control loss

2.78 15.48 29.63 35.13 28.82

200.12 192.81 138.20 36.55 0.00

−161.20 −2.36 −0.59 −0.03 0.00

Best performance loss −159.51 −13.91 −7.27 −6.04 −7.78

Table 43.3 shows the number of optima and cycles and the values of the best attained control and performance for different values of the probability that the principal vetoes a policy change she does not like in the O8 organizational structure. Note that control and performance are measured as losses from the optimum, that is, as rank distance between the actual policy vector and the most wanted or best fit one. Thus a loss of control 0 means that the principal obtains exactly her most preferred policy vector and a loss of performance 0 means that the chosen equilibrium is the best policy vector in the true ranking. The results change somehow if instead of the mere power to veto changes of the status quo that are against her preferences, the principal can impose by fiat her most preferred subset of policies to each agent. Table 43.4 summarizes these results. Obviously if the principal always intervenes by fiat she can get full control, but the number of possible equilibria and best performance are considerably lower than when only veto power can be exerted. Also the reduction of cycles is less sharp than when veto is used. Using fiat in organizations with coarser partitions of decisions makes control even easier, but the outcome is worse in terms of performance (and, as we will see below, also in terms of learning), because coarser organizations produce a smaller number of possible organizational equilibria. For instance, Table 43.5 presents the results obtained by increasing levels of probability of intervention by fiat in a O2 type organization. If we consider the case in which agenda repetition is not allowed and an organizational outcome is reached after all agents have taken their decisions only once, our results become stronger. In this case we have already noticed that the number of organizational outcomes is larger and the use of veto or fiat power makes it even larger. These findings are reported in Table 43.6 for veto power (similar results are obtained for fiat power). Not too surprisingly, throughout our simulation experiments “more power” – in terms of depth and probabilities of its exercise – yields more organizational control over agents’ behaviors. And in that case coordination is easier.

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Table 43.5 The effect of fiat in O2 P(fiat) 0.0 0.3 0.5 0.8 1.0

No. optima

No. cycles

0.99 8.32 9.84 9.47 8.29

154.08 164.39 119.49 25.64 0.00

Best control loss −156.86 −0.20 −0.01 0.00 0.00

Best performance loss −161.69 −28.65 −24.99 −26.16 −31.14

Table 43.6 The effect of veto in O8, without agenda repetition P(veto) 0.0 0.3 0.5 0.8 1.0

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Interestingly, organizational performance also grows with the exercise of authority, but up to a point. There is no monotonicity here and there appear to be three “phases” in the system, namely, (i) with no or low exercise of authority coordination is difficult and organizational performance is low; (ii) with robust exercise of authority coordination is easy and performance high; (iii) with extremely deep and detailed exercise of authority coordination is easy but performance is worse. The worsening of performance in the last phases is a consequence of the higher level of control that is achieved by more frequent use of veto or fiat power. Limited use of authority increases coordination and generates a landscape with multiple equilibria or outcomes, but if the frequency in the use of authority increases further, the number of equilibria and outcomes begins shrinking as control increases further and the organizational landscapes become more similar to the principal’s landscape (which is single peaked by definition). As we shall shortly see, these properties are broadly corroborated by set-ups involving different types of learning. Competence Competence can be defined in our framework as the correlation between the individual subjective ranking of policy vectors and the true one. Individuals whose ranking is more correlated with the true one have a better representation of the environment in which the organization operates. The question we briefly address in this subsection is where more competent individuals should be placed in our organization. The question is not completely new and has already been addressed in some papers (see for instance Garicano 2000 and Garicano and Rossi-Hansberg 2006), but here we develop it in our framework of highly heterogeneous agents and delegation. First, and quite trivially, if the principal is fully competent (i.e. knows the right ranking

The dynamics of organizational structures and performances 715 of policy vectors), she should exercise a maximum degree of authority by always overruling the agents’ decisions which are not in accordance with her preferences. In this limit case the principal should not delegate, or, equivalently, should delegate all decisions to only one agent and always overrule him. More interesting is the case in which the principal is more competent than all the agents but her correlation with the true ranking is less than one. In this case, we are back to a case in which the highest control and highest performance can be achieved by partitioning and delegating decisions (structure O8 allows the achievement of the highest levels of control and performance) together with the exercise of an intermediate level of authority. Let us now turn to agents’ competence. We have built populations of agents characterized by different degrees of competence, that is, correlation between their subjective ranking and the true one, and we have tested whether delegating more decisions to more competent agents always increases performance. The answer in general is no, except in the limit case in which one agent is fully or almost fully competent (his ranking is equal to or very highly correlated with the true one) and all or almost all decisions are delegated to him. As the competence of the most competent agent decreases, the highest performing organizational structure quickly switches to O8, thus more competent agents are delegated as few decisions as less competent ones. Both results show a clear discontinuity: if the allocation of decisions is (almost) optimal and someone’s competence is (almost) at the maximum, then the most competent agent should also be given highest decision power, otherwise there is no reason to give more power to more competent agents. It must be noticed that this result is strictly dependent on our rather extreme assumption on complexity: since both the organizational and the true landscapes are randomly generated, they both tend to be uncorrelated, therefore two policy vectors which are relatively close (in Hamming distance) may be far away in ranking both in the organizational and in the true landscape. In less complex environments, where instead policy vectors which differ in very few policy items are also very close in ranking, indeed more competent agents should be allocated a larger portion of the decisions. Learning In our model learning can only take place through trial and error: by experimenting with different organizational equilibria the principal and the agents can acquire information on the relative value of different policy vectors and adapt their subjective rankings accordingly. Therefore, the existence of a multiplicity of organizational equilibria is a fundamental driver for learning. As we have already pointed out, the number of organizational equilibria or outcomes is higher either when decisions are highly partitioned and authority is used in order to prevent cycles, or when the agenda is not repeated. A careful balance between partition of decisions and use of authority is therefore needed to increase learning. Figure 43.1 plots the average Spearman rank correlation coefficient for organizations O8 and O2 for different values of veto probabilities when πpadapt 5 1 (i.e. the principal always updates her ranking when new organizational equilibria are tested in the environment) and when the agenda can be repeated. It is worth noting the inverted U-shape of the relation between the use of veto power and the principal’s adaptation in O8: too little

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use of veto power decreases learning because of the high frequency of cycles; too much use of it also decreases learning because it decreases exploration (many policy changes that would produce good organizational equilibria are vetoed). In O2, this inverted U-shape does not appear and learning steadily (though slowly) increases with the frequency of veto. A similar, but even stronger, result is obtained when fiat instead of veto power is considered, as shown in Figure 43.2. If we do not allow for agenda repetition we obtain very similar patterns (for the sake of brevity we do not report the corresponding graphs), but the values of the average Spearman rank correlation coefficients are significantly higher than the ones obtained with repetition because, as we showed above, without agenda repetition the number of organizational outcomes is considerably higher and therefore there can be more exploration.

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organizational structure O8 with many agents. The instability instead is much lower in O2 where only two agents must coordinate. Indeed, these results appear to suggest that an organizational set-up particularly conducive to learning involves multiple decentralized searches but also a centralized “exploitation” of the outcomes of such exploratory efforts. Figure 43.6 shows the effect of veto power on average agents’ learning in O8 and O2. Finally, we can introduce agents’ adaptation to the principal’s preferences, what we called agents’ docility. Obviously, agents’ docility greatly increases principal’s control, provided the principal exerts some authority, as shown by Figure 43.7, where we plot average and best control for different values of veto probability when πdocil is set to 1 in the organization O8. Recall that in our model agents’ adaptations to the principal’s preferences are actually triggered by the exercise of authority, as the latter is the means the principal can use to reveal her preferences to the agents.

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Figure 43.9 Average and best performance with high or low agents’ docility in O8 for different probabilities of veto effect on learning and performance. The reason is that docility makes coordination easier (agents slowly converge to a common ranking of policy vectors, that is, the one of the principal) and decreases the number of cycles, which tends to be high when veto and fiat are infrequently used. Finally, by considering the results presented so far together, we can outline a possible evolutionary pattern of organizations in relation to the environment they face. When facing new ill-structured problems, high heterogeneity, cognitive diversity and conflict are conducive to high levels of exploration, and an organizational structure with finely partitioned decision rights may further enhance it. However, at the same time, diversity and conflict must be balanced by a considerable authority intervention, in order to preserve coordination and control. As learning proceeds and the principal acquires a better representation of the problem (and of course if the environment is stable), the structure of delegation becomes less relevant and docility can effectively substitute veto and fiat power.

CONCLUSIONS Power and authority, on the one hand, and cognitive and behavioral adaptation, on the other, are fundamental dimensions of an economic organization or, for that matter, of all social institutions. In this work we have presented a simple computational model which allows the analysis of some of the links between these two dimensions. First, we show, not surprisingly, that authority and exercise of power significantly facilitate coordination. Second, and much less intuitively, a robust exercise of veto and fiat power by a superimposed authority greatly enhances organizational performance up to a point: ubiquitous exercise of power yields easy coordination but worsens performance. Hence the third finding: higher organizational performance comes with some balance between decentralized local coordination on the one hand and centralized authority on the other. These properties are corroborated and indeed strengthened when one allows for organizational learning, both by the principal and by the agents. Our fourth result is that the

The dynamics of organizational structures and performances 721 exercise of authority not only makes coordination easier, but also collective learning more effective. However, the proposition only holds as long as some balance between exploration and exploitation is preserved. A too strict exercise of authority degrades the learning abilities of the organization. Moreover, the most effective organizational set-ups appear to be those in which exploration (learning) is decentralized while exploitation (the ensuing coordinating rules) is centralized by the principal. What about “docility”, that is, adaptation by the agents in their cognition, preferences and behavioral rules? Our fifth set of findings shows that docility is the “high-powered” version of (and indeed largely substitutes for) authority. It is more effective than the latter in achieving coordination, but it can hinder exploration if too strong and too fast. An organization made of fully docile members coordinates very smoothly but learns relatively little, since all the learning has to be picked up by the principal, losing all the efficacy of decentralized search. Our model is highly simplified, but captures, we believe, some fundamental aspects of the tension between centralized and decentralized decision making in complex organizations where decisions are highly interdependent. Our main point is that power and authority are institutional mechanisms that can produce stability in such circumstances and lead to higher organizational performance. Decentralized decision making in a setting characterized by high degrees of complexity and interdependence usually fails to produce a stable outcome, but if combined with the exercise of some authority it can on the contrary produce a variety of outcomes, increasing the possibility of both coordination and learning. Acknowledgements This is a revised version of the paper: “The dynamics of organizational structures and performances under diverging distributions of knowledge and different power structures”, published by the authors in the Journal of Institutional Economics in 2015. We would like to thank the Journal of Institutional Economics for granting permission to republish this paper.

REFERENCES Alchian, A., and H. Demsetz (1972): “Production, information costs and economic organization”, American Economic Review, 63, 772–795. Brusoni, S., L. Marengo, A. Prencipe and M. Valente (2007): “The value and costs of modularity: A problemsolving perspective”, European Management Review, 4, 121–132. Cohendet, P., Parmentier, G., and Simon, L (2017): “Managing knowledge, creativity and innovation”, in The Elgar Companion to Innovation and Knowledge Creation, ed. by H. Bathelt, P. Cohendet, S. Henn and L. Simon, pp. 197–214. Edward Elgar Publishing, Cheltenham, Northampton, MA. Demsetz, H. (1995): The Economics of the Business Firm. Cambridge University Press, Cambridge. Ethiraj, S., and D. Levinthal (2004): “Modularity and innovation in complex systems”, Management Science, 50, 159–173. Garicano, L. (2000): “Hierarchies and the organization of knowledge in production”, Journal of Political Economy, 108, 874–904. Garicano, L., and E. Rossi-Hansberg (2006): “Organization and inequality in a knowledge economy”, Quarterly Journal of Economics, 121, 1383–1436. Grossman, S. J., and O. D. Hart (1986): “The costs and benefits of ownership: A theory of vertical and lateral integration”, Journal of Political Economy, 94, 691–719. Hayek, F. (1945): “The use of knowledge in society,” American Economic Review, 35, 519–530.

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Jensen, M., and W. Meckling (1992): “Specific and general knowledge, and organizational structure”, in Contract Economics, ed. by L. Werin and H. Wijkander, pp. 251–274. Blackwell, Oxford. Kauffman, S. A. (1993): The Origins of Order. Oxford University Press, Oxford. Levinthal, D. (1997): “Adaptation on rugged landscapes”, Management Science, 43, 934–950. Lindblom, C. (1977): Politics and Markets. Basic Books, New York. Luhmann, N. (1979): Trust and Power. Wiley, Chichester. Lukes, S. (2005): Power: A Radical View. Palgrave Macmillan, London. Marengo, L., and C. Pasquali (2011): “The construction of choice: A computational voting model”, Journal of Economic Interaction and Coordination, 6, 139–156. Marengo, L., and C. Pasquali (2012): “How to get what you want when you do not know what you want: A model of incentives, organizational structure and learning”, Organization Science, 23, 1298–1310. Marengo, L., and S. Settepanella (2010): “Social choice on complex objects”, Annali della Scuola Normale Superiore di Pisa – Classe di Scienze., 13, 1039–1064. Milgram, S. (1974): Obedience to Authority: An Experimental View. Tavistock Institute, London. Moore, B. (1958): Political Power and Social Theory: Six Studies. Harvard University Press, Cambridge, MA. Rajan, R. G., and L. Zingales (1998): “Power in a theory of the firm”, Quarterly Journal of Economics, 113, 387–432. Siggelkow, N., and J. Rivkin (2005): “Speed and search: Designing organizations for turbulence and complexity”, Organization Science, 16, 101–122. Simon, H. (1976): Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations. The Free Press, New York, 3rd edn. Simon, H. (1981): The Sciences of the Artificial. MIT Press, Cambridge, MA, 2nd edn. Simon, H. (1991): “Organizations and markets”, Journal of Economic Perspectives, 5, 25–44. Simon, H. (1993): “Altruism and economics”, American Economic Review, 83, 156–161. Weber, M. (1978): Economy and Society. University of California Press, Berkeley, CA, translation of Wirtschaft und Gesellschaft, 1922. Williamson, O. E. (1975): Markets and Hierarchies: Analysis and Antitrust Implications. Free Press, New York. Williamson, O. (1995): “Hierarchies, markets and power in the economy: An economic perspective”, Industrial and Corporate Change, 4, 21–49.

44. Learning through governance Neil Bradford and David A. Wolfe

INTRODUCTION As innovation becomes the driving force of growth and development in the economy, increasing attention has focused on the knowledge-based factors that are crucial to economic prosperity. While a variety of perspectives offer differing theories and accounts of success, one influential approach focuses on the social characteristics that underlie both the innovation process itself and the broader political arrangements and policy mechanisms that condition change and enable adaptation. Rather than looking narrowly at economic institutions or state regulations, this perspective emphasizes the dynamic nature of innovation and the governance interactions among firms, across sectors and between economic actors and governments. The chapter starts from this dynamic and interactive perspective, highlighting key contributions to the literature and exploring the changing nature of the innovation process in the knowledge-based economy, while also exploring the implications for governments and public policy. The aim of the chapter is to clarify the nexus of social relations in knowledge-based economies that connect innovation, learning and governance. To this end, the discussion adopts the concept of the “innovation system” as a lens to frame and interpret contemporary processes of economic transformation playing out in local and regional settings across Organisation for Economic Co-operation and Development (OECD) countries. The chapter is organized around three themes. First, we address the fundamental role that knowledge plays in today’s economy, emphasizing the importance of collaboration in the generation, dissemination and application of new insights. We then link ideas about collaborative knowledge to the structures and processes of governance that organize and drive innovation. Conceptualizing governance relations at the local “associational” scale and the national “multilevel” scale, we highlight the interdependence among governments and a host of knowledge-based economic actors in driving local and regional innovation. Emphasizing that knowledge and collaboration generate innovations through institutional processes of learning, we discuss two specific types of learning: broad intersectoral forms of social learning, and the more bounded policy learning that occurs within government institutions. Recognizing the importance of connecting these two types of learning in innovation processes, we discuss how associational and multilevel governance can “join up” in geographic spaces and policy networks. The chapter concludes with a consideration of how ideas about knowledge, governance and learning are being applied in practice through innovation systems operating at local and regional scales in several OECD jurisdictions.

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INNOVATION IN A KNOWLEDGE-BASED ECONOMY Over the past decade, key policy-making bodies, such as the OECD, and many national governments have come to view the emerging digital economy as knowledge-based. This perspective follows from the central role that knowledge-based activities play in the production process, as well the rising proportion of the labour force that deals with the production, distribution and processing of cognitive and cultural material in comparison to the proportion that handles tangible goods. If knowledge is understood to include not just research and development (R&D), but also design, engineering, advertising, marketing and management, then cognitive and knowledge-based inputs are becoming the defining feature of both manufacturing and service industries in the new economy (OECD 1996; Scott 2012). Indeed, the rapid pace of technological change over the past decade and a half portends even more dramatic changes yet to come, in new technologies, new products and whole new industries, as we are currently witnessing with the rapid proliferation of mobile technologies, cloud computing, the application of big data and the rapid emergence of the Internet of Things. The World Wide Web, itself the product of the rapid integration of computer, telecommunications and multimedia industries a brief two decades ago, is currently being transformed from an elite tool for scientific research into a fundamental platform for the complete transformation of business processes. These dynamics have given rise to a broad reappraisal of theories of economic growth and development. Though long neglected in mainstream economics, the role played by knowledge in economic transformation is increasingly viewed as one of the most important factors in understanding change. Recognition of knowledge as a fundamental variable flows from the tradition of evolutionary economists and new growth theorists, commencing with Schumpeter’s Theory of Economic Development (1928) and extended by Nelson and Winter’s An Evolutionary Theory of Economic Change (1982) and Paul Romer’s work on “increasing returns” (1986). Each of these landmark texts places questions of innovation and knowledge accumulation at the centre of the analysis. At the core of this body of work lie several generalizations about knowledge and the role of markets and institutions in capitalist development (Cohendet and Simon, Chapter 3, this volume). Most general is the recognition that the underlying basis of economies is transformed over time through the growth of knowledge. New knowledge, coordinated through a decentralized system of markets and supported by an institutional framework, opens up new economic spaces, giving rise to new opportunities for economic growth. This process is an interactive one whereby the generation and application of knowledge both influences and is influenced by the structural changes of the economy (Metcalfe et al. 2002). Central to this formulation is the insight that the generation and accumulation of new knowledge is a virtually unlimited process. Knowledge generates more knowledge as each development or activity gives rise to new possibilities, which expands the underlying knowledge base. Data, a fundamental building block of information, and ultimately new knowledge, is increasing at an unprecedented pace. According to a recent analysis by the Boston Consulting Group, the production of data increased 2,000-fold between 2000 and 2012 and the stock of data is expected to continue doubling every two years. “Since larger data sets yield better insights, big is beautiful. Data wants to be big, and businesses struggle to keep up” (Evans and Forth 2015). As a consequence, capitalism itself is restless as new knowledge is captured and transformed by firms into new opportunities,

Learning through governance 725 ultimately changing the relative importance of different economic activities. Products, industrial sectors, manufacturing processes, regions and, at times, entire countries, all shift in their relative importance as a result of knowledge accumulation and its application (Metcalfe 2001). While knowledge has long been an integral—if often understudied—part of economic processes, what is changing in the “new economy” is the type of knowledge driving both patented discoveries and, in turn, the growth of new industries as these breakthroughs are commercialized. Production is increasingly dependent on specialized, complex and scientifically intensive knowledge from a wide diversity of fields (OECD 2000; Wolfe 2010). One indication of this shift is the steady rise of citations of academic papers in patent applications in recent decades. One study of paper citations in US patents found that such citations increased considerably between 1985 and 1995 in a range of technology sectors, from chemicals and electronic components to, particularly, biotechnology. Moreover, 73 per cent of papers cited in US patents were from public science, suggesting that the importance of government support of science and technology is increasing (Narin et al. 1997; Block and Keller 2009). A consequence of this increasing dependence of technological innovation on the rapidly moving scientific frontier is that no one firm can be in command of the wide range of technological competencies needed for successful innovation. Indeed, as technology has become more complex, firms have come to rely ever more on collaborations as a way of leveraging the escalating risks and costs of R&D in the face of mounting global competition. Given the increasing complexity of innovation, research consortia, cross-licensing agreements and research contracts have all become essential forms of cooperation. They help firms to access new knowledge and share development costs and associated risks, particularly in the more knowledge-intensive sectors such as information technology and biotechnology. This trend is also reflected in the increasing importance of research alliances for the competitive strategies of leading firms and the importance of those alliances to the earnings of leading firms (Rycroft and Kash 1999). These collaborations—among firms, government agencies, research laboratories and universities—have thus become a key variable to understanding economic success and, consequently, important focus of government policy. Their importance suggests that much of the useful knowledge in the innovation process is derived not only internally from within the firm and its employees, but also from the broader linkages of the firm to its “innovation system”, including its interactions with suppliers, customers, sources of knowledge or collaborators (Cooke 1997; Wolfe 2011). The concept of an innovation system focuses on the constantly evolving relationships between a wide spectrum of innovation partners and draws attention to how their interactions affect knowledge creation, the rate of knowledge diffusion, knowledge transformation to innovation and the expansion of that innovation. A systems perspective emphasizes the leveraging of local and regional infrastructures to fuel the innovation process through research parks, universities, large research-driven firms, start-ups, investors and other professionals (Bramwell et al. 2012). Knowledge accumulation is therefore an intrinsically uneven process, both spatially and temporally. Indeed, far from being a steady process through time, it accumulates at an unpredictable rate as new knowledge spawns new innovations, giving rise to further opportunities for more innovation. The process, in effect, creates a clustering of new

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knowledge in time for any industry or technology sector and is constantly opening “new windows of locational opportunity” on a geographic basis (Scott and Storper 1987). Successful innovation increasingly depends on the ability to coordinate the collective activities of a diverse range of actors involved in the innovation system, especially at the regional and local levels. The question of how relations among these actors can be coordinated acquires greater significance, drawing attention to the governance dimension of the innovation process.

GOVERNANCE: HORIZONTAL AND VERTICAL RELATIONS Analyzing the nature of the innovation process in the modern knowledge-based economy requires a better understanding of the role of “governance”. This emphasis on the role of governance, as opposed to government activities, stems from the insight drawn from the innovation system approach that outcomes depend on the interaction among a wide range of social and economic actors, including provincial or state and local governments, the private sector and not-for-profit organizations. Central to the concept is the development of styles of governing in which the boundaries between public and private actors and even across different levels of government become blurred. Governance focuses on “steering mechanisms” which no longer rely on the authoritative distribution of resources derived through traditional bureaucratic structures (Stoker 1998). The novelty of the approach lies in the accent placed on the processes of consultation and deliberation, rather than the exercise of formal authority through organizational hierarchies and administrative structures. It focuses on “the process through which public and private actions and resources are coordinated and given a common meaning and direction” (Peters and Pierre 2004). Reflecting broader shifts in the political science and administrative science literatures, governance scholars argue that political relations and policy making have moved from a hierarchical pattern associated with the bureaucratic state-managed mode of development in the post-World War II era to a more heterarchical set of relations in the current era (Osborne 2010). The governance perspective builds on insights from the policy literature that there is often a critical gap between the formulation of policy and its implementation (Paquet 1997). National and provincial or state governments may legislate in any one of a number of areas within their jurisdictional authority, but the effectiveness of policy is determined “on the ground” in a specific geographic context. The extent to which governments achieve their desired goal depends on the pattern of interaction between public authorities operating a variety of arm’s-length agencies, private sector firms and a wide range of industry and other voluntary associations. The effectiveness of national and regional research policies thus depends on robust “co-production”, whereby governments rely on private firms, public and private laboratories, post-secondary educational institutions and intermediary associations to help design and deliver initiatives (Bradford and Wolfe 2012; Feldman and Lowe, Chapter 42, this volume).

Learning through governance 727 Associative Governance: Horizontal Relations Associative governance is the process of managing networks of diverse actors, where notions of power rest more on recognition of mutual dependence among decentralized networks than unilateral assertions of interest or top-down impositions of authority (Bradford and Bramwell 2014). In a number of articles, Morgan, among others, outlines this concept in a manner that is suitable to the context of a learning economy (Morgan 1997; Cooke and Morgan 1998; Ruttan and Boekma 2007). The key factor is not the boundary drawn between the state and private economic actors, but rather a framework for appreciating the effective interaction between the two, coordinated by new sets of governance arrangements. The appeal of the associative approach to governance stems from the fact that it substitutes a mix of public and private roles for the exclusive role of the public bureaucracy, and emphasizes the context of institutional structures, cooperative incentives and mutual learning. Government establishes the basic rules governing the operation of the economy, but places greater emphasis on the devolution of responsibility to a wide range of associative partners—those firms, organizations and communities that will enjoy the fruits of its success, or live with the consequences of its failure. The quality of governance cannot be reduced to the actions of any one actor in either the public or private spheres, but results from their combined interaction across a wide range of socio-political-administrative interventions (Rhodes 1996). Policy development and implementation works with and through the combined resources of governmental and non-governmental actors in the form of horizontal, self-organizing and “self-governing inter-organizational networks” (Rhodes 1996). A strategic priority in the associative model is to find the effective balance between the state’s need to provide direction and ensure accountability and the desirability of providing greater voice and responsibility to socio-economic partners through consultation, deliberation and dialogue (Morgan and Nauwelaers 1999). When such balance is achieved, the knowledge flows crucial to both the innovative capacity of firms and the policy performance of governments are channeled and leveraged most productively (Bradford and Wolfe 2013). In his influential account of the associational model, Amin explores this associational balance through discussion of a “reflexive state” that includes four key principles. The first is a degree of decision-making pluralism, which involves delegating decision-making authority to the levels and bodies at which policy effectiveness can best be achieved. The second involves the notion that the state provides strategic leadership and capacity to coordinate. This is not a role that follows from the politics of command and control. Effective leadership requires the combining of authority with a capacity for consensus building in the appropriate arenas. The third point involves the adoption of a process of dialogic rationality. The relevance of dialogic democracy involves a lasting consensus that results from interactive reasoning. The fourth point involves the commitment in the process of democratic practices to transparent and open government. This approach to governance seeks to “break away from the constraints of the traditional dual choice between market-centred and state-centred approaches”, and emphasizes the development of “governance capability across, and between, a broad range of institutional fields of economic life” in the form of institutionalized local governance structures based on “networks of organization and representation” (Amin 1996).

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Implementing associative forms of governance poses a number of challenges, however, especially for public sector partners more accustomed to operating in the traditional context of a hierarchically structured bureaucracy. For governments to operate in this mode of governance they must establish the conditions under which key actors at the community level can engage in a consultative and interactive fashion with public agencies as well as learn themselves to collaborate with these actors in a more distributed pattern of authority (Cooke and Morgan 1998). Governments need to “let go” of some power and “join up” with others. The ability to collaborate in this way involves the delegation of certain tasks from formal government agencies to accredited business associations or community organizations. The latter possess relevant assets, such as knowledge of, and credibility with, their members, which public organizations need to enlist in order to ensure the effectiveness of their support policies. The dispersal of power to this broader range of actors creates the opportunity for more meaningful dialogue to take place at the regional and local levels (Cooke 1997). This is important because dialogue or discussion is central to the process by which parties come to reinterpret themselves and their relationship to other relevant actors within the local economy (Morgan and Nauwelaers 1999; Paquet 1999). Multilevel Governance: Vertical Relations The concept of associative governance also implies the devolution of power in the state system from remote bureaucratic ministries at the national level to local and regional levels of government better positioned to build lasting and interactive relations with firms and business associations in their regions. Devolving power to the lower levels of government creates the opportunity for more meaningful dialogue to take place at the regional level, and for better intelligence to inform policy intervention. The associative approach to governance is thus closely linked to a related concept, that of multilevel governance, which is derived from a term pioneered by Gary Marks in his work on the relations between levels of government within the European Union (EU) (Marks 1993). It represents a new model of political architecture where political authority and policy-making influences are dispersed across the different levels of the state as well as to non-state actors. Whereas the associative governance literature focuses on the integration of a broader array of non-governmental actors into governing processes, the idea of multilevel governance emphasizes the need for greater cooperation across different levels of government who share overlapping or competing spheres of jurisdictional responsibility across a related set of policy areas. At the core of the idea is a recognition that the national level no longer monopolizes policy making and instead engages in collective decision making with other levels of government and relevant actors, and in so doing, cedes control over some aspects of the policy-making process. Decision-making competencies are therefore shared among a range of governmental actors, with no one level exercising a monopoly over another. Accordingly, subnational levels are said to be interconnected to national, and at times supranational, arenas rather than nested within the national state (Hooghe and Marks 2001). In jurisdictions outside of Europe, where federalism is more common, the concept of multilevel governance helps recognize that relevant areas of jurisdictional responsibility have long since ceased to be the “watertight compartments” of classical theories of

Learning through governance 729 federalism (Horak and Young 2012). The interdependent nature of governmental roles and jurisdictional responsibilities, as well as the contributions from informal actors not explicitly recognized in the constitutional division of powers, is of increasing importance for effective policy supports for innovation. The sharing of decision making with lower levels of government promotes a process of interactive dialogue that is essential to economic success and targeted policy at the regional and local scales. Regional and local actors are a necessary source of knowledge in policy networks, assisting in the process of intelligence gathering for knowledge-intensive firms and for strategic government interventions. This integrated view is endorsed by Scott et al. who suggest that governance ideas are now widely deployed to describe the multifaceted aspects of social and economic coordination in an increasingly interdependent world where various tiers of government must collaborate with each other, as well as with a range of non-governmental actors, to achieve their goals. They point out that the governance of city-regions in particular, must be viewed as part of a larger project of coordination across multiple geographic scales and jurisdictional levels. This “sense of the term sees governance as involving a set of complex institutional reactions to the broader problems of economic and social adjustment in the emerging global–local system” (Scott et al. 2001). From this perspective, innovation occurs at the intersection of multilevel and associational governance networks in local and regional settings where firms can access the optimal mix of knowledge inputs and policy resources to help meet the global competition.

GOVERNANCE AND LEARNING If knowledge is integral to the innovation process in a knowledge-based economy, then learning is the most important social process according to Lundvall’s (1992) seminal work on innovation systems (see also Lundvall, Chapter 29, this volume). A premium is placed on the ability to acquire, absorb and diffuse relevant knowledge and information throughout the various institutions that affect the process of economic development and change. Organizations need to become more reflexive and adaptive, by tapping into the knowledge and capabilities that their members and their competitors possess. Increasingly, the challenge for both public and private organizations is how to structure knowledge and intelligence in social ways, through learning processes that share insights collectively and express the complementarity among different forms of knowledge—scientific, technical, tacit or local (Paquet 1999; Wolfe and Gertler 2002). The increasing complexity of innovation policy-making, involving interactions among different economic actors and governance structures, “necessitates effective policy learning mechanisms which allow policy makers to learn from past experiences, ongoing implementation processes and the assessment of future trends” (Koschatzky 2005). As Lundvall stresses, learning, far from being an individual affair, is fundamentally an interactive process that requires the presence of networks and varies strongly according to the degree of social interaction among the members of those networks (1992). Indeed, both Freeman (1987) and Lundvall (1992) emphasize the relative importance of the patterns of interaction between firms as part of a collective learning process in the acquisition and use of new technical knowledge. This flows from their belief that innovation is increasingly tied to a process of interactive learning and collective entrepreneurship,

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particularly in terms of the relationship between producers and users of new technology. For governments, the capacity to absorb new inputs, identify gaps in current policy, and assist firms in accessing and applying knowledge, will determine their relevance in the innovation process (Bradford and Wolfe 2013). Learning in this sense, for both private and public sectors, refers to the building of new competencies and the acquisition of new skills, not just gaining access to information. It is the capability of individuals, firms and governments to adapt to rapidly changing economic circumstances that will determine their future economic success in the global economy (Lundvall and Borrás 1998). From this perspective, two distinct, though related, types of learning are crucial to the innovation process: social and policy. Social Learning The concept of social learning is critical for the kinds of organizational changes associated with the emerging, knowledge-based economy. The capacity for social learning is essential for tapping into the intelligence of individual organizations as well as the collective knowledge of firms within a given geographic space. The nature of the governance arrangements that regulate the patterns of interaction among firms and institutions within this space is thus critical to their capacity for social learning. In this concept of a shared or networked form of social learning, neither the public sector nor individual private enterprises are the repository of all forms of relevant information and knowledge; rather, the process of innovation and institutional adaptation depends upon an effective pattern of interaction between public and private actors. Social learning is thus essential for building a capacity to sustain growth and facilitate the adjustment process from declining economic activities to expanding ones in response to the process of globalization and altered competitive conditions (Wolfe and Gertler 2002). The conditions of extreme uncertainty that characterize periods of rapid economic and technological change place a premium on the ability to acquire, absorb and diffuse relevant knowledge and information throughout the various organizations that affect the process of economic development. In addition to effecting a transverse dispersal of authority across a wider range of social, economic and political actors that requires the adoption of more consensual forms of decision making, periods of rapid economic and technological change fundamentally alter the definition and achievement of organizational objectives. In periods like this, organizations need to become more reflexive and adaptive; in other words, they need to become capable of learning both what their goals are and how to adapt the means to reach them in the context of a rapidly shifting environmental context. The most effective way of doing this is by tapping into the knowledge and capabilities that members of those organizations possess by facilitating processes of social learning (Paquet 1999). Underlying this notion of social learning is the concept of reflexivity which is derived from a number of sources—not least the work of Anthony Giddens. For Giddens, reflexivity is grounded in the structures of social practice that are fundamental to his social analysis. “Continuity of practices presumes reflexivity, but reflexivity in turn is possible only because of the continuity of practices that makes them distinctively ‘the same’ across time and space. ‘Reflexivity’ hence should be understood not merely as ‘self-consciousness’ but as the monitored character of the ongoing flow of social life”

Learning through governance 731 (Giddens 1984). Moreover he ascribes the characteristics of reflexivity not only to individuals but also to institutions. This element of institutional reflexivity described by Giddens is further elaborated by Cooke (1997), who suggests that a capacity for self-monitoring must be viewed as an aspect of the institutionalized intelligence required to cope with the need for constant innovation in the face of continuous change and uncertainty. He suggests that the kind of institutional intelligence implied by the notion of institutional reflexivity implies a higherorder level of learning, learning-by-learning, which requires the ability to self-monitor and learn from past successes and failures: in other words, to learn how to learn. This suggests a higher order of learning by institutions—one based on the ability to apply institutional memory and intelligence to monitor their own progress in adapting to ongoing changes in the environment. The capacity for self-monitoring is an intrinsic aspect of the institutionalized intelligence required to cope with the need for constant innovation; as such, it is fundamental for the learning capacity of an organization, institution or region. Cooke and Morgan view reflexivity as “the systematic process which combines learning and intelligence such that, in a number of feedback loops, the system receives guidance” (1998). Storper also emphasizes the importance of fostering public institutions that encourage concerned parties to commit to the kinds of conventions and relations that support an institutionalized learning economy. Building trust among economic actors in a local or regional economy is a difficult process that requires a constant dialogue between the relevant parties so that interests and perceptions can be better brought into alignment. He sees talk as an essential process for generating these kinds of conventions and shared understandings. The value of talk arises from the need for communicative interaction that goes beyond the mere transfer of information between parties to build the conditions essential to achieve mutual understanding and acceptance: “Talk refers to communicative interaction, designed not simply to transmit information and relay preferences, but to achieve mutual understanding. In the case of prospective learning, information from other experiences where learning has worked . . . can be valuable as a stimulus” (Storper 2002). Where this process succeeds, these institutions foster social learning and consequently play an important role in supporting the innovation process within a local or regional economy. In institutions that promote learning by monitoring, actors can gauge the benefits they are gaining through their involvement without making themselves overly vulnerable. Sabel suggests that this process may be particularly beneficial in the emerging knowledge-based economy, where the production of complex goods requires the coordination of many specialized firms across diverse branches of the industrial and service sectors (Sabel 1994). When learning by monitoring has successfully been institutionalized in this way, it allows actors to assess where cooperation is advantageous and mutually beneficial, thus contributing to a broader process of social learning throughout the regional economy (Wolfe and Gertler 2002). Policy Learning The kind of interactive learning based on a degree of reflexivity, or the ability to selfmonitor and learn from past successes and failures, poses important questions about the

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nature of policy formation in modern democracies. For it is not only private institutions that must learn and adapt to the changing realities of a more innovative economy, but public ones as well, especially in the context of more associative and multilevel forms of governance. Thus, Lundvall’s emphasis on the centrality of learning processes for innovation in the knowledge-based economy applies equally to policy processes in situations where accessing and integrating private sources of knowledge in the context of more associative forms of governance is both necessary and crucial to ensure the effectiveness of policy outcomes. However, the ability to acquire, share and disseminate policy-relevant knowledge within novel governance arrangements poses new challenges for government departments and agencies accustomed to operating in the more traditional command and control mode of conventional bureaucracies or the jurisdictional boxes of classical federalism (Bennett and Howlett 1992). The concept of policy learning has not been fully elaborated, and much of the research from this perspective remains focused on cognitive processes internal to the government (Hall 1993; Bradford 1998). We view policy learning as a specific form of social learning that applies to policy-making processes that occur internally within governmental organizations, but also in the broader governance processes that link public and private sector actors in policy networks and policy communities. As such, the concept of learning found in the policy literature must be linked to the broader notion of social learning outlined above and placed in the context of the emerging forms of associative and multilevel governance that are critical for innovation processes in a knowledge-based economy (Bradford and Wolfe 2013). Learning dynamics increasingly cross the public and private sectors, joining a host of economic actors in processes of mutual discovery as to what strategies and interventions generate value. A good start toward such linking involves recognizing that policy learning involves specific forms of the organizational learning which intersect or overlap in different governance networks. Policy learning can thus take place inside individual public sector organizations, within organizations in the same network or systems, or across various organizations in different networks or systems. The recent recognition in the policy literature that rationality is bounded and that conflicting choices and values underlie policy decisions requires a more nuanced approach to learning processes (Bradford and Wolfe 2013; Beland and Cox 2011). From this perspective, policy analysis contributes to the discourse and bargaining that frames policy formation. The organizational and institutional structures within which policy is made are also critical. The design of appropriate policy depends to a large extent on the design of organizational structures capable of learning and adapting to what is learned both within the public sector and in broader, more associative and multilevel forms of governance (David and Foray 1995). Policy learning in this sense is described as “a change in thinking” that occurs in a structured and directed way (Koschatzky 2009) and is focused on the refashioning of policy tools to resolve a policy issue or achieve a specific goal or objective (Kemp and Weehuizen 2005). Central to the process of policy learning is the acquisition and absorption of new forms of knowledge on the part of those officials who bear responsibility for political decisions. As is the case with the innovation process, policy learning is cumulative; policy makers build on their past knowledge and competences to adapt to changing circumstances in a reflexive manner (Koschatzky 2009; Burton-Jones 1999). Policy learning by definition includes “policy forgetting”; in other words, the ability to abandon outdated policy approaches that are

Learning through governance 733 no longer effective or may lead to counter- productive results when working in new and different modes of governance. The networked dimension of policy learning acquires even greater significance in the context of an associative or multilevel model of governance where issues of steering and accountability loom large. Here policy learning is especially complex, as it must extend across the boundaries of several different organizations—including both public and private ones—at more than one level of political jurisdiction. On this basis, Nauwelaers and Wintjes distinguish between three different modes of policy learning in different organizations: intra-organizational learning, intra-system learning and inter-system learning. The first mode involves primarily learning-by-doing with respect to the internal practices or routines of the individual organization. The relevant policy knowledge is mostly internal, embedded in the experience and tacit understanding of the policy makers in the organization. The second mode applies to learning processes that extend across the boundaries of different organizations that are linked together in the same policy system. This can include the transfer of person-embodied forms of knowledge through meetings by individuals in the system or more codified transfers of knowledge through the conduct of benchmarking processes or the creation of scorecards, which have been used extensively to evaluate the performance of national and regional innovation systems. The final mode involves the conduct of comparative evaluation exercises across organizations in different systems. This type of policy learning has been used extensively by policy officials in international organizations trying to transfer insights and best practice across organizations located in different national and regional systems (Nauwelaers and Wintjes 2008). The analytical strength of this framework resides in distinguishing between different modes, enabling the comparative study of interactive learning dynamics within and across jurisdictions. The social and policy dimensions of learning can be drawn together for more robust system-level explanations of innovation.

INNOVATION AND GOVERNANCE: REGIONAL INNOVATION SYSTEMS While the preceding discussion might suggest that the concepts of governance and social and policy learning apply at a high level of abstraction, in fact they have begun serving as an important guide for policy formation to promote innovation, especially at the local and regional level (see also Feldman and Lowe, Chapter 42, this volume; Rallet and Torre, Chapter 26, this volume; Bathelt and Henn, Chapter 28, this volume). The drive to establish policies and institutional frameworks to promote competitiveness in a knowledge-based economy is widespread. To this end, policy makers across a growing number of jurisdictions in Europe and North America have adopted the regional innovation system or innovation ecosystem approach to intensify efforts in supporting and leveraging the flow of knowledge throughout their local and regional economies (Bramwell et al. 2012). This approach reflects a sophisticated way of viewing the innovation process, enabling policy makers to focus attention on the collaborative, interdependent nature of the innovation process and identify the best means of stimulating productive networks and relationships within and across local networks of innovation actors to stimulate sectors of comparative advantage. Furthermore, the innovation system approach focuses on the constantly

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evolving relationships between a wide spectrum of economic partners and draws attention to how their interactions affect knowledge creation, the rate of knowledge diffusion, knowledge transformation to innovation and the expansion of that innovation. As we have argued elsewhere, Rather than targeting institutional research infrastructures to specific sectors or specializations, regional innovation systems are shifting to “hubs” or “communities for innovation” that allow for more agile responses to shifting technology and market conditions [and] leveraging of local and regional infrastructures to fuel the regional innovation process through the collaboration of multiple partners including research parks, universities, large research-driven firms, start-ups, investors and other professionals. (Wolfe 2011)

The innovation system approach has found fertile ground at both national and regional levels across OECD countries. Since the early 1990s, the approach has been embedded in the Cohesion Policies of the European Union, particularly through the initiatives to promote the adoption of regional innovation strategies in the less favoured regions and subsequently through the Regional Programs of Innovative Actions (Morgan and Nauwelaers 1999; McCann and Ortega-Argilès 2013). While recent approaches in the US and Canada have emphasized the promotion of innovation ecosystems and cluster strategies, the underlying dynamics of associative governance and the support for mechanisms of social learning reflects the extent of policy learning that has occurred on both an intra-system and inter-system basis (Ketels 2013, Ebbekink and Lagendijk 2013). The US approach has tended to focus on strong national support for the transformation of entire industries, ranging from automobiles, steel, chemicals and materials, to pharmaceuticals, computers and information technologies, emerging biotechnologies, and the internet, with increasing recognition that these industries are grounded in local and regional complexes of firms, research institutes, venture funding and local governance organizations. At the subnational level, robust regional innovation systems are evident in California’s digital economy, Northeast Ohio’s and Boston’s Route 128 high-tech industries, and New York City’s burgeoning Fintech sector. All are strong examples of innovation hotbeds that have benefitted from the strong collaborative partnerships forged between local research universities, government, venture capital communities, entrepreneurial companies and individuals to leverage the flow of knowledge exchange and technology transfer and bolster regional economic development. The Obama administration committed to a series of coordinated policy initiatives to strengthen and support regional innovation clusters. The various lobbying efforts for a more concerted federal strategy to support regional innovation clusters, underway for a number of years, found strong support in the federal budget for FY2011 and updated one year later. In a series of interrelated measures, the budget introduced several proposals to support the growth of regional innovation clusters across different departments. The centrepiece of these measures was the Economic Development Administration’s (part of the US Department of Commerce) proposal to establish a $75 million program to support Regional Innovation Clusters with funds for regional planning efforts, and matching grants to support cluster initiatives (Wessner 2013). The rationale for the US government’s approach to regional economic development was spelled out in a speech given by John Fernandez, former Mayor of Bloomington, Indiana and then Assistant Secretary of Commerce for Economic Development. He noted that dynamic and innovative compa-

Learning through governance 735 nies thrive in places where scientists, business people, highly skilled workers and venture capitalists cluster together with similar and interrelated firms. “Place matters” according to Fernandez: “Entrepreneurs and researchers and innovators want to be around each other. They want to feed off the shared creative energy. They want access to a shared talent pool. They want to build relationships” (Fernandez 2010). The adoption of this approach both embodies a social learning approach at the regional level, but more significantly, represents a critical example of policy learning at the national level. While Canada lacks a coordinated approach at the national level, there are a number of thriving regional/local innovation ecosystems. In Ontario, regions like KitchenerWaterloo, the Greater Toronto Area (GTA) and Ottawa demonstrate the capability of innovation systems to successfully harness the greatest potential of different technologies, sectors and markets in support of localized knowledge transfer (Wolfe 2012). For instance, the Waterloo innovation system provides a rich interface for thousands of innovation actors and organizations across different sectors, including students and faculty from four leading post-secondary education institutions (University of Waterloo, University of Guelph, Wilfrid Laurier University and Conestoga College); staff from 150 local research institutes; an established venture capital community; and other enabling organizations like the Communitech Hub and the Accelerator Centre (Wolfe 2015). While less coordinated than the EU’s efforts to support regional innovation strategies and the smart specialization approach, numerous local and regional initiatives in the more decentralized American and Canadian contexts are mobilizing their assets and resources to support the development of key industries, including high tech, financial services, education/research, manufacturing, service sector and health/life sciences. Regional innovation systems become sustainable when the requisite assets and resources are available for maintaining relationships between partners, adapting to changing internal and external catalysts, and translating knowledge generated by research organizations to industry investors. In governance terms, dynamic innovation systems and local innovation clusters are embedded in dense networks of actors that interact to support social learning processes. Forged initially through the associational relations afforded by geographical proximity at the local and regional level, the longer term viability of these innovation systems and innovation clusters depends increasingly on the ability of governments to incorporate the lessons derived from social learning processes into higher-level processes of policy learning that feed into multilevel governance approaches (OECD 2014).

CONCLUSION This chapter has explored the issues and challenges of innovation in the knowledgebased economy. Moving beyond traditional approaches that tend to focus on economic institutions or government structures, we adopt an interactive view of the innovation process, capturing linkages among knowledge flows, governance relations and learning mechanisms. In reviewing key contributors to this perspective, the chapter ranged across academic disciplines for an integrated interpretation capturing the economic, social and policy dimensions of innovation. From our discussion, it is apparent that innovation in the knowledge-based economy is a complex, multifaceted process, one that challenges actors from government, business, education and communities to adapt through

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collaboration and learning. Indeed, much of the literature reviewed emphasizes the obstacles and barriers to such change. However, our discussion also reveals opportunities in both the scholarly and practical domains. In the case of the former, a better understanding of the role that knowledge plays in the economy, coupled with new insights into governance and learning models, offers a framework to analyze whether and how national governments and local and regional actors align their resources to support firms, sectors and clusters. From a practical perspective, there is growing evidence that such processes are already well underway in many places as local and regional innovation systems gather momentum across the OECD countries. Further work linking theory and practice will strengthen the knowledge platform for innovation in the economy, in the community and in policy.

REFERENCES Amin, A. (1996) “Beyond Associative Democracy”, New Political Economy 1, 3: 309–333. Bathelt, H. and Henn, S. (2017) “National and Regional Innovation Systems”, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, 457–471. Beland, D. and Cox, H. (eds) (2011) Ideas and Politics in Social Science Research. Oxford: Oxford University Press. Bennett, C. and Howlett, M. (1992) “The Lessons of Learning: Reconciling Theories of Policy Learning and Policy Change”, Policy Sciences 25, 3: 275–294. Block, F. and Keller, M. (2009) “Where Do Innovations Come From? Transformations in the US Economy, 1970–2006”, Socio-Economic Review 7, 3: 459–483. Bradford, N. (1998) Commissioning Ideas: Canadian National Policy Innovation in Comparative Perspective. Toronto: Oxford University Press. Bradford, N. and Wolfe, D. (2012) “Playing Against Type? Regional Development Policy in the Harper Era”, in B. Doern and C. Stoney (eds) How Ottawa Spends, 2012–2013: The Harper Majority, Budget Cuts, and the New Opposition. Kingston and Montreal: McGill-Queen’s University Press, 71–89. Bradford, N. and Wolfe, D. (2013) “Governing Regional Economic Development: Innovation Challenges and Policy Learning in Canada”, Cambridge Journal of Regions, Economy and Society 6, 2: 331–347. Bradford, N. and Bramwell, A. (2014) Governing Urban Economies: Innovation and Inclusion in Canadian City-Regions. Toronto: University of Toronto Press. Bramwell, A., Hepburn, N. and Wolfe, D. (2012) Growing Innovation Ecosystems: University–Industry Knowledge Transfer and Regional Economic Development in Canada. Knowledge Synthesis Paper on Leveraging Investments in HERD prepared for the Social Sciences and Humanities Research Council of Canada. Toronto: Munk School of Global Affairs. Burton-Jones, A. (1999) Knowledge Capitalism: Business, Work and Learning in the New Economy. Oxford and New York: Oxford University Press. Cohendet, P. and Simon, L. (2017) ‘Concepts and Models of Innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, 33–55. Cooke, P. (1997) “Institutional Reflexivity and the Rise of the Region State”, in G. Benko and U. Strohmayer (eds) Space and Social Theory: Interpreting Modernity and Post-Modernity. Oxford: Blackwell, 285–303. Cooke, P. and Morgan, K. (1998) The Associational Economy Firms, Regions, and Innovation. Oxford: Oxford University Press. David, P. and Foray, D. (1995) “Accessing and Expanding the Science and Technology Knowledge Base”, OECD STI Review 16: 13–68. Ebbekink, M. and Lagendijk, A. (2013) “What’s Next in Researching Cluster Policy: Place-Based Governance for Effective Cluster Policy”, European Planning Studies 21,5: 735–753. Evans, P. and Forth, P. (2015) Borges’ Map: Navigating a World of Digital Disruption. Boston, MA: The Boston Consulting Group. Feldman, M. and Lowe, N. (2017) ‘Innovation, Governance and Place’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, 685–701.

Learning through governance 737 Fernandez, J. (2010) Remarks Prepared for Delivery to the Chicago Rail Summit. Washington, DC: Economic Development Administration, US Department of Commerce. Freeman, C. (1987) Technology Policy and Economic Performance: Lessons from Japan. London and New York: Pinter Publishers. Giddens, A. (1984) The Constitution of Society: Outline of the Theory of Structuration. Berkeley, CA: University of California Press. Hall, P. (1993) “Policy Paradigms, Social Learning, and the State: The Case of Economic Policymaking in Britain”, Comparative Politics 25: 175–196. Hooghe, L. and Marks, G. (2001) Multi-level Governance and European Integration. Lanham, MD: Rowman and Littlefield Publishers. Horak, M. and Young, R. (eds) (2012) Sites of Governance: Multilevel Governance and Policy Making in Canada’s Big Cities. Kingston and Montreal: McGill-Queen’s University Press. Kemp, R. and Weehuizen, S. (2005) Policy Learning: What Does It Mean and How Can We Study It?, Publin Report No. D15, Oslo: NIFU-STEP. Ketels, C. (2013) “Recent Research on Competitiveness and Clusters: What Are the Implications for Regional Policy?” Cambridge Journal of Regions, Economy and Society 6, 2, July: 269–284. Koschatzky, K. (2005) “Foresight as a Governance Concept at the Interface between Global Challenges and Regional Innovation Potentials”, European Planning Studies 13, 4: 619–639. Koschatzky, K. (2009) The Uncertainty in Regional Innovation Policy: Some Rationales and Tools for Learning in Policy Making. Working Papers Firms and Regions No. R6/2009. Karlsruhe: Fraunhofer ISI. Lundvall, B.-Å. (2017) ‘National Innovation Systems and Globalization’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, 472–489. Lundvall, B.-Å. and Borrás, S. (1998) The Globalising Learning Economy: Implications for Innovation Policy. Targeted Socio-Economic Research Studies, DG XII, Commission of the European Union. Luxembourg: Office for Official Publications of the European Communities. Lundvall, B.-Å. (1992) “Introduction”, in B.-Å.Lundvall (ed.) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. London: Pinter Publishers, 1–23. Lundvall, B.-Å. (1998) “Why Study National Systems and National Styles of Innovation”, Technology Analysis and Strategic Management 10, 4: 407–421. Marks, G. (1993), “Structural Policy and Multilevel Governance in the EU”, in A. Cafruny and G. Rosenthal (eds) The State of the European Community. New York: Lynne Rienner, 391–411. McCann, P. and Ortega-Argilès, R. (2013) “Modern Regional Innovation Policy”, Cambridge Journal of Regions, Economy and Society 6, 2, July: 187–216. Metcalfe, J. (2001) “Institutions and Progress”, Industrial and Corporate Change, 10, 3: 561–586. Metcalfe, J., Foster, J. and Ramlogan, R. (2002) Adaptive Economic Growth. CRIC Discussion Paper No. 59. Manchester: Centre for Research on Innovation and Competition. Morgan, K. (1997) “The Learning Region: Institutions, Innovation and Regional Renewal”, Regional Studies 31, 5: 491–503. Morgan, K. and Nauwelaers, C. (1999) “A Regional Perspective on Innovation: From Theory to Strategy”, in K. Morgan and C. Nauwelaers (eds) Regional Innovation Strategies: The Challenge for Less Favoured Regions. London: Stationery Office, 1–18. Narin, F., Hamilton, K. and Olivastro, D. (1997) “The Increasing Linkage between US Technology Policy and Public Science”, Research Policy 26: 317–330. Nauwelaers, C. and Wintjes, R. (2008) “Innovation Policy, Innovation in Policy: Learning within and across Systems and Clusters”, in C. Nauwelaers and R. Wintjes, Innovation Policy in Europe: Measurement and Strategy. Cheltenham and Northampton, MA: Edward Elgar Publishing, 225–269. Nelson, R. and Winter, S. (1982) An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press. OECD. (1996) “Special Theme: The Knowledge-Based Economy”, in Science, Technology and Industry Outlook 1996. Paris: Organisation for Economic Co-operation and Development, 229–256. OECD. (2000) A New Economy? The Changing Role of Innovation and Information Technology in Growth. Paris: Organisation for Economic Co-operation and Development. OECD. (2014) OECD Regional Outlook 2014. Regions and Cities: Where Policies and People Meet. Paris: Organisation for Economic Co-operation and Development. Osborne, S. (ed.) (2010) The New Public Governance? Emerging Perspectives on the Theory and Practice of Public Governance. New York: Routledge. Paquet, G. (1997) “States, Communities and Markets: The Distributed Governance Scenario”, in T. Courchene (ed.) The Evolving Nation-State in a Global Information Era: Policy Challenges. Kingston: John Deutsch Institute for the Study of Economic Policy, Queens’ University, 25–56. Paquet, G. (1999) Governance through Social Learning. Ottawa: University of Ottawa Press.

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Peters, G. and Pierre, J. (2004) “Multi-level Governance and Democracy: A Faustian Bargain?” in I. Bache and M. Flinders (eds) Multi-level Governance. Oxford and New York: Oxford University Press, 75–89. Rallet, A. and Torre, A. (2017) ‘Geography of Innovation, Proximity and Beyond’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham and Northampton, MA: Edward Elgar Publishing, 421–439. Rhodes, R. A. W. (1996) “The New Governance: Governing without Government”, Political Studies 44: 652–667. Romer, P. (1986) “Increasing Returns and Long-Run Growth”, Journal of Political Economy 94, 5: 1002–1037. Rutten, R. and Boekma, F. (2007) The Learning Region: Foundations, State of the Art, Future. Cheltenham and Northampton, MA: Edward Elgar Publishing. Rycroft, R. and Kash, D. (1999) “Innovation Policy for Complex Technologies”, Issues in Science and Technology, 16, 1, Fall. Sabel, C. (1994) “Learning by Monitoring: The Institutions of Economic Development”, in N. Smelser and R. Swedberg (eds) The Handbook of Economic Sociology. Princeton, NJ and New York: Princeton University Press and Russell Sage Foundation, 137–166. Schumpeter, J. (1928) The Theory of Economic Development. Oxford: Oxford University Press. Scott, A.J. and Storper, M. (1987) “High Technology Industry and Regional Development: A Theoretical Critique and Reconstruction”, International Social Science Journal 112: 215–232. Scott, A. (2012) “A World in Emergence: Notes towards a Resynthesis of Urban-Economic Geography for the Twenty-First Century”, in P. Cooke (ed.) Re-framing Regional Development: Evolution, Innovation, and Transition. London and New York: Routledge, 29–54. Scott, A., Agnew, J., Soja, E. and Storper, M. (2001) “Global City-Regions” in A. Scott (ed.) Global CityRegions: Trends, Theory, Policy, Oxford and New York: Oxford University Press, 11–33. Stoker, G. (1998) “Governance as Theory: Five Propositions”, International Social Science Journal 155: 17–28. Storper, M. (2002) “Institutions of the Knowledge-Based Economy”, in M. Gertler and D. Wolfe (eds) Innovation and Social Learning: Institutional Adaptation in an Era of Technological Change. Basingstoke, UK: Palgrave Macmillan, 135–159. Wessner, C. (ed.) (2013) Best Practices in State and Regional Innovation Initiatives: Competing in the 21st Century. Washington, DC: National Academies Press. Wolfe, D. (2010) From Entanglement to Alignment: A Review of International Practice in Regional Economic Development. Toronto: Mowat Centre For Policy Innovation. Wolfe, D. (2011) Clusters, Collaboration and Networking: Review of International Best Practice and Implications for Canada. Report prepared for the Strategy and Research Branch, National Research Council of Canada. Toronto: Munk School of Global Affairs. Wolfe, D. (2012) “Civic Governance, Social Learning and the Strategic Management of City-Regions,” in D.B. Audretsch and M.L. Walshok (eds) Creating Competitiveness: Entrepreneurship and Innovation Policies for Growth. Cheltenham and Northampton, MA: Edward Elgar Publishing, 6–25. Wolfe, D. (2015) “Resilience and Governance in City-Regions: Lessons from Waterloo, Ontario”, in K. Jones, A.  Lord and R. Shields (eds) City-Regions in Prospect? Exploring the Meeting Points between Place and Practice. Kingston and Montreal: McGill-Queen’s University Press, 187–213. Wolfe, D. and Gertler, M. (2002) “Innovation and Social Learning: An Introduction”, in D. Wolfe and M. Gertler (eds) Innovation and Social Learning: Institutional Adaptation in an Era of Technological Change. Basingstoke: Palgrave Macmillan, 1–25.

45. Global value chains and innovation Ari Van Assche

INTRODUCTION In recent decades, many firms have undertaken rapid transformations that are changing the way products and services are produced. Thanks to reduced communication and transportation costs, they have abandoned the practice of producing goods and services themselves in a single country. Through offshoring and outsourcing, they have sliced up their value chains and dispersed production activities across the globe, creating global value chains (GVCs). Bombardier’s Global Express aircraft is a good example of a product made with a GVC. Its production involves dozens of companies that are located in several countries around the world. The fuselage comes in part from the Bombardier plant in Belfast and another part from its Japanese partner. The fuel system is produced in Europe, the wings are made in Japan, and the tail is made in Canada. All major components are shipped to the Bombardier plant in the vicinity of Montreal for final assembly. The Global Express is hardly an isolated case, but rather part of a larger trend. Currently, trade in intermediate inputs has grown to account for roughly two-thirds of international trade (Johnson and Noguera 2012). And a new dataset on trade in value added (TiVA) compiled by the Organisation for Economic Co-operation and Development (OECD) and World Trade Organization (WTO) provides evidence that the share of foreign value added embodied in a country’s exports increased for virtually all countries around the world between 1995 and 2011. A vast literature has analyzed GVCs by investigating the drivers of firms’ decisions to fragment their production internationally (Grossman and Rossi-Hansberg 2008; Schmeisser 2013; Van Assche 2008), the type of firms that are more likely to offshore production activities (Defever and Toubal 2013; Farinas and Martín-Marcos 2010; Kurz 2006; Tang and Van Assche 2016; Tomiura 2007), and the effects of offshoring on a firm’s productivity, employment, and wages in both the home and host countries (Amiti and Konings 2007; Goldberg et al. 2010; Hummels et al. 2014; Kasahara and Rodrigue 2008). A question that has hitherto received surprisingly little scholarly attention, however, is how the globalization of value chains affects a firm’s innovation capabilities. As we will explain in this chapter, economists and business scholars generally conjecture that the effect should be positive for two reasons. The movement of productive activities offshore helps firms free up resources, which should give them leverage to put them into highervalue activities like innovation (Farrell 2005; Bardhan and Jaffee 2005). Furthermore, expanding value chains around the globe exposes companies to foreign technologies and knowledge that are not available locally, thus improving their innovation capabilities (Berry 2014; Cantwell 1989; Phene and Almeida 2008; Pietrobelli and Rabellotti 2011). A number of recent studies, however, suggest that this view is too simplistic. Technology is too often taken as fixed in these studies, yet this is not necessarily the case. Particularly 739

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in emerging industries, technologies keep changing fast and unpredictably, potentially undermining the perceived advantages of offshoring on innovation (Pisano and Shih 2009). Adding to this, offshoring decisions can in certain circumstances influence a firm’s technology choice, negatively affecting both the nature and pace of technological change (Fuchs and Kirchain 2010; Fuchs et al. 2011). In this chapter, we will take into account these new academic insights to provide a more nuanced view of the link between GVCs and innovation. We structure the chapter as follows. The second section presents the economic logic of GVCs and analyzes how this matters for a firm’s innovation capabilities. The third section then investigates how technological unpredictability and technology choice affects this relation. The last section provides concluding remarks and suggests areas for future research.

ECONOMIC LOGIC OF GLOBAL VALUE CHAINS It is instructive to start with Porter’s (1985) concept of “value chain” to understand the economic logic of slicing up production and dispersing activities to different locations around the globe. A value chain is a sequence of activities or tasks that a company performs to design, produce, sell, deliver, and support its products. It not only consists of the physical transformation processes (so-called primary activities) that go into producing a good, but also support activities. These include research and development (R&D), procurement, human resource management, and many tasks that are regarded as high-value-added activities. The value chain has two important features which affect the location decision of value chain activities. On the one hand, activities have heterogeneous input requirements, which in line with the theory of comparative advantage generate a centrifugal force for firms to geographically disperse them across the globe. On the other hand, activities are intrinsically inter-connected, which act as a countering centripetal force for firms to concentrate activities in the same location. We will discuss these two opposing forces in detail and evaluate the implications for a firm’s innovation capabilities. Centrifugal Force Locations vary in their knowledge and resource endowment profiles (Cantwell and Iammarino 1998; Chung and Yeaple 2008; Furman et al. 2002), and this pushes firms to slice up their value chain and disperse activities across multiple countries so as to tap into complementary pockets of knowledge and resources. Two strands of literature have looked at these centrifugal forces from different angles. On the one hand, studies on GVCs have focused on the “vertical” efficiency-seeking motive for firms to disperse value chain activities across multiple countries (Grossman and Rossi-Hansberg 2008). Studies on global knowledge sourcing, then again, have concentrated on the “horizontal” knowledgeseeking motive for firms to set up operations in multiple countries (Berry 2014; Cantwell 1989). Studies on global production networks have set up an overarching framework that combines both horizontal and vertical motives for firms to slice up their value chain and disperse activities across multiple countries (e.g. Coe et al. 2008).

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(a) Efficiency-seeking The GVCs literature has focused on vertical efficiency-seeking motives for firms to disperse value chain activities across multiple countries. The production of an activity requires a mix of labor, capital, and knowledge, and this input composition differs greatly along the value chain. To give an example, R&D is a knowledge-intensive task that requires significant investments in human capital, while manufacturing assembly requires relatively more unskilled labor. At the same time, locations differ in their resource endowment profiles, with some locations that are abundant in human capital and others abundant in unskilled labor (Dunning 1998). In line with the classical theory of comparative advantage, firms have the incentive to reduce their production costs and maximize their competitive advantage by keeping the knowledge-intensive activity in a developed country where skilled labor is abundant and to move the labor-intensive activity to a developing country with cheap labor (Kogut 1985; McCann and Mudambi 2005). Empirical studies provide substantial support that offshoring value chain activities strengthens a firm’s competitiveness. Using firm-level data from Germany, Wagner (2011) finds strong evidence that offshoring increases a firm’s productivity. In a similar vein, Amiti and Konings (2007) use Indonesian data and show that the rise in input imports due to a 10 percentage point drop in input tariffs leads to a 12 percent productivity gain for local firms. Kasahara and Rodrigue (2008), Kasahara and Lapham (2013), Topalova and Khandelwal (2011), and Goldberg et al. (2010) all show similar large gains for Chile and India. While few studies have made a direct link between offshoring and innovation, there are good reasons to expect that it matters for firms in both developed and developing countries (Turkina and Van Assche 2016). For firms in developed countries, offshoring can free up resources that they can invest in higher-value activities such as R&D, improving its innovation capabilities (Bardhan and Jaffee 2005; Farrell 2005). For firms in the recipient location, then again, the inclusion in GVCs can provide them access to valuechain-specific knowledge from other locations which may allow them to move up the chain (Pietrobelli and Rabellotti 2011). (b) Knowledge-seeking The global knowledge sourcing literature, then again, has focused on horizontal knowledge-seeking motives for firms to disperse value chain activities in different locations. The starting point is that firms benefit from knowledge diversity (Cantwell 1989) and that locations differ markedly in their knowledge profiles (Cantwell and Iammarino 1998; Chung and Yeaple 2008; Furman et al. 2002). As a consequence, firms have the incentive to set up activities in different locations to gain access to complementary knowledge pockets that are not available locally (Cantwell and Santangelo 1999; Berry 2014). They may do so within their firm boundaries by setting up knowledge-creating subsidiaries in targeted clusters (Cantwell and Mudambi 2005; Feinberg and Gupta 2004). Or they may develop trans-local technological collaborations with other firms in the targeted locations (Archibugi and Iammarino 2002; Nieto and Rodríguez 2011). Once the external knowledge is obtained, the company can then use reverse knowledge transfer to enhance the parent firm’s innovation and productivity in its headquarter location through combinative knowledge generation (Berry 2014; Chung and Yeaple 2008). For this too, there is empirical evidence to back it up. Chung and Yeaple (2008) show

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that US multinational firms primarily set up R&D subsidiaries in countries with similar technology profiles. Berry (2014) and Nieto and Rodríguez (2011) find that R&D offshoring is positively related to firms’ innovation performances, particularly when it occurs within firm boundaries. Finally, Turkina and Van Assche (2016) show that the innovation performance of firms in developed-country clusters is primarily related to trans-local connectedness in “horizontal” partnership relations, whereas the innovation performance of firms in emerging-market clusters is mainly related to trans-local connectedness in “vertical” buyer–supplier and investment linkages. While this chapter mainly focuses on the link between GVCs and innovation at the firm level, the theoretical mechanisms also apply to the cluster/regional level (see for example Li et al. 2012; Mudambi and Santangelo 2016; Turkina and Van Assche 2016). Centripetal Forces Enticing as these benefits of creating a GVC may be, dispersing a value chain geographically comes with substantial costs. First, moving a value chain activity offshore entails fixed costs which negatively influence a firm’s willingness to spread out their value chain. Adding to this, the production of a value chain activity often requires resources and knowledge to be sent from other stages of a value chain. The costs associated with coordinating these interdependencies between value chain stages – the spatial transaction costs – tend to increase with distance, thus acting as a countervailing pull force to co-locate value chain activities in the same location. (a) Fixed costs Companies face a fixed cost when relocating a value chain activity offshore (Tang and Van Assche 2016). If a company sets up its own subsidiary in a foreign location, there is the cost of searching an appropriate venue, setting up the plant or office, and coordinating activities across borders. If a firm offshores an activity at arm’s length to an external firm, it in addition needs to spend substantial resources to find the most suitable outsourcee and set up the appropriate governance structure. The existence of a fixed cost affects firms differently depending on their size and performance. Antràs and Helpman (2004) show that in an industry with heterogeneous firms and identical fixed costs, only the more productive firms can profitably jump over the fixed cost hurdle of offshoring, while less productive firms are better off producing locally. This prediction is backed up by empirical evidence that firms which offshore indeed tend to be larger and more productive than those that source locally (Defever and Toubal 2013; Farinas and Martín-Marcos 2010; Kurz 2006; Tang and Van Assche 2016; Tomiura 2007). The fact that only large and performing firms choose to create GVCs casts an important caution onto any empirical firm-level study that links GVCs with innovation. A couple of recent papers find that firms which offshore are more heavily engaged in R&D activities, have a more sophisticated human capital base, more frequently introduce new products, and invest more frequently in advanced process technologies (Veugelers 2013; Dachs and Ebersberger 2013). One should be wary of taking this as evidence that offshoring induces innovation since the direction of causality may go the other way. Larger and more productive firms innovate more, and those are the ones that also happen to be more likely to offshore.

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(b) Spatial transaction costs The location decision of a task also depends on costs related to the inherent links that exist between the task and other value chain nodes. To produce a task, a firm generally requires information, materials, and capital that have been generated and transferred from other value chain activities. Transferring goods and information between value chain nodes implies spatial transaction costs, and these tend to increase when activities are located at a distance from each other. The literature has primarily focused on two types of spatial transaction costs. The first type are the trade costs associated with physically moving products across geographical space (Hummels 2007). They are essentially the difference between the total landed cost of a good or service (the price of a product once it has arrived at a buyer’s door) and its production cost, and can be broadly divided into three categories: international transportation costs, border-related costs and beyond-the-border costs (Van Assche 2016). Since all three costs tend to increase when international borders are crossed, trade costs act as a deterrent against offshoring, and especially for time-sensitive goods as well as “heavy” products with a high weight-to-value ratio that are more expensive to transport (Ma and Van Assche 2016). The second type of spatial transaction costs are knowledge transfer costs. Many processes of knowledge exchange are tacit and spatially sticky, thus requiring direct and repeated face-to-face contact between people for it to be transmitted (Storper and Venables 2004). Firms can minimize the knowledge transfer costs by co-locating an activity with other value chain tasks. If activities are located at a distance from each other, however, exchanging and monitoring knowledge flows becomes more cumbersome, requiring companies to create routines that allow for the temporary mobility of individuals (Bathelt and Turi 2011; Rallet and Torre, Chapter 26, this volume). Evidence indeed shows that increased geographic distance reduces knowledge transfers (Cummings et al. 2009; Gibson and Gibbs 2006). The knowledge transfer cost depends on the nature of the transaction, with certain types of information more cheaply transferable from a distance than others. A first characteristic is the complexity of the transaction. Complex tasks are less easy to offshore than routine tasks since it is difficult to specify them into simple instructions and teach them to foreign workers with little misunderstanding (Autor et al. 2003; Levy and Murnane 2004). They require complex thinking, judgment, and human interaction which are in need of extensive coordination and frequent face-to-face interaction. In line with this, Liu et al. (2011) find that American firms are more likely to offshore services which are more routine and less complex. Second, it depends on the codifiability of the transaction. Codifiable information can be expressed digitally and can thus be easily transferred over the internet (Blinder 2006; Leamer and Storper 2001). Tasks that intensively rely on codifiable information from other value chain activities can therefore be performed more easily at a distance. Tacit information, in contrast, cannot be conveyed in symbols. Tasks that intensively rely on tacit information therefore require frequent face-to-face contact, which makes it difficult to offshore its production. Companies can to a certain extent minimize the knowledge transfer costs by adapting their governance structure. Kogut and Zander (1993), for example, find that firms choose to produce an activity in-house (hierarchy) if an activity requires complex and tacit transactions, and decide to conduct an activity at arm’s length (market) if transactions

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Table 45.1 Gereffi et al.’s (2005) five governance types Governance type

Complexity of transactions

Ability to codify Capabilities in the transactions supply base

Degree of explicit coordination and power asymmetry

Market Modular Relational Captive Hierarchy

Low High High High High

High High Low High Low

Low

High High High Low Low

High

Source: Adapted from Gereffi et al. (2005).

are simple and codifiable. Gereffi et al. (2005) add three types of hybrid governance structures that lie between hierarchy and market (see Table 45.1). If transactions with a capable supplier are complex yet easily codifiable, the lead firm and supplier build a modular outsourcing relation where they use sophisticated software such as CAD/CAM technology to transfer information digitally. If knowledge transactions are complex and difficult to codify, then again, the lead firm and supplier often develop a relational outsourcing structure which inevitably involves a high proportion of mutual interdependence and face-to-face interaction. When suppliers lack competencies, a lead firm can set up a captive outsourcing relation in which it provides detailed information to the supplier and exerts high levels of monitoring and control. The governance type not only affects the knowledge transfer costs, but also a firm’s opportunities to learn from its value chain partner. Technological capabilities are often tacit and knowledge exchanges with value chain partners can therefore act as an important source for upgrading a firm’s capacities. Local suppliers, for example, may receive aid from their foreign buyers to upgrade their technological capabilities through sharing of production techniques and assisting with technology acquisition (Paus and Gallagher 2008). Local firms may also receive advice on quality insurance and the organization of product lines from their value chain partners (Crespo and Fontoura 2007; Javorcik 2008). The extent of knowledge spillovers from value chain partners depends on the type of GVC upgrading. Humphrey and Schmitz (2002) distinguish between four types of GVC upgrading: process, product, inter-sectoral, and functional. ●

●

●

●

Process upgrading refers to an increase in domestic value added due to the introduction of better technology or more efficient production methods for an existing production activity. In other words, it refers to productivity growth in existing activities in the value chain. Product upgrading involves conducting a similar activity as before for more sophisticated product lines (which can be defined in terms of increased unit values). Inter-sectoral upgrading takes place when a firm uses its acquired production knowledge to move into new, more sophisticated sectors with higher-value-added shares. Functional upgrading refers to performing new activities in the chain with a higher skill and value-added content.

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A foreign lead firm’s willingness to support a local supplier to upgrade varies widely across upgrading type (Humphrey and Schmitz 2002; Schmitz and Knorringa 2000). Lead firms are generally willing to offer favorable conditions for upgrading within production (product, inter-sectoral, or process upgrading) since it helps foster the complementarity between value chain partners. They may face discouragement and even obstacles, however, when it comes to functional upgrading as this may encroach on the competences of other value chain actors (Schmitz and Knorringa 2000; Giuliani, Chapter 22, this volume; Lundvall, Chapter 29, this volume). How does the existence of fixed and spatial transaction costs affect the link between GVCs and innovation? It is safe to say that it only plays a moderating role. The existence of spatial transaction costs adds constraints to a company which affects its feasibility of creating a GVC. For firms that find it optimal to create a GVC given the constraints related to spatial transaction costs, nonetheless, the two channels through which offshoring spurs innovation continue to exist. On the one hand, the firms can save on resources which can be invested into R&D. On the other hand, they can gain access to foreign pockets of knowledge which can increase their innovation capabilities.

THE FALLACY OF FIXED TECHNOLOGY A number of recent studies caution that one needs to be careful to avoid painting an overly rosy picture of the link between GVCs and innovation (Pisano and Shih 2009; Fuchs and Kirchain 2010). It is not that these articles question the economic logic of GVCs presented in above per se. Rather, they suggest that setting up GVCs is often so complex that companies are unable to foresee the full consequences of offshoring, making it impossible to make precise estimations of the costs of implementing offshoring activities abroad (Larsen et al. 2013). If such hidden costs to offshoring are sufficiently large, it can outweigh the benefits of creating a GVC, in some situations overturning the positive link of GVCs on innovation. Hidden costs of offshoring are not a new concept, with examples of firms underestimating the costs of moving a value chain activity overseas abounding (Porter and Rivkin 2012). Many firms complain that transferring knowledge internationally requires more control than originally expected. Other companies experience that local labor costs increase beyond expectations or that high rates of personal turnover reduce productivity more than expected. A particularly important hidden cost, nonetheless, is what may be called the fallacy of fixed technology. Companies that consider offshoring often assume that technology is fixed and will remain so in the medium to long term. As a result, they do not take into account the effects of potential future technological change on the economic logic of offshoring. A number of recent studies have highlighted the flaw that lies in this assumption and have demonstrated that this can negatively affect innovation. We will discuss these arguments in the remainder of this section.

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Technological Maturity Technology is rarely fixed over time. Rather, it can change rapidly and unpredictably, and this can affect the dynamics of spatial transaction costs, potentially undermining the advantages of creating a GVC. To understand this logic, it is useful to consider the concept of product modularity. Modularity is a property of a product’s technological architecture that determines how components interact with each other to elicit the full potential of a final product (Baldwin and Clark 2000). When a product is non-modular or integral, components are specifically adjusted to one other, leading to a low level of codifiability of transactions between components. On the contrary, modular products consist of loosely coupled components that interact with one another through well-defined and codified architectural standards. Particularly in industries with low process maturity, the degree of product modularity is prone to change unexpectedly (Chesbrough 2003). Facing new supply or demand pressures, companies frequently alter the interaction requirements between value chain activities as they attempt to improve the production process. This technological unpredictability can undermine the presumed benefits of creating a GVC. To see this, consider a Canadian company that decides to outsource a labor-intensive activity to a qualified supplier in Mexico based on the observation that the technological architecture of the product ensures codifiable buyer–supplier transactions. Following Gereffi et al.’s (2005) GVC governance structure classification, the firm may decide to opt for a market or modular relation with the Mexican supplier. After a year, the technological architecture of the product suddenly becomes more integral, making the same buyer–supplier transaction highly non-codifiable. In that case, the resulting rise of spatial transaction costs may wipe out the original benefit for the Canadian company of offshoring the value chain activity. Furthermore, if the Canadian company no longer has the capability to do the value chain activity itself at home, it may lose control of its value chain. This is precisely the argument that Pisano and Shih (2009; 2012) use when they suggest that offshoring manufacturing may undermine a company’s technological edge. Similar to our notion of hidden spatial transaction costs, they argue that the (underappreciated) interdependence between R&D and manufacturing determines if GVCs has an adverse effect on innovation. Two features affect this (hidden) interdependence: (1) the codifiability or “modularity” of the transactions between R&D and manufacturing; and (2) the “process maturity” of the technology (see Table 45.2). If the transactions between R&D Table 45.2 Pisano and Shih’s (2012) modularity–maturity matrix Modularity

Process maturity

High Low

Source: Adapted from Pisano and Shih (2012).

Low

High

Process-embedded innovation Process-driven innovation

Pure product innovation Pure process innovation

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and manufacturing are modular and the process technology is mature (pure product innovation), the risk is limited that the company underestimates the spatial transaction costs both now and in the future. In that case, offshoring entails limited dangers for a firm’s innovation capabilities. On the other hand, when R&D and production are interdependent and/or manufacturing technologies are immature, offshoring can be dangerous since it involves high and unpredictable spatial transaction costs. In that case, the value of integrating R&D and manufacturing activities in the same location is high, and offshoring can have important negative implications on innovation. Technology Choice Offshoring may even force firms to select inferior innovation paths. In contrast to the extant literature (e.g. Van Assche 2008), firms do not necessarily choose the optimal technology first and then adapt their GVC structure. They often start off by choosing the location of their value chain activities and only then adjust their technology (Fuchs and Kirchain 2010). This is important because offshoring can in some circumstances change the cost–benefit balance related to a specific technology, pushing firms to embark on suboptimal innovation paths. Research by technological strategy scholars argues that a firm’s product architecture is often a managerial decision rather than an attribute of the knowledge itself (Henderson and Clark 1990). According to Ulrich (1995) and Schilling (2000), firms can choose from a variety of product architectures for any set of functional requirements. To see this, it is once again useful to focus on product modularity. The key benefit of a modular product architecture is that it reduces the interdependencies between modules, therefore allowing firms to independently concentrate their capabilities on innovating a single module and permitting the multiplication of design options through the mix and match of modules (Baldwin and Clark 2000). This, however, does not come without a cost. Modularity narrows the degrees of freedom that researchers have in the design process as they need to adhere to the fixed interfaces (Christensen et al. 1999), which in the long term can lead to fewer innovative breakthroughs than integral systems (Fleming and Sorenson 2001). Furthermore, outsourcing modules to external suppliers may cause firms to lose their ability to control key intellectual property (Zirpoli and Becker 2011) and may in certain cases lead to the creation of future competitors (Arrunada and Vázquez 2006). Offshoring can alter the cost–benefit balance of a specific technological architecture and as such influence its economic viability. To better coordinate its offshoring relations, for example, a company may decide to codify its interfaces with its suppliers, ignoring the fact that this ultimately undermines the synergistic productivity gains of a more integral product architecture. Similarly, a company’s decision to offshore its manufacturing can reduce the economic viability of developing emerging technologies. Fuchs and Kirchain (2010) and Fuchs et al. (2011) find that, as production in the optoelectronics industry has been offshored to East Asia, the manufacture of better-performing designs developed in the US no longer pay off. In sum, the fallacy of fixed technology can undermine the positive link between GVCs and innovation. If firms ignore technological unpredictability and technological choice, it  may force them to adopt suboptimal product architectures and inferior innovation paths.

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CONCLUSION In this chapter, we have analyzed the various channels through which the globalization of value chains affects a firm’s innovation capabilities. We have highlighted that GVCs can stimulate a firm’s innovation performance in a number of important ways. They help companies reduce their production costs through offshoring, which allows them to free up resources that can be invested in R&D. Furthermore, they permit companies to tap into new pockets of knowledge which are not available locally, as a result strengthening their local knowledge base. However, we have also emphasized that one should be careful in painting an overly rosy picture of GVCs. Companies face important challenges and costs when setting up GVCs, and if not managed correctly these “hidden costs” may overturn the positive link between GVCs and innovation. A particularly important hidden cost is the fallacy of fixed technology. Companies often assume that technology is fixed in the medium to long term and therefore do not take it into account when deciding how to organize their GVC. We have demonstrated how – particularly in industries with low process maturity – offshoring can steer companies onto inferior technological paths. Our chapter points out the need to further explore the dynamic relationships between technology and GVC governance. It highlights an interesting theoretical dualism: firms do not necessarily choose the optimal technology first and then adapt their GVC structure after. They often do the opposite. They choose the location of their value chain activities and only then adjust their technology to fit their most pressing constraints. More research is needed to investigate both theoretically and empirically the  consequences of this sequential decision-making process for firms and regions.

REFERENCES Amiti, M., & Konings, J. 2007. Trade liberalization, intermediate inputs, and productivity: Evidence from Indonesia. American Economic Review, 97(5), 1611–1638. Antras, P., & Helpman, E. 2004. Global sourcing. Journal of Political Economy, 112(3), 552–580. Archibugi, D., & Iammarino, S. 2002. The globalization of technological innovation: Definition and evidence. Review of International Political Economy, 9(1), 98–122. Arrunada, B., & Vázquez, X. 2006. When your contract manufacturer becomes your competitor. Harvard Business Review, 84(9), 135–145. Autor, D., Levy, F., & Murnane, R. 2003. The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. Baldwin, C. Y., & Clark, K. B. 2000. Design Rules: The Power of Modularity (Vol. 1). Cambridge, MA: MIT Press. Bardhan, A., & Jaffee, D. 2005. Innovation, R&D and Offshoring. Research Report. Fisher Center for Real Estate and Urban Economics, University of California, Berkeley, Fall. Bathelt, H., & Turi, P. 2011. Local, global and virtual buzz: The importance of face-to-face contact in economic interaction and possibilities to go beyond. Geoforum, 42(5), 520–529. Berry, H. 2014. Global integration and innovation: Multicountry knowledge generation within MNCs. Strategic Management Journal, 35(6), 869–890. Blinder, A. 2006. Offshoring: The next industrial revolution? Foreign Affairs, 85(2), 113–128. Cantwell, J. 1989. Technological Innovation and Multinational Corporations. Oxford: Basil Blackwell. Cantwell, J., & Iammarino, S. 1998. MNCs, technological innovation and regional systems in the EU: Some evidence in the Italian case. International Journal of the Economics of Business, 5(3), 383–408. Cantwell, J., & Mudambi, R. 2005. MNE competence-creating subsidiary mandates. Strategic Management Journal, 26(12), 1109–1128. Cantwell, J., & Santangelo, G. 1999. The frontier of international technology networks: Sourcing abroad the most highly tacit capabilities. Information Economics and Policy, 11(1), 101–123.

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Chesbrough, H. 2003. Towards a dynamics of modularity: A cyclical model of technical advance. In A. Prencipe, A. Davies, & M. Hobday (eds.) The Business of Systems Integration. Oxford: Oxford University Press, pp. 174–200. Christensen, C., Verlinden, M., & Westerman, G. 1999. Product modularity, vertical disintegration and the diffusion of competence. Harvard Business School Working Paper, No. 99-124, April. Chung, W., & Yeaple, S. 2008. International knowledge sourcing: Evidence from US firms expanding abroad. Strategic Management Journal, 29(11), 1207–1224. Coe, N., Dicken, P., & Hess, M. 2008. Global production networks: Realizing the potential. Journal of Economic Geography, 8(3), 271–295. Cummings, J., Espinosa, J., & Pickering, C. 2009. Crossing spatial and temporal boundaries in globally distributed projects: A relational model of coordination delay. Information Systems Research, 30(3), 420–439. Crespo, N., & Fontoura, M. 2007. Determinant factors of FDI spillovers – what do we really know? World Development, 35(3), 410–425. Dachs, B, & Ebersberger, B. 2013. The Effects of Production Offshoring on R&D and Innovation in the Home Country. FIW-Research Reports 2013/14 No. 01, Vienna, June. Defever, F., & Toubal, F. 2013. Productivity, relationship-specific inputs and the sourcing modes of multinationals. Journal of Economic Behavior and Organization, 94, 345–357. Dunning, J. 1998. Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29(1), 45–66. Farinas, J. C., & Martín-Marcos, A. 2010. Foreign sourcing and productivity: Evidence at the firm level. World Economy, 33(3), 482–506. Farrell, D. 2005. Offshoring: Value creation through economic change. Journal of Management Studies, 42(3), 675–683. Feinberg, S., & Gupta, A. 2004. Knowledge spillovers and the assignment of R&D responsibilities to foreign subsidiaries. Strategic Management Journal, 25(8–9), 823–845. Fleming, L., & Sorenson, O. 2001. Technology as a complex adaptive system: Evidence from patent data. Research policy, 30(7), 1019–1039. Fuchs, E., Field, F., Roth, R., & Kirchain, R. 2011. Plastic cars in China? The significance of production location over markets for technology competitiveness in the United States versus the People’s Republic of China. International Journal of Production Economics, 132(1), 79–92. Fuchs, E., & Kirchain, R. 2010. Design for location? The impact of manufacturing offshore on technology competitiveness in the optoelectronics industry. Management Science, 56(12), 2323–2349. Furman, J., Porter, M., & Stern, S. 2002. The determinants of national innovative capacity. Research Policy, 31(6), 899–933. Gereffi, G., Humphrey, J., & Sturgeon, T. 2005. The governance of global value chains. Review of International Political Economy, 12(1), 78–104. Gibson, C. B., & Gibbs, J. L. 2006. Unpacking the concept of virtuality: The effects of geographic dispersion, electronic dependence, dynamic structure, and national diversity on team innovation. Administrative Science Quarterly, 51(3), 451–495. Giuliani, E. 2017. Industrial clusters in global networks. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 360–371. Goldberg, P., Khandelwal, A., Pavcnik, N., & Topolova, P. 2010. Imported intermediate inputs and domestic product growth: Evidence from India. Quarterly Journal of Economics, 125(4), 1727–1767. Grossman, G. M., & Rossi-Hansberg, E. 2008. Trading tasks: A simple theory of offshoring. American Economic Review, 98(5), 1978–1997. Henderson, R. M., & Clark, K. B. 1990. Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9–30. Hummels, D. 2007. Transportation costs and international trade in the second era of globalization. Journal of Economic Perspectives, 21(3), 131–154. Hummels, D., Jørgensen, R., Munch, J., & Xiang, C. 2014. The wage effects of offshoring: Evidence from Danish matched worker–firm data. American Economic Review, 104(6), 1597–1629. Humphrey, J., & Schmitz, H. 2002. How does insertion in global value chains affect upgrading in industrial clusters? Regional Studies, 36(9), 1017–1027. Javorcik, B. 2008. Can survey evidence shed light on spillovers from foreign direct investment? World Bank Research Observer, 23(2), 139–159. Johnson, R. C., & Noguera, G. 2012. Accounting for intermediates: Production sharing and trade in value added. Journal of International Economics, 86(2), 224–236. Kasahara, H., & Lapham, B. 2013. Productivity and the decision to import and export: Theory and evidence. Journal of International Economics, 89(2), 297–316.

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The Elgar companion to innovation and knowledge creation

Kasahara, H., & Rodrigue, J. 2008. Does the use of imported intermediates increase productivity? Plant-level evidence. Journal of Development Economics, 87(1): 106–118. Kogut, B. 1985. Designing global strategies: Comparative and competitive value added chains. Sloan Management Review, 26(4). Kogut, B., & Zander, U. 1993. Knowledge of the firm and the evolutionary theory of the multinational corporation. Journal of International Business Studies, 24(4), 625–645. Kurz, C. J. 2006. Outstanding Outsourcers: A Firm- and Plant-Level Analysis of Production Sharing. Finance and Economics Discussion Series (No. 2006-04). Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, DC. Larsen, M. M., Manning, S., & Pedersen, T. 2013. Uncovering the hidden costs of offshoring: The interplay of complexity, organizational design, and experience. Strategic Management Journal, 34(5), 533–552. Leamer, E., & Storper, M. 2001. The economic geography of the internet age. Journal of International Business Studies, 32(4), 641–666. Levy, F., & Murnane, R. 2004. The New Division of Labor: How Computers Are Creating the Next Job Market. Princeton, NJ, Princeton University Press. Li, P. F., Bathelt, H., & Wang, J. 2012. Network dynamics and cluster evolution: Changing trajectories of the aluminium extrusion industry in Dali, China. Journal of Economic Geography, 12(1), 127–155. Liu, R., Feils, D., & Scholnick, B. 2011. Why are different services outsourced to different countries? Journal of International Business Studies, 42(4), 558–571. Lundvall, B.-Å. 2017. National innovation systems and globalization. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 472–489. Ma, A., & Van Assche, A. 2016. Spatial linkages and export processing location in China. World Economy, 39(3), 316–338. McCann, P., & Mudambi, R. 2005. Analytical differences in the economics of geography: The case of the multinational firm. Environment and Planning A, 37(10), 1857–1876. Mudambi, R., & Santangelo, G. 2016. From shallow resource pools to emerging clusters: The role of multinational enterprise subsidiaries in peripheral areas. Regional Studies. 50(12), 1965–1979. Nieto, M. J., & Rodríguez, A. 2011. Offshoring of R&D: Looking abroad to improve innovation performance. Journal of International Business Studies, 42(3), 345–361. Paus, E., & Gallagher, K. 2008. Missing links: Foreign investment and industrial development in Costa Rica and Mexico. Studies in Comparative International Development, 43(1), 53–80. Phene, A., & Almeida, P. 2008. Innovation in multinational subsidiaries: The role of knowledge assimilation and subsidiary capabilities. Journal of International Business Studies, 39(5), 901–919. Pietrobelli, C., & Rabellotti, R. 2011. Global value chains meet innovation systems: Are there learning opportunities for developing countries? World Development, 39(7), 1261–1269. Pisano, G., & Shih, W. C. 2009. Restoring American competitiveness. Harvard Business Review, 87(7/8), 114–125. Pisano, G., & Shih, W. C. 2012. Does America really need manufacturing? Harvard Business Review, 90(3), 94–102. Porter, M. 1985. Competitive Advantage: Creating and Sustaining Superior Performance. New York: Simon & Schuster. Porter, M., & Rivkin, J. 2012. Choosing the United States. Harvard Business Review, 90(3), 80–91. Rallet, A., & Torre, A. 2017. Geography of innovation, proximity and beyond. In H. Bathelt, P. Cohendet, S. Henn, & L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation. Cheltenham, Northampton, MA: Edward Elgar Publishing, 421–439. Schilling, M. 2000. Toward a general modular systems theory and its application to interfirm product modularity. Academy of Management Review, 25(2), 312–334. Schmeisser, B. 2013. A systematic review of literature on offshoring of value chain activities. Journal of International Management, 19(4), 390–406. Schmitz, H., & Knorringa, P. 2000. Learning from global buyers. Journal of Development Studies, 37(2), 177–205. Storper, M., & Venables, A. 2004. Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 4(4), 351–370. Tang, J., & Van Assche, A. 2016. Multinationals and offshoring: Firm-level evidence from Canada. Mimeo. Tomiura, E. 2007. Foreign outsourcing, exporting, and FDI: A productivity comparison at the firm level. Journal of International Economics, 72(1), 113–127. Topalova, P., & Khandelwal, A. 2011. Trade liberalization and firm productivity: The case of India. Review of Economics and Statistics, 93(3): 995–1009. Turkina, E., & Van Assche, A. 2016. Trans-local connectedness and cluster performance in knowledge-intensive industries. Mimeo. Ulrich, K. 1995. The role of product architecture in the manufacturing firm. Research Policy, 24(3), 419–440.

Global value chains and innovation

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Van Assche, A. 2008. Modularity and the organization of international production. Japan and the World Economy, 20(3), 353–368. Van Assche, A. 2016. Global value chains and the rise of a global supply chain mindset. In S. Tapp, A. Van Assche, & R. Wolfe (eds.) Redesigning Canadian Trade Policies for New Global Realities. Queen’s University Press, pp. 183–208. Veugelers, R. 2013. Innovative Firms in Global Value Chains. Innovation for Growth Policy Brief No. 21, European Commission. Wagner, J. 2011. Offshoring and firm performance: Self-selection, effects on performance, or both? Review of World Economics, 147(2), 217–247. Zirpoli, F., & Becker, M. 2011. The limits of design and engineering outsourcing: Performance integration and the unfulfilled promises of modularity. R&D Management, 41(1), 21–43.

46. Innovation, development and global destruction networks Andrew Herod, Graham Pickren, Al Rainnie and Susan McGrath-Champ

INTRODUCTION In this chapter we explore how the detritus of modern life – discarded computers, television sets, iPods, cars, ships and a whole host of other manufactured goods – can serve not simply as something that must be disposed of as waste but also, sometimes, as items which may function as raw materials for new rounds of manufacturing and so of innovation and development. To do so we first outline the concept of what we are here calling Global Destruction Networks (GDNs). These are networks of places where products are disassembled and their constituent elements are extracted for processing and reuse as inputs into new productive processes. What emerges out of the end of a GDN, then, represents not only the termination of one commodity but also potentially the beginning of another. In this regard, GDNs can serve not only as important conduits bringing waste from one part of the world to another – often, but not exclusively, linking communities in the Global North with those of the Global South – but also as the catalysts for new industrial production. Thus, to give but one example, the shipbreaking that takes place in Bangladesh generates large quantities of steel and other components which can then be recycled and transformed into new products within the workshops and industrial facilities of that country, some of which may then be exported and some of which may be used domestically (Gregson et al. 2012). Having sketched out what we mean by GDNs we then draw upon three activities – the recycling of e-waste, ships and vehicles – to show how the breaking apart of discarded commodities so as to retrieve their constituent elements for use in new commodities can generate incomes, jobs and new ideas about how to organise the work process. In doing so, we argue that understanding the connections between GDNs and the Global Production Networks (GPNs) to which they supply components provides a way to trace the transfer of value (used here in the Marxist sense of ‘congealed labour’) from one part of the globe to another. This raises important questions concerning what role GDNs might play in market making and the production of the unevenly developed geography of capitalism.

GLOBAL DESTRUCTION NETWORKS During the past two decades or so a substantial body of research has emerged on the way in which the various sites involved in putting together commodities and bringing them to the market are networked together. Starting with the 1994 publication of Commodity Chains and Global Capitalism by Gereffi and Korzeniewicz, this literature has evolved 752

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into three broad sub-fields as various scholars have argued for understanding the connections between these sites in terms of Global Production Networks (GPNs – Coe et al. 2004; 2008; Henderson et al. 2002), in terms of Global Value Chains (GVCs – Kaplinsky 1998; Gereffi et al. 2005) and in terms of Global Commodity Chains (GCCs – Gereffi and Korzeniewicz 1994). Although these three approaches have some important differences (for a summary see Rainnie et al. 2011), they do share at least one commonality: they have all predominantly adopted a linear view of the life of commodities, tracing them temporally and geographically from the places involved in their creation to the places where they are sold to consumers (see also Giuliani, Chapter 22, this volume; Van Assche, Chapter 45, this volume). In this regard they have certainly been helpful in defetishising commodities by allowing academics, labour activists, consumers and others to trace their journeys across the planet, from (perhaps) sweatshops in China to high-end retail outlets in New York, London or Moscow. However, in the past few years a growing dissatisfaction with such linear ‘follow the things’ (Appadurai 1986) approaches to understanding the temporal and geographical lives of commodities has emerged, most particularly in relation to their silence on what happens to commodities once they are used up and thrown away – once, that is, they have become ‘waste’. As a result, a number of scholars (e.g., Gregson et al. 2010; Lepawsky and Mather 2011; and Lepawsky and Billah 2011) have argued for the need to focus upon commodities’ afterlives and the processes whereby some of them are disassembled so that their parts can be retrieved and used as inputs in other commodities. These scholars have called for a more critical understanding of the nature of waste (especially the supposedly hard-and-fast discursive delineation between ‘useful product’ and ‘waste’), together with how a commodity’s status as ‘waste’ may be both an ending and a beginning of commodity circulation within the totality of capitalist accumulation, rather than just an ending. These calls for developing a critical understanding of waste have helped advance understandings of the nature of commodities under capitalism. However, they have tended to focus upon the transformation of commodities’ physical forms and/or their semiotic status as a way to understand what happens to them after they are discarded. Hence, Lepawsky and Mather’s (2011: 247) recounting of the recycling of e-waste in Bangladesh and Canada argues for an analysis largely based in semiotics in which components of one commodity are ‘enacted as something else’ – they give the example of how e-waste’s ‘copper wires or gold circuitry bec[o]me unrecognisable as electronics [as they are turned into,] for example, copper ingots or gold bars’. What these approaches have less explicitly done, though, is to theorise the geographical movement of waste from one part of the globe to another and its subsequent physical transformation in terms of the spatial transfer and capture of value. This failure is significant because following the movement of value across the Earth’s surface, we would argue, allows for an understanding of processes and patterns of economic development in ways that more profoundly tie such development into the deep structural forces of the capitalist global economy than does focusing merely upon the superficial transformation of objects’ physical form. In what follows, then, we want to contribute to the analysis of what happens to commodities once they transition from being useful (i.e., having use value as the products that they were originally designed to be) to being ‘waste’ and thence from being ‘waste’ to being the potential source of raw materials for new commodities. However, we also want to push the discussion in a new direction, one which more explicitly links the activity of waste

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processing to the deep dynamics of capitalist accumulation. Hence, rather than focusing upon semiotics and the physical transformation of commodities from, say, copper wires to copper ingots, what we want to do is to outline a framework for understanding how value/ congealed labour is moved across the surface of the Earth through the process of taking apart commodities and then extracting their constituent elements so that they might be used as raw materials for new products. To do so we have developed the concept of what we are calling Global Destruction Networks, which we see very much as the inverse correlates of GPNs – whereas GPNs link different communities together in the process of production, GDNs do so in the process of commodity disassembly and constituent element reclamation. The usefulness of the concept of GDNs, then, is that it provides a means to understand the movement of not only material goods from one part of the world to another but also, more importantly (in our view), the spatial movement of value and how the specificities of the labour process affect the onward motion of this value. To understand how this is so, though, we must first briefly examine what Marx (1867 [1990]) had to say about the transfer of value within the production process, for such value can also be transferred in the process of breaking down old commodities. This transfer, we aver, has important implications for understanding both patterns of economic development in the communities around the world which have become centres of waste processing and how these communities are then linked to the outside world through their providing raw materials that may be inputs into various GPNs.

VALUE, DEVALUATION AND DEVALORISATION In Volume One of Capital Marx explored the production process and the creation, capture and movement of value. Specifically, in detailing his famous capital circuit of M(oney)–C(ommodity)–M(oney), Marx argued that the value that is incorporated into the machines which are used to produce new commodities gets transferred to those new commodities during the production process. Newly produced commodities, then, contain the value appropriated during their own manufacture and part of the value that was incorporated into the machines used to produce them when those machines were themselves constructed, as well as part of the value incorporated in the raw materials from which these newly produced commodities are made – perhaps the labour of miners who dug the iron ore and coal out of the ground which was then used to make the steel from which various components of the new commodities are manufactured. As Marx puts it (1867 [1990]: 509), Machinery, like every other component of constant capital, creates no new value, but yields up its own value to the product it serves to beget. In so far as the machine has value and, as a result, transfers value to the product, it forms an element in the value of the latter.

This process of transferring value from a machine to the commodities made by it continues until there is no more value left to transfer and the machine reaches the end of its normal working life, a process which Smith (1981) has termed the machine’s complete ‘devalorisation’. However, it is also possible that a machine may be abandoned before it has come to the end of its normal working life, as when it is replaced by a newer, more efficient model. In this context, the machine still has value tied up in it from when it was

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itself constructed, value that could be transferred to products it might potentially manufacture. Such an instance Smith called the machine’s ‘devaluation’. This is a very different situation from when the machine is devalorised. Thus, Smith argued (1990: 126, emphasis added), whilst devalorisation represents a transfer of value from machine to commodity, devaluation ‘represents an absolute destruction of value’. In considering the breaking up of discarded commodities, we can make a similar distinction. Thus, the machines and tools used when breaking up e-waste or ships or anything else transfer a certain amount of their own value to the newly recovered components coming from those commodities – they are devalorised as their value is transmitted to the recovered components that may become the raw materials for new commodities. Furthermore, the components from old commodities which may now become raw materials for new commodities will contain value that was embedded in them when they were first manufactured, as well as value added by those workers involved in the breaking apart of the old commodities and the extraction of their constituent elements. The old commodities working their way through GDNs and the new ones which will come out of the GPNs which such GDNs are supplying with inputs, then, are deeply connected through the process of value conveyance. At the same time, though, it is important to understand that there may be limits placed upon whether or not a commodity’s components can be reused and thus whether value can be transferred from old commodities to new ones. Such limits can come from one of two sources. On the one hand, certain materials are limited by their chemistry as to how many times they can be recycled. Thus, whereas most metals are almost infinitely recyclable, many other things are not. Paper, for instance, can usually be recycled five to seven times before its fibers become too short to make it usable whereas many plastics degrade during reprocessing and so have only one successful recycling, after which they often are turned into items like plastic lumber used in garden benches and drop out of the recycling cycle and so cease to be involved in the forward transfer of value (Bureau of International Recycling n.d.; Mulvaney 2011). In situations where it is no longer physically possible to recycle something because its chemistry has irrevocably changed, we can consider these components to have reached the end of their working lives and to have been devalorised. However, there are also instances in which commodities’ components could potentially be recycled but are not because it is simply not cost-effective to do so under current market conditions. In such situations the value incorporated within them that could be transferred to new commodities is lost and we can consider them to have been devalued. It is important to recognise, though, that this latter is not a naturally imposed state of affairs but a socially imposed one. Thus, should market conditions change – perhaps the price of a particular metal increases or the technology for extracting it from the carcasses of abandoned commodities improves dramatically – then these components may once again enter the recycling stream and thus become inputs into a GPN. Indeed, should the price of certain components increase sufficiently, this can even lead to landfill mining in which waste buried several decades ago is dug up to access the value it still contains (see van der Zee et al. 2004). Equally, new technologies may extend the recyclability of certain materials – Woody (2010), for instance, reports on how scientists are developing new types of plastics that can be recycled a greater number of times. Following from the above, we want to argue that there are several reasons why the concept of GDNs and distinguishing between processes of devalorisation and devaluation are important for thinking about issues of development and innovation. First,

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conceptualising the process of breaking up discarded commodities in the search for useful components and recapturable value in terms of the operation of GDNs provides a fruitful way for imagining how various parts of the global economy are networked spatially, in much the same way that the concept of GPNs does. This allows us to think about how what happens in one part of the globe (the discarding of a commodity) can have a domino effect elsewhere (the making available of potential inputs that may go into a new GPN). Second, given that GDNs are often intimately imbricated with GPNs, the concept of GDNs provides a mechanism for thinking about how the labour practices involved in taking components apart can have very significant impacts upon how GPNs are structured and function. For instance, in the Global South the dismantling of e-waste is usually done in a much more labour-intensive manner than it is in the Global North, where a higher proportion of e-waste tends to be dismantled using large machinery. This has important implications for recovery rates of various materials. Thus, as Wang et al. (2012: 2136) have shown, ‘manual dismantling achieves higher liberation rates without breaking the original form of components and materials, which is easier to sort and improves re-usability’. By way of contrast, the capital-intensive recovery processes used in the Global North recover less repair and reuse-grade material compared to the labour-intensive methods of the Global South but are better able to safely extract metals and other components from spent items. In sum, the labour processes of a GDN shape to a large degree what raw materials are then made available for use as inputs into GPNs and how, consequently, these GPNs are structured and operate. There is, then, clearly an important geographical specificity to different GDNs (those based in the Global North often operate quite differently to those in the Global South) and to how they are linked to GPNs, even those associated with the dismantling of the same types of commodities (say, computers). Third, differentiating between processes of devalorisation and devaluation provides a way to conceptualise how value generated in one part of the world may be captured in another and also to distinguish analytically between which of the components of an old commodity may have the potential to become inputs into a new GPN and which are truly waste that must simply be thrown away because they have no potential for reuse. This has implications for thinking about how the importing of discarded commodities into, say, countries of the Global South may or may not shape local development – in some instances the devalorised waste from the Global North may simply have to be dumped into a landfill, thereby creating one type of job and one set of environmental outcomes, whereas in others the waste which still contains value that may be captured (either now or at some future point) can actually serve as the catalyst for local economic development, thereby creating different types of jobs and environmental outcomes. Amongst other things, this has consequences for questions of social justice. Hence, whereas the fact that much of the detritus of the ‘throw-away’ lifestyle of modern consumers in the Global North frequently ends up in the Global South has often been seen as a kind of neo-colonialism in which Global South environments suffer because of the profligate lifestyle of Global North consumers, this waste may contain materials and value that can be captured by Global South economic actors and reinserted into the commodity and value flows of the global economy by being used in new products coming out of various GPNs. This reality muddies easy arguments about the neo-colonial nature of waste exports from the Global North.

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Fourth, linking GDNs with GPNs provides a lens through which to contextualise at least one new innovation within manufacturing, namely the design of products in particular ways so that they can be more easily taken apart to facilitate harvesting their constituent components for reuse and so recapturing value. Although this ‘Design for Recycling’ or ‘Design for Disassembly’ is a relatively recent phenomenon, the desire to design products that may be more readily taken apart for recycling is leading some manufacturers to dramatically reshape how they conceive of and fabricate their products. In so doing, growing numbers are making increased use of smart technology in their commodities (Chiodo and Jones 2012 have termed this ‘Active Disassembly using Smart Materials’). Some of this ‘active disassembly’ technology involves the use of ‘shape-memory’ alloys and polymers that ‘remember’ their original pre-use shape and return to it when heated (Yang et al. 2014), which has the potential to radically transform how both GDNs and GPNs are structured and function. For instance, in the mid-2000s Nokia developed a cell phone prototype that can disassemble itself in only a few seconds when heat is applied to it (Bhamra and Lofthouse 2007). Whereas historically manufacturers have tended to focus upon designing ways to most easily put commodities together, now, thanks to mandates like the European Union’s Waste Electrical and Electronic Equipment directive that came into force in 2004, they are increasingly also focusing upon ways to most easily take them apart at the ends of their lives. As we shall argue, then, not only can the ways in which GDNs function shape how GPNs are structured but, increasingly, manufacturers are consciously designing commodities and production methods so as to shape how GDNs operate. Having laid out a brief conceptual framework we now explore three case studies to contemplate how the breaking up of commodities can provide a basis for new patterns of local development and how the geographical transfer of value allows the mixing of value created in one part of the world (where the commodity was initially assembled) with that being produced in another (where the commodity is being broken apart), together with what this means for understanding the structural dynamics of capitalist accumulation.

THREE EXAMPLES OF WASTE PROCESSING – E-WASTE, SHIPS AND VEHICLES E-waste We are truly in the age of electronics. Consequently, we are also in the age of e-waste, especially given the market pressures to continually upgrade items such that many products’ half-lives are becoming ever shorter (Slade 2007) – the iPhone is perhaps iconic in this regard, with new versions emerging about every 12 months or so (Bilton 2012). Indeed, whereas in the past manufacturers often touted the durability of their products as a marketing tool, increasingly manufacturers seem to be promoting the devaluation of their commodities through the planned obsolescence brought about by continual ‘improvements’ rather than encouraging consumers to allow them to be devalorised as they deteriorate through normal wear and tear. This logic of devaluation has been explicitly recognised by Andrew Grove, former CEO of Intel, who remarked, regarding the launch of a new chip: ‘This is what we do. We eat our own children, and we do it

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faster and faster . . . that’s how we keep our lead’ (quoted in Ramstad 1994). This means that there are vast amounts of value left to be recaptured from the e-waste which can potentially serve as inputs into new GPNs, either through the removal of still-functioning chips and other components from older products or through the melting down and reuse of the metals and other components contained in these products. As a result, the term ‘demanufacture’ is appearing more frequently within the electronics industry as firms recognise that recapturing the value tied up in used electronics’ various components can be important sources of profit. For instance, Motorola’s ‘Material Demanufacturing Center’ in Plantation, Florida, has its origins in 1993 when the company faced a semiconductor shortage and decided to recycle assembled, preconsumer circuit boards as a way to secure the components it needed. In terms of the actual dismantling of e-waste, this is generally done using one of two types of labour process. Considering the labour processes involved in the demanufacturing of e-waste is important because how this is done has significant implications for value recapture. Hence, whereas in the Global North some components (such as Cathode Ray Tubes from televisions) are taken apart by hand because they contain fairly toxic substances (lead, mercury, barium, cadmium and phosphorous, amongst others) that could be more easily released into the environment should machinery be used, for the most part dismantling is done by machinery within the formal economy. In the Global South, on the other hand, it is mostly (but not entirely) conducted by hand as an informal economic activity. In the Global North, then, electronics are typically brought to a centre to be sorted, with materials that can be reused, repaired or refurbished (i.e., materials that have not been devalorised) being either resold locally or through online channels, or both. Importantly, different elements within a commodity may have different futures, depending upon whether they still contain value or not and, if so, how much. Thus, the monitor from a seven-year-old desktop computer may be deemed reuseable and resaleable as is after a series of testing procedures (see Pickren 2015), whilst the rest of the hardware might be dismantled and its parts separated into hazardous and non-hazardous components and reuseable and non-reuseable parts. Some items (like batteries and circuit boards) may then be sent downstream to more specialised processors able to handle this potentially hazardous material. Once the remaining e-waste has been sorted and dismantled, it is usually shredded into fairly small pieces and then further separated by machine and, if necessary, sent for further processing (if its elements still contain easily recapturable value) or to the landfill (if completely devalorised or if the value left in it is deemed not worth recovering because of present market conditions). As several authors (e.g., Lepawsky and Mather 2011; Pickren 2015) have noted, however, the continuous movement of value from these GDNs back into GPNs can involve more than just the physical movement of commodity grade scrap and reusable electronics. Hence, in the Global North especially, the production of certificates of secure data destruction and privacy protection are equally important to the business models of many e-recycling firms that serve large banks, governments, health care firms and others who may be concerned that information held on their computers could end up in the public arena. In the Global South, the process is broadly similar (if more labour-intensive) but can also be quite different in some respects. Thus, Reddy (2013) has indicated that informal recyclers who dismantle e-waste in Bangalore, India, first typically recover reusable working components (e.g., circuit boards and hard drives) to sell either directly to

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assemblers of non-brand name, locally produced computers or to computer repair shops who use the components to mend broken computers. They also strip and separate the non-working parts into recyclable materials to sell to wholesalers, who are connected to regional and even global commodity markets. Meanwhile, other informal recyclers Reddy studied have developed a sub-niche by chemically processing certain computer e-waste elements so as to recover precious metals. What this shows is both that not only is much labour expended to separate those components which have been devalorised from those which still have value that may be recaptured but also that the distinction between informal and formal sectors is a false binary (see Rainnie et al. 2015). Hence, as Grant and Oteng-Ababio (2012: 4) have concluded from their work in Ghana, those ‘making a living’ on their own in the informal sector rather than ‘earning a living’ in the formal, waged sector ‘do not operate in a separate economic realm since informal local e-waste circuits depend on an extra-local formal economy’. In other words, the complex relationships between GDNs and GPNs increasingly make the formal–informal distinction with regard to economic activities difficult to sustain. The result of these labour processes is the reclamation of several different types of components which can serve as inputs into GPNs. Depending upon the types of components about which we are talking, there will be very different geographies to the GDNs which are breaking up old commodities and the GPNs for which these components are now raw materials and thus for the ongoing motion of value. For instance, the types of equipment used vary considerably across the globe – large machines in the Global North versus, perhaps, sledgehammers and screwdrivers in the Global South – and this has implications for the forward movement of value incorporated in that equipment as it becomes devalorised through being employed to break up e-waste. Equally, in cases where components like still-useable chips or circuit boards are taken from one product and used as is as inputs into another, either as spare parts or as the building blocks of whole new products (as when technicians in Bangalore use harvested parts to build new, non-brand computers for sale in local markets), the value incorporated within them remains in motion. It is then added to by the labour of those who are harvesting the parts and those who are putting them together into new commodities. In other cases, parts from e-waste may be shredded and sent onwards for manufacturing into entirely new types of goods – toys, car parts, medical devices and a plethora of other products (Lepawsky and Billah 2011). In these instances the value incorporated within them remains in motion but becomes incorporated into very different-looking commodities. In turn, when these goods reach the end of their new lives some of them and/or their parts will have become devalorised (depending upon how they have been used) and so no longer will be recyclable whereas others will potentially be launched into yet more commodities after again having been harvested and processed. Finally, it is important to stress here that not only does the labour process found in the GDN shape the future movement of value into new GPNs but also that the organisation of GPNs – their logics, design strategies and labour processes – impacts the kind of work that recyclers do in GDNs. As Pickren (2015: 416) argues, ‘the dynamics of planned obsolescence, proprietary production choices and competition occurring “upstream” have a direct impact on the “downstream” recovery of value and hazards management by creating highly complex, heterogeneous products that often have proprietary specifications’. In the case of electronics, then, many products are designed and manufactured in ways

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that make it extremely difficult for recyclers to determine whether a used item should be reused, repaired or recycled. For example, Apple’s iPads and iPhones have a ‘sealed-box design’ that requires a proprietary set of Apple screwdrivers to open, thus preventing most users from upgrading their hardware and extending their products’ life. For such products devaluation, rather than devalorisation, is thus typically the end result of their constituent elements’ life cycles. Apple’s business philosophy, then, is quite different from that of companies who are adopting the model of Designing for Recycling or Designing for Disassembly, in which products are designed specifically to make their disassembly – and thus the recovery of their constituent elements – easy. What this shows is that the labour processes within a GDN therefore exist in contingent relation with different GPNs, both those that ‘send’ used materials and those that ‘receive’ reworked items. Ships The GDNs involved in the breaking up of old ships share some similarities with those involved in the breaking up of e-waste, but they also, as might be expected, have many differences. For instance, whereas e-waste processing usually occurs in large urban areas in both the Global North and Global South, as these are close to where the waste is often generated, shipbreaking occurs, by necessity, in coastal areas. Equally, although in the early 20th century much shipbreaking took place in the industrial countries of the Global North, today over 95 per cent occurs in the Global South, as the cost of labour is so much less and environmental regulations are so much more lax than in the North (for a history of shipbreaking in the United Kingdom, see Bowen 1936 and Buxton 1983; for an overall examination of the industry in the Global South, see Hossain and Islam 2006). Thus, more than 100 shipbreaking companies operate in Alang, India, whilst other maritime communities like Gadani in Pakistan (which employed some 30,000 workers in the early 1980s until work began to move to even cheaper locations), Chittagong in Bangladesh and Aliağa in Turkey have also developed a specialisation in such work. Usually, ships enter a shipbreaking GDN when they are deemed to have come to the end of their working lives (typically, after about 25 to 30 years of service), either because it no longer makes economic sense to continue repairing and/or retrofitting them (i.e., they are reaching the point of becoming devalorised) or because newer, more competitive models have come online (such as the newest generation of ‘Ultra Large Container Vessels’ [ULCVs]) that can generate higher profits for their owners (i.e., when they are about to be devalued). In 2009 some 1,200 ships were scrapped globally, representing a capacity of approximately 25 million gross tonnes (Sarraf et al. 2010: 3). Shipbreaking GDNs are often highly transnational, with headquarters in places like London or Dubai that connect the actual breaking yards with brokers operating in major ports around the world who serve to secure the ships. Given that the clock starts ticking for a breaking yard the moment that a ship to be scrapped is purchased, the breaking companies must sail that ship as quickly as possible to their yards. When the ship gets close to the breaking yards it will be forced to wait in international waters to be certified as gas free and then, after the issuance of the relevant permits, it will come into the breaking yard. The standard cargo ship takes between three and six months to dismantle (Hossain and Islam 2006). As with e-waste so with ships, there are different ways of breaking up these entities. For instance, in Alang, India, the physical geography of the continental shelf – considered by

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many to be the best in Asia for shipbreaking – provides a ten-metre tide, which makes it possible to accommodate big ships and to run them aground high up the beach so that they remain exposed to workers for longer periods of time. The ships are then largely broken up by hand, a job that is grimy, sweaty and dangerous and exposes workers to carcinogens such as asbestos and heavy metals. By way of contrast, in China ships are typically broken up in docks using large-scale machinery. This is principally because Chinese workers are less willing to tolerate the poor conditions and low wages found in India and Bangladesh, with the result that the breakers have sought to replace workers with laboursaving technologies (Minter 2011). Because of this, Chinese shipbreakers are seen by many to be more environmentally friendly, which can give them an edge with some sellers of ships (who want their ships broken up in a more environmentally friendly way) and with some consumers (who will only purchase metals and other components coming from ships that have been broken up in a more environmentally friendly way) (Puthucherril 2010). Meanwhile, profit rates can vary enormously – from about 16 per cent per ship in Bangladesh in 2009 compared to about only 3 per cent in Pakistan (Sarraf et al. 2010: 4). In terms of questions of development and innovation, then, not only can the availability of labour and its willingness to work under certain conditions affect where and how a ship is broken up but, also, what the parts from those ships are used for is a reflection of broader connections between a particular GDN and the end-users which it supplies. Hence, in some cases items like tables and seating are taken out of ships and sold in the local market for use in family homes. Because it has little likelihood of ever being recycled again, this furniture thereby drops out of the recycling cycle and is valued only for its use value – whatever value/congealed labour may be left in it, in other words, does not continue to circulate. Drawing upon Marx’s distinction between productive and nonproductive labour, we can therefore think of the work involved in retrieving such furniture from the ships for sale to local families as non-productive of future value. However, in other instances the materials extracted from the ships can indeed be productive of future surplus value. Thus, ships’ engines and generators are often sold to garment manufacturing factories whilst their boilers frequently end up in rice mills, garment-washing plants and knitting plants (Hossain and Islam 2006). In yet other instances, the metals cut from the hulks of ships can carry the value held in them forward as they are used as inputs into new GPNs – perhaps being melted down to produce door panels for cars which may then be melted down again in a few years and used for something else (maybe parts for a computer?) when those cars finally go to the scrap yard, or as end products which are unlikely to be recycled anytime soon, as when they are used for steel girders for construction (although, at some future point, these steel girders could be retrieved when the buildings into which they are being put are demolished and so they could re-enter the recycling stream several decades from now). The metals made available through shipbreaking, then, can serve as crucial inputs for industrial production and so as onward carriers of value. In the case of Bangladesh, for instance, the shipbreaking industry is pivotal to supplying the nation’s steel needs because the country does not have any indigenous iron mining. Indeed, in the mid-2000s breaking yards provided some 80–90 per cent of Bangladesh’s steel, compared to only about 15 per cent of India’s (Hossain and Islam 2006). Karim (2010) suggests that this is the reason why the Bangladeshi government is particularly lax in punishing shipbreaking firms who violate the country’s environmental and labour laws. However, although shipbreaking

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satisfies relatively little of India’s steel needs, the fact that metal recycled from ships sells at about half the cost of regular blast-furnace-produced steel (Rousmaniere and Raj 2007: 361) means that the GPNs to which the yards deliver metal constantly pressure them to generate ever larger amounts – a clear illustration of how the demands of a downstream GPN can shape the activities of the upstream GDN which is supplying it. In similar fashion, Chinese GPNs have also been reshaping how shipbreaking GDNs are structured, as the country’s construction boom has created an almost insatiable appetite for steel (as well as for concrete – as Smil 2013 notes, between 2011 and 2013 China used more concrete than the United States used during the whole of the 20th century [some 6 gigatonnes versus 4 gigatonnes]!). This demand for steel for construction led Chinese steel manufacturers in the early 2000s to offer higher prices for recycled metal which, in turn, resulted in Chinese shipbreaking operations offering higher prices for ships on the global market, such that fewer went to India and Bangladesh. Significantly, this came at a time when reductions in Indian import tariffs for steel (the result of the neoliberalisation of the economy), together with the rather anachronistic and more expensive demolition methods employed by Indian shipbreakers, meant that many Indian steelmakers began to import scrap metal rather than sourcing steel coming from shipbreaking yards (Basu 2004). In the case of steel and other metals, then, the fact that the metal is more or less infinitely recyclable means that it can continue to carry forward value from several generations of workers who have laboured to produce it (the original ore miners, the smelters of the original metal, the shipbuilders who incorporated it into oceangoing vessels and various recyclers and manufacturers who use the metal coming from the ships). Used Vehicles The trade in used vehicles and their parts links together many regions of the globe. Historically, the used-vehicle trade was largely limited to high-end cars and antiques. However, in the 1970s the export of lower-end used vehicles began to expand dramatically, especially as Japanese government restrictions on the domestic resale of relatively new used cars began to lead to a glut of good-quality vehicles available for export (Clerides 2008). These vehicles themselves usually have one of three immediate futures: they are sold to local consumers to be driven on roads; they are taken apart so that their components can be used pretty much as is as spare parts for vehicles already in-country; or they are broken up for scrap, so that the metal, glass, rubber, plastics, textiles, leather (such as that coming from seats) and other parts can be used to make something else or in something else. Hence, some of the thermoplastic materials recovered from old vehicles can be granulated and used in a variety of new products and in the construction and agricultural sectors, whilst many vehicle batteries are bought to power TV sets in rural homes (in which case they fall out of the value conveyance cycle) and/or remanufactured for resale (in which case they may continue within it). In other cases, used cars are broken up for their parts within the countries of their manufacture or initial use. In the United States, for instance, every year some eight million cars and five million trucks are recycled, making them the number one recycled product (US Environmental Protection Agency 2012). Once dismantled these vehicles’ parts are often shipped overseas. In the early 2000s the global market for remanufactured and used motor vehicle parts and components was

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estimated to be worth between US$60 and US$70 billion (Czaga and Fliess 2005). As they are exported for reuse, they carry with them the value which they still contain. Several areas in the Global South have become hubs for the used-car trade. Abu Shagara, a residential area in the emirate of Sharjah, UAE, is one of the largest such centres in the Middle East, whilst Dubai has emerged as a leading exporter to Africa and the Indian subcontinent of vehicle spare parts, most of which are second-hand parts sourced from damaged Global North cars sold by insurance companies or from vehicles seized by police agencies. In the case of Central America, between 2005 and 2008 over 2.5 million used vehicles were exported from the United States to Mexico (Davis and Kahn 2010). For their part, a number of Pakistani trading families have been central in developing a network exporting used vehicles from Japan to East Africa, importing them via South Africa for resale in countries like Mozambique (Brooks 2012). The largest market in Africa for used vehicles, though, is in West Africa, with vehicles mostly coming from Europe and North America. Indeed, West Africa is one of the largest destinations anywhere for exports of used vehicles from Global North countries, with an estimated 500,000 imported annually, although given that many vehicles are imported in such a way as to avoid being taxed, this figure is an educated guess. The value of used-vehicle imports from Europe to West Africa jumped from a few million euro in the early 1980s to just under €1 billion by 2000 (Beuving 2009). A principal port at which such vehicles are landed is Cotonou, Benin. Upon arrival many are traded in the port, whilst the remainder move on to one of Cotonou’s car markets to await potential buyers. Only about 10 per cent or so of these vehicles remain in Benin, however. The rest are shipped to other West African countries, particularly Nigeria, which by the early 2000s was importing some 100,000 used cars compared to only about 10,000 new ones (Agbo 2011a). Whereas vehicles coming from Europe but destined for Nigeria mostly come through Benin, those coming from the United States – typically from dealers in Texas and New Jersey with whom Nigerian traders have established relationships – are usually shipped directly to Nigeria. In 2008, about 50,000 vehicles valued above US$2,000 were exported to Nigeria directly from the United States (Connors 2011). The GDNs for used vehicles, then, stretch from exporters in Europe, Japan and the United States, as well as other countries, to those places where the vehicles will be broken up to recover useful materials. In this regard, they can serve as important conduits of raw materials and value into the economies of the receiving countries. At the same time, though, some parts of the vehicles will fall out of the value circulation system, either because they are devalorised or because, although they still hold value, the market is presently such that it does not make economic sense to try to recapture that value (i.e., they are devalued). In such instances they may perhaps either be used for non-productive activities (parts of car bodies that are not processed for sending on as inputs into a GPN might be used as roofing materials in local homes, for example) or be landfilled, thereby providing some (minimal) local employment but also potentially threatening local environmental and human health. Indeed, according to Fuse et al. (2009), in 2005 some 3.4 million tonnes of iron, 310,000 tonnes of aluminium, 75,000 tonnes of copper, 32,000 tonnes of lead and 27,000 tonnes of zinc held in used passenger cars were not recycled in the country of these vehicles’ first sale but, rather, moved in a global flow out of them. Whereas the top three exporting countries were Japan, Germany and the United States, the dominant importers were emerging and/or resource-rich countries like Russia, Australia, Lithuania, the

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Czech Republic, Kazakhstan and the UAE. However, many also ended up being directly exported to Global South countries, such as those of West Africa. Furthermore, given that Fuse et al.’s data only tracked the direct country of first import, it is impossible to know with any great accuracy how many cars imported into, say, the UAE were subsequently re-exported to other countries in the Gulf region, East Africa and South Asia. Equally, most of the spare parts imported into places like the UAE are then sent on to third countries – each year, for instance, an estimated 100,000 tourists from the former Soviet republics travel to the UAE and spend, on average, about US$2,500 on automobile spare parts and related products (Anon n.d.). As with e-waste and ships so it is with used vehicles: the GDNs in the Global North are generally quite different from those in the Global South. This has implications for patterns of local development. Hence, in countries like the United States, vehicles are typically broken up using large equipment, whereas in the Global South the work is generally done by hand, using simple tools like axes. The fact that the taking apart of used vehicles is done by hand in the Global South means that workers are generally more likely to be exposed to noxious chemicals and also that less of the vehicle overall is successfully recycled (i.e., that less value is successfully recaptured) because the workers do not have the appropriate tools or technology to secure from it many parts for processing and reuse – it is often not possible to separate the different metals, for instance, because of a lack of the equipment necessary to do so. In the Global North, on the other hand, large hammermill machines break up the vehicle carcasses into fist-sized pieces, such that the metals can then be separated for reuse – the ferrous metals are typically recovered using magnets whilst nonferrous metals are generally recovered using eddy-current separators (Rem et al. 1998), with the result being that more value is recaptured. Moreover, the geographical location where the taking apart occurs can affect what kinds of GPNs the recovered materials then enter. Hence, whilst in places like the United States and Europe much of the recycled material eventually finds its way back into automobile GPNs (i.e., the value generated within this sector stays within it), in the Global South much of the recovered iron and steel is used to produce materials for the building and construction industry, whereas aluminium is turned into cooking utensils (i.e., the value initially generated in the automobile sector is transferred to other sectors). The materials that are completely devalorised or are devalued because it is too expensive to recapture the value incorporated in them often end up in landfills rather than continuing to circulate in the local, regional, national or global economy.

DISCUSSION The production of waste is a central element in modern capitalist systems. Indeed, the model of accumulation today is increasingly dependent upon the rapid replacement of products, with consumers encouraged to replace them before their use value has been exhausted. The planned obsolescence of commodities and their devaluation, then, has become key to the accumulation process at least since the emergence of the modern consumer society in the post-WWII era when, as an article in the Journal of Retailing proclaimed in the spring of 1955, the new model of consumption being developed required that ‘things [be] consumed, burned up, worn out, replaced, and discarded at an ever increasing pace. We need to have people eat, drink, dress, ride, live, with ever more

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complicated and, therefore, constantly more expensive consumption’ (Lebow 1955: 7–8). However, as we have outlined above, through its passage through various GDNs such waste has the potential to be reused and so to provide significant resources which can serve as catalysts for local manufacturing and hence of economic development in places where there may or may not presently be manufacturing occurring. Several important points, we feel, emerge out of the above discussion. The first of these is that the processing of waste can clearly provide countries with significant supplies of raw materials to which they might not otherwise have access. Thus, Agbo (2011b) has suggested that the importation of used vehicles into Nigeria provides the country with about 196,000 tonnes of scrap materials each year, potentially an important source of raw materials for local manufacturing. Likewise, shipbreaking in Bangladesh affords the country an important source of iron and steel to which it does not have access through domestic sources. Meanwhile, in the case of Australia it has been estimated that each year there is between five and six million tonnes of metal content with an estimated worth of more than A$5 billion in the country’s waste streams which, were it recovered and recycled, could cover 60–70 per cent of annual metal consumption within the country (Corder and Golev 2014). This raises policy questions about how might importing waste be used as a way to kickstart industrialisation in places presently without it and how might global or national trade rules be restructured so as to shape the planetary movement of used goods – many countries, for instance, restrict the importation of used vehicles for all sorts of reasons (safety concerns, protection of local manufacturers, etc.), which affects the geography of where they may be disassembled so as to extract raw materials. Countries – especially those in the Global South – might increasingly have to decide, then, whether to keep imported used vehicles out of their territories as a way to protect nascent vehicle manufacturers or whether to allow them in as a potential source of inputs for other manufacturing sectors or the construction industry (in the case of metal that is melted down to make, say, steel girders). As might be imagined, this could have myriad implications for political conflicts between different segments of domestic capital. Second, the types and quality of resources which might be harvested from waste can be dramatically shaped by the labour processes involved in taking apart old products. This has implications for patterns of local economic development and market making. It also has implications for the kinds of investments in particular places that local or national governments or private enterprise may wish to make in the purchasing of machinery and in the training of workers, should they wish to use the processing of waste as an economic development strategy. For instance, although the use of machinery means that the rate of recovery of certain types of constituent elements may be higher in countries like the United States than in the Global South, where more of this work is done by hand, the higher cost of labour means that recovering, repairing and directly reusing in new products recovered components like computer chips tends to be lower. Instead, recovered components are typically simply melted down, with their constituent elements used to make new chips (or other products). The prohibitive cost of testing and repairing large volumes of e-waste for resale, then, means that the vast majority of the US industry’s e-waste recycling output is commodity-grade material (Daoud 2011: 13). In the Global South, on the other hand, a higher proportion of computer parts are recovered whole and sold for reuse as they are. This raises questions about whether and how such Global South countries might successfully brand new products made from directly repaired and

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reused components, especially if consumers in the Global North (but also elsewhere) are increasingly seeking products that are recycled because of the sense that doing so is more environmentally friendly. This is particularly so because the recycling and secondary use of metals tends to save significant amounts of energy and produce less carbon dioxide relative to digging up and processing fresh ores, thus potentially minimising environmental impacts and buttressing sustainable development through the more efficient use of resources. For instance, according to the Bureau of International Recycling (2007), using recycled steel provides energy savings of 74 per cent relative to producing steel from primary raw materials, using recycled aluminium provides energy savings of 95 per cent, using recycled copper produces savings of 85 per cent and using recycled lead produces savings of 65 per cent. Producing paper using recycled paper results in 35 per cent less water pollution and 74 per cent less air pollution. Should, then, governments in countries importing waste encourage training for workers in the proper dismantling and repair of such components as a potential ‘green’ development strategy? Third, and closely related, the lack of appropriate machinery means that many things which could potentially be reused as inputs into new GPNs are lost to the future – they are permanently devalued – and so end up being simply discarded in landfills. However, as new technology is developed the range of recovered goods that emerge out of the end of GDNs seems to increase exponentially. Hence, several companies are developing technologies which can convert into synthetic oil, natural gas and carbon products the ‘automobile shredder residue’ (consisting of glass, fibre, rubber, automobile liquids, plastics and dirt) that remains after vehicles have been disassembled and the readily reusable parts have been recovered (Taylor 2012). This means that, once these technologies have been perfected and brought to market, countries which lack their own sources of oil and/ or natural gas could potentially invest in them and use the importation of old vehicles and other waste as a means to gain a greater degree of energy independence, especially as these Waste-to-Energy (WtE) technologies become more efficient. This has myriad implications, both environmental but also geopolitical and geoeconomic – greater energy independence will likely have negative impacts upon the ability of oil-producing and natural-gas-producing countries to shape the global political economy as we move into the future. In a similar fashion, a fourth consideration relates to how the growth of GDNs can have important implications for innovations in the design of products so that they are more easily recycled after their initial use. In the days before much thought was given to how discarded goods might be disassembled so that their constituent elements could be harvested for reuse, the key design consideration was usually functionality. Today, however, growing numbers of manufacturers are redesigning their products so that they still have functionality but that they are also more easily disassembled, making it easier for them (and others) to recover materials (McDonough and Braungart 2002). Hence, in the 1990s the Big Three US automobile manufacturers (Ford, General Motors and Chrysler) collaborated with the Vehicle Recycling Development Center located in Highland Park, Michigan, to develop more effective ways of designing vehicles so that they could more easily be dismantled at the end of their lives and their constituent components retrieved for reuse as used parts and/or recycled for fresh manufacturing. Several other companies engaged in similar efforts, including Siemens (coffeepots), Caterpillar (tractors), Xerox (photocopiers) and Eastman Kodak (cameras) (Bylinsky 1995). More recently

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there have been efforts to redesign LED lightbulbs to make them more readily recyclable (Webb 2013). The growth of GDNs, in other words, helps situate the growth of Design for Disassembly efforts so that we can understand these latter relationally rather than as developing in isolation. Fifth, in thinking about how the growth of GDNs can serve to stimulate economic development in those places which are the destination points for discarded products, it is also important to contemplate what might be some of the negative consequences for those places which were previously the producers of the raw forms of the inputs for GPNs. For example, should recycling rates of metals ever get high enough, this could have deleterious effects on mining communities in places like the Pilbara of Western Australia, which is a huge producer of iron ore. Certainly, the effects will be sectorally and geographically uneven, for different metals have quite different recycling rates. Hence, the lead in batteries is readily retrieved and the value of gold in electronics makes it worth recovering. However, in cases where materials are used in small quantities (e.g., tantalum in electronics) or where their price is presently not that high, recycling is physically more challenging and/or less economically worthwhile (for a more detailed breakdown of the recycling rates of various metals, see UNEP 2011). Nevertheless, the expansion of GDNs has the potential to destroy some local economies even as it stimulates others. Finally, bringing things back to the theoretical realm, the discussion of GDNs allows us to link the above analysis into a larger body of literature concerning the geographical mobility of value (e.g., Hadjimichalis 1984) and what this means for understanding patterns of global uneven development. In this regard, we can perhaps draw upon Amartya Sen’s work. Although he was not talking about waste, Sen’s (1962) paper on the export of used machines to ‘underdeveloped countries’ was perhaps the first to explore the transfer to low-waged countries of goods which had reached the end of their useful life in high-waged ones. He suggested that there were several reasons for this transfer to be economically worthwhile. For one, whereas in an ‘advanced economy’ a fall in the machine’s efficiency might put a producer at a competitive disadvantage, the lower wages and levels of efficiency in ‘underdeveloped’ ones mean that this is less of an issue. In similar vein, for manufacturers in an ‘underdeveloped country’, buying an older machine rather than a newer one may actually yield higher profit rates because of the depreciation rates of older versus newer machines and the relative proportions of constant and variable capital (i.e., machinery and labour) in the production process. For Sen (p. 348), then, the principal shaper of the economic logic of this transfer is the ‘differences in the condition of labor supply’ in the two contexts. We can draw similar conclusions with regard to the processing of waste, since the availability of labour, its cost and the type of machinery present (if any) will shape where waste ends up being processed and how. In turn, this will shape what kinds of products emerge out of the end of the GDN – high-quality melted-down metal versus parts like computer chips which can be reused as is, plastics which can be recycled versus those which must simply be thrown away because of a lack of reprocessing facilities, and so forth. Perhaps most important, though, is that the distinction between devalorised and devalued waste made above provides a mechanism for differentiating between that waste which is imported and has no utility in a productive sense – it is devalorised and is being sent overseas simply to be dumped and/or to be used for its use value (as with furniture taken from ships) and so will not be returned to the Money–Commodity–Money cycle at some future point – and that which is being imported and which has the potential

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to continue in motion as inputs into GPNs (i.e., it is devalued waste). In this regard, the discussion of GDNs and the distinction between devalorisation and devaluation provides an entrée into exploring the broader dynamics of global capitalist accumulation and the role of ‘waste’ therein.

REFERENCES Agbo, C. (2011a) A critical evaluation of motor vehicle manufacturing in Nigeria. Nigerian Journal of Technology, 30.1: 8–16. Agbo, C. (2011b) Recycle materials potential of imported used vehicles in Nigeria. Nigerian Journal of Technology, 30.3: 118–129. Anon (n.d.) Meeting the rising demand for auto parts. Africa Business Pages (online resource). Available at www. africa-business.com/features/spares.html; last accessed 11 June 2017. Appadurai, A. (ed.) (1986) The Social Life of Things: Commodities in Cultural Perspective. Cambridge: Cambridge University Press. Basu, I. (2004) Indian ship-breaking drifts to China. Asian Times 22 April. Available at www.atimes.com/atimes/ South_Asia/FD22Df05.html; last accessed 11 June 2017. Beuving, J. (2009) Striking gold in Cotonou? Three cases of entrepreneurship in the Euro-West African secondhand car trade in Benin. In J. Gewald, S. Luning and K. van Walraven (eds) The Speed of Change: Motor Vehicles and People in Africa, 1890–2000, pp. 127–147. Leiden, Netherlands: Brill Publishing. Bhamra, T., and Lofthouse, V. (2007) Design for Sustainability: A Practical Approach. Aldershot: Gower Publishing. Bilton, N. (2012) Disruptions: you know you can’t live without Apple’s latest glass rectangle. Available at https:// bits.blogs.nytimes.com/2012/10/28/disruptions-you-know-you-cant-live-without-apples-latest-glass-rectangle/? mcubz52; last accessed 11 June 2017. Bowen, F. (1936) The shipbreaking industry. In C. Winchester (ed.) Shipping Wonders of the World (2 volumes). London: The Amalgamated Press. Available at www.naval-history.net/WW1NavyBritish-Shipbreak.htm; last accessed 11 June 2017. Brooks, A. (2012) Networks of power and corruption: the trade of Japanese used cars to Mozambique. Geographical Journal, 178.1: 80–92. Bureau of International Recycling (n.d.) Ten questions on paper recovery and recycling. Available at www.bir. org/industry/paper/ten-questions-on-paper-recovery-and-recycling; last accessed 11 June 2017. Bureau of International Recycling (2007) About recycling. Available at http://web.archive.org/web/2007092 7175746/http://www.bir.org/aboutrecycling/index.asp; last accessed 11 June 2017. Buxton, I. (1983) A century of British shipbreaking: the growth and decline of an industry. In A. Ambrose (ed.) Jane’s Merchant Shipping Review, pp. 151–159. London: Jane’s Publishing Company Limited. Bylinsky, G. (1995) Manufacturing for reuse: designing products to be torn apart into reusable pieces keeps the Earth greener and can make a profit for practitioners. CNN Money online. Available at http://archive.fortune. com/magazines/fortune/fortune_archive/1995/02/06/201830/index.htm; last accessed 11 June 2017. Chiodo, J., and Jones, N. (2012) Smart materials use in active disassembly. Assembly Automation, 32.1: 8–24. Clerides, S. (2008) Gains from trade in used goods: evidence from automobiles. Unpublished paper, University of Cyprus, Department of Economics. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id5640902; last accessed 11 June 2017. Coe, N., Dicken, P., and Hess, M. (2008) Global production networks: realizing the potential. Journal of Economic Geography, 8.3: 271–295. Coe, N., Hess, M., Yeung, H., Dicken, P. and Henderson, J. (2004) ‘Globalizing’ regional development: a global production networks perspective. Transactions of the Institute of British Geographers, New Series, 29.4: 468–484. Connors, W. (2011) In Nigeria, used cars are a road to status. Wall Street Journal 18 January. Available at http:// online.wsj.com/article/SB10001424052748704515904576076622892749928.html; last accessed 11 June 2017. Corder, G.D., and Golev, A. (2014) Industrial ecology forum ‘Shifting the Australian resources paradigm’, 28 March 2014, Sydney – Outcomes and Findings Report. Prepared for Wealth from Waste Cluster, by the Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland. Brisbane, Australia. Czaga, P., and Fliess, B. (2005) Used goods trade: a growth opportunity. OECD Observer, No. 246/247, December 2004–January 2005. Available at www.oecdobserver.org/news/archivestory.php/aid/1505/Used_ goods_trade.html#sthash.CmowtKHC.flLhJ6SW.dpuf; last accessed 11 June 2017.

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Daoud, D. (2011) Inside the U.S. Electronics Recycling Industry. Report by International Data Corporation. Available at http://ewasteguide.info/files/IDC_2011_ISRIl.pdf; last accessed 11 June 2017. Davis, L.W., and Kahn, M.E. (2010) International trade in used vehicles: the environmental consequences of NAFTA. American Economic Journal: Economic Policy, 2.4: 58–82. Fuse, M., Nakajima, K. and Yagita, H. (2009) Global flow of metal resources in the used automobile trade. Materials Transactions, 50.4: 703–710. Gereffi, G., Humphrey, J. and Sturgeon, T. (2005) The governance of global value chains. Review of International Political Economy, 12.1: 78–104. Gereffi, G., and Korzeniewicz, M. (eds) (1994) Commodity Chains and Global Capitalism. Westport, CT: Greenwood Press. Giuliani, E. (2017) Industrial clusters in global networks. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation (pp. 360–371). Cheltenham, Northampton, MA: Edward Elgar Publishing. Grant, R., and Oteng-Ababio, M. (2012) Mapping the invisible and real ‘African’ economy: urban e-waste circuitry. Urban Geography, 33.1: 1–21. Gregson, N., Crang, M., Ahamed, F., Akhter, N. and Ferdous, R. (2010) Following things of rubbish value: endof-life ships, ‘chock-chocky’ furniture and the Bangladeshi middle class consumer. Geoforum, 41.6: 846–854. Gregson, N., Crang, M., Ahamed, F., Akter, N., Ferdous, R., Foisal, S. and Hudson, R. (2012) Territorial agglomeration and industrial symbiosis: Sitakunda-Bhatiary, Bangladesh as a secondary processing complex. Economic Geography, 88.1: 37–58. Hadjimichalis, C. (1984) The geographical transfer of value: notes on the spatiality of capitalism. Environment and Planning D: Society and Space, 2.3: 329–345. Henderson, J., Dicken, P., Hess, M., Coe, N. and Yeung, H. (2002) Global production networks and the analysis of economic development. Review of International Political Economy, 9.3: 436–464. Hossain, M., and Islam, M. (2006) Ship Breaking Activities and Its Impact on the Coastal Zone of Chittagong, Bangladesh: Towards Sustainable Management. Young Power in Social Action (YPSA), Chittagong, Bangladesh. Available at www.shipbreakingbd.info/report/Ship%20Breaking%20Activities%20and%20 its%20Impact%20on%20the.pdf; last accessed 11 June 2017. Kaplinsky, R. (1998) Globalisation, Industrialisation and Sustainable Growth: The Pursuit of the Nth Rent. Discussion Paper #365, Institute of Development Studies, University of Sussex. Available at www.ids.ac.uk/ files/Dp365.pdf; last accessed 11 June 2017. Karim, M. (2010) Environmental pollution from the shipbreaking industry: international law and national legal response. Georgetown International Environmental Law Review, 22.2: 185–240. Lebow, V. (1955) Price competition in 1955. Journal of Retailing, 31: 5–10, 42, and 44. Lepawsky, J., and Billah, M. (2011) Making chains that (un)make things: waste-value relations and the  Bangladeshi rubbish electronics industry. Geografiska Annaler: Series B, Human Geography, 93.2: 121–139. Lepawsky, J., and Mather, C. (2011) From beginnings and endings to boundaries and edges: rethinking circulation and exchange through electronic waste. Area, 43.3: 242–249. Marx, K., (1867 [1990]) Capital (Volume 1). London: Penguin. McDonough, W., and Braungart, M. (2002) Cradle to Cradle: Remaking the Way We Make Things. New York: North Point Press. Minter, A. (2011) The shipbreakers of China. The Atlantic 4 March. Available at www.theatlantic.com/international/archive/2011/03/the-shipbreakers-of-china/71976/; last accessed 11 June 2017. Mulvaney, D. (2011) Green Technology: An A-to-Z Guide. Thousand Oaks, CA: Sage. Pickren, G. (2015) Making connections between global production networks for used goods and the realm of production: a case study on e-waste governance. Global Networks: A Journal of Transnational Affairs, 15.4: 403–423. Puthucherril, T. (2010) From Shipbreaking to Sustainable Ship Recycling: Evolution of a Legal Regime. Leiden, Netherlands: Martinus Nijhoff Publishers. Rainnie, A., Herod, A., and McGrath-Champ, S. (2011) Review and positions: global production networks and labour. Competition and Change, 15.2: 155–169. Rainnie, A., Herod, A., McGrath-Champ, S. and Pickren, G. (2015) Wasted commodities, wasted labour? Global production and destruction networks and the nature of contemporary capitalism. In K. Newsome, P. Taylor, J. Bair and A. Rainnie (eds) Putting Labour in Its Place: Labour Process Analysis and Global Value Chains, pp. 249–265. Basingstoke, UK: Palgrave Macmillan. Ramstad, E. (1994) Intel to demonstrate next generation chip within a year. AP News Archive 27 January. Available at www.apnewsarchive.com/1994/Intel-to-Demonstrate-Next-Generation-Chip-Within-a-Year/id5c180eea20a7dde3aab1e9322e20800b; last accessed 11 June 2017. Reddy, R. (2013) Revitalising economies of disassembly: informal recyclers, development experts and e-waste reforms in Bangalore. Economic and Political Weekly, 48.13: 62–70.

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Rem, P.C., Beunder, E.M. and Kuilman, W. (1998) Grade and recovery prediction for eddy current separation processes. Magnetic and Electrical Separation, 9.2: 83–94. Rousmaniere, P., and Raj, N. (2007) Shipbreaking in the developing world: problems and prospects. International Journal of Occupational and Environmental Health, 13.4: 359–368. Sarraf, M., Stuer-Lauridsen, F., Dyoulgerov, M., Bloch, R., Wingfield, S. and Watkinson, R. (2010) Ship Breaking and Recycling Industry in Bangladesh and Pakistan. Report for the World Bank. Available at http://siteresources.worldbank.org/INTPOPS/Publications/22816687/ShipBreakingReportDec2010.pdf; last accessed 11 June 2017. Sen, A.K. (1962) On the usefulness of used machines. Review of Economics and Statistics, 44.3: 346–348. Slade, G. (2007) Made to Break: Technology and Obsolescence in America. Cambridge, MA: Harvard University Press. Smil, V. (2013) Making the Modern World: Materials and Dematerialization. New York: Wiley. Smith, N. (1981) The concepts of devaluation, valorization and depreciation in Marx: toward a clarification. Unpublished manuscript, Department of Geography and Environmental Engineering, The Johns Hopkins University, Baltimore. Smith, N. (1990) Uneven Development: Nature, Capital and the Production of Space (Second edition). Oxford: Blackwell. Taylor, B. (2012) Breakthrough moment. Recycling Today 15 May. Available at www.recyclingtoday.com/ rtge0512-auto-shredder-residue-debate.aspx; last accessed 11 June 2017. UNEP (United Nations Environment Programme) (2011) Recycling Rates of Metals: A Status Report. Paris: UNEP. US Environmental Protection Agency (2012) Compliance Assistance at Work in Auto Salvage Yards and Auto Recycling Facilities. San Francisco, CA: EPA Pacific Southwest. Available at www.epa.gov/region9/enforcement/auto-compliance.html; last accessed 11 June 2017. Van Assche, A. (2017) Global value chains and innovation. In H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation (pp. 739–751). Cheltenham, Northampton, MA: Edward Elgar Publishing. van der Zee, D.J., Achterkamp, M.C. and de Visser, B.J. (2004) Assessing the market opportunities of landfill mining. Waste Management, 24.8: 795–804. Wang, F., Huisman, J., Meskers, C., Schluep, M., Stevels, A. and Hagelüken, C. (2012) The best-of-2-worlds philosophy: developing local dismantling and global infrastructure network for sustainable e-waste treatment in emerging economies. Waste Management, 32.11: 2134–2146. Webb, F. (2013) Light bulb moment: redesigning LEDs for recyclability. The Guardian online 20 June. Available at www.theguardian.com/sustainable-business/led-light-bulbs-redesign-recycle; last accessed 11 June 2017. Woody, T. (2010) Scientists develop highly recyclable plastic. New York Times 9 March. Yang, W.G., Lu, H., Huang, W.M., Qi, H.J., Wu, X.L. and Sun, K.Y. (2014) Advanced shape memory technology to reshape product design, manufacturing and recycling. Polymers, 6.8: 2287–2308.

47. Innovation and the global eco-industry Bernard Sinclair-Desgagné

INTRODUCTION One of the most remarkable business outcomes of the past four decades is the emergence of a large specialized industry that focuses on mitigating the environmental impacts of human activity. Still insignificant in the late 1960s, this so-called eco-industry now compares with the aerospace and pharmaceutical sectors in size, employs more people than the automobile or chemical industries, accounts for an important and fast growing part of international trade, and currently holds close to 8 percent of all awarded patents (see, for example, the OECD’s environmental reviews – OECD 2013; EBI Report 2020 – EBI 2011; and Ernst & Young 2006). Many factors might in general explain the emergence of an industry. The existing studies habitually emphasize the role of technological innovation, altered relative costs or prices, entrepreneurial initiatives, or new consumer needs and other sociological changes. (For literature surveys, the reader may refer to Forbes and Kirsch 2011, Tether and Stigliani 2012, Gustafsson et al. 2016, Dewald and Truffer, Chapter 37, this volume.) One peculiarity of the eco-industry, though, is its critical reliance on regulations and social demands. Indeed, in industrialized countries, in response to people’s growing health and environmental concerns, the first national laws dealing with waste management, air and water pollution, noise reduction, and soil protection (measures such as the Resource Conservation and Recovery Act, the Clean Air Act, the Clean Water Act, the Noise Control Act, and the Comprehensive Environmental Response, Compensation and Liability Act, or CERCLA, in the United States) were adopted three or four decades ago. Since neither the governments nor any pressure group were then able to provide the needed abatement technologies, it was up to the private sector to make them available. This does not justify, however, why some specialized firms, not the targeted polluters, would increasingly take charge. This chapter’s objective is to put forward a rationale which has generally been overlooked in the literature on industry emergence but seems to match the eco-industry’s history, namely the old and well-known prediction made by Adam Smith in The Wealth of Nations, and formalized later by Stigler (1951), that ‘the division of labor is limited by the extent of the market.’ According to this statement, the combination of sustained economic growth (hence the general expansion of markets) and more stringent environmental pressures and regulations had to lead polluting firms to outsource the production and delivery of abatement goods and services. Once activated, this Smithian process – this chapter will next submit – sets a proper landscape to continuously innovate and create value. As Adam Smith (1776, p. 123) pointed out: ‘Men are much more likely to discover easier and readier methods of attaining any object when the whole attention of their minds is directed towards that single object (thanks to the division of labor and its consequent specialization) than when it is dissipated among a great variety of things.’ One might add that the new specialized firms 771

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are also strongly encouraged to make their business last. This entails creating value for society, first of all, by making the control of polluting emissions more effective and less costly; for the eco-industry itself, by giving it the opportunity to capture part of the benefits from having a cleaner environment; and for the regulated polluters, finally, by bringing down compliance costs and, more importantly, by inducing abatement suppliers to seek remedies which also enhance their clients’ competitiveness. It will be stressed, however, that achieving these outcomes (particularly the last one) is conditional on better coordinating certain public policies and changing some managerial mindsets and practices. In what follows, the second section provides data about the current state of the ecoindustry and its future prospects. On this basis, the third section next argues that innovation in greener products and technologies results in fact from a Smithian process that generates knowledge through further division of labor and specialization. The fourth section then outlines what policymakers and managers should do to make the most of this process. A fifth section sketches new avenues of research and concludes this chapter.

THE ECO-INDUSTRY – SOME FACTS AND TRENDS In 1998, in a report to the U.S. Department of Commerce, David Berg et al. (1998, p. 7) observed at that time that: ‘The domestic industry that provides environmental products and services is one of the least understood sectors within American industry, despite its size and economic importance.’ Things are changing. Several industrial syndicates and trade associations have come into existence; they now closely monitor their members’ activities, publish forecasts, and make recommendations to entrepreneurs and policymakers. Public organizations such as the OECD and Eurostat gather more and more precise data on green techs, and seek explicitly to come up with consensual definitions and classifications that will allow collecting comparable statistics. The advent of trade associations and sectoral definitions corroborates an important finding of the literature, reported by Gustafsson et al. (2016, p. 37): it contributes to forming an industry identity and is thereby an essential part of industry emergence. So far, however, relatively few economists and management scholars seem to have taken notice of the existence of the eco-industry. To my knowledge, no current environmental economics textbook mentions it, and only a limited number of articles in refereed journals have considered it explicitly. Among them, some papers actually focus on incipient sub-segments of the eco-industry, such as solar photovoltaic technologies (Calori 1985; Kapoor and Furr 2015), wind energy (Russo 2003; Garud and Karnøe 2003; Sine and Lee 2009), green building (York and Lenox 2014), and recycling (Lounsbury et al. 2003), emphasizing the impact of social demands, industry structure or firm strategy. Before looking at some of the figures provided by these organizations, let us first state some key definitions and thereby clarify the scope of the eco-industry. What Is the Eco-industry? In a landmark document published in 1999, the OECD and Eurostat (1999, p. 9) define the environmental goods and services industry as the set of ‘activities which produce goods and services to measure, prevent, limit, minimize or correct environmental damage

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to water, air and soil, as well as problems related to waste, noise and eco-systems; these include cleaner technologies, products and services which reduce environmental risk and minimize pollution and resource use.’ More recently, in order to get a better grasp at the impact of environmental policies on employment and track the evolution of the so-called green economy, Eurostat (2009, pp. 29–30) has decided to consider from now on all the socalled eco-activities. These refer to ‘the production of goods and services contributing to environmental protection and the management of natural resources.’ They can be carried out by private enterprises or public administrations, and include ‘some auxiliary activities which are not traded but still constitute an expense.’ The term ‘eco-industry’ then refers to ‘the eco-activities undertaken by an industrial sector.’ All in all, eco-activities are thus defined by the environmental objective they pursue. According to the European System for the Collection of Economic Information on the Environment, which abides by Eurostat’s decisions, environmental protection comprises seven general areas and two cross-sectional sets of activities: air and climate protection, wastewater management, solid waste management, soil and underground water remediation, noise and vibrations mitigation, biodiversity and landscape preservation, the prevention of radiations, environmental research and development (R&D), and environmental management. For natural resources management, on the other hand, the proposed classification is based on resource types: one speaks about the management of water, non-farmed forests, wildlife, mineral resources, energy, and R&D. This recent classification is now being discussed in several international forums and might still change in the future. Admittedly, the above definition contrasts with the most common one derived from industrial economics, which rather holds that an industry is a group of firms delivering products which are close substitutes for one another (Porter 1980). Products that reduce noise have little to do with products that deal with waste; goods and services to cope with water pollution hardly substitute for goods and services to improve air quality. Putting such a wide and heterogeneous set of products within the same perimeter might reflect specific public policy objectives. In its more traditional posture, the regulator wants to enhance competition and consumer surplus. Here, on the other hand, a policymaker’s goal is to foster sustainable development. Note that the activities aiming to lower fossil fuel consumption are not considered to be part of those seeking to mitigate global warming, but are rather included in one of the subareas of natural resources management. Sorting of solid waste is embedded within natural resources management, namely as a ‘recovery’ activity. Biological agriculture belongs to the subfield of ‘soil protection,’ ecotourism to a category called ‘others’ in environmental protection. The prospection and exploration of natural resources, along with asbestos removal and the disinfection of buildings, the protection against natural and technological risks (which was previously included in the OECD/Eurostat 1999 manual), and activities targeting ‘urban quality of life’ (such as parks and gardens), are all excluded. Eco-activities are also defined with respect to the delivery of certain goods and services called ‘eco-products.’ It was proposed to distinguish two categories of eco-products: ‘functional eco-products,’ which refer to goods and services that explicitly and directly meet an environmental protection goal, and ‘other eco-products,’ which comprise products initially meant for some other use (such as, for example, pumps and filters) but were modified to fulfill an environmental purpose. It is of course hard to ring-fence the

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latter category; Eurostat leaves it up to European Union member states to choose which statistical treatment will be used for these products. Knowing these (still evolving) classifications is important before one reads and starts interpreting the data. The observations to be presented next relate to functional ecoproducts and eco-products associated with resource management, which constitute the core of eco-activities. Size and Growth of the Sector Table 47.1 shows the recent evolution of global revenues for the eco-industry’s main segments. The OECD reports that the eco-industry’s worldwide revenues added up to US$858 billion in the year 2012. Prominent areas, such as solid waste management and water treatment works, comprise what are essentially end-of-pipe activities. This should not hide the fact that the search for and implementation of upstream remedies, or ‘cleaner production,’ is now subject to increasing efforts. Employment in eco-activities seems to have done rather well during the recent economic downturn, with an increase of 3 percent from 2007 to 2008, at the heart of the crisis. In a country like France, it is expected that the so-called green growth could generate up to 600,000 new jobs between now and the end of the present decade (Farthouat 2010). The Institut national de la propriété industrielle and the Ministère de l’économie, de l’industrie et de l’emploi say its main engines will be the areas of renewable energy (solar energy, wind power, biofuels, etc.), energy management (through heating and insulation), climate change prevention (CO2 capture, notably), remediation (soil and underground water cleanup), and waste recovery (sorting, recycling, energy generation, etc.). Table 47.2 next shows the amounts of environmental goods and services traded across the world, and the various regions’ respective share of this trade. The United States, Western Europe, and Japan – all developed countries – are well ahead in volume and hold large trade surpluses. Meanwhile, Asia runs a significant trade deficit. This situation explains in part the current deadlock in negotiations to liberalize the sector (Delabroye et al. 2016).

THE DYNAMICS OF THE ECO-INDUSTRY – A SMITHIAN PROCESS The preceding section conveyed one important stylized fact: the eco-industry delivers what are mostly end-of-pipe solutions to pollution problems. Another key fact is that the sector’s main segments are dominated by a few large firms: 10 percent of companies actually account for 80 percent of operating revenues (Ecorys 2009). The latter is indicative of the presence of economies of scale and scope. Altogether, these facts suggest a plausible explanation for the existence of the ecoindustry: it is a natural consequence of the broader division of labor which, as Adam Smith pointed out early on, both follows and fosters economic development. To see this, consider Stigler (1951)’s well-known demonstration of Smith’s theorem, which is reproduced in Figure 47.1. End-of-pipe pollution abatement activities (like solid waste

775

52.0 40.6 7.6 33.4 3.6

130.1 21.4 36.1 37.7 4.7 88.0

97.2 43.3 30.5

Equipment Water Equipment & Chemicals Air Pollution Control Instruments & Info Systems Waste Mgmt Equipment Process & Prevention Tech

Services Solid Waste Management Hazardous Waste Management Consulting & Engineering Remediation/Ind’l Services Analytical Services Water Treatment Works

Resources Water Utilities Resource Recovery Clean Energy Systems & Power

Source: EBI (2011, pp. 18–19).

2004

Market Segment

100.5 49.0 40.3

133.6 21.8 38.3 40.0 4.8 90.6

54.2 42.5 8.2 34.1 3.7

2005

103.5 53.9 59.2

136.7 22.2 40.1 42.0 4.9 93.1

56.4 43.9 8.6 35.1 3.9

2006

106.5 63.6 76.9

139.7 22.6 41.7 44.1 5.1 95.9

59.3 45.3 8.9 36.3 4.1

2007

111.5 50.0 99.1

142.8 22.9 45.2 45.7 5.3 100.8

63.2 45.1 9.3 36.6 4.4

2008

113.8 42.5 111.0

142.1 21.7 44.7 44.3 5.2 104.2

60.7 38.8 8.9 33.7 4.5

2009

116.4 43.8 123.3

144.3 21.5 46.8 44.8 5.3 106.8

62.5 39.7 9.2 34.0 4.5

2010

118.7 44.6 136.1

146.1 21.7 48.2 45.3 5.4 108.5

65.0 40.7 9.6 35.1 4.6

2011

Table 47.1 The global market for environmental goods and services, by industry segments, 2004–2012 (in billion US$)

121.1 45.5 150.2

148.0 21.9 49.6 45.7 5.5 110.2

67.6 41.8 10.1 36.1 4.7

2012

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Table 47.2 World trade in environmental goods and services, by region, 2009 (in billion US$) Region United States Western Europe Japan Asia Mexico Latin America Canada Australia/New Zeland Central and Eastern Europe Middle East Africa Total

Market size

Exports

Imports

Balance

292.1 219.6 95.8 67.0 6.3 20.6 19.7 12.9 15.0 18.5 8.7 776.2

40.5 55.8 19.1 5.2 0.8 1.1 2.8 3.7 0.9 0.6 0.2 130.7

27.5 39.8 7.2 22.2 3.0 8.5 3.1 1.8 6.2 7.4 4.2 130.7

13.0 16.0 11.9 −17.0 −2.2 −7.4 −0.3 1.9 −5.3 −6.8 −4.0 0.0

Source: EBI (2011).

Price

Average total cost

Y2 Y3

Y1 0

Quantity

Note: Using this figure, Stigler (1951, p. 187) demonstrates Adam Smith’s assertion as follows: ‘. . . we partition the firm, not among the markets in which it buys its inputs but among the functions or processes which constitute the scope of its activity. . . . If the cost of each function depends only on the rate of output of that function, we may draw a unique cost curve for it. . . . We should expect to find many different patterns of average costs of functions: some falling continuously (Y1); some rising continuously (Y2); some conventionally U-shaped (Y3). . . . with the expansion of the industry, the magnitude of the function subject to increasing returns may become sufficient to permit a firm to specialize in performing it. The firms will then abandon the process (Y1), and a new firm will take it over.’ Source: Stigler (1951, p. 187).

Figure 47.1

Stigler’s well-known demonstration of Adam Smith’s assertion

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management and recovery, wastewater treatment, and contaminated site remediation) correspond to function Y1 in the chart; they are subject to economies of scale and display little synergy with other production activities (European Cluster Observatory 2013). As the argument now goes, polluting firms will prefer to outsource them and benefit thereby from lower costs as soon as the market is able to support specialized suppliers. The latter condition was achieved over the past decades, thanks to growing social demands, more stringent environmental regulations, and sustained economic growth which jointly gave rise to a critical mass of clients for the eco-industry. As our stylized facts already suggest, this rationale applies mostly to environmental protection activities (with the nuances specific to each segment). For resources management activities (such as those pertaining to renewable energy), however, social demands and regulations might have been sufficient to attract new external entrepreneurs (Sine and Lee 2009). It should be noticed, moreover, that the largest segments of the eco-industry (notably those pertaining to water treatment and solid waste) are infrastructure components for the economy as a whole and certain industrial sectors (such as mining, energy generation, aluminum production, and chemical manufacturing). Their goods and services are thus somewhat generic, meeting the needs of as many polluters as possible while coping reasonably well with each firm’s specific problems. Bresnahan and Gambardella (1998) consider this feature to be an essential (but missing) ingredient of Stigler’s argument: vertical specialization must indeed result from a significant increase in the number of client enterprises (which was the case here) rather than a simple rise in firm size (which might have instead led polluting firms to produce abatement technologies themselves). A similar Smithian process lies behind the emergence of several other sectors, such as freight and machine-tool. In a recent empirical study, Arora et al. (2009) find that the history of the chemical industry, for instance, corroborates Bresnahan and Gambardella (1998)’s finer formulation of the Smith–Stigler theorem. Considering the rapid emergence of the eco-industry, other factors must have also played a role in accelerating the process in this case. Over the past 20 years, for instance, following Prahalad and Hamel (1990)’s influential article, a key precept of business strategy actively taught in business schools and diffused by management consultants was that firms should focus on their core functions. Since pollution management, especially when it is end-of-pipe, hardly qualifies to be such a function, managers were then willing to externalize it whenever they could. The evolution of risk management practices might also have influenced polluters’ decision to externalize environmental technologies. One effective way of dealing with uncertainty – in this case, regulatory and scientific uncertainty – is indeed to share part of the burden with a third party, especially if the latter happens to offer some key knowhow and if its interests will thereby be better aligned with those of the client (Blocki 2002). As has happened before, the broader division of labor at play here has already brought advantages to society. There is also, of course, a well-known downside to the division of labor (Legros et al. 2014, p. 797): lost economies of individual scope, extra costs of communication among specialists, more time spent coordinating tasks between disparate entities, and additional resources expended to bring independent devices together. The innovations and knowhow generated so far, and the consequent improvements in environmental quality and public health, however, seem to have compensated sufficiently. Thanks

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to the ensuing expertise and the presence of market incentives, the treatment of certain pollutants has become more effective and less costly (OECD 2013), professionalism and high-technology are now hallmarks of waste management (Wilson 2007), and the efficient reduction of odors, lead emissions, and discharges of toxic particles could not be achieved without the particulate scrubbers manufactured and continuously improved by specialized firms (EBI 2011). I will now argue that, under appropriate public policies and managerial practices, the Smithian process currently at work could bring even greater benefits.

THE ECO-INDUSTRY AND VALUE CREATION – AN OUTCOME STILL IN THE MAKING Nowadays, the assertion that the eco-industry generates knowledge and creates value might still run into some skepticism. The industry clearly answers society’s demands for a cleaner environment. Yet, it still seems overly dependent upon environmental regulation and policy rather than being a true engine of economic growth. In the latter case, one would see the eco-industry trigger a virtuous circle where, by improving its clients’ competitiveness, it would make its market grow and create thereby the conditions of its own expansion. Such a development, however, is ingrained in all Smithian processes. One may thus conjecture that the eco-industry’s main contribution to innovation and knowledge creation is yet to come, provided an appropriate business landscape is put in place. Revisiting Public Policies Since the production of environmental goods and services increasingly matters for environmental protection, employment, and international trade, several governments – notably in the United States, Canada, Germany, the United Kingdom, France, China, and India – and the European Union (the latter after adopting the so-called Lisbon strategy) are now actively and openly promoting their domestic eco-industry. In France, for instance, such a policy is spelled out in a document named Ecotech 2012, and the agency responsible for implementing it is the Strategic Committee for the Eco-Industries (COSÉI). Other major players are the National Research Agency (ANR), which administers the ‘Eco-technologies and sustainable development research program,’ the Agency for the Environment and Mastery of Energy (ADEME), which fosters the diffusion of eco-technologies and the expansion of environmental goods and services suppliers on the French and foreign markets, and OSÉO, a public investment bank which financially supports innovation in small and medium enterprises. These actions emphasize public funding of environmental R&D, the economic intelligence necessary to identify and enter foreign markets, the design of public procurement, the advent of business alliances and partnerships between private firms and public research institutes, and the availability of venture capital. Actions like these are again typical for nascent industries (Gustafsson et al. 2016, pp. 30–31). In principle, they should give an additional thrust to the ongoing Smithian process of broader division of labor. But they might not have the expected impact on innovation and knowledge creation without better coordinating with other policies. First of all, traditional environmental policy, which relies on mandatory technical

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standards, taxes, quotas, tradable permits, and voluntary agreements, should be revised according to its effect on the structure of the eco-industry. For environmental policy not only determines the size of the market for environmental remedies, it also influences the price-elasticity of demand for abatement goods and services. (This observation was first made by David and Sinclair-Desgagné (2005). Part of the economic literature that subsequently examined some of its consequences is covered in Sinclair-Desgagné (2008).) A polluting firm subject to a technical standard, for instance, will be less sensitive to the price charged by its abatement suppliers than if it can choose between paying some extra pollution taxes versus further lowering its emissions by acquiring certain technologies. It follows that an oligopolistic eco-industry will usually charge higher markups under the former policy than under the latter. This conclusion has received (indirect) empirical support. For example, Lange and Bellas (2005) find that the price of scrubbers that control sulfur dioxide emissions dropped drastically in the United States, following the reform of the Clean Air Act which put forward market instruments (in this case, tradable permits) in replacement of the previous mandatory technical standards. The data reveal that this drop is not due to a sudden burst of innovativeness nor to an increase in competition (market concentration rather increased) within the eco-industry. The residual explanation is that the price-elasticity of demand for scrubbers went up. The choice and design of environmental policy instruments can thus affect significantly the prices of environmental goods and services, hence the resulting rent of abatement suppliers, with obvious consequences for their efforts to innovate. On the one hand, prices which are too high will hamper the polluters’ competitiveness (hence possibly undermining environmental policy); on the other hand, prices which are too low will deter green entrepreneurship and innovation. Like innovation in general, environmental innovation is also stimulated by public policies that foster competition (Baumol 2002; Spulber 2013). Competition must be tuned up and managed in such a way that innovation and value creation – not just regulation – also become essential for survival in the eco-industry. Subsidies and programs inviting new entrants thus go in the right direction and should be pursued, if not enhanced. At the same time, competition policy must apply vigorously to the eco-industry, considering the increasing market concentration in certain segments (Solid Waste Treatment and Analysis and Testing, notably). David and Sinclair-Desgagné (2010) have shown that granting subsidies to the ecoindustry while taxing polluting emissions can be economically (Pareto) efficient. The role competition policy can play to limit concentration in the eco-industry is analyzed by Canton et al. (2012). The reader might note here that, while environmental regulation often tends to favor higher market concentration in polluting industries (which entails a drop in production, hence a reduction in polluting emissions), it would rather decrease it in the environmental goods and services sector. Finally, regulatory uncertainty should be lowered as much as possible; for by increasing the initial capital necessary for a firm to self-protect against future rules, regulatory uncertainty generates significant entry barriers for new entrepreneurs in the eco-industry. Rarely mentioned, but as important for the expansion of eco-activities, would finally be governmental actions aiming at harmonizing definitions and international classifications, and countering corruption which often plagues the construction sector and solid

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waste management in certain countries. The former will allow lifting several barriers to trade in environmental goods and services, thereby increasing competition within the ecoindustry and enlarging the market for abatement suppliers (Delabroye et al. 2016). This might further enhance the division of labor (particularly in waste management, which relies on the successive interventions of gathering, sorting, treatment, and recycling specialists) and generate greater economies of scale and scope. Actions against corruption, on the other hand, will not only benefit the environment by improving compliance with rules and regulations, they will also foster professionalism and expertise in the delivery of environmental goods and services. All these policies, provided they are properly applied and synchronized, will strongly drive firms in the eco-industry towards innovation and value creation. To achieve this goal, however, they will also have to be met by appropriate managerial practices. Revisiting Business-to-Business Relationships In the mid-1990s, the eco-industry in the United States was in crisis. As opposed to the situation that had prevailed throughout the previous decades, suppliers of environmental goods and services could not anymore base their entire strategy on the assumption that environmental regulation would get more and more stringent, and demand for environmental technology less and less elastic, because the regulator was now relying increasingly on market instruments, such as taxes and tradable permits, instead of command-and-control (David and Sinclair-Desgagné 2005). Some trade associations then started recommending their members to not just deliver end-of-pipe remedies but to also look for means to prevent polluting emissions; this, they said, would lead to reconsidering the client’s whole production process and, while doing so, seeking ways to enhance its competiveness. (More detailed accounts on this change of position can be found in Diener et al. (2000), Sinclair-Desgagné (2008), and EBI (2011, pp. 32–34 of Chapter 1).) This approach has gained ground in the largest segments of the ecoindustry, as a recent report by Environmental Business International (EBI 2011, p. 34 of Chapter 1) confirms: Pollution control, waste management and cleanup driven by regulation still represent the majority of revenues in the environmental industry. However, customer demand is replacing these services with pollution prevention and resource recovery investments not wholly dependent on regulations. For example, water treatment equipment for discharge is losing market share to water treatment and purification equipment for reuse. Expenditures on waste management equipment manufactured for containment, collection and transportation of solid waste for efficient disposal are increasingly being replaced by investments in equipment for sorting, processing and baling materials for recovery. Waste management services are focusing on recovery, and companies are generating profits from both services rendered and sale of recovered materials. Demand for compliance-oriented consulting is drying up, while demand for strategic environmental management and pollution prevention goes unmet. [Emphasis added]

This brings up one key remark: trade associations clearly saw that making the ecoindustry sustainable requires what most management scholars see as being the path towards innovation and value co-creation in business-to-business (B2B) relationships, which is to mutually seek to foster each other’s competitiveness. This change of mind happened because the Smithian process of broader division of

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labor exposes suppliers to incentives that will make them eager to create value in this sense. Meeting this objective implies, however, that the relationship between polluters and their abatement suppliers needs to be revised drastically. Current contracts between polluting firms and providers of environmental goods and services are generally reactive and ‘minimalist’: the goal is essentially to comply with existing regulations up to the industry’s common practice. An alternative approach would rather make the client and supplier maximize their joint benefits. The argument that contract framing has an impact on value creation was recently made by Weber and Mayer (2011). They use the terms ‘prevention frame’ and ‘promotion frame’ to denote ‘minimalist’ and ‘maximalist’ frames respectively. In their own words (p. 54): A prevention frame leads to an interpretation of a goal as minimal (something that must be met), which induces high-intensity negative emotions if the goal is not achieved and low-intensity positive emotions if the goal is met. . . . Conversely, under a promotion frame, parties view the same goal as maximal (something that would be ideal if reached). If a maximal goal is missed, lowintensity negative emotions are experienced, whereas if a maximal goal is reached, high-intensity positive emotions are induced. Thus, in an effort to reach the maximal goal and avoid sins of omission, parties display more flexible and creative behavior. [Emphasis added]

This new mindset will allow business strategy across firms in the eco-industry to evolve. Abatement suppliers might then consider offering ‘augmented’ products (a term used by Lindgreen and Wynstra 2005) that systematically deliver beyond the customer’s expectations. Their approach to B2B contracting might further change from a ‘goods logic’ to a ‘service logic,’ whereby the client’s core activities are placed at the heart of the transaction. Quoting Grönroos (2010, p. 241): According to a traditional manufacturing approach, following what could be labeled a goods logic, the supplier, for example producing and selling a production machine, would concentrate on how well the machine fits the customer’s production process – on what can be called operational efficiency. . . . It remains the responsibility of the customer to make sure that it can make effective use of the resource so that value can be created out of the resource purchased. A service business, i.e. a firm that has adopted a service logic, would take a much further-reaching responsibility for a customer’s everyday practices and how they ultimately support the customer’s business. . . . The core customer process (e.g. a production process) is supported by the core of the supplier’s market offering (e.g. a production machine), whereas the customer’s business is supported by the entire extended offering, including the machine and support to other customer processes important to the business. [Emphasis added]

Simultaneously, polluting firms will interact differently with their providers of environmental goods and services, by upgrading the quality and density of interpersonal linkages at the interface and involving upper management (Palmatier 2008; Merkel, Chapter 35, this volume; Faulconbridge, Chapter 41, this volume). Better knowledge of each other’s needs and preferences will certainly arise in this context. Greater proximity between suppliers and clients will also make them aware of their respective capabilities and routines, which will then facilitate organizational change (Sinclair-Desgagné 1999a). Empirical research shows indeed that such a mindset fosters creative solutions. The following examples are extracted from case studies conducted by the Institute of Clean Air Companies (ICAC), a non-profit U.S. association of firms working on the control and monitoring of stationary-source air pollution. They respectively show how

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collaborating more closely with, and giving more leeway to, a clean-tech provider can actually pay off. Example 1: A commercial bakery in California, Gold Coast Baking of Santa Ana, wanted an emission abatement system that would make its new production line comply with volatile organic compound (VOC) emission regulations. The company then asked its abatement supplier to build a catalytic oxidizer as an integral component of the new bakery oven. The unique design – incorporated as part of the operation process, not as an end-of-pipe system – resulted in substantial time and money savings. For instance, making the heat circulate in the oven eliminated the need for an additional heat exchanger and lowered fuel consumption by 25 percent. Example 2: Engineers at a major manufacturing plant in California needed to replace a faulty wet venturi scrubber operating on a waste-wood-fueled boiler. They wanted a system that would drastically lower fly-ash output, operate efficiently on the variable fuel, and resist fires. This system had to be engineered, fabricated, and installed within eight months. The company looked at various ways it could clean the stack emissions generated by its fixed-grate stoker boiler, including fabric filters and dry electrostatic precipitators (ESP). A pilot study conducted on site by a control technology supplier, however, convinced the manufacturer that a wet ESP would meet their requirements. Thanks to the acquired information, the supplier then designed, engineered, and installed an air pollution control system that is keeping emissions well below current regulatory thresholds. The company has thereby ensured that it will maintain a competitive edge over other manufacturers as regulation becomes tighter. Example 3: A manufacturing company selling an array of flexible packaging to customers across the United States needed to control its VOC emissions and treat a wide range of solvents (its plant was located in a residential section of town with a hospital across the street). Its 15-year-old carbon-bed recovery system required a lot of maintenance work and consumed a large amount of fuel. The system also yielded unrecyclable solvents that resulted in significant monthly disposal fees. In addition, the restricted solvent diet that the system could handle was limiting manufacturing flexibility. To address the problem, a regenerative thermal oxidizer (RTO) was built offsite and trucked to the packaging facility in close collaboration with engineers from an industrial equipment installation firm. Stack testing of the installed oxidizer produced an actual destruction efficiency of 98.9 percent – more than enough to satisfy the regulatory agency. Compared to the old solvent recovery system, the new system eliminated disposal fees and brought important savings in operating costs, with fuel consumption reduced by 80 percent. The RTO also eliminated the need for 9,000 pounds of water previously used for daily steam downs. Finally, the plant has been able to transfer the people in charge of the old solvent recovery system maintenance into value-adding activities. By considering its clean-tech supplier a partner rather than some arm’s-length dealer, this company was able to convert environmental compliance into a process of knowledge and value creation. Unfortunately, most current practices do not yet reflect this new mindset. The first section of this chapter suggested that most eco-activities are still largely carried out within a minimalist framework. In light of the 1990s experience (and some theoretical work, such as Nimubona and Sinclair-Desgagné 2011), however, one can nevertheless be hopeful that the actual business landscape will soon change for the better.

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CONCLUSION This chapter has argued that the emergence of the eco-industry and green innovation result from a Smithian process of broader and broader division of labor, in which polluting firms increasingly choose to outsource the control and treatment of their effluents to specialized suppliers of abatement goods and services. Smithian processes lie at the heart of economic development and knowledge creation; most industrial sectors have experienced such a process in the past, to see knowledge, innovation and society’s welfare improve significantly. There is little reason to expect a different outcome here, as long as public policies altogether favor sound innovation-based competition and the managers involved adopt an approach to B2B contracting which centers on the polluter’s core activities and value co-creation. This text is certainly not the first one to say that environmental protection can go hand in hand with innovation, competitiveness, and economic growth. The so-called Porter Hypothesis, for example, which states that well-designed environmental regulation can benefit not only the environment but also polluting firms, has generated a large literature. This assertion initially found support in theoretical work (Sinclair-Desgagné 1999b) and many case studies (Porter and van der Linde 1995), but the theoretical and empirical evidence drawn later on has delivered mitigated conclusions (Ambec and Lanoie 2008; Brännlund and Lundgren 2009). This chapter adds to this stream of research in two important ways: it highlights vertical relationships in pollution management, and it shows that the emergence of the eco-industry fits a well-known process which is inherently knowledge- and value-creating. As expressed here, however, the argument made to support the Smithian process is rather broad and needs to be refined, contextualized, and further supported by data. To be sure, the Smithian logic will not work the same across segments of the eco-industry, as different factors would play different roles and interact differently in diverse areas. Thanks to the heterogeneity characteristic of the eco-industry, future empirical research might lead to (1) an understanding of the initial conditions and various stages of industry emergence (as called for by Gustafsson et al. 2016), (2) an appraisal of multiple causes (several eco-industry segments, for instance, do not have their origin in exogenous technical invention), actors and strategies (as York and Lenox 2014 did for the green building segment), and (3) an understanding of the transitions between stages, particularly when a given segment enters a mature phase then governed by creative destruction and Schumpeterian logic. Acknowledgements This paper builds on an invited commentary delivered in Montreal on June 28 2010 at the colloquium ‘The Porter hypothesis at 20: Can environmental regulation enhance innovation and competiveness?’, in response to Professor Michael Porter’s keynote address. I wish to thank the editors, Sebastian Henn and Harald Bathelt, for comments and suggestions that helped improve significantly the content and presentation of the initial draft. My background knowledge of the eco-industry owes greatly to previous research collaborations and discussions with Joan Canton, Maia David, Alain-Désiré Nimubona, and Grischa Perino. My colleague Aurélia Durand also drew my attention to the relevant

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marketing literature on value creation in business-to-business relationships. I remain of course the only one responsible for all mistakes, omissions, and other shortcomings.

REFERENCES Ambec, S. and Lanoie, P. (2008) ‘Does it pay to be green? A systematic overview’, Academy of Management Perspectives, 23, 4, 45–62. Arora, A., Vogt, W. B. and Yoon, J. W. (2009) ‘Is the division of labor limited by the extent of the market? Evidence from the chemical industry’, Industrial and Corporate Change, 18, 5, 785–806. Baumol, W. J. (2002) The Free Market Innovation Machine, Princeton, NJ: Princeton University Press. Berg, D. R., Ferrier, G. and Paugh, J. (1998) The U.S. Environmental Industry, Washington, DC: U.S. Department of Commerce – Office of Technology Policy. Blocki, S. W. (2002) ‘Outsourcing environmental compliance’, Chemical Engineering Progress, October, 42–46. Brännlund, R. and Lundgren, T. (2009) ‘Environmental policy without costs? A review of the Porter hypothesis’, International Review of Environmental and Resource Economics, 3, 75–117. Bresnahan, T. and Gambardella, A. (1998) ‘The division of inventive labor and the extent of the market’, in E.  Helpman (ed.) General Purpose Technologies and Economic Growth, Cambridge, MA: MIT Press, pp. 253–281. Calori, R. (1985) ‘Effective strategies in emerging industries’, Long Range Planning, 18, 3, 55–61. Canton, J., David, M. and Sinclair-Desgagné, B. (2012) ‘Environmental regulation and horizontal mergers in the eco-industry’, Strategic Behavior and the Environment, 2, 2, 107–132. David, M. and Sinclair-Desgagné, B. (2005) ‘Environmental regulation and the eco-industry’, Journal of Regulatory Economics, 28, 2, 141–155. David, M. and Sinclair-Desgagné, B. (2010) ‘Pollution abatement subsidies and the eco-industry’, Environmental and Resource Economics, 45, 2, 271–282. Delabroye, S., Nimubona, A.-D. and Sinclair-Desgagné, B. (2016) ‘International trade and the environmental goods and services industry’, in A. Espinola-Arredondo and F. Muñoz-Garcia (eds.) Natural Resources and Environmental Policy in the Era of Global Change – Volume 1: Game Theory, World Scientific Publishing, pp. 295–328. Dewald, U. and Truffer, B. (2017) ‘Market formation and innovation systems’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 610–624. Diener, B. J., Terkla, D. and Cooke, E. (2000) The Massachusetts Environmental Industry: Facing the Challenges of Maturity, University of Massachusetts Donohue Institute. EBI (2011) Report 2020: The U.S. Environmental Industry, Environmental Business International. Ecorys (2009) Study on the Competitiveness of the EU Eco-Industry, Report for the European Commission. Ernst & Young (2006) Eco-industry, Its Size, Employment, Perspectives and Barriers to Growth in an Enlarged EU, European Commission – DG Environment. European Cluster Observatory (2013), Eco-industries – Analysis of Industry-Specific Framework Conditions Relevant for the Development of World-Class Clusters, Report to the European Commission. Eurostat (2009) The Environmental Goods and Services Sector – A Data Collection Handbook, Office for Official Publications of the European Communities. Farthouat, A. (2010) ‘Les éco-activités résistent-elles à la crise?’, Novethic, accessed 10 October 2014 at http://www.novethic.fr/empreinte-terre/economie-circulaire/isr-rse/les-eco-activites-resistent-elles-a-la-crise123040.html. Faulconbridge, J. R. (2017) ‘Relational geographies of knowledge and innovation’ in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 671–684. Forbes, D. P. and Kirsch, D. A. (2011) ‘The study of emerging industries: Recognizing and responding to some central problems’, Journal of Business Venturing, 26, 589–602. Garud, R. and Karnøe, P. (2003) ‘Bricolage versus breakthrough: Distributed and embedded agency in technology entrepreneurship’, Research Policy, 32, 277–300. Grönroos, C. (2011) ‘A service perspective on business relationships: The value creation, interaction and marketing interface’, Industrial Marketing Management, 40, 2, 240–247. Gustafsson, R., Jääskeläinen, M., Maula, M. and Uotila, J. (2016) ‘Emergence of industries: A review and future directions’, International Journal of Management Reviews, 18, 28–50. Kapoor, R. and Furr, N. R. (2015) ‘Complementarities and competition: Unpacking the drivers of entrants’ technology choices in the solar photovoltaic industry’, Strategic Management Journal, 36, 416–436.

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Lange, I. and Bellas, A. (2005) ‘Technological change for sulfur dioxide scrubbers under market-based regulation’, Land Economics, 81, 4, 546–556. Legros, P., Newman, A. F. and Proto, E. (2014) ‘Smithian growth through creative organization’, Review of Economics and Statistics, 96, 5, 796–811. Lindgreen, A. and Wynstra, F. (2005) ‘Value in business markets: What do we know? Where are we going?’, Industrial Marketing Management, 34, 7, 732–748. Lounsbury, M., Ventresca, M. and Hirsch, P. M. (2003) ‘Social movements, field frames and industry emergence: A cultural-political perspective on US recycling’, Socio-Economic Review, 1, 71–104. Merkel, J. (2017) ‘Coworking and innovation’, in H. Bathelt, P. Cohendet, S. Henn and L. Simon (eds) The Elgar Companion to Innovation and Knowledge Creation, Cheltenham, Northampton, MA: Edward Elgar Publishing, pp. 570–586. Nimubona, A.-D. and Sinclair-Desgagné, B. (2011) ‘Polluters and abaters’, Annals of Economics and Statistics, 103/104, 9–24. OECD (2013) Environmental Performance Reviews, OECD Publishing. OECD/Eurostat (1999) The Environmental Goods and Services Industry, OECD Publishing. Palmatier, R. W. (2008) ‘Interfirm relational drivers of customer value’, Journal of Marketing, 72, 4, 76–89. Porter, M. E. (1980) Competitive Strategy, New York: The Free Press. Porter, M. E. and van der Linde, C. (1995) ‘Toward a new conception of the environment–competitiveness relationship’, Journal of Economic Perspectives, 9, 4, 97–118. Prahalad, C. K. and Hamel, G. (1990) ‘The core competencies of the corporation’, Harvard Business Review, 90, 3, 79–91. Russo, M. V. (2003) ‘The emergence of sustainable industries: Building on natural capital’, Strategic Management Journal, 24, 317–331. Sinclair-Desgagné, B. (1999a) ‘Le mariage organisationnel’, in Michel Poitevin (ed.) Impartition – Fondements et Analyse, Quebec, Canada: Presses de l’Université Laval, pp. 137–150. Sinclair-Desgagné, B. (1999b) ‘Remarks on environmental regulation, firm behavior, and innovation’, CIRANO Working paper 1999s-20. Sinclair-Desgagné, B. (2008) ‘The environmental goods and services industry’, International Review of Environmental and Resources Economics, 2, 1, 69–99. Sine, W. D. and Lee, B. H. (2009) ‘Tilting at windmills? The environmental movement and the emergence of the U.S. wind energy sector’, Administrative Science Quarterly, 54, 1, 123–155. Smith, A. (1776) An Inquiry into the Nature and Causes of the Wealth of Nations. New York: Everyman’s Library, 1991. Spulber, D. F. (2013) ‘How do competitive pressures affect incentives to innovate when there is a market for inventions?’, Journal of Political Economy, 121, 6, 1007–1054. Stigler, G. J. (1951) ‘The division of labor is limited by the extent of the market’, Journal of Political Economy, 59, 3, 185–193. Tether, B. and Stigliani, I. (2012) ‘Towards a theory of industry emergence: Entrepreneurial actions to imagine, create, nurture and legitimate a new industry’, accessed 6 June 2016 at http://druid8.sit.aau.dk/acc_papers/ jeotvl03aa19ro7agvvnsr70rji1.pdf. Weber, L. and Mayer, K. J. (2011) ‘Designing effective contracts: Exploring the influence of framing and expectations’, Academy of Management Review, 36, 1, 53–75. Wilson, D. C. (2007) ‘Development drivers for waste management’, Waste Management and Research, 25, 198–207. York, J. G. and Lenox, M. J. (2014) ‘Exploring the sociocultural determinants of de novo versus de alio entry in emerging industries’, Strategic Management Journal, 35, 1930–1951.

Index

abduction 142 abductive learning 142–3 absolute advantage 473–4 absorptive capacity 278, 300, 363, 628, 677 academic entrepreneur 627–9, 631 academic spinoff 627–9 acceleration of modernity xiv acceleration school 219–20 access to assets 95 accumulation of capital 56–7, 625 capitalist 753–4, 757, 764–5, 768 of knowledge 46, 49, 204, 220, 362, 724–6 R&D 102–4 technological 363 acquisitive strategy 339 active consumption 235 active disassembly using smart materials 757 active innovation seekers 513 actor-network theory (ANT) 48, 202, 239, 247–8 actors’ social position 655 AD see Alzheimer’s disease (AD) ad-hoc innovation 263 adaptation behavioral 715, 717–18, 720–721 dynamic and mutual 557 of preferences 709 to user needs 618 adoption of best practice 560 of biosafety ordinance 694, 699 of technologies 152–4, 158, 166, 172–3, 435 advanced countries 75–83 advantage absolute 473–4 comparative 424, 474, 484, 733, 740–741 jurisdictional 500 affordances 154, 158, 161–2, 537, 540, 550, 579 Africa 763–4, 776 agencement 558–9, 565–7, 600–607 see also market-agencement agency embedded 129, 653, 655 importance in economic activity 672 role in shaping routines 558 social 673

agenda characterizing organizational structure 707–8, 710 repetition 708–16 agglomeration economies 215–17, 225–6, 423, 442–7, 452 forces leading to 480 models 217 Alzheimer’s disease (AD) field of treatment 655–6 implications for practice 664–5 and institutional logics 652–5 nature of disease 655–6 study data and methods 657–63 ambidexterity 89–90 Amin, Ash 9, 11, 15, 46–7, 63, 70–71, 122, 208, 232, 240, 342–3, 345–6, 350–352, 360, 392, 495, 538–9, 571, 576, 578, 580, 596–7, 671–5, 677, 680, 727 angel investment 629–30, 632 ANT see actor-network theory (ANT) Antwerp 645–8 Apple iPhone 152, 159–62, 757, 760 Apple Macintosh 156–7, 159 apprenticeship 346, 351, 394, 577, 675 arduous innovation 124–5, 130 arena scientific 64–5 socio-economic 65–6 technological 65 artefact 307–8, 346–7, 557–61, 564–7 artist-run center 253 artistic knowledge 104 assemblage 490, 502, 558–9, 567 assemblage theory 499–500 assets access to 95 complementary 60, 104, 108 intangible 105 ownership of 95, 265 tangible 265–6 assimilation perspective 260–261 associative governance 727–9, 734 asynchronicity 550–552 attachment 599, 603–4, 606–7 atypical visitors 519 augmented products 781 autopoietic systems 461, 466

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B2B see business-to-business relationships (B2B) Baden-Württemberg 265–6, 459, 465 baking powder 124 Bangladesh 752–3, 760–762, 765 Basel 657–60 BASF 62, 128 Bathelt, H. 3, 5, 8–12, 15, 41, 121–3, 128, 131, 222, 239, 269, 360, 364, 392, 395, 397, 400–401, 423–4, 428, 440, 448, 453, 459, 462–7, 493, 495, 509–10, 512–14, 516, 518–20, 523–4, 526, 529, 533–4, 538, 548, 552, 572, 578, 580, 612, 617, 627–8, 630–631, 638, 642, 646, 671, 673–6, 680, 686, 743 Bauhaus fixed by clichés and limited perception 291–3 and innovation 293–7 results summary 299 Bavaria 264 being there 246, 397–8, 577 Belgium 459, 646–7 benchmark of observation process 9 of trade fairs 519 benevolent creativity 308, 310, 315, 319 Berkeley 686, 692–6, 699 Bern 658–9, 662–4 bilateral transactions innovation as competition to establish 596–600 market-agencement conception 589, 601–6 spectre of 593–4 biomedicine 417 biosafety ordinance 686–9, 693, 699 biotech industry 686–9, 694, 699 biotechnology 630, 686–9, 692–700, 725 black box ideas as 198, 206 of innovation 59, 235–6 knowledge as 238 organizational 704 process of creation 235 board membership 697–8 Bohr, Niels 61 bonding social capital 343, 354, 545 Boston 133, 399, 627, 687 Boston Route 128 399, 626, 734 boundaries of communities 348, 558, 677 of reverse innovation 76–7 of technological systems 612 territorial 461–5, 543 boundaries of the firm 34–6, 44–5, 51, 60, 112, 183, 202, 353, 425, 679, 733, 741–2

boundary processes 348 Bradford, N. 442, 726–7, 730, 732 brain drain 367, 640, 643–4, 648 brainstorming 106, 201, 206, 278, 379 brokers 185–6 Burger-Helmchen, T. 77, 202, 353 business current 88 emerging 89–90 exploitation of existing 92–3, 207, 210, 721 exploration of new 93–4, 96, 207, 210, 620, 721 future 93, 96 business climate 685, 690–691 business network 338, 343, 360, 523, 672 business services 69–70, 267–70, 272 business-to-business relationships (B2B) 780–82 buzz and pipelines 423, 477, 480, 493, 498 C-K design theory 106, 201, 209, 300–301, 692–3 Callon, M. 48, 70–71, 202, 239, 247, 500, 556–9, 565–6, 590, 597, 600–603, 605, 611, 614 Cambridge controversy over recombinant DNA research 686–7, 692 developing first municipal-level biosafety ordinance 687–9 differences with Berkeley case 693–5 importance of policy 699 Cantner, U. 106, 165, 169, 172, 175–6, 178–9 Canton Fair 518 capital financial 448, 629, 657 human 42, 57, 270, 406, 410, 473, 628, 741–2 mental 474, 487 see also social capital capital accumulation 56–7, 625 car (industry) 127, 762–4 Catalonia 133 catching up categories for understanding success or failure of 169, 485 countries successful in 474–5, 486 factors for 475, 477–8 causation 131–2 centrifugal force 740 centripetal forces fixed costs 742 spatial transaction costs 743–5 chain-linked innovation model 59, 62, 458, 460 change, institutional 126–7, 655–64

Index Chesbrough, Henry William 3, 9, 34, 40, 45–6, 50, 60, 71, 87–8, 93–7, 125, 183, 260, 353, 372–3, 432, 596, 746 China 333, 349, 362, 397, 486, 518, 643, 647, 753, 761–2, 778 circulation 490, 497–9, 502 circus, contemporary 244, 247, 249 Cirque du Soleil 210, 247, 252 citation patent 111, 188, 398, 427 publication 64–5, 460, 725 cities see urban bias; urban diversity citizen participation 434 city council 687–9, 692–3 classification cluster 423–4 diaspora 639 eco-industry 772–4 hybrid virtual communities 540 industrial 217, 232 technologies 111 closed model of innovation 35–45, 50 clothing fabric 523, 524–33 clues 143 contextual 548 sensory 552 cluster cluster-based approach 422–4, 680 collective efficiency in 481, 484 concept of 422–4 creative 41 diamond 646–8 emergence and development 686, 691, 699 as example of territorial innovation model 492–3, 498 and ‘fallacy of composition’ 484–5 formation 477, 479–81 as geographical nodes of knowledge and innovation 671 green 435 and horizontal learning 395–9 innovation occurring more readily in 440 and knowledge-seeking 741–2 offshoots from 424–6 permanent 422–6 regional 480, 734–5 technology 268, 626, 630, 644, 695 temporary 510, 580 and urban bias 440–443, 445–6, 452–3 see also industrial cluster; innovation cluster; innovative cluster Cluster Go International 467 cluster innovation 366 cluster learning 364 Cluster-Netzwerke-International 467

789

cluster strategies 734 co-conception 377 co-creation with consumers, strategies for changes in approaches to marketing and consumers 375–6 consumer competence, motivation and engagement 378–9 downstream and upstream 376–8 supports 379–80 targets for advantages and limits of 386–7 emergent nature consumers 384–5 lead users 381–4 co-evolution of technology and markets 610, 613–22 co-innovation 386–7 co-location 351, 427, 429, 463, 548, 581–2, 675 co-opetition 185, 534 co-presence organized 463, 545–6, 552, 581 physical 550, 552 co-production 377, 726 co-production of innovations 596–8 codebook 203, 209 codifiability 483, 743, 746–7 codified knowledge 39, 70, 239–40, 252, 350–351, 427, 479, 483, 493, 538, 543, 733 cognitive activities 100–101, 200, 209, 392 cognitive dissonance 348–9 cognitive distance 348, 577–8 cognitive proximity 348–9, 615 cognitive resonance 349 Cohendet, P. 3–4, 8–9, 11, 46–7, 63, 70–71, 183–4, 202–3, 207–8, 210, 232, 240, 245, 252, 344–5, 349, 353, 364, 392, 395, 414, 467, 514, 518, 538, 558, 566, 578, 580, 597, 671–5, 677 Colbertist state 68 collaborative innovation 373 collaborative project 498, 661 collaborative work 579 collective action 279, 493, 523, 528, 533–4, 575, 601–2, 699 collective decisions 728–9 collective efficiency 481, 484 collective invention 184, 396, 400–401 collective learning effects in innovation process 59 function of VC firms in Silicon Valley 337–8 importance of interaction patterns between firms 729 practice perspective depending on 145–6 combination 6, 30, 91, 173, 184, 218–27 combinatory invention 218–19

790

The Elgar companion to innovation and knowledge creation

commercial innovation 405–17 commodities 246–7, 752–9, 764–5 communities craft 350–351, 499–500, 675 creative 204–5, 208, 251 epistemic 9, 46, 71, 208, 344–5, 395, 580 hybrid 539–40, 543, 549, 552 innovation 342, 380 of innovation practice 147–9 knowing 3, 9, 49, 70, 202, 204–5, 208–10 local 397, 561, 686–95, 699 online 379–80 open source software 352, 395–6 organizational 558, 561, 565, 567 of practice 46, 71, 147–8, 240–241, 344–55, 675–8 professional 351–4, 430, 645 task-based 350, 578 virtual 537–53 Community Innovation Survey (CIS) 43–4, 104–8, 263, 445 community members co-located 345, 352, 545, 678 cognitive proximity 348 cognitive work of 46, 202 contribution to firms 49 craft/task-based activities 350 dislocated practice 349 ideas 204, 208 online 379–80 practice as source of coherence 346 pre-existing characteristics and preferences 354–5 comparative advantage 424, 474, 484, 733, 740–741 competence(s) of agents 706–7, 715 “artful” 146 building 474, 486 consumer 376, 378–9 firm 39–40, 51, 66–7 individual 70–71 partner 184–5 of principal 714–15 uneven distribution between producer and user 473, 476, 483 venture capitalist 336–7 competition policy 170, 779 competitive advantage dynamic view on 94–5 sustainable 92–4, 97 transient 92, 94–7 competitive diffusion 165 competitiveness international 473–5

national 354–5, 442 structural 473, 475 competitor innovation 519 complementarity 51, 112, 429, 477, 729, 745 complementary assets 60, 104, 108 completeness of networks 329, 332–4 complex innovation abductive learning routines enabling 142–3 and practice perspective 149 problems of 140 Silicon Valley’s network of 334–5, 339 complex network theory (CNT) as applied to Silicon Valley 330, 332–3, 338–40 as learning and robust system of heterogeneous actors 329–30 use of 327–8, 339 complex systems 58, 67, 138, 140–143, 328, 337 complex technologies 178, 188–9, 387 complexity 64, 139–42, 330–331, 340, 436, 483, 743–4 complexity theory 42 computer server transfer 560–564 computers 69, 154–7, 160, 234–5, 333, 758–9 concepts of innovation 25–7, 30, 57–8, 233, 258 see also model of innovation conceptual history of innovation 25–31 conceptualization of institutions correlated behavior 123–4 as organizations 122–3 as rules and regulations 122–3 stabilized interaction patterns 133 concertation process 528–34 congealed labour 752, 754, 761 consensus community 692 on future trends 529–30 process of building 528, 685, 690, 692–5, 697–9, 727 of reciprocity 395 Washington 478, 590 constellations of practice 552, 673 consumers active integration of 373 consumption of culture 250 critical mass of 171 design for 152–4, 157–60 emergent nature 384–5 of fashion goods 524–5, 533 Global South and Global North 756, 766 involvement of 432–6 nudging forwards 162 online 379–80 product cycle 75, 234

Index reflexive relationship with producers 235–6 strategies for co-creation with 375–9 two types of 173 user-led innovation 375 contemporary circus 244, 247, 249 context clues 143, 548 coworking 575, 578 cultural 167, 352–3, 354, 397–8, 576, 580 economic 51, 343, 344, 392 geographical 459, 611–13, 615, 621 high-technology 626 highly regulated 352, 663–4 local 133, 363, 365–8, 397–9, 436, 448, 499, 546–7, 699 localized 467 malevolent 307–9 material 537–8, 546, 548, 552 maturation 616–17 science 61, 63, 66 see also institutional context contextuality 8 contingency geographical 130–133 of knowledge production 581 principle of 123 of relational action 673 controlled innovation 231–2 controversial innovation 128 convergence 278, 285, 289, 295, 297–301, 465, 485–6 convergent thinking 199, 275, 278, 298–9 conversational space 686–7, 690, 694–5, 697, 699 conversion (strategies) 127 cooperation networks 433, 573 coordination-oriented approach 426–30 copyright 48, 103, 108–12 copyright system 394–6 correlated interaction 123–4 correlated patterns of behavior 5 corrosion of creativity 250 corruption 779–80 cost disease 264–5 cost innovation 78, 81 counter-terrorism 317–18, 320 country advanced 75–83 developing 75–84, 361–3, 367, 459–60 “coupled practice” 50 coworking in academic literature 576 benefits of 574–6 in creative industries 571–5, 581 as new innovation model 579–80

791

as new social organization of work 570, 573–6 rise of independent, freelance workforce 571–3 spaces collaborative working in 579 existing research on 576–7 as innovation landscapes 570, 576 as new knowledge sites 580–582 social learning in 577–8 craft communities 350–351, 499–500, 675 creative class 105, 215, 237, 570, 574 creative clusters 41 creative communities 204–5, 208, 251 creative criminals 318–20 creative destruction 34–5, 65, 452, 492, 605, 783 creative economy 7, 232, 241, 342, 570–571 creative firm 69, 103 creative industry 6–7, 206–7, 210–211, 571–5, 581 creative milieu 580 creative process 63, 70–71, 197–201, 207–8, 217–18, 220–221, 499, 673 creative reservoir 208–9 creative slack 202 creative spark 201–2, 209, 446 creative task 100, 105, 249 creativity 4Ps of 307–11 benevolent 308, 310, 315, 319 community as facilitator of 353–5 corrosion of 250 and innovation 6–8, 344–5 and learning 347–8 malevolent 307–20 negative 307, 310, 313, 315, 320 three levels of, in science 64–6 types of 310 urban diversity and economic development 217–23 see also malevolent creativity creativity issues 275–80, 297–9, 302 creativity lab 581 criminal activities 307, 310–311 criminal behaviour 311–12, 316, 318–20 criminal entrepreneurship 311, 318–19 Cropley, D.H. 307–13, 316–18 cross-functional team 225 cross-licensing 189, 725 crowdfunding 433 crowdsourcing 106, 184, 186, 201, 226, 254, 263, 372, 377, 387, 433 cultural context 167, 353–4, 397–8, 576, 580 cultural differences 14, 221, 353, 676

792

The Elgar companion to innovation and knowledge creation

cultural diversity 221, 223 cultural economy contrast with manufacturing economy 233–4 and creativity 7, 237–8 effect of regulatory changes 233–4 and innovation normative practices 231–5, 241–2 problem of 241 value of 230 as sub-section of all production 232 transfer mechanisms 235–8 translation 239–41 and value 235–42 cultural economy ecosystem 232–3 cultural industries burgeoning literature in 244 cultural innovation integrating influences outside commodity chain 246–7 integrating role of material culture 247–8 integrating role of production-related actors 248–9 cultural production risks 249–50 emerging challenges 254 innovation as production of symbolic knowledge 245–6 nature and extent of 244 support for creation of informal spaces 252 institutions to ensure inclusive networks 253 provision of affordable spaces 251 cultural policies 250 cultural product(s) 48, 232–3, 244, 247–50 cultural production 232–3, 246–54, 577 cultural value 230, 232–3, 235, 240, 417 cultural workers 245–6, 249, 251–3 culture entrepreneurial 132, 630–632 organizational 563, 625, 627, 631 regional 395, 625, 631–2 cumulative learning 549–50 current business 88 D’Adderio, L. 556–7, 559–60, 564, 566–7 dark side application domains 310–311 concept 307–10 research 311–20 decentralized innovation 128, 518, 616, 618–19, 628, 672, 735 decision making decentralized 330, 721 organizational 707–9 pluralism 727

decision rights allocation 702–3, 707, 717, 720 decisions, delegation of 705–7, 711–12, 714–15, 720, 727 decomposability of decision tasks 706 of flow of innovation 79 of innovation process 205, 451 of knowledge 702 of work roles 144–5 defensive patents 443 degree of novelty 101, 107, 261 degrees of openness 424, 441 demand-pull approach 34–6, 40, 58–9 depreciation rate 102–3, 767 design dominant 169, 336, 401, 524–5, 532–3 organizational 695–9 robust 126–7, 153–5, 157, 160–162 design capabilities 278, 280, 298, 302 design for disassembly 757, 760, 767 design for recycling see design for disassembly design management 275, 289 design organization 278–9, 285, 299, 301 design oriented organizations (DO2) 301 design process 278–9, 285, 289, 299, 557, 747 design space 49, 301 design theory 276–7, 285, 291, 297–9 Desrochers, P. 224 devalorisation 754–60, 763–4, 767–8 devaluation 754–7, 760, 763–4, 766–8 developing countries 75–84, 361–3, 367, 459–60 deviance 131–2 devolution of power 728 of responsibility 727 Dewald, U. 610–613, 616, 618–20 diamond industry 645–8 diaspora entrepreneurship 354, 639–41 differentiation and domestication cycling between 157 example of cycle 159–61 managing the cycle 158 in evolution of research of service innovation 261–2, 269 market 234–5 nature of, in design 153 product 248, 400, 595, 598 differentiation by design 153–4, 158, 161–2 differentiation perspective 261–2 diffusion of innovations 42, 47, 345, 478, 620–621 diffusion of knowledge 43, 47, 237, 281, 365, 394, 397, 576, 629, 725, 734

Index digital photography 126, 129 digital technologies 152, 155–7, 159–62, 258, 270, 431–2 digitalisation 417 direct impact of research 67 disassembly 752–4, 757, 760, 765–7 discontinuous design improvements 158 innovation 154 discovery research 142 disengagement 93, 95–6 disruption high-end 79, 81 low-end 79, 81 disruptive innovation 76, 78–9, 81, 107 distance cognitive 348, 577–8 cultural 476, 640–41 geographic(al) 226, 236, 269, 430, 449–50, 476, 477–87, 540–543, 581, 638–9, 673, 676 knowledge transfer over 269, 352, 467, 537, 543, 570–71, 577–8, 581, 638, 642–8, 742–3 technological 226, 236, 430, 543 distributed authority 728 distributed knowledge 349, 538, 702–3, 705–6, 711, 717 divergence 278, 285, 289, 295, 297–301, 485–6, 563 divergent thinking 199, 209, 285, 298–9, 308, 314 diversity of agents 327–9 ambiguous role of 581 urban 215–27, 447 division of labor corporate 491 in knowledge production function 61 in management 268 between manufacturing and services 267–9 science characterized by 71 Smithian process of broader 771–2, 774–81, 783 division of service labor 267–8 DO2 (design oriented organizations) 301 domesticating innovation 152–62 domestication by design 153–6 domestication–differentiation cycle 157–62 dominance of alternatives 165, 170–172, 178 degrees of 482–3, 485 peripheral 127–8 dominant design 169, 336, 401, 524–5, 532

793

dominant model of innovation 2, 33–5, 39–40, 42–4, 50–51 Dosi, G. 2, 40, 69, 121, 125, 172–5, 486, 492 Dougherty, D. 138–48 Douglas, Y. 5, 124, 126, 152, 154, 156–7, 160 downstream co-creation 376–8 downward causation 131–2 drift avoidance (strategies) 127 drug innovation 125, 140, 142–3, 542, 656 Dvorak Simplified Keyboard 125 dynamic capabilities 40, 169, 197, 205 dynamic market failure 170 dynamic view on competitive advantage 94–5 e-waste 753, 755, 757–60, 765 eco-activities 773–4, 779, 782 eco-industry defining 772–4 dynamics of 774–8 emergence of 771–2 size and growth of sector 774 Smithian process 774–8, 783 and value creation business-to-business relationships 780–82 public policies 778–80 eco-product 773–4 ecologies of creativity 8, 12, 246, 252 ecologies of knowledge 9, 12–13, 467, 509–10, 520, 679 economic arena 65–6 economic development factors crucial for 477–8, 485 science- and technology-based 696–7, 699 and servitization 265–70 urban diversity and creativity 217–23 waste as strategy for 753–4, 756, 765, 767 economic discovery in conception of innovation 408 as powerful diffusion machine 416 procedure 405, 407 processes 408–9 in Space X episode 415 start-ups 417 in telecommunications domain 415 temptation to avoid stage of 410 transposing military technology into 414 economic diversity 215, 223, 225–7 economic geography of innovation 427–30 economic growth based on human creativity 60 innovation as main basis for 57 knowledge and innovation as contributors to 42–3 organizations promoting 458–9 positive effect of patents 182

794

The Elgar companion to innovation and knowledge creation

and servitization 264–7 sustained 771, 777 technological innovation serving 29 economic practice 122, 133, 672–3 economics of ideas 50–51, 58, 60, 87, 94–6, 373, 383, 407–8, 411, 417 economics perspective 335, 362–4, 479 economies agglomeration 215–17, 225–6, 423, 442–7, 452 localization 216–18, 671 of scale 237, 423, 426, 774, 777 of scope 237 urbanization 216–17, 225 economization, modalities of 590 economy creative 7, 232, 241, 342, 570–571 developed 78–9, 81, 481, 643 developing 75–84, 109, 641 emerging 77–9, 478 ecosystem cultural economy 232–3 entrepreneurial 629, 632 industrial and business 424–6 innovation 50–51, 91, 733–5 Edison, Thomas competitors 157 lighting system 126–7, 154–5 limitations of DC power 157–8, 161 as typical inventor 61 world view 162 effectuation 71, 209 efficiency-seeking 741 electric car 127 electric light 126–7, 154–5 electronics 62, 183, 188, 220, 375, 413, 560, 562, 753, 757–60, 767 see also microelectronics embedded agency 129, 653, 655 embedded knowledge 70, 139, 146–7, 237, 239–41, 246, 347 embeddedness of actors 425, 460 of agents 329, 333 of cultural differences 353 in diverse social networks 14, 338 of entrepreneurs 332, 338–9, 630, 632 firms’ local 360, 453 of innovation 58, 231, 746 of networks 241 of path-dependent processes 167 regional 493–4, 496–7, 500 ‘social-spatial’ 493, 498 socially embedded learning 396–8, 402 embedding agent 338–9

embodied knowledge 239, 350–351, 394, 396, 676, 711, 733 emergent nature consumers 384–5 emerging business areas 89–90 emerging economies 77–9, 478 encounters face-to-face 518–19, 537, 545–6, 548, 550–552, 678 formal and informal 246 market 603 online 537 planned or chance 252 short-term 11–12 endogeneity trap 494, 496, 501–2 endogenous (development) perspective 494 endogenous growth 42, 57, 60, 334–5, 442, 446–7 engagement collective 548 community 211, 346, 353, 539, 549, 675, 692, 699 of consumers 378–9 engineering 167, 268, 399–400, 407, 562–3 engineering design 106, 275–6, 278–81, 285, 289, 291, 298–9, 409, 557 enrolment collective 203 as means of knowledge production and innovation 70–71 entrepreneur academic 627–9, 631 institutional 662–3 migrant 398, 639–40, 642–4 transnational 641 entrepreneurial climate 630, 632 culture 132, 630–632 discovery 406, 414, 495, 498 ecosystem 629, 632 knowledge 337–9 opportunities 13, 570, 626 university 627–8 entrepreneurship criminal 311, 318–19 definition 625 diaspora 354, 639–41 green 779 institutional 14, 129–30, 652–65 transnational 638–49 user 387 entropic death 360 environmental goods and services 772–3, 774–6, 778–81 issues 425, 434–5 policy 772, 778–9

Index product 772 protection 773, 777, 778, 783 R&D 778 regulation 760, 777, 778–9, 780, 783 epistemic communities 9, 46, 71, 208, 344–5, 395, 558, 580 epistemic culture 142, 558 epistemic object 540–541, 543–4, 550 epistemic uncertainty 140 equilibrium/equilibria 165–6, 172–4, 179, 236, 474, 706–16, 719 equipment manufacturer 265, 449 ethical challenges 311–12, 314–18 ethnic enclave 639–40 ethnic entrepreneurs 640 ethnography 379–80, 581 European Union (EU) accounting frameworks 103 ‘balanced development’ 495 fostering regional innovation 497, 501 freelance workers 572 policy 69, 501, 734 program 467 relations between government levels 728 waste directive 757 evolutionary dynamic 465, 612 evolutionary economic geography 169–70, 493–4 evolutionary economics 58, 165, 175, 372, 724 evolutionary model of innovation 34, 39 evolutionary perspective 2, 428, 472, 479 evolutionist approach 200 evolutionist theory 336 exaptation 219, 223 exhibitor perspective on trade fairs 512–14 exit decisions 95–6 expertise 91, 132, 139–40, 144–5, 147–8, 210, 220–222, 225–6, 344, 382, 385–6, 543 explicit knowledge 39, 207, 674 exploitation of existing business 92–3, 207, 210, 271 exploration of new business 93–4, 96, 207, 210, 620, 721 export base model, modified 268–9 extensive marketization 590–591 externalities agglomeration 167 cities as sites of dynamic 446–7, 452 generated by choices 42, 168 human capital 216 knowledge 58, 68, 427, 446 large cities offering positive 269 Marshallian 421, 427 network 167–8, 173, 187

795

extra-cluster knowledge 360–361, 364–5 network 360 fabless firms 185 face-to-face (interaction) 41, 225–6, 330, 350–352, 394, 427, 478, 518–19, 537–8, 545–6, 548–52, 615, 638, 675, 678–9, 743–4 face-to-object (interaction) 546 fallacy of composition 484–5 fallacy of fixed technology 745–8 false negative 125 family 226–7, 253, 361, 640, 643–7, 665 family ties 396–7 fashion 244, 247, 249–50, 394, 518, 524–34 fashion cycle 247, 524–5 fashion trends 527–32 Faulconbridge, J.R. 349, 552, 671, 673–81 FDI (foreign-direct investment) 128, 476, 478, 483 feasibility 141–2 federalism 728–9, 732 feedback negative 174–9 positive 166, 168, 174, 176–8 Feldman, M.P. 12, 56, 67, 142, 145, 148–9, 422, 500, 625–6, 630, 632, 685–7, 689, 693, 695–6, 699–700 Ferrary, M. 133, 330, 332, 338–9, 340 fiat power 708–10, 713–14, 716–18, 720 field, organizational 125, 128–9, 513, 652, 655 filière 493, 498 financial capital 448, 629, 657 fintech 13, 734 fit with firm, assessing 141–2 fixation 275–81, 285–93, 297–302 fixation effect 277, 280, 285, 297, 300–302 fixed cost 742 fixed technology 745–8 flagship fair 512, 518 Foray, D. 48, 405, 407, 410–411, 490, 495–6, 732 foreign-direct investment (FDI) 128, 476, 478, 483 formal economy 758–9 forms of knowledge 48–9, 57, 60, 239, 362, 485, 572, 580, 729, 732–3 forward citation 111 framing competition 92 conceptual 122 contract 781 coworking as cultural 574, 577–8 creativity in malevolent context 308

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models and theories 556–7 routines 564–5 for structuring of collective action 602–4 for understanding 201 Frascati Manual 3, 38, 50, 100–101 fraud 309, 311, 315 freelancer 571–3 Freeman, Christopher 472–8, 483–6 frequency of interaction 449–50 friend networks 226, 250, 361, 396–8, 645 frugal innovation 75, 77–8 functional upgrading 362, 744–5 future business 93, 96 fuzzy front end 205–6, 278 gaming software 662–3, 665 Gandhian innovation 78 garbage-can model (of organizational choice) 519 garden leave 399 gatekeeper 360–361, 364–6, 425, 630 gatekeeping 365, 452 GCC see global commodity chain (GCC) GDN see global destruction network (GDN) Geilinger, N. 6, 13, 14, 129 generativity 300–301 genetic engineering 686–7 geo-localizing innovation 447 geographic concept 446–7 geographical context, innovation systems in 611–13 geographical contingency of institutions 130–134 geographical industrialization 491–4, 496, 498 geographical perspective 131–4 geographical proximity 38, 67, 226, 421–30, 448, 459, 687, 692, 735 geographies of market formation 621–2 relational, of knowledge and innovation 671–82 geography of diffusion 620–621 geography of innovation concept 421–2 coordination-oriented approach 426–30 economic 427–30 motivation for studying 442 new research questions 453–4 obscuring role of non-technological dimensions 431 perspectives 364–7 use of patent activity as indicator of 443–4 geography of knowledge transfer 236, 638, 642–9 Giuliani, E. 360, 362–7, 424, 484

global buyer 360–361, 367, 392 global buzz 510, 520 global cluster networks 467 global commodity chain (GCC) 481, 484, 753 global connection 360–362, 422, 452 global destruction network (GDN) growth impact 766–8 value, devaluation and devalorisation 754–7, 766–8 and waste commodities 752–4, 766 waste processing examples e-waste 757–60 shipbreaking 760–762 used vehicles 762–4 global eco-industry 771–83 global innovation map of flows 80 as not easily applicable locally 436 process 84 as rapidly evolving phenomenon 83 requirements 83 Silicon Valley as hub of 133 global knowledge transfers 638–49 global networks industrial clusters in 360–368 policy investing in 680 global North 481, 752, 756, 758–60, 763–6 global production network (GPN) 740, 752–64, 766–8 global South 752, 756, 758–60, 763–6 global value chain (GVC) approach 361, 481–6 centrifugal force 741–2 centripetal forces 742–5 definition 361 economic logic of 740–745 example of product made with 739 fallacy of composition 484–5 fallacy of fixed technology 745–8 and learning processes 367 modes of governance in 482–3 relationships in, types of 482 relevance to industrial clusters 366 research question arising 487 and technological change 739–40, 745 transaction interface 483 upgrading 361–2, 744–5 global warming 773 Glückler, J. 5, 8–11, 15, 121–5, 128, 131–2, 239, 264–6, 268–9, 392, 396, 462, 538, 617, 671, 673–4, 680 Godin, B. 25, 29, 33, 36, 38, 99–101, 231 Golfetto, F. 518, 523–34 goods passivaction 602

Index as platforms 592–3, 596 qualification of 592–606 goods logic vs. service logic 781 governance associative 727–9, 734 definition 685 in global value chains 482–3 horizontal and vertical relations 726–9 and innovation 14–15, 733–5 and learning 393–6, 729–33, 735–6 modes of 483 multilevel 728–9, 735 regional 495, 733–5 territorial 679–81 types of 744 governance and management of innovation 14–15 governance from below 15 governance relations 726–9 GPN see global production network (GPN) Grabher, G. 4, 9, 237, 245–7, 250, 269, 360, 537, 539–40, 548–9, 553, 571–3, 580, 613–14, 617 Granovetter, M.S. 8, 41–2, 123, 128, 329–32, 338, 340, 343, 430, 548, 674 green entrepreneurship 779 green growth 774 growth horizons 89 growth objectives 89–90 GVC see global value chain (GVC) habit 46, 122, 125–8, 153–4, 245, 306, 614 Haefliger, S. 387 Hamdan-Livramento, I. 2 Hargadon, A. 5, 124, 126, 152, 154, 157, 160, 203–4, 278, 300, 379, 548, 550, 581 harmful creativity 309, 312, 315 harmful novelty 311, 314 Hatchuel, A. 276, 279, 300–301 headquarters of firm 82, 109, 128, 252, 364, 443–5, 491, 741, 760 healthcare sector hybrid communities 540 and institutional entrepreneurship 664–5 institutional logics in 653–4, 663–4 market-agencement competition 605–6 marketization 601, 604 need for innovation 138 and professional communities 350, 352 technology applications for 665 see also Alzheimer’s disease (AD) heedfulness 141, 550 Henn, S. 13, 401, 467, 580, 638, 642, 645–6, 648 Héraud, J.-A. 58, 68–9

797

Herod, A. 15, 638, 753, 759 heresy xiv heterogeneous networks 328–33, 335, 339, 565 hidden cost 745, 748 high-end disruptions 79, 81 high-tech start-up 328, 332, 334–7 high-technology cluster 268, 626, 630, 644 see also technology cluster high-technology context of entrepreneurship 626 Highland Park, Michigan 766 highly regulated context 663 history dependent process 165, 170, 175, 179 history of innovation changing fortunes of 27, 29–31 as collective process 30 as conspiracy 30 design theory and creativity 277–302 economic geography 427 frequency of term over time 28 ideation process 198–200 as instrument of progress 27–9 as magic word 29 and order 25–7 as political 27 post-World War II 29–30 rehabilitations of 27, 29 theorizing 29–30 as useful 27 hold-up 183, 186–7, 190–191 horizontal interaction 9, 393, 396 horizontal learning among rival firms 360 concept of 392–3 governance 393–6 importance 401–2 mechanisms 396–401 human capital 42, 57, 270, 406, 410, 473, 628, 741–2 human values 412 Hussler, C. 3, 4 hybrid collective 559 hybrid firm involved in informal cooperation 262 rise of 265–7, 269–70 hybridization 12, 266, 269–70 hysteresis 169 see also institutional hysteresis Ibert, O. 232, 240, 537–8, 540, 553, 573, 575, 577–81 IBM 152, 155–6 idea conversion 202–4, 209 execution 201, 203–4, 209

798

The Elgar companion to innovation and knowledge creation

generation 201–9 selection 200, 203, 205–6 socialization 204–5, 209 valuation 202–7, 209 ideas management 200, 205–9 ideation process concept of 198–200, 204 coupling with innovation process 207 intention phase 201, 203–4, 207, 209 landing (execution) phase 203–4, 209 managing 198–205 social construction (conversion) phase 202–4, 209 spark phase 201–4, 209 tension with innovation process 205–11 IIDEX/NeoCon Canada 512 IKE group see Innovation, Knowledge and Economic Dynamics (IKE) group, Aalborg imaginaries 497, 499, 501 imaginarium 408 immaterial labour (labor) 248–9 immaterial product 237–8 impact of research direct 67 indirect 67–8 impact of trade fairs on innovation acquiring information about user needs 518 collecting information about the market, technology and policy environment 518–19 coming across problem solutions 519–20 compiling information about competitor innovation 519 presentation of innovation 518 searching for problem solutions 519 types of 517–20 implicit knowledge 538 imprinting 126 impromptu innovation explorers 513 incontestability 166–8, 175 increasing returns lock-in 166–8, 172–4, 177–8 to scale 42, 125–6, 266, 478–9, 486 incremental innovation 3, 6, 108, 217, 263, 327, 352, 378, 518–19 incubator 497, 575–6, 580, 627, 629, 649, 662 India 76–8, 82–3, 333, 459, 643, 645–8, 741, 758–63, 778 indicators of innovation 99, 107–11, 443–4 see also measurement of innovation indigenous innovation 78, 478 indirect impact of research 67–8 industrial cluster beyond local networks 360–361

caveats on research 367–8 differing from technology cluster 626 and global value chains 361–2, 366–7 and multinational enterprises 364, 366–7 technological gatekeepers in 364–6 upgrading of 361–2, 366 see also cluster industrial complex 423, 427, 475–6 industrial design 108–12, 275–6, 279–98, 516–17 industrial district 38, 218, 225, 327, 397, 400–401, 421–2, 492–3, 498–9 industrial ecosystem 424–6 industry emergence 610, 771–2, 783 industry identity 772 industry perspective on trade fairs 514–17 industry spillacross 247 industry spillover 13, 38, 215–17, 225–7 inferior technology 230, 748 inflexibility 166–70, 178–9 inflexible outcomes 165, 170, 172, 178–9 informal economic activity 758–9 informal space 252 information acquiring, about user needs 518–19 collecting, on market, technology and policy environment 518–19 compiling, about competitor innovations 519 sources, for innovation 510–511, 515 infused design 300–301 innovation ad-hoc 263 arduous 124–5, 130 based on communities 348–9 blowback 78 at bottom of the pyramid 77–8 collaborative 373 collective nature of 59, 138, 148, 183–4, 244–5, 523 controlled 231–2 controversial 128 cost 78, 81 cultural 246–9, 251 decentralized 128, 518, 616, 618–19, 628, 672, 735 disruptive 76, 78–9, 81, 107 domesticating 152–62 frugal 75, 77–8 Gandhian 78 green 772, 778–80, 783 incremental 3, 6, 108, 217, 263, 327, 352, 378, 518–19 indicators 443–4 indigenous 78, 478

Index institutional 435 Jugaad 75, 77–8 malevolent 309 mode 373 networks of 327, 330–340, 733 open 45–51, 87–98 see also open innovation as outcome xiv, 231, 260 peripheral 128, 130, 441 practice 231–5 as process xiv, 6, 30 product 140, 144–5, 149, 177, 232–3, 443, 475–7, 589, 592, 595, 600–606, 746–7 radical 70–72, 206, 217, 263, 278, 350–352, 355, 378, 382, 433, 627 recombinant 263 resource-constrained 78 reverse 4, 75–84 sequential 30, 50, 183, 190–191, 203 Shanzhai 78 social 27, 405, 431, 662 style 524, 532 technological 1, 29, 35–6, 91, 104–12, 218–19, 431–4, 725 theorizing 29–30 trickle-up 78 unsatisfactory 473, 476, 483 user 372–87 user-driven 59–60 see also geography of innovation innovation and creativity 6–8 communities of practice as sites of 345–53 community as facilitator or inhibitor of 353–5 occurring in cities 440, 447 innovation and cultural economy 230–231 normal innovation, normal science 231–5 transfer 235–8 translation 239–42 innovation and cultural industries 244–6 expanding cultural innovation analysis 246–9 risks and need to socialize risks 249–54 innovation and entrepreneurship 13–14, 625–6 entrepreneurial culture and impact 630–633 high-technology context 626 regional/local systems of 629–30 university spinoffs 627–9 innovation and institutions 4–6, 121 conceptualization of institutions 122–4 geographical contingency of 130–134 relation between 124–30 innovation and knowledge 57–9 knowledge creation as central tenet of 197 managing in organizations 677–8 production 59–61

799

role of manager in coupling with ideas 207–9 viewed relationally 674–7, 681–2 innovation and lock-in 165–6 inescapability 170–175 literature 166–70 neo-Schumpeterian illustration 175–9 innovation and market making 13–14 innovation and order 25–7 innovation and process 83–4, 205–9 innovation and technical progress 66–8 innovation and trade fairs 509–10 impact, types of 517–20 importance of 512–17 trade fairs as sources of information 510–511 innovation and urban diversity birth defects of Jacobs spillovers 215–17 case studies 223–7 creativity and economic development 217–23 innovation as concept 2–4 innovation as instrument of progress 27–9 innovation capabilities 105, 361, 372, 398, 512, 739–41, 745, 747–8 innovation capacities 407–8 innovation cluster 465–6, 734–5 innovation communities 342, 380 innovation cycle 152–3, 261, 448, 525, 534 innovation ecosystem 10, 50–51, 91, 467, 733–5 innovation failure 125–6 innovation, governance and place 685–6 Cambridge case 686–9 new technologies 689–92 organizational design 695–9 regional conversation space and improvisation 699–700 regulation and consensus 692–5 innovation in permanent settings 9–11 innovation in practice perspective 138–9 actual work of innovation 139–45 carrying out work of innovation 145–9 innovation in temporary settings 11–12 innovation in virtual settings 11–12 Innovation, Knowledge and Economic Dynamics (IKE) group, Aalborg 472–3, 475 innovation landscape 270, 576 innovation leaders 512–13 innovation management see management of innovation innovation models chain-linked 59, 62, 458, 460 coworking as new 579–80 interactive and closed 39–45 linear (technology-push) 2–3, 33–9, 50–51

800

The Elgar companion to innovation and knowledge creation

open and interactive 45–51 territorial 345, 492–5, 498 innovation network 9, 91–2, 105, 334–5, 432, 604–5 innovation policy 14–15, 29, 58, 421, 436, 467, 632–3, 729 innovation process global 84 managing tension with ideation process 205–9 Schumpeterian 36–7 Smithian 771–2, 774–8 two-stage 451 innovation-replication dilemma introduction 556–7 performativity and routines transfer 557–60 towards performative perspective on transfer 564–7 transfer of computer server 560–564 innovation research as beyond and across disciplinary boundaries 15–16 challenges 4, 13 focuses of 2, 11, 258, 260 future research avenues 6 gaps in 11–13, 88, 260–262 pushing boundaries of 92, 98 strengths 140 innovation survey Canadian 444–5 confirming importance of non-R&D inputs 104 as direct measure of innovation output 106–7 indirect measures in 108–9 main direct innovation output indicators 107–8 solution to measurement problem 112 see also Community Innovation Survey (CIS) innovation system concept of 457, 485 in geographical context 611–13 metropolitan 459 new streams of analysis 477 sectoral 457 social 467 see also national innovation system; regional innovation system; technological innovation system; territorial innovation system innovation theory overview 446–7 reconciling non-urban innovation with 447–52

innovation versus technological achievement common base and specific stage 406–8 disconnection or symbiosis 412–13 distinction 409–10 feedback concerning education and research institutions 410 institutions and values 411–12 introduction 405 productivity 416 routes 408–9, 413–15 study conclusions 416–17 temporality 409 innovation work see work of innovation innovative cluster agents of 328, 338–9 concept of 327 contribution of VC firms 335–40 Silicon Valley 327, 330–340 innovative governance xiv innovative milieu 41, 330, 421–2, 427–8, 440, 446, 459, 493, 498, 638 innovative practice 75, 433, 652, 663–4 innovative process 33, 36–7, 39–41, 46, 81, 362, 364, 570–571, 581 innovative sales 108 innovative thinking xiv, 201–2, 209, 219, 275, 344–5 innovators isolated 448–9, 453 types of 441, 448 inside-out open innovation 45, 60, 90, 96 institution definition 123 to ensure inclusive networks 253–4 inadequate 413–16 organizations, rules and regularities 122–3, 134 proto- 129 as stabilized interaction patterns 133 and values 411–12 see also conceptualization of institutions; institutions and innovation institution-building 463, 495 institutional change 126–7, 655–64 institutional context adjusting innovation to 126–7 advantages of systematic analysis of 4–6 conceptualization of institutions 122–4 drift avoidance 127 future research avenues 133–4 geographical perspective 131–3 layering 127 practice 128–30, 132–3 relations between institutions and innovation 124–34

Index success of innovation resting on design and redesign of 133 and transnational entrepreneurs 642–3, 649 institutional entrepreneurship activities 662–3 concept 655 creating new institutions to enforce innovations 129–30 data and methods 657–63 definition 653 enabling conditions for 663 factors crucial to success of 663 field of AD treatment 652, 655–6 implications for practice 664–5 institutional logics 652–5, 663–4 main challenge for 14 process model of 655 institutional framework 7, 67, 169, 411, 462–3, 466–7, 549, 647, 724, 733 institutional hysteresis 4, 10, 125–6 institutional innovation 435 institutional practice 129–30, 134 institutional resistance 127–8 institutional rigidity 128 institutional set-up 467 institutional strategies 127 institutional variety 130–131, 133 institutionalization 123, 499, 620, 652, 655, 658, 661 institutions and innovation 4–6, 121 conceptualization of institutions 122–4 geographical contingency 130–134 relation between 124–30 insurance industry 152, 155–6 intangible assets 105 integration perspective 262–4 integrationist model of creativity 200 intellectual property rights 94, 109–10, 260 see also patents intensive marketization 590–591 intention 201–5, 207, 209 inter-sectoral upgrading 362, 744–5 interaction information and isolated innovators 448–9 innovation and institutions 462–3 institutional framework for 467 and monitoring 399–400 user–producer 472, 475–7, 479–80, 613 virtual 544–53 interactionist model of creativity 200 interactive and closed model of innovation 3 characteristics 39–40 comparisons 50–51 multidisciplinary building of 40–43 questioning of 43–5

801

interactive dialogue 729 interactive learning based on reflexivity 731–2 concept of 479 innovation generated by processes of 446 innovation tied to process of 729–30 in regional innovation systems 478–81 relational proximity as precondition for 578 role of services in 268 interactive model of innovation 2–3 see also interactive and closed model of innovation; open and interactive model of innovation interdependence of knowledge 222, 551–2 interdependencies, untraded 479 interface-market 589, 592–5, 598–607 interindustrial spillovers 215–16, 225, 227 intermediaries 60, 71–2, 185–6, 250, 351, 377, 425, 519, 580 internal capacity 39, 441 international business perspective 362–4 international competitiveness 473–5 international development perspective 361–2 international product life cycle 75 international trade 472, 474, 485, 590, 739, 771 internet 42, 112, 159–60, 226, 249–50, 270, 333, 342, 377–80, 393, 395–6, 447, 449, 509, 515, 539, 675, 724, 743 intersectoral upgrading 362, 744–5 Interstoff 526–8 intraindustrial spillovers 216 invention collective 184, 396, 400–401 definition 65, 219 and design 152–5 distinction with innovation 27, 30, 36–8, 406–7 examples 373 historical 48, 280–291 and patents 66, 109, 111 as result of creative activity 64 taking to market 345 inventor 48–9, 61, 111, 182, 190, 220–221, 225–6 iPhone 152, 159–62, 757, 760 iPod 160 isolation 171, 345, 392, 444–5, 449, 457, 460, 559, 574 Jacobs-Rosenberg-Bairoch model 216 Jacobs spillovers critique of 215–17, 222–3 definition 215 fostering 225–7 observation and learning for 224–5

802

The Elgar companion to innovation and knowledge creation

job mobility 224, 226 Jugaad/Gandhian innovation 75, 77–8 jurisdictional advantage 500 key enabling technologies (KET) 495, 498 keyboard 125, 168, 170–171 KIBS see knowledge-intensive business services (KIBS) kinship networks 397 kinship relations 397, 638, 647 Knightian uncertainty 140 know-how 47, 104, 139, 147, 218–19, 224, 226–7, 245, 365, 380, 382, 394–5, 397–8, 483, 603, 628, 685 know-who 184, 245, 252–3, 483 knowing in action 350–352, 576, 578 knowing-in-doing 146 knowing communities 3, 9, 49, 70, 202, 204–5, 208–10 knowledge artistic 104 bases 49, 198, 203, 227, 300, 366, 407, 446, 570, 577, 580, 676 codified 39, 70, 239–40, 252, 350–351, 427, 479, 483, 493, 538, 543, 733 concept of 237–8 creation 56–60, 64–5, 69, 71, 197, 238 in design 278–80, 295, 299 for design 285, 298, 300 diffusion 43, 47, 237, 281, 365, 394, 397, 576, 629, 725, 734 distributed 349, 538, 702–3, 705–6, 711, 717 domains 540–543, 551 ecologies 9, 12, 467, 509–10, 520, 679 economic 406–10, 412–13 embedded 70, 139, 146–7, 237, 239–41, 246, 347 embodied 239, 350–351, 394, 396, 676, 711, 733 entrepreneurial 337–9 in evolution 63–4 exchange 365–6, 393, 395–7, 399–400, 427–8, 480, 545, 580, 743–4 explicit 39, 207, 674 filter 628 generation 354–5, 459, 511, 516–17, 520, 570, 576–7, 580–581, 613, 741 implicit 538 management 43, 197–8, 207–9, 346–7, 677–8 management practices 106 practical 239, 281, 299, 497, 577 practice 538 production 57, 64, 70–71, 245–6, 549 spillover 215–17, 224–5, 227, 394, 399, 744 structure 300, 396

symbolic 244–6, 252, 676–7 tacit 48, 67, 70, 207, 239, 246, 330, 350, 427, 486, 493, 538, 628 technological 406–7, 411–13, 416 knowledge and innovation 57–9 knowledge creation as central tenet of 197 managing 677–8 production of 59–61 viewed relationally 674–7, 781–2 knowledge-based economy important role of transnational entrepreneurs 647 innovation in 724–6, 735–6 patents in 191 regions as cogs in 500 social learning 730–731 term 43 today’s crowning imaginary 497 knowledge-based production 342 knowledge-based view 40, 42, 184 knowledge collaboration open spaces of collaboration facilitating 202 in virtual communities 537–53 knowledge flows into clusters 360 through communities 353 to competitors 396 cross-border 638, 640, 646 from east to west 290 exchanging and monitoring 743 and horizontal learning 402 importance of knowledge-intensive services 268 and labor mobility 398, 402 leveraging 733–4 and multinational enterprises 12, 367 across network nodes 240–241 and open innovation 87, 373 patent citation data used to track 111 points of confluence of 223 reverse 50, 80, 84in trade fairs 511 virtual interactions providing 11–12 knowledge-intensive business services (KIBS) 69–70, 268–70, 672 knowledge-intensive services 264, 268–9, 514, 516 knowledge making 231, 239–40 knowledge production as exogenous mechanism 57 for innovation 59–61 three levels of 64–6 translation and enrolment as means of 70–71 knowledge-seeking 741–2 knowledge spillovers avoiding uncontrolled 394

Index Silicon Valley 133 on switching jobs 399 and upgrading 744–5 see also Jacobs spillovers knowledge transfer cost 743–4 global 638–49 leaky pipe analogy 231, 235 localized 735 over distance 638, 642–7 knowledge translation 231 knowledge work 570, 573, 580–582 knowledge worker 138–9, 344, 365, 574, 632 Kodak 36, 126, 129, 334, 766 labor, divisions of corporate 491 in knowledge production function 61 in management 268 between manufacturing and services 267–9 science characterized by 71 Smithian process of broader 771–2, 774–81, 783 labor market 169, 218, 268, 399, 421, 479–80, 571–4, 577, 626, 648, 698 labor mobility 363, 393, 396, 398–9, 402 labour congealed 752, 754, 761–2 material vs. immaterial 248–9 process 754, 756, 758–60, 765, 767 structure 563 Lagendijk, A. 491, 495, 497, 499, 610, 613, 615–16, 734 Lancasterian product 262 landscape model 706–10, 714–18 language game 591–2, 606 large scale projects 413, 436 law firm 105, 330–332, 335–6 law of supply and demand 594 layering (strategies) 127 Le Masson, P. 201, 300–302, 605 lead user (lead-user) 40, 44–5, 381–5 leading firm 360–362, 365–6, 530, 725 learning abductive 142–3 capabilities 329, 338–9 by combining 141–3 by creating 141–3 by doing 59, 167, 733 economy 479, 727, 731 through governance 729–33, 735–6 by imitation 3 by interaction 3 by monitoring 400, 731 by observation 3

803

organizational 703–6, 709–10, 715–21 policy 731–3 by recombining 141–3 region 440, 446, 479, 493 situated 146–7, 347–9, 353–5, 578 social 576–8, 723, 730–731, 735 socially embedded 396–8 types of 723, 730–733 vertical 360, 392, 402 see also collective learning; horizontal learning; interactive learning; learning process learning process centrality of 732 collective 729 communities of practice 348 distributed 43 horizontal 394–7, 402 interactive 509, 518 internal 361 market–technology linking 142 organizationally instituted 46 in production sphere 474 for robustness 329 social 397, 735 specific 362 legal system 109, 132 legitimacy 38, 125, 127, 209, 249, 394, 533, 616, 622, 663, 691 legitimate peripheral participation 345–6 Leppälä, S. 224 Leslie, D. 244, 247–53 Lhuillery, S. 102, 104–5, 107, 109 Li, P. 128, 361, 364, 397, 400, 467, 509, 742 Light + Building 512 LightFair International 512–13 lighting industry 170, 512 limits of neoclassical economics 42, 476 linear process 47, 207, 432–3 linear (technology-push) model of innovation 2–3 characteristics 35–6 comparisons 50–51 emergence and development 33–4 multidisciplinary building of 36–8 postulation 33 questioning of 38–9 lines of business 102 Linux 44–5, 373, 543 liquid networks 221, 223, 226 List, Friedrich 474, 478, 483, 486–7 Living Labs 433, 436 local and global equilibria 706, 709 local buzz 395, 397, 480 local community 397, 561, 686–95, 699

804

The Elgar companion to innovation and knowledge creation

local-global nexus 360, 367 local impact of innovation 453–4 local innovation system see regional innovation system local institutions 128, 133, 367, 647 local knowledge 448, 676, 705, 748 local networks 42, 82, 366, 421, 630, 632, 733–4 local regulation 690–694, 699 localization 424, 428, 479, 493, 499 localization economies 216–18, 671 localized knowledge transfer 735 localized learning 246, 360, 398, 459 lock-in breakup 171–2 concept of 165–6, 179 effects 42 as evolutionary-inspired notion 165 extra-cluster knowledge to avoid 360 inescapability of 165, 170–175, 178–9 as ‘inflexibilities’ of outcomes 166 literature 166–70 neo-Schumpeterian illustration 175–8 overcoming 170–172 and patents 190 and path dependence 165–70 and QWERTY standard 125, 168, 170–171 and regional economic specialization 227 replicator dynamics model 165, 175–8 role of new alternatives 170–172 unlikely 172–5 lock out 126–7 logic academic 498, 500–501 of competition 598–9 economic 740–745 goods vs. service 781 service-dominant 261 long-term growth 89–90, 94 long waves (of economic development) 1, 172 low-end disruptions 79, 81 Lowe, N. 685–6, 693, 695–6, 699–700 Lundvall, B.-Å. 3–4, 6, 9, 10, 40, 58, 67, 105, 121, 130–131, 133, 237, 345, 392, 432, 457–64, 472–3, 475–80, 483–5, 487, 509, 518, 612–13, 729–30, 732 Malecki, E.J. 3, 8, 447–8, 451, 493, 495, 612, 626, 630, 632 malevolent creativity application domains 310–311 concept of 307–10 research 311–20 malevolent innovation 309 management of innovation in closed interactive models 43

and governance 14–15 main challenge 197 managing tension with ideation process 205–11 in organizations, relational viewpoint 677–8 managerial challenges, types of 82–3 manifesto 203, 209 manufacturing 62, 95, 107, 142, 185–8, 219–20, 259–70, 362, 443–4, 519–20, 644–7, 746–7, 757–9, 765–6, 781–82 MAR spillover see Marshall-Arrow-Romer (MAR) spillover Marengo, L. 40, 43, 172, 705, 710–712 market interface 589, 592–5, 598–607 mass 597–8, 617, 620–621 multisided 593 niche 615, 630 market-agencements ambivalence of 604 comparison with interface markets 589, 592, 604–7 competition 589, 592, 599, 601, 603–6 loosely connected innovation and competition 604–5 market as marketization 601–4, 607 move from interface markets 596, 600–606 market competition 335, 589, 592–3, 600, 603, 606 market failure 108, 170, 703 market formation in context of TIS maturation 616–17 different forms of, within renewable energy TIS 613–16 in environmental technologies sector 610–611 identifying geographies of 621–2 and innovation 13–14 innovation systems in geographical context 611–13 market segment 614, 618–22 market transaction 614–15, 618–22 spatial dynamics of 617–21 user profile 615–16, 622 market growth 617, 619–20, 622 market intelligence 270 market legitimacy 6, 13, 129, 514, 518 market making 13–14, 752, 765 market offering 258, 263, 269–70, 781 market pull 610, 618–19, 621 market segment formation 614, 618–22 market-technology linking 141–2, 146–8 market transaction formation 614–15, 618–22 marketing function 482, 491, 510–511 marketing instruments 509

Index marketization extensive 590–591 implications of 600–606 and innovation 596–600 intensive 590–591 interface-markets 592–5, 606–7 and the market 590–592 as modality of economization 590–591 markets for technology 183–8 Marshall-Arrow-Romer (MAR) spillover 216 Marx 440, 754 mass customization 377 mass market 597–8, 617, 620–621 mass production 230–231, 234, 601 material context 537 material culture 247–8 material labour (labor) 248 materiality 544–8, 551–2, 559–60, 567 maturation different types of knowledge transfer for 338, 615 ideal-type processes 613 stage in photovoltaic technology 620–621 of technological innovation system 616–17 McGrath, Rita 88, 92–7 McGrath-Champ, S. 15, 638, 753, 759 measurement of innovation broadening scope of 99 inputs 100–106 limitations and future directions 111–12 outputs 106–11 patent-based 432 use of biodata 317–18 use of manuals 2–3, 100–101, 106–7 mediator 502, 559–60, 565–7 medical care see healthcare sector medical treatment see Alzheimer’s disease (AD) memory 549–50, 552 mental capital 474, 487 mental model 125, 128 Merkel, J. 573, 576, 578–9 method of ratios case illustration 282–3 fixed by existing objects 280–282 and innovation 283–5 knowledge 285 results summary 299 metropolitan area 225–6, 443–5, 449–52, 495 metropolitan innovation system (MIS) 459 micro-electronics on international competitiveness (MIKE) project 475–6 microelectronics 290, 475, 696–700 Microelectronics Center of North Carolina (MCNC) 695–700

805

middleground 9, 207, 252 migrant entrepreneurs 398, 639–40, 642–4 migrants 367, 638–49 migration 226–7, 337, 367, 640–642, 645, 649 MIKE (micro-electronics on international competitiveness) project 475–6 MIS (metropolitan innovation system) 459 MNE see multinational corporation/enterprise (MNC/MNE) mobility (of innovators) 447, 453–4 modalities of economization 590 Mode 1 63 Mode 2 63–4, 68, 549 model of creativity entrepreneurial 250 multilevel, interactionist and integrationist 200 model of innovation attempts to produce typology 34–5 demand-pull 34–6, 40, 58–9 evolution of economic contexts 51 literature 34 multidisciplinary aspects of 33–4 science push 35, 58–9 see also chain-linked innovation model; concepts of innovation; interactive and closed model of innovation; linear (technology-push) model of innovation; open and interactive model of innovation model of the economy, three-sector 267 modernity xiv, 31 modularity 112, 207, 525, 706, 746–7 monitoring 399–400 monopolization 165–6, 170, 173–8 Montreal 251–2, 349, 450, 739 motivation of consumers 378–9 as factor influencing creativity 308, 310, 314 intrinsic 61, 169, 199–201, 208, 539, 549 of lead users 382, 385 of scientists 56, 61–2, 64 for studying geography of innovation 442, 444 of technological gatekeepers 365 types of 378, 382 multidisciplinary team 140–141, 225, 227 multilevel governance 728–9, 735 multilevel model of creativity 200 multinational corporation/enterprise (MNC/ MNE) as actors of local–global nexus 360 codes of communication 486 identification of R&D allocation 103 innovation of business model 62, 128

806

The Elgar companion to innovation and knowledge creation

relational proximity 676, 680 relevance to industrial clusters 366–7 and reverse innovation 75–6, 78, 81–3 and technological spillovers 362–4 multisided market 593 music 48, 160, 234, 240, 248–50, 657 nanoscience 417 nascent industry 778 national competitiveness 354–5, 442 national innovation system (NIS) approach 10, 58, 457–61, 467, 613 approach characterized by some stagnation 466–7 capability to reproduce dynamics 467 on co-development of institutional settings 130 concept of 458–9, 472–3 convergence vs divergence among 485–6 as critical for economic development 477–8 criticism of being overly introspective 612 definition 40–41 emphasis on role of institutions 462–3 failing to address early industry formation processes 610 and globalization 477 importance of building strong 484–5 and international competitiveness 473–5 linking sector performance analysis to 485 and organization of work 480 product innovation and user–producer interaction 475–7, 480 relating global value chain approach to 483–4, 486–7 as self-referential systems 457, 463–5 and social system 461–2 national system of innovation (NSI) 473–5, 477, 483–4 national system of production 130 natural resources management 773 negative creativity 307, 310, 313, 315, 320 negative feedback 174–9 neoclassical economics limits 42, 476 Netherlands 132 netnography 543–4 network approach 41 network, cooperation 433, 573 network effect 167, 169–72, 174–5, 434 network externalities 167–8, 173, 187 network theory see actor-network theory; complex network theory (CNT) networker 338 networks inclusive 253–4 and innovation 8–9, 327–40

of innovation 327, 339–40, 733 types of 483 New Argonauts 643–5, 647–8 new industrial spaces 491–2, 496, 498 new product development 87, 90–92, 94, 97, 523–6, 531, 533 new technology-based firms 625, 644 new-to-the-firm 107–8 New York 155, 158, 218–19, 251, 442, 512, 528, 574–5, 579–80, 646, 734, 753 Newton 159–61 niche market 615, 630 Nigeria 763, 765 NIS see national innovation system (NIS) no-innovation-system 466 non-buyers 519 non-compete covenants 399 non-practicing entities 186–7 non-R&D input 104–6 non-selling function 519 Nord-Pas de Calais 269 norm-violation 310 normal innovation 3, 231–5 normal science 231–5, 238 North Carolina Biotechnology Center 695–700 not-for-profit organization 138, 726 novel and effective idea 308 novelty adaptation to 37 capturing 107 as combinations 219 cooperation increasing degree of 261 customers unconvinced by 81 false negative 125 financial fraud utilizing 311 generation of effective 309 grammar of usage 49 harmful 314 impact of design domesticating 157 as innovation 31, 434 of lies 314 patentability criteria 109 production of 2, 6–7, 33, 310, 320 plus relevance equals creativity 69 in R&D 101 relying on tacit knowledge 48 schema incongruity creating 153–4 threshold on 110–111 traits 307 as universal 307 NSI see national system of innovation (NSI) object’s career 597 observation process 9 OECD 3, 34, 38, 43, 50, 99–103, 105–7,

Index 259–60, 264, 342, 346, 416–17, 431, 434, 459, 473, 495, 497, 723–5, 734–6, 739, 771–3, 778 OECD Directorate for Science Technology and Industry (DSTI) 473 Oetker, August (Dr.) 124 offshoring efficiency-seeking 741 fixed costs 742 hidden costs 745, 748 knowledge seeking 741–2 in literature 739–40 reducing production costs 748 spatial transaction costs 743–7 technology choice 747 Ogawa, S. 3, 9, 375 ongoing specialization 12, 465 online communities 379–80 online forum 383, 540–541, 550 open and interactive model of innovation 3–4 characteristics 45–6 combining forms of knowledge creation 60 comparisons 50–51 multidisciplinary building of 46–9 questioning of 50–51 open business model 97 open innovation Chesbrough’s adoption of 3, 60 and competitive advantage 91–7 concept of 87–9, 94, 97, 432, 446 coupled 45, 50 different “faces” of 88–90 emerging literature on 71 inside-out 45, 60, 90, 96 making relevant to economic players 90–92 model 47–8, 50, 62, 425 and new product development 87, 90–92, 94, 97 outside-in 45, 60, 90, 96 paradigm 45–6, 372–3, 448 and patents 71, 183–5, 191–2 relating to McGrath’s strategic insights 92–7 science in context of 61, 66 open-source communities 46, 395–6 open-source software communities 352, 395–6 open-source software projects 393, 395 open system of intellectual property 45 of management 60 R&D as 373 openness and creation of sustainable competitive advantage 97 degrees of 424, 441

807

as positively correlated with economic performance 478 in terms of (real) option approach 96–7 operative closure 462, 464 order 25–7 ordinance 686–9, 692–5, 699 organization for innovation 147–9, 295, 297, 301 organization of value chains 482–3, 740–745 organizational capability 138 organizational communities 558, 561, 565, 567 organizational conflict 126, 347, 354, 564, 653–4, 694 organizational culture 563, 625, 627, 631 organizational decision making 707–9 organizational design 695–9 organizational dynamics decentralization 702, 717–18, 720–721 knowledge distribution 702–3, 705–6, 711, 717 model 705–10 organizational structure 706–8, 710–713, 715, 717–18, 720 performances 706, 710–715, 717, 719–21 power, authority and hierarchies 703–5 results 710–720 organizational equilibrium 708–13, 715–21 organizational field 125, 128–9, 513, 652, 655 organizational innovation 106–7 organizational knowledge 140, 197 organizational learning 138–9, 347–8, 709, 715–21, 732–3 organizational memory 463 organizational structure dynamics of 702–3, 720–721 model 705–20 power, authority and hierarchies 703–5 organized anarchies 519 organized co-presence 463, 545–6, 552, 581 organized markets 476 organized proximity 11, 426, 428, 430 organizing innovation 144 Oslo Manual 3, 43–4, 50, 100, 106–7, 431 out-of-the-box thinking 275 outside-in open innovation 45, 60, 90, 96 overflowing 556–7, 563–5 ownership of assets 95, 265 Pachidou, F. 6, 13, 14, 129 Palanpuris 645–7 PalmPilot 159 paradox of embedded agency 129 Parmentier, G. 202–3, 208 participative operations 377 passivaction of goods 602

808

The Elgar companion to innovation and knowledge creation

passive innovation reviewers 513 Pasteur’s quadrant 61–2 patent brokers 186 patent count 108–10 patent families 109, 111 patent law 191–2, 394 patent litigation 186–7 patent pool 189 patent system 109, 182–6, 189–92, 394–5, 401 patent thicket 188–9 patent trolls 186–8, 191 patent value 111 patents citations in US 725 and complex technologies 188–9 as coordination devices 183–5 and copyrights 48 defensive 443 double incentive role 182 in eco-industry 771 formalized knowledge captured in 676 as indicator of innovation 64–5, 108–11, 443–4 as instrument of exclusion 183–5, 191 and lock-in 190 and open innovation 71, 183–5, 191–2 as outside-in process 60 “second best” theory of 182–3 sequential innovations 190–191 strategic uses in technology markets 185–8 transforming into open innovation accelerators 191–2 use as proxy for innovation 237–8 path, concept of 170 path dependence concept of 169–70, 173–5 definition 168 degrees of 173 essence of 176 hysteresis as special case of 169 and innovation 8, 190 of knowledge and creative capabilities 110 lock-in 165–6, 168–70 and normal science 232 ongoing debate on real pressure exerted by 170–171 of regional trajectories 494 and social action 123 and technological change 479 technological monopolization implicit in 173 Pénin, J. 71, 183–7, 191–2 performativity computer server transfer 560–564 new framework 566–7 performative struggles 556, 563–7

and routine dynamics 557–60, 564–7 routines transfer 557–60, 567 theory of 556–7 towards performative perspective on transfer 564–6 peripheral dominance 127–8 peripheral innovation 128, 130, 441 permanent innovator 6 persistence 10, 83, 125, 167–70, 174, 329, 337, 366, 556, 558 personality emergent nature consumers 384–5 Five Factor Model of 200 and malevolent creativity 309, 312–16, 318–19 Pfizer 125, 656 pharmaceutical industry 62, 125, 183, 186, 188, 540, 542, 656, 662–4 phat dependence 174, 179 Phelps, Edmund 50, 60–61, 71, 406–9, 411–12, 416, 443 philosophy of science 231–2, 238 photography 126, 129 photovoltaic technology designing local proto-markets 617–19 shaping geography of diffusion 620–621 stabilization and spatial expansion 619–20 physical co-location 537, 548, 551 physical co-presence 546, 550, 552 Pickren, G. 758–9 pipelines 423–4, 477, 480, 493, 498, 630, 638, 680 place dependence 169 place-specific advantage 685–6 platforms, goods as 592–3, 596 policy co-production 726 policy forgetting 732–3 policy learning 723, 729, 731–5 policy-making 494–5, 501, 724, 728–9, 732 policy rationale 498, 500–501 Polya urn 171–2, 174–5, 178 population heterogeneity 172–3 Porter, Michael Eugene 67, 216, 222, 327, 392, 399, 423, 440, 459, 481, 490, 493, 740, 745, 783 Porter spillovers 216 positive feedback 166, 168, 174, 176–8 power in communities of practice 675 definition 704 fiat 708–10, 713–14, 716–18, 720 organizational 703–21 types of 703 and user innovation 372–3 veto 708–10, 713–20

Index power relationships 441, 593–4, 601, 673, 675–7, 679 practical knowledge 239, 281, 299, 497, 577 practice of work 139, 145, 579 practice perspective benefits of 149 as dependent on collective learning 145–6 literature 138–9 on organizing for innovation 147–9 tenet of 674 understanding of knowledge and workers 139 work of innovation 138, 145–9 practices of institutional entrepreneurs 652–3, 655 Pratt, A.C. 232–4, 236–7, 240, 244, 248, 253, 572 pre-conception 206, 377 Première Vision concertation 528–32 description and history 526–8 as leading clothing fabric trade show in Europe 524 organizers’ farsightedness 533 as primary site for learning and knowledge exchange 532 prescription 123, 210, 473, 484, 560, 632 presentation of innovation 518 prevalence 167, 176, 179 price formulation 603 price index 102 principal agent model 705–21 pro-sumption 235 problem solutions coming across 519–20 firms producing customized 6 searching for 519 problem-solving 59, 123, 170, 201, 221, 275–6, 311, 329, 463–4, 551, 696, 698 process-goods 596–8, 601 process of creation (creativity) 43, 235, 254 process upgrading 362, 744–5 producer-user interaction 472, 475–7, 479–80, 518, 613 product characteristics-based 262–3 concept of 139, 258, 387 definition 262 and malevolent creativity 307, 310–311, 314–15 product development collaborative 377–8 and design reasoning 290 joint programs 362 lead-user communities 353

809

new 87, 90–92, 94, 97, 523–6, 531, 533 and value 233 Product Development Management Association (PDMA) 140, 147 product innovation 108–10, 140, 177, 475–7, 595, 600–601, 746–7 product life cycle as akin to separate paradigms 238 international 75 reversed 79 product upgrading 362, 744–5 production chains 218, 481–2 ‘cleaner’ 774 convergence with services 259, 262–4, 269–70 of craft activities 350 cultural 246–51, 253–4 in cultural economy 232–4 of environmental goods 778, 780–82 expansion of 218–19 fabric 531–2 of global production networks 361–2, 365, 367 of global value chains 739–48 of “imaginaries” 497 interdependence with institutional arrangements and innovation 463–4, 466 of knowledge 7, 37–8, 46, 48–9, 57, 61, 64–6, 70–71, 245–6, 298–9, 345, 549, 581, 610, 675–9, 681 in law of supply and demand 594 learning processes 474 of malevolent creativity 307, 314–15, 320 mass 230–231, 234, 601 networks 638, 643 of novelty 2, 33, 198, 310, 320 organization of cultural 577 from platform-goods to process-goods 596–8 in region–innovation nexus 491–2 scientific 37, 61, 63, 65, 69 settings 224–5 and transfer 235–7 production system deepening divisions of service labor in 267–8 four functions of 267 holistic view of 232 local 422–5 national 130, 472–3 and process upgrading 362 “reflexive” 572 productivity coworking increasing 574 of exploration and extraction 91

810

The Elgar companion to innovation and knowledge creation

firm performance 112 gains 107, 258, 263–5, 741, 747 high rates of turnover reducing 745 improvements 363 innovation leading to higher 452 offshoring increasing 741 paradox 431 problem of developed countries 416 and process upgrading 744 spillovers 363, 411 total factor 57 professional communities 351–4, 430, 645 professional practitioners 139, 145–6, 149 professionals creative 251 mobility 398, 645 role of 145–6 progress, innovation as instrument of 27–9 project, collaborative 498, 661 project ecologies 571–2 proprietary knowledge 365, 394 proprietary production choices 766 proto-institution 129 proximity approach 427–30 cognitive 348–9, 615 concept of 428–30 geographical 38, 67, 226, 421–30, 448, 459, 687, 692, 735 organized 11, 426, 428, 430 relational 537, 576, 578, 676–8 spatial 11, 38, 572, 612, 614 technological 429 types of 428–9 proximity school 421, 430 public financing of research 410 public investment impact on science 68–70 need for 475 public policy 422, 467, 581, 604–5, 678–9, 773 public safety 686, 690–691, 694 qualification of goods 592–606 qualitative methods 674 quality-based innovation 464 quality production 249–50, 365, 526–7 quasi-anonymity 548–9, 552 QWERTY 125, 168, 170–171 R&D see research and development (R&D) radical innovation 70–72, 108, 206, 217, 222, 263, 278, 302, 333, 350–352, 355, 378, 382, 433, 442, 464, 480, 579–80, 626–7, 629 radio broadcasting 128

Raffo, J. 109, 111 Rainnie, A. 753, 759 Rallet, A. 10, 67, 427–8, 436 Rantisi, N.M. 244, 247–52, 671–2 rate of return, social 66–7 ratio method 277, 280–281, 285, 287, 297–9 re-creation 556, 566–7 real option approach 96–7 real options 96 reciprocal relation 366 recombinant DNA research 686–7 recombinant growth 219 recombinant innovation 263 recombination 9, 81, 171, 201–2, 204, 207, 263, 579–80, 625–6 recycling 425–6, 435, 752–3, 755, 757–8, 760–761, 765–7 Redtenbacher, Ferdinand 279–85, 293, 298–9, 301 reflection-in-action 146–7 reflective practitioner 138, 146 reflective reframing 548, 550–551 reflexive state 727 Reformation 25–6, 29–30 reframing artefacts and communities 565 cycles of 557 learning routine 143 reflective 548, 550–551 region–innovation nexus (un)boundedness 493, 498 academic logics and policy rationales 498, 500–501 circulation 497, 499 concept 490 corporate divisions of labor 491 endogeneity trap 494, 496, 501–2 new industrial spaces 491–2, 498 ontological-relational perspective 496–501 regional trajectories 494, 498–500 smart specialization 495–6, 498 territorial innovation models 492–5, 498 territorial trap 495–6, 501 regional agglomeration 479, 671 regional culture 395, 631–2 regional decline 449–52 regional development 267–9 regional economic development 266, 495, 625, 734–5 regional economy 269, 627, 731 regional governance 495, 499 regional innovation 449–52 regional innovation cluster 734 regional innovation strategy (EU) 734–5 regional innovation system (RIS)

Index approach characterized by some stagnation 466–7 concept of 460, 465 critique of 465–6, 612 as device to mobilize innovation in localized contexts 467 embracing concepts at subnational scale 630 and entrepreneurship 629–30 failing to integrate market dimension 610 importance of geographical location for 41 interactive learning in 478–81 learning, innovation and governance 733–5 literature 10, 58, 457, 459–61, 613, 622 little understanding of role of services in 268 as nexus of social and economic links 625 regional innovation perspective 498 types of 466 and social system 461–2 strength of approach 467 regional policy 467, 494–5, 679 regional thickenings of technological systems 466 regional trajectories 494, 498–500, 502 regionalized national systems of innovation 466 regulation cultural economy 233–4 environmental 777–83 governance 690–695 in institutions 130–134 in production system 267 regulation and innovation 690–695 regulatory uncertainty 779 related variety 217, 494, 615–16 relational approach principles 672–4 relational innovation landscapes 570, 576 relational knowledge 237–8 relational perspective 672, 674–8, 681 relational policy challenges 678 extra-territorial 679–81 managing corporate relationality 679 orientation to 682 relational proximity 537, 576, 578, 676–8 relational space 673, 679–80 relational understanding (of action) 123 relational value 232, 235 relationality 496–501, 674, 679, 681 relationships in value chains, types of 482 Remington Rand 152, 155–6 remittance 640 remote areas mundane technology altering relations with urban areas 447 patents in 443–4

811

research questions arising 453–4 swamping of observations in 444–5, 453 urban bias in 440–445, 447–53 renewable energy (technologies) forms of market formation within 613–16 photovoltaic 617–21 replication–innovation dilemma 556–67 replicator dynamics 165, 172, 175–9 replicator model 165, 175–8 research and development (R&D) and Canadian service firms 514–16 capital intensity 108 environmental 773, 778 and external assets 90, 95 “Golden Age of in-house” 372 inputs 100–104 within linear and closed model 36–7 manufacturing assembly comparison 741, 746–7 offshoring 742, 745 as often located in cities 443–4 as open system 373 overlooked avenue for activities 112 as part of firms’ core business in many sectors 62 and patents 108–11, 182, 185–8 pure internal 60 representing one-third of innovation costs 104 researchers in private departments 63, 66 spillovers 67–9 and systematic design 277 upgrading 482 Research Innovation Development (RID) 301 research institutions 60, 410, 627 research method analytical framework to study historical cases 279–80 malevolent creativity 311–12 relational approach principle 673–4 research organization 11, 62, 65, 100, 112, 183, 220, 268, 410, 465, 735 resilience of local systems 422 as robustness 328–9 of Silicon Valley 47 territorial 428, 433 resource-based view 40, 197 resource-constrained innovation 78 resourceful crime 311 retardation school 219–20 returns to scale 42, 125–6, 176, 266, 478, 486 reuse of existing knowledge 287, 297–8 of known solutions 282, 289

812

The Elgar companion to innovation and knowledge creation

of obsolete design rules 287, 298–9 of product components 752, 755–68 of technologies 81, 190, 192 reverse engineering 399–400 reverse innovation blurred borders with other concepts 76–7 challenging process of 83–4 concept of 4, 75–6 definition 78 internal definitional fuzziness 77–9 managerial challenges associated with 82–3 map of global innovation flows with 80 and multinational corporations 75–6, 78, 81–3 one-dimensional representation of 76 two types of 79–83 types of 79–81 reverse product cycle 260–261 reversed product life cycle 79 RID (Research Innovation Development) 301 Rinallo, D. 518, 523–34 RIS see regional innovation system (RIS) risk evaluation 337 risk-taking 6 risks associated with cultural production and need to socialize risks 249–54 Roberts, J. 208, 342–3, 345–7, 349–54, 538–9, 571, 576, 578, 675 robust design 126–7, 153–5, 157, 160–162 robustness 289, 301, 328–30, 332–4, 337, 339 role of institutions 5, 131, 462 roll-film camera 124, 129 Rosenberg, N. 2, 8, 34, 39, 59, 62, 216, 409, 412, 432, 457, 616 Route 128 399, 626, 734 routine dynamics 557–60, 564–7 routine replication 556, 558, 564 routine transfer of computer server 560–564 novel characterization of 566–7 as outcome of performative struggles 556, 566–7 and performativity 557–60, 567 as re-creation 566 role of artefacts 565–7 royalty stacking 183, 188–9 RSI see regional innovation system (RIS) rules regulations and policies 5, 122–3, 131–2, 134, 466, 653 and resources 148 rural areas Brazil 424 France 440 India 76

social systems 467 Spanish 133 schema incongruity 153 schematic fit 159 Schumpeter, Joseph Alois 1–2, 6–7, 35–6, 45, 58, 65, 106, 182, 431, 594, 598–9, 625, 724 Schumpeterian perspective 6–7, 35–8, 58–9, 65–6, 99, 165, 172, 175–8, 491–3 Schumpeterian successive approaches 58–9 science coarse definition 56 as complex system 64 creativity in 64–6, 71–2 discovery research 142 economics of 56–9 fundamental deceit at core of 238 in hierarchy of knowledge creation 240 impact of public investment in 68–70 and knowledge production 59–61, 70–71, 239 need for entrepreneurial spirit 72 as path dependent 232 scientific creativity 63 scientific research 61–3, 66–7, 71 translation and enrolment 70–71 science and innovation collective nature of 59 contribution of scientific research to 66–7 production of knowledge for 59–61 radical 70–71 relationship between 57–9, 71, 231–5, 238 science-based innovation 139, 429 Science Policy Research Unit, Sussex 472–3 science push model 35, 58–9 scientific arena 64–5 second best theory 182–3, 191 secrecy fighting 182, 192 reliance on 443–4, 449 strategic tool 64, 66 sectoral innovation system 457, 612 sectoral-territorial approach 464 self-referential system 457, 461, 463–7 self-reproduction 463 semiotics 753–4 sequential innovation 30, 50, 183, 190–191, 203 sequential perspective 205–7 serendipity 61, 63, 201, 209, 448–9 service business-oriented 268–9 customer-oriented 259–63, 266, 269–70 knowledge-intensive 264, 268–9, 514, 516 operational 269 service cluster 269

Index service-dominant logic 261 service industries and trade fairs 514–17 service innovation assimilation 260–261 becoming evident and important 269–70 concept of 260–261 differentiation 261–2 double ambiguity of services and innovation 259–60 integration 262–4 process-oriented 264 and regional development 267–9 service labor, division of 267–8 service triangle 259–60 services concept of 258, 260, 270 convergence with production 258–9, 262–4, 269–70 definition 259–60 role in economic growth 264–5 services and innovation consistent integration into research 269 double ambiguity of 259–60 and regional development 268–9 underdeveloped literature 258 servitization paradox 266 Shanzhai innovation 78 shared local context 546–7 shared practice 539, 546–8, 552, 570, 578 shared workspace 574–5, 578, 580 sharing of collaboration dividends 185 consumption experiences 385 in coworking context 575, 578 of cultural products 250 expertise 268–9, 661, 676 as governance mechanism 539 of ideas 206 knowledge 106, 133, 346, 350, 354, 365, 392–7, 400–402, 427, 538, 543, 570, 632, 646, 649 practice 544–8, 552 resources 348 same interests 65 tools and methods 49 Shearmur, R. 422, 441–3, 445, 447–53, 625 shipbreaking 755, 760–762, 765, 767 ships 752, 755, 760–762, 767 Silicon Valley as complex innovation network 334–5 as complex system 328 contribution of VC firms 335–9 horizontal knowledge sharing 401 immigrant high-tech entrepreneurs 349, 367, 397–8, 643–5, 648

813

impressive economic prosperity 133 as innovative cluster 327, 330–333, 339–40, 423–4 job hopping in computer industry 398–9 originator of link between innovation and entrepreneurship 626 as regional innovation system 459, 465, 629 as self-generating ecosystem 47 spin-off processes 627 Simon, L. 202, 207–8, 210, 345, 566 Sinclair-Desgagné, B. 779–83 singularization 600–607 situated innovation 139, 143–4, 231 situated learning 146–7, 347–9, 353–5, 578 situated practice 346–7, 349, 352–3 skeuomorphism 127, 161 skeuomorphs 154, 156, 158–9 slow innovators 453 small and medium-sized enterprise (SME) 3, 14, 78, 87–8, 105, 132, 421, 444, 447, 493, 498, 523, 525, 531, 533, 573, 778 smart activities 105 smart specialization 490, 495–6, 498, 500–502, 735 SME see small and medium-sized enterprise (SME) Smith, Adam 264, 392, 474, 592 Smithian (innovation) process 771–2, 774–81, 783 social capital bonding 343, 354, 545 bridging 343, 349, 354, 545 shared 348 transnational 640, 642, 647 social construction of ideas 198, 201–5, 209 social curation 579 social dynamics 352, 538, 548, 570–571, 577, 581 social innovation 27, 405, 431, 662 social learning 577–8, 723, 730–731, 734–5 social network complex network theory 328–30 horizontal learning through 396 “local buzz” 395, 397 observing communities in 383 problem of traditional analyses 238 for professional knowledge collaboration 540 Silicon Valley as 327–8 stable patterns of social interaction in 133 transnational entrepreneurs 642, 647 social network analysis 339–40, 581 social norms 46, 343–4, 685 social organization of work 570, 573–6 social position 655 social practice institutions 132

814

The Elgar companion to innovation and knowledge creation

social practices 121, 131, 349, 463, 538 social rate of return (from academic research) 66–7 social system 148, 329, 458, 460–462, 464, 466–7 social value 31, 230, 233, 343, 412 Socialist 27 socially embedded learning 396–8, 402 socio-economic arena 65–6 socio-spatial dynamics 251–3 sociology of scientific knowledge (SSK) 232, 239 sociomaterial assemblage 567 solid waste 773–5, 777, 779–80 sophistication cost 132 space coworking 576–82 creation of informal 252 new industrial 491–2, 498 provision of affordable 251 Spain 133, 366, 516, 618, 620 spatial disparities 1, 130 spatial dynamics of market formation 617–21 spatial perspective 5, 12, 121, 457 spatial proximity 11, 38, 572, 612, 614 spatial settings, innovation in 9–11 spatial transaction costs 743–7 spatial transfer 236 spatiality 246, 466, 671–6, 681 specialization, smart 490, 495–6, 498, 500–502, 735 Spigel, B. 629, 631–2 spillacross 247 spillovers interindustrial 215–16, 225, 227 intraindustrial 216 Jacobs 215–17, 222–7 knowledge 215–17, 224–5, 227, 394, 399, 744 and technological achievement 413–14 spinoff economic 426 processes 398 university 497, 627–9, 631 splitting knowledge 392 SSK (sociology of scientific knowledge) 232, 239 stabilization phase of market formation 619–20 stabilizations (of economic interaction) 462 stabilized interaction patterns 133 stage-gate process 205–6, 275 stages of innovation 309 standard affirmation 523, 532–3 standard committees 523, 532–3 standard wars 523, 532

standards accounting 103 adoption of joint 532 architectural 746 biosafety 687 color 530 of conduct 351 environmental 691 of excellence 655 global 562 industry 492, 577, 690, 692 living 475 needed to reduce uncertainty in innovation 525 novel vs. established 125 for organization of value chains 482 patents 187 product 680 quality 361 technical 82, 778–9 as welfare-enhancing lock-ins 170 start-up funding 334–7 start-up life cycle 330–331, 335–6 start-up selection 336 start-up signaling 336–7 steering mechanisms 726 sticky information 252 strategic driver 91–2, 97 strategy as central concept in open innovation 94 and competitive advantages 92–3 strong ties 339, 580, 630, 648 structural competitiveness 473, 475 structuration theory 148 style innovation 524, 532 subsidiary 12, 75–7, 82–3, 103, 128, 218, 353, 363–4, 366–7, 509, 672, 676, 679, 741–2 subsidies 614, 619, 779 subsystem 143, 462 supersystem 461–2 support policy 121, 619–20, 696, 728 survey of innovation 375, 510, 514–17 sustainability 77–8, 237, 254, 396, 529, 601, 654 sustainable competitive advantage 94, 97 sustainable creative process 207 sustainable governance 15 Swiss watches 126 switching cost 126, 167–8 Switzerland 126, 265, 409, 617, 652, 656–7, 660, 662–3 symbolic knowledge 244–6, 252, 676–7 systematic design consequences for innovation 290 and design management 289 diagram and process for 288

Index origins and model of 285–7 principles of reasoning 287, 289 results summary 299 success abroad 290 systems of entrepreneurship 629–30 systems of innovation national 10, 40–41, 58, 67, 345, 466, 473, 485 regional 41, 345, 478–81, 485 social 467 technological 457 see also innovation system; national innovation system; regional innovation system; technological innovation system Szurmak, J. 7, 11, 571 T-shaped skills 140 tacit knowing 538 tacit knowledge 48, 67, 70, 207, 239, 246, 330, 350, 427, 486, 493, 538, 628 Taiwan 333, 349, 486, 643–4, 647–9 tangible assets 265–6 task-based communities 350, 578 tax credit 100–101, 105, 250 team cross-functional 225, 561 multidisciplinary 140–141, 225, 227 team player 140 technological achievement Airbus 410 beneficiaries 417 Concorde 408–9, 410 digitalization, biomedicine and nanoscience 417 disconnection or symbiosis 412–13 feedback concerning education and research institutions 410 great transformation and bogged-down countries problem 416–17 inadequate values and institutions 413–15 innovation capacities 407–8 and innovation, distinction between 405, 409–10 institutions and values 411–12 productivity 416 routes to innovation 406–16 spillovers 413–14 Sputnik to new economy of California 412–13 temporalities 409 transposition 414–15 technological arena 65 technological capability 477–8, 744 technological change 1–3, 58, 127, 133, 179, 219, 245, 249, 254, 260, 401, 417, 479, 523, 610, 612, 724, 730, 740, 745

815

technological choice 616, 747 technological competition 168–79 technological gatekeeper 364–6 technological infrastructure building national as focus for development strategies 478, 485 and international competitiveness 473–5 technological innovation 29, 35–6, 66, 91, 104–12, 218–19, 261–2, 431–4, 725, 771 technological innovation system (TIS) concept and pillars of 611–13 emerging markets in literature on 610 forms of market formation within renewable energy 613–16 market formation in context of maturation 616–17 photovoltaic 617–21 technological maturity 746–7 technological monopolization 172–3 technological proximity 429 technological search 12, 512 technological spillover 362–4 technology complex 178, 188–9, 387 contribution of scientific research to progress in 66–7 fallacy of fixed 745–8 firms and clusters 626 impact of public investment in 68–70 inferior ‘winning’ example 230 knowledge 141, 146 technological arena 65 technological mercantilism 68 use of 133, 152, 166 virtual, in treatment of AD 662–3, 665 technology achievement 414–17 technology-based firms 625, 644 technology cluster 626, 695 see also high-technology cluster technology development 3, 41, 58, 186, 402, 513–14, 611, 613, 616, 621–2, 691, 696–7, 699 technology policy 29, 38, 414, 457, 486 technology providers 616–17, 619–20 technology push model 2–3, 33–9, 50–51 technology transfer 37, 69, 184–6, 218, 363–4, 423, 465, 627, 631, 643, 648, 734 temporary cluster 510, 580 temporary proximity 12, 680 temporary settings, innovation in 11–12 see also coworking; trade fairs tension between design and creativity, types of 277–8 territorial governance 679–81

816

The Elgar companion to innovation and knowledge creation

territorial innovation model (TIM) 345, 492–5, 498 territorial innovation system attracting interest 457 problems of literature 10 relation to national innovation systems 458 territorial trap 495–6, 501 territory 41, 51, 234, 421, 427–8, 433, 463–4, 493, 495, 501 terrorism 307, 309, 311, 313, 317–18 textile industry 284, 361, 524–34 theorizing innovation 29–30 theory of social systems 461–2 theory of structuration 148 thinking convergent 199, 275, 278, 298–9 divergent 199, 209, 285, 298–9, 308, 314 out-of-the-box 275 third space 251–2 three-sector model of economy 267–8 ties strong 339, 580, 630, 648 weak 339–40, 343, 349, 545, 548, 580, 630 TIS see technological innovation systems (TIS) Torre, A. 10, 67, 422, 424, 427–8, 430, 448, 453 trade association 353, 514–15, 523–4, 526, 530, 533–4, 772, 780 trade fair benchmark function 519 competitor innovation 519 ‘global buzz’ 510, 520 impact on innovation, types of 517–20 importance for innovation 512–17 introduction 509 knowledge transfer 638 as sources of information 510–511, 515, 615 trade fairs and innovation different views of role 509–10, 520 exhibitor perspective 512–14 impact, types of 517–20 industry perspective 514–17 trade show concertation 529–34 trade shows collective action at 528, 533–4 concertation process 529–34 important role of 523–4, 534 innovation in fabric for fine fashion apparel 524–6 Première Vision 526–8 trademark 108–12 traduction 70–71 trajectories innovation 523–4, 528, 533 regional 494, 498–500, 502

technological 141, 171, 179, 190–191, 479–80, 492–3, 520, 613, 616, 622 trajectory of ideas 205 transaction cost analysis (theory) 483 transaction cost theory 482, 492 transaction costs, spatial 743–7 transfer mechanisms and cultural economy 235–6 knowledge 237–8 organisation and scale 236–7 spatial 236 transfer of value 752, 754–5, 757 transient competitive advantage 92–7 translation concept of 69 and enrolment 70–71 of knowledge and innovation 239–41 knowledge creation mechanism related to 69 making in 231, 239–42 as means of knowledge production and radical innovation 70–71 progressive 202 transnational entrepreneur concept of 640–642 diaspora entrepreneurship 639–40 impact on regional development Asian economies 643 case studies highlighting 647–8 three influences 648 important role in knowledge-based economy 647 knowledge transfer and innovation through family businesses in diamond sector 645–8 kinship relations 642–3 New Argonauts 643–5, 647–8 literature 638 policy implications 648–9 transposition 414–15 trend concertation see concertation process trends, assessing 141–2 trickle-up innovation 78 triple helix 68, 130, 498, 627 TRIZ 106, 201, 300–301 trolling (patent) 183, 186–8, 191–2 Truffer, B. 610–613, 615–20 trust 132, 225, 246, 250, 343–4, 351–4, 574–5, 580, 614, 643, 731 types of creativity 310 of impacts of trade fairs on innovation 517–20 of innovation 283–5 of innovator 441, 448 of learning 723, 730–733

Index of of of of of of of of of

managerial challenges 82–3 motivation 378, 382 networks 483 power 703 proximity 428–9 regional systems 466 relationships in value chain 482 reverse innovation 79–81 tension between design and creativity 277–8 of upgrading 361–2 uncertainty epistemic 140 Knightian 140 regulatory 779 standards needed to reduce 525 underground 4, 9, 207, 252, 376, 395 uneven development 767 unintended research outcome 124–5 United States (U.S.) biosafety ordinance 686–9, 693–6 eco-industry 778, 779, 780, 782 ethnic linkages 640 national innovation systems 458–9 patent trolls 187 purchasing manager survey 511 radical innovation patterns 464 research and development 407–8 from Sputnik to New Economy 412–13 Taiwanese engineers 643–5, 647–8 trade in environmental goods and services 776 used vehicles 762–5 waste management laws 771 UNIVAC 152, 155–7 university, entrepreneurial 627–9 University of California 644, 692–4 university spinoff 497, 627–9, 631 unrelated variety 217 unsatisfactory innovation 476, 483 untraded interdependencies 479–80, 485, 492, 498, 500 untraded linkages 402 upgrading as capacity building 485 challenge of industrial 284–5 concept of 361–2, 481–2 functional 362, 744–5 global value chains 484–5, 487, 744–5 of industrial clusters 361–2, 366 inter-sectoral 362, 744–5 process 362, 744–5 product 362, 744–5 research and development 482

817

sectoral 485 types of 361–2 upperground 4, 9, 207, 252 upstream co-creation 376–8 upward causation 131–2 urban agglomeration 225–6, 447, 492, 570 urban bias in innovation studies concept 440–441 innovation theory 446–7 reconciling non-urban innovation with theory 447–52 research questions arising 453–4 sources of empirical bias 442–5 study conclusions 452–3 urban development 217–23 urban diversity and innovation case studies 223–7 creativity and economic development 217–23 Jacobs spillovers 215–17, 222–3, 225–7 regional development policies 227 urban (economic) diversity 217–23 urbanization economies 216–17, 225 U.S. see United States (U.S.) use of technology 133, 152, 166 use value 753, 761, 764, 767 user empowerment 376 user entrepreneurship 387 user heterogeneity 172 user innovation advantages and limits of co-creation and coinnovation with users 386–7 innovation and users 373–5, 387 lead users and emergent nature consumers 381–5 power to users 372–3 strategies for co-creation with consumers 375–9 virtual tools and online communities 379–80 user–producer interaction 472, 475–7, 479–80, 518, 613 value creation, capture and movement of 754 creation in eco-industry sector 778–83 and cultural economy 235–42 of e-waste 757–60 geographical mobility of 767 geographical transfer of 757 recapture 756–9, 763–4 of shipbreaking 760–762 transfer and capture of 753 transfer from machine to commodities 754–5 transfer from old to new commodities 755–6 use 753, 761, 764, 767

818

The Elgar companion to innovation and knowledge creation

of used vehicles 762–4 of waste 756 values human 412 inadequate 413–16 and institutions 411–12 valuing agencies 602–3 Van Assche, A. 739, 741–3, 747 Vanhaverbeke, W. 87, 91, 95 Vannuccini, S. 172, 179 variety institutional 130–131, 133 related 217, 494, 615–16 unrelated 217 VC see venture capital Vehicle Recycling Development Center 766 vehicles, used 762–6 vehicular idea 497, 500, 502 Vellera, C. 3, 9 venture capital industry 649 venture capital (VC) collective learning function 337–8 contribution to Silicon Valley 328, 335–6 embedding function 338–9 financing function 335 for financing innovation 412, 629–30, 778 position in Silicon Valley innovative cluster 331 selection function 336 signalling function 336–7 as source of robustness in Silicon Valley 334–5 Vernette, E. 375–6, 381, 385–6 vertical interaction 9, 402 vertical learning 360, 392, 402 veto power 708–10, 713–20 Viagra 125 video games 316–17 virtual communities advantages of 548–51 asynchronicity and reflective reframing 550–551 case selection 541–3 co-presence of people and objects 545–6 cumulative learning, selection and memory 549–50 hybrid 539–40 knowledge collaboration 537–8 knowledge practices 538 low multiplexity and quasi-anonymity 548–9 materiality 544–8 mediated interaction in localized practices 546–8 netnography 543–4

research design 540–544 study conclusions 551–3 virtual, notion 537, 551 virtual interaction advantages of 548–52 ambiguities of 552–3 materiality of 544–8, 551–2 virtual reality 658–9, 662–3, 665 virtual settings, innovation in 11–12 virtual technologies in treatment of AD 662–3, 665 virtual tools 379–80 visceralization 141–2 vision creation 663–4 visual art 244, 248, 252–3 Volkswagen 127 von Krogh, G. 46, 655, 657, 664 Washington consensus 478, 590 waste implications of growth of GDNs 766–8 implications of processing 764–6 processing examples 757–64 production 764–5 Waste Electrical and Electronic Equipment directive 757 waste management 771, 773, 774–5, 778, 779–80 waste processing e-waste 757–60 implications of 764–6 ships 760–762 used vehicles 760–764 waste-to-energy (WtE) 766 water treatment 7734–5, 777, 780 weak ties 339–40, 343, 349, 545, 548, 580, 630 wealth of nations 405–6, 411, 414, 416 Weil, B. 300, 605 whale watching 129–30 winner takes all market 233, 235 Wolfe, D.A. 442, 460, 725–7, 729–32, 734–5 work of innovation compared to team sport 140 conventional view of work prohibiting 144–5 emergent, situated and concrete 143–4 four communities of practice 147–8 insights into 138–9 learning processes 141–3 practice perspective 138, 145–7 understanding as practice 149 work role of innovators 140–141 workspace 570, 573–8, 580–581 world-first innovation 442, 445 WtE (waste-to-energy) 766