Knowledge Creation, Diffusion, and Use in Innovation Networks and Knowledge Clusters : A Comparative Systems Approach Across the United States, Europe, and Asia 9780313083235, 9781567204865

Citation preview

KNOWLEDGE CREATION, DIFFUSION, AND USE IN INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

This page intentionally left blank

KNOWLEDGE CREATION, DIFFUSION, AND USE IN INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS A Comparative Systems Approach across the United States, Europe, and Asia

Edited by

ELIAS G. CARAYANNIS DAVID F. J. CAMPBELL

TECHNOLOGY, INNOVATION, AND KNOWLEDGE MANAGEMENT SERIES SERIES EDITORS ELIAS G. CARAYANNIS and SHANTHA LIYANAGE

PRAEGER

Westport, Connecticut London

Library of Congress Cataloging-in-Publication Data Knowledge creation, diffusion, and use in innovation networks and knowledge clusters a comparative systems approach across the United States, Europe, and Asia / edited by Elias G. Carayannis and David F. J. Campbell. p. cm. — (Technology, innovation, and knowledge management) Includes bibliographical references and index. ISBN 1-56720-486-4 (alk. paper) 1. Technological innovations—Economic aspects—United States. 2. Technological innovations—Economic aspects—Europe. 3. Technological innovations—Economic aspects—Asia. 4. Knowledge management—United States. 5. Knowledge management—Europe. 6. Knowledge management— Asia. I. Carayannis, Elias G. II. Campbell, David F. J., 1963- 111. Series. HC110.T4K59 2006 658.4'038'011—dc22 2005020952 British Library Cataloguing in Publication Data is available. Copyright (Q 2006 by Elias G. Carayannis and David F. J. Campbell All rights reserved. No portion of this book may be reproduced, by any process or technique, without the express written consent of the publisher. Library of Congress Catalog Card Number: 2005020952 ISBN: 1-56720-486-4 First published in 2006 Praeger Publishers, 88 Post Road West, Westport, CT 06881 An imprint of Greenwood Publishing Group, Inc. www. praeger. com Printed in the United States of America

The paper used in this book complies with the Permanent Paper Standard issued by the National Information Standards Organization (Z39.48-1984). 10 987654321

CONTENTS

Preface

vii

Introduction and Chapter Summaries

tx

1

"Mode 3": Meaning and Implications from a Knowledge Systems Perspective Elias G. Carayannis and David F. J. Campbell

1

2

Productive Research Teams and Knowledge Generation Frank T. Anbari and Stuart A. Umpleby

26

3

Re-Thinking Science: Mode 2 in Societal Context Helga Nowotny, Peter Scott, and Michael Gibbons

39

4

Knowledge Production: Competence Development and Innovation—Between Invention and Routine Wolfgang H. Guttel

5

6

7

The University/Business Research Networks in Science and Technology: Knowledge Production Trends in the United States, European Union, and Japan David F. J. Campbell Innovation in Clusters and the Liability of Foreignness of International R&D Max von Zedtwitz and Philip Heimann The Emergence of Regional Technological Capabilities and Transatlantic Innovation Networks: A Btbliometnc Study of Public-Private, EU-U.S. R&D Partnerships Elias G. Carayannis and Patrice Eaget

52

67

101

123

vi

8

9

10

11

12

13

14

15

16

Contents

Measuring Science-Technology Interaction in the Knowledge-Driven Economy Martin S. Meyer

144

The Different Dynamics of the Biotechnology and ICT Sectors in Finland Christopher Palmberg and Terttu Euukkonen

158

The Transformation of the German System of Innovation: The Case of Biotechnology Edgar Grande and Robert Kaiser

183

"Competence Centers" in Germany: How Can Policymakers Support the Improved Diffusion of Knowledge? Susanne Buhrer

203

Cooperation and Networking as a Side Effect of the German Delphi '98 Kerstin Cuhls

223

Certification and Knowledge Management: An Approach Applied to the Space Transport Industry Frederic Fontane, Patrice Houdayer, and Franck Vasseur

237

Innovation Policy in the Knowledge-Based Economy: The Israeli Case Guy Ben-Ari

253

Using National Innovation Systems to Enhance S&T Policy: A Knowledge-Based Approach with Examples from Japan Mark S. Hewitt

283

Visualization of Research Universities: Raising the Right Questions to Address Key Functions of the Institution Thomas Pfefjer

307

Conclusion: Key Insights and Lessons Learned for Policy and Practice Elias G. Carayannis and David F. J. Campbell

331

Index

343

PREFACE Book Motivation and Scope

I

n this book, we attempt to address some fundamental "What" and "How" questions in b o t h a conceptual and an applied manner. These questions include: 1. What is knowledge in research and technology development and deployment? 2. What is the role of emerging, dynamically adaptive, and transdisciplmary knowledge and innovation systems and infrastructures (such as innovation networks of networks and knowledge clusters of clusters) in the science enterprise? 3. What are the implications and lessons learned for science and technology public sector policies and research and development private sector practices? 4. How do the emerging network-centric and cluster-based knowledge infrastructures shape and become simultaneously shaped by science and technology policies and practices? 5. How does the organizing of the science enterprise according to "Mode 3"—namely, dynamically adaptive, continually reconceptualizing, redefining, and recombining systems and their elements, functions, and borders—impact the science enterprise and become in turn shaped by its evolving dynamics? 6. What policy learning should result from our collection of transatlantic perspectives? 7. Finally, could "Mode 3" serve as a means of inferring and identifying meaningful patterns in the chaotic dynamics of the ebb and flow of knowledge or, in other words, the punctuated processes of knowledge creation, diffusion, and use within and around the science enterprise?

In this book, we have attempted to compile an eclectic configuration of perspectives and treatises of mutually complementary and reinforcing themes on science and technology (S&T) as well as on research and development (R&D).

VIII

Preface

We hope that it will prove of interest and use for S&T policymakers, R&D managers, business decision makers, and students of innovation and knowledge dynamics alike. Elias G. Carayannis David F. ]. Campbell Washington, D.C./Vienna September 2005

INTRODUCTION AND CHAPTER SUMMARIES ELIAS G. CARAYANNIS DAVID F.J. CAMPBELL

T

here is ample and growing evidence that intangible resources such as knowledge, know-how, and social capital will prove to be the coal, oil, and diamonds of the twenty-first century for developed, developing, and emerging economies alike1. Moreover, there are strong indications and emerging trends that show qualitative and quantitative differences between the twentieth- and the twenty-first-century drivers of economic growth: 2 The world economy is in the midst of a profound transformation, spurred by globalization and supported by the rapid development of ICT (information and communication technologies) that accelerates the transmission and use of information and knowledge. This powerful combination of forces is changing the way we live and redefining the way companies do business in every economic sector.

We are currently going through a dynamic era for the economies of the world where a country can transition fast either upward (see the case of Ireland) or downward (see the case of Japan), and this trend has become increasingly pro3 nounced and accelerated since the mid-1990s. This new era is punctuated by: • Development ol a service-based economy, with activities demanding intellectual content becoming more pervasive and decisive. • Increased emphasis on higher education and lifelong learning to make effective use of the rapidly expanding knowledge base. • Massive investments in research and development, training, education, software, branding, marketing, logistics and similar services.

X

Introduction and Chapter Summaries

• Intensification of competition between enterprises and nations based on newr product design, marketing methods, and organizational forms. • Continual restructuring of economies to cope with constant change. The challenge and the opportunity in particular for advanced, developing, and transitioning economies is to evolve and possibly leapfrog from lower- to middle-income, knowledge-, technology-, and know-how-importing and -using countries to high and sustainable income, knowledge-, technology-, and knowhow-generating and exporting ones. For such a transition to be effective and sustainable, key success factors are innovation and knowledge clusters and networks linking public and private, domestic, regional, and global sector research and technological development entities:4 Innovation through the creation, diffusion, and use of knowledge has become a key driver of economic growth and provides part of the response to many new social challenges. However, the determinants of innovation performance have changed in a globalizing, knowledge-based economy, partly as a result of information and communication technologies. Innovation results from increasingly complex interactions at the local, national, and world levels among individuals, firms, and other knowledge institutions. Governments exert a strong influence on the innovation process through the financing and steering of public organizations that are directly involved in knowledge generation and diffusion (universities, public labs), and through the provision of financial and regulatory incentives. The knowledge economy, while relying on and leveraging technology and especially ICT, also needs a harmonious policy and institutional environment, a consistent regulatory framework, and a plausible business environment to promote innovation. Yet this does not necessarily imply that the government is the sole actor responsible for developing toward the knowledge economy. Examples of viable strategies and interventions have shown how the knowledge economy and e-development allow for better integration and cooperation between the private and the public sectors. The significance and relevance of technology is twofold. In one case, it widens the gap, leaving developing countries lagging. In the other, technology can optimize and maximize development efforts. Deeper cooperation among international donors and recipient countries is needed to allow the optimization role of technology to overcome the widening effect it imposes on the gap between North and South. The convergence of transformations and discontinuities both in the means of production and in the nature of the outcomes of economic activity (products and services) and the pronounced shift from product-focused, tangibles-based economies to service-focused, intangibles-relying ones necessitate rethinking and possibly reinventing ways and means of supporting the mission (as well as the business) of global, regional, and national policies and practices of economic development.

Introduction and Chapter Summaries

XI

In this context, the validity of Joseph Schumpeters and the Austrian School of Economies' principle of creative destruction is further corroborated. This principle underscores the importance, as both a challenge and an opportunity, of the continual replacement, renewal, and reinvention of socioeconomic, technological, and political institutions, practices, and infrastructures. Hence, the role of private and financial sector development as an enabler, catalyst, and accelerator of bottom-up, entrepreneurial initiatives coupled with top-down creative and realistic innovation policies in developed, developing, and transitioning economies becomes increasingly central. At the core of our proposed domain of intellectual discourse and especially using a systems approach, lie the processes of higher-order economic (see Matthew)^ and technological learning (Carayannis)1 as cited in Dyker and Radosevich:' The concept of economic learning captures the notion that some economies seem to be able to accommodate changes (eg products, technologies, markets) better than others. They do so partly through the flexibility of their firms themselves, but also through their capacities to promote inter-organizational linkages and collaboration and, above all, through the capacity of public institutions to imbibe and develop innovations, and then disseminate those innovations in various forms to firms, thus accelerating the process of adaptation... .Matthew makes a useful distinction between first-, second-, and third-order economic learning. First-order learning takes place within firms (organizations). Second-order learning takes place between firms through arrangements like sub-contracting, licensing, consortia, equity partnerships or joint ventures. Third-order economic learning takes place both outside and within firms but m such a way that their operating conditions are changed. It is "meta-learning," or learning how7 to learn; it takes place at the level of the economic system as a whole. In this book, we focus on a number of themes related to the creation, diffusion, and use of knowledge and especially within and across networks and grids of innovation networks and knowledge clusters. Geographically the book encompasses perspectives from the United States, Europe, and Asia, thus providing useful contrasts and insights for science and technology policy and practice through the immersion in diverse socioeconomic, technological, and cultural regimes and realities. We list below a number of indicative themes and concepts that the chapters in this book address as well as summaries of each book chapter to provide the reader with a conceptual guide to the book's contents:

KEY ISSUES AND RESEARCH QUESTIONS ADDRESSED

• What is knowledge? —How does knowledge relate to R&D, S&T, and innovation? —Knowledge creation, knowledge production, knowledge use, and knowledge diffusion.

Introduction and Chapter Summaries

XII

• What is "Mode 3" in knowledge creation, diffusion, and use? • What are innovation networks and knowledge clusters? —Innovation, innovation systems, innovation networks, innovation clusters. —University-industry (busmess)-government interactions, and policies for S&T, R&D, and innovation. —Policy challenges: top-down versus bottom-up issues. • Crucial indicators for defining and measuring the impact of knowledge on economic performance (R&D, S&T, and innovation) and the use of knowledge for benchmarking. —Knowledge production (e.g., bibliometric indicators). —Networking and innovation networks (e.g., the scope, scale, and types of local/ regional, national, and supranational interactions). —Knowledge diffusion: What would be appropriate indicators? —Impact of knowledge (R&D, S&T, and innovation) on economic performance. —Knowledge-based economy and society. —Creativity, invention, innovation, S&T, and R&D: linkages (also theoretical and/or conceptual linkages), complementarities, and synergies. —Homogeneous versus heterogeneous growth of knowledge: quantitative versus qualitative indicators. —Knowledge and complexity.

KEY CONCEPTS DEFINED "Mode 3"

"Mode 3" for knowledge creation, diffusion, and use: "Mode 3V is a multilateral, multinodal, multimodal, and multilevel systems approach to the conceptualization, design, and management of real and virtual knowledge stock and knowledge flow, modalities that catalyze, accelerate, and support the creation, diffusion, sharing, absorption, and use of cospecialized knowledge assets. "Mode 3" is based on a system-theoretic perspective of socioeconomic, political, technological, and cultural trends and conditions that shape the coevolution of knowledge with the "knowledge-based and knowledge-driven, global economy and society."

Innovation Networks

Innovation networks'" are real and virtual infrastructures and mfratechnologies that serve to nurture creativity, trigger invention, and catalyze innovation in a public and/ or private domain context (for instance, government-university-industry publicprivate research and technology development co-opetitive—a combination of cooperative and competitive—partnerships). ' Knowledge Clusters

Knowledge clusters are agglomerations of cospecialized, mutually complementary and reinforcing knowledge assets in the form of knowledge stocks and

Introduction and Chapter Summaries

XIII

knowledge flows that exhibit self-organizing, learning-driven, dynamically adaptive competences and trends in the context of an open systems perspective.

KEY ISSUES FOR KNOWLEDGE, R&D, S&T, AND INNOVATION

• National systems of knowledge, R&D, S&T, and innovation. • Policy learning in knowledge: best practices and lessons learned from the United States, the European Union, and Asia (e.g., Japan). • Knowledge (R&D, S&T, and innovation) as an engine for economic growth. • Knowledge as an asset for economic performance: incentives, measurement, and protection of knowledge. • Different country profiles of knowledge and economic growth and economic competitiveness profiles. • Proper knowledge-oriented policy interaction between government, university, and business. • Knowledge networks and alliances, for example, private-public partnerships for knowledge, S&T as public or private goods. • What is the proper role of government and public policy for knowledge? • Science supply versus market demand. • Local/regional, national, supranational, and global innovation systems. • Small countries, large countries, supranational entities: What is the balance between the the United States, the European Union, and Japan? Can East Asia form a supranational union? What are the challenges for Japan to remain a global player? • National and global knowledge diffusion: What are appropriate policy strategies for developing or newly industrialized countries to enter the global market?

CHAPTER SUMMARIES CHAPTER 1: Elias G. Carayannis and David F. J. Campbell, "Mode 3": Meaning and Implications from a Knowledge Systems Perspective

As an analytical point of departure, Elias G. Carayannis and David F. J. Campbell focus on systems and systems theory as they relate to knowledge creation, diffusion, and use matters. The emerging and growing interest in developing possibilities for using theory (and concepts) for practical application serves as motivation for the study of potential ramifications of systems theory for knowledge creation, diffusion, and use. Systems theory matured over time as a comprehensive and sophisticated spectrum of conceptual tools. In the empirical world, knowledge creation, diffusion, and use are regarded as crucial events and processes for the sustainable and satisfactory growth rates of developed knowledge-based economies and societies. Embedding concepts of knowledge creation, diffusion, and use in the context of general systems theory could prove mutually

XIV

Introduction and Chapter Summaries

beneficial and enriching for systems theory as well as knowledge-related fields of study. It could: 1. reveal for systems theory a new and important field of application and, 2. at the same time, provide a better conceptual framework for understanding knowledge-based and knowledge-driven events and processes in the economy and hence reveal opportunities for optimizing public sector policies and private sector practices. This linkage of systems theory and knowledge-related events and processes is pursued, by the authors, along the following lines: 1. Via developing conceptual equivalents between the elements/self-rationale of a system and clusters/networks; clusters and networks are being considered as important for innovation. 2. Via linking a multilevel conceptual architecture design to knowledge creation, diffusion, and use that is motivated by a perspective of multilevel knowledge and innovation systems. In this context, the "national system of innovation" perspective is recontextualized and reembedded, while not being replaced, in a larger global framework. 3. The authors introduce the term Mode 3 (as juxtaposed to Mode 1 and Mode 2, also discussed in this book) to identify and profile an emerging multilevel knowledge metasystems perspective. CHAPTER 2: Frank T. Anbari and Stuart A. Umpleby, Productive Research Teams and Knowledge Generation

The chapter contributed by Frank T. Anbari and Stuart A. Umpleby falls into two major sections. In the first part of their analysis, they deliver a comprehensive overview of how knowledge is being generated, by tracing this important research question across a whole spectrum of diversified issues: U.S. federal government support for research; research networks; implications of research for society; research in companies; and managing research teams. One general theme, decisively underlying these different approaches, is how different modes of knowledge production and use can be tied effectively together. For firms, more specifically, the challenge of how to reconcile and integrate horizontal (competitive) and vertical (precompetitive) collaboration is the result. Networks can link together organizations with a complementary knowledge expertise, leveraging opportunities, but also creating challenges for the management of such collaborative enterprises. In the second part of their analysis, they deliver a summary in retrospect of the Biological Computer Laboratory (BCL) at the University of Illinois in Urbana-Champaign, which operated from 1958 until 1975. Anbari and Umpleby classify the BCL as an example of a "highly productive research team," which, thirty years after its closure, still attracts increasing attention. BCL was directed by Heinz von Foerster, an academic entrepreneur who immigrated from Austria after

Introduction and Chapter Summaries

xv

1945. The following features fed into the innovativeness of BCL: interdisciplinary research; art and analogical reasoning; many modes of learning; involvement of people at several levels of education; a large and grand vision; support and encouragement for all contributions; social activities; transparent information; and research on several levels. In their final plaidoyer, Anbari and Umpleby express the idea that an improved understanding of effective organization and generation of knowledge should link to an advanced innovation rate.

CHAPTER 3: Helga Nowotny, Peter Scott, and Michael Gibbons, Re-Thinking Science: Mode 2 in Societal Context

The New Production of Knowledge was one of the most successful knowledge books in recent years, which defined and developed the knowledge concept of Mode 2. Helga Nowotny, Peter Scott, and Michael Gibbons structure their analysis in three sections: again summarizing the key arguments of The New Production of Knowledge; a reflection of "trends in the research environment"; and presenting a summary of their new7 arguments, developed in Re-Thinking Science. Re-Thinking Science represents a follow-up book to The New Production o/ Knowledge, on the one hand, reflecting and discussing criticism of the earlier book and, on the other hand, developing the earlier concepts further. ReThinking Science stresses four aspects: 1. Elaborating the interaction between "science" and "society," emphasizing a coevolution of science and society under Mode 2 premises. 2. Extending the context of application to different forms of contextualization. 3. Emphasizing the new character of Mode 2, which does not just represent a secondary phenomenon of traditional scientific research. 4. Introducing two new ideas: first, underpinning contextualization by referring to an agora as "problem-generating and problem-solving environments,,- encouraging and demanding participation of knowledge producers and users; second, thinking further about the ramifications and consequences oi the application contexts.

CHAPTER 4: Wolfgang H. Guttel, Knowledge Production: Competence Development and Innovation—Between Invention and Routine

Wolfgang H. Guttel states a paradigmatic shift of strategic management theories from market-based to resource-based views, implying that the mtracorporate potentials and competences, in connection with the production of knowledge, should be given a greater consideration for achieving sustainable competitive advantages. Guttel offers the following definition for organizational competences: "Organizational competences are routines (= processes, i.e., observable, quite stable patterns of behavior) based on systems of rules (= structures), which are grounded in the corporate culture." Out of an interest in conceptually

Introduction and Chapter Summaries

XVI

explaining competence development, innovation, and knowledge production, a conceptual matrix is introduced and discussed: 1. Competence development may focus on three levels of change, which are invention and modifications of invention activities; innovation and change processes, and their modifications; and organizational change capability and modifications of that. 2. Complementanly to these change levels, different alternatives of change are available: skills; incentives; structures/processes; and corporate culture. Corporate culture represents in that logic the final reference, since changes of the organizational change capability demand a change of the corporate culture. This implies substantial learning processes, in which the "meta change routines and rules (dynamic capabilities)" are impacted through pervasive self-reflection processes, altering the frame of reference. CHAPTER 5: David F. J. Campbell, The University/Business Research Networks in Science and Technology: Knowledge Production Trends in the United States, European Union, and Japan

In his chapter, David F. J. Campbell focuses on knowledge production and research networks. Referring conceptually to the idea of multilevel systems of innovation, different knowledge concepts are related to each other: research (R&D), science and technology (S&T), and innovation. More specifically, four fundamental concepts of knowledge creation, diffusion, and use and their implications are presented and discussed in detail: 1. 2. 3. 4.

Mode 1 Mode 2 Triple helix Technology life cycles

These four concepts all emphasize the importance of university/business (university/society) research networks in science and technology, creating manifold linkages between university research and business R&D. Based on such conceptual considerations, the empirical analysis then compares knowledge production trends in the United States, the European Union, and Japan. On the one hand, intensity levels of university and business R&D expenditure are observed, and on the other, the "efficiency" of university research (based on scientific publications) and business R&D (based on patent data) is assessed. The conclusion summarizes the empirical findings, relating them again to the introduced knowledge concepts. In addition, some comparative hypotheses are presented for discussion about the national innovation systems (knowledge production systems) of the United States, the European Union, and Japan.

Introduction and Chapter Summaries

XVII

CHAPTER 6: Max von Zedtwitz and Philip Heimann, Innovation in Clusters and the Liability of Foreignness of International R&D

Knowledge (technology) spillover opportunities drive multinational companies to set up foreign R&D centers in different local regions (clusters) around the world and represent also an incentive for regions/clusters to attract foreign companies. Since knowledge spillover between foreign companies and local regions often cannot be directly monetarized, they depend on leveraging the imperative o^ reciprocity. The importance of tacit knowledge, particularly for explorative R&D, leads to a renaissance of the region in the context of R&D globalization, acknowledging the power of local proximity in knowledge sharing both explicit and especially tacit. Max von Zedtwitz and Philip Heimann analyze innovation processes at the interface of international company R&D and regional clusters. Social and cognitive access barriers constrain knowledge spillover, reflecting the liability of being foreign (LOF) that takes into account the not-invented-here syndrome. Zedtwitz and Heimann propose a conceptual model, as follows: 1. It uses as point of departure a corporate innovation system and a regional innovation system. 2. And then it defines the compatibility between the company's innovation system (performing foreign R&D investment) and the region's innovation system (attracting foreign R&D) as crucial for lowering the costs of knowledge spillovers. The absorptive capacities of regions and companies determine the degree of compatibility. Flows of FDI (foreign direct investment), in fact, could be used as proxy indicators for measuring compatibility patterns empirically. Zedtwitz and Heimann suggest a four-step process for the absorption of knowdedge that is being created during collaboration. Case studies of four advanced knowledge clusters— Boston (U.S.), Cambridge (UK), Sophia Antipolis (France), and Stockholm (Sweden)—indicate practical insights and lessons to be learned for strategic decision making. CHAPTER 7: Elias G. Carayannis and Patrice Laget, The Emergence of Regional Technological Capabilities and Transatlantic Innovation Networks: A Bibliometric Study of Public-Private, EU-U.S. R&D Partnerships

At the beginning of their analysis, the authors Elias G. Carayannis and Patrice Laget present an overview of different conceptual approaches to defining innovation systems. They focus on the national innovation system, which can be understood as a specific arrangement of institutions, involving also government and government policy, that intends enhancing the creation, storage, and transfer of knowledge, particularly emphasizing technology. Collaboration and communication between

XVI li

Introduction and Chapter Summaries

various national institutions can be assessed according to criteria of efficiency and effectiveness. The globalization of knowledge creation, diffusion, absorption, and use events and processes, as well as related market, network, and knowledge spillover effects, seriously challenge the national innovation systems. However, instead of making them obsolete, such phenomena necessitate the revisiting of the policies and practices of national innovation systems and highlight the need for a multilevel, metasystems approach to twenty-first-century innovation and knowledge systems policies and practices [Editor's Note: What we call Mode 3 in this bookl. As long as nation states and national politics exist, it is legitimate to speak of national innovation systems. In that context Carayannis and Laget refer to the two terms of technonationalist and technoglobalist: 1. One interpretation of technonationalism implies understanding international cooperation as a possibility for compensating possible (specific) weaknesses of a national innovation system. 2. Technoglobalism, on the contrary, emphasizes that international cooperation enhances the national innovation systems substantially and gradually leads to their integration into a global research network, with multinational corporations playing a key role. For their empirical analysis, the authors apply a bibliometric approach by focusing on article publications in international peer-reviewed journals in science and the social sciences (SCI database, put together by 1ST), where the institutional affiliations of the article authors express the following characteristics: 1. At least one institution must be located in the United States, and at least on in the EU (EU 15). 2. Furthermore, at least one institution must be private (defined as a firm), and at least one public (primarily, but not only, universities). These propositions fulfill the requirements for cross-national and cross-sectoral collaborations. The time window was the whole publication period 1988-1997. Based on the premises that national innovation systems become increasingly globally embedded and that collaborative research networks gain in importance, it was decided to define this specific subset of article publications as crucial. The presented sample covered a total of 18,869 articles. The key research questions focused on the scope and intensity of cross-sectoral transatlantic publications. This should enable the reader to determine whether transatlantic article cooperation follows more the logic of a complementary collaboration or an outsourcing-based collaboration. Empirical findings indicate that the primary axis of institutional article cooperation links an American firm with a European university. Broader institutional clusters consist of U.S. firms, U.S. universities, and European universities. European firms, clearly, are less frequently represented in transatlantic bibliometric cooperation patterns than U.S. firms.

Introduction and Chapter Summaries

XIX

CHAPTER 8: Martin S. Meyer, Measuring Science-Technology Interaction in the Knowledge-Driven Economy

In his contribution, Martin S. Meyer focuses on science-technology interaction by investigating two distinct areas of intellectual property rights (IPR): patent citations and academic patents. Analyses of patent citations conventionally investigate the nonpatent literature references on the front pages of patents. Particular attention is given to the scientific nonpatent references, being interpreted as a measurable input of science into technology and business. In that context, Meyer prefers to interpret science citations by patents as an indication of a general, two-way, often reciprocal information flow and interaction between science and technology, where one-way conclusions about the directionality from science to technology often are premature. Meyer emphasizes, in addition to patent citations, two alternative perspectives and methodologies of study: academic publication activities of researchers in business firms and technological patent activities of academic researchers at universities, which he defines as academic patents. Academic patents again may be divided into three groups: 1. purely academic patents, where the inventors, at the time of the invention, worked primarily at the university 2. collaborative patents, where the university inventors cooperated with the industry inventors 3. industrial patents, where the academic university inventor, at the time of the invention, worked primarily in a business setting Meyer tests patent citations and academic patents for Finland in pursuit of empirical corroboration of his ideas. One conclusion reflects differing patterns in different technology fields. Patent citations are more frequent in biotechnology and less frequent in ICT (information and communication technologies). Socalled academic industrial patents concentrate on ICT, whereas in biotechnology and pharmaceuticals the purely academic patents and the collaborative patents dominate. This reinforces the premise that distinct technology fields reveal different knowledge transfer patterns from science to technology (business). In a second empirical step, Meyer compares the science-technology interaction in Finland with results from another small-sized economy, Flanders (Belgium). CHAPTER 9: Christopher Palmberg and Terttu Luukkonen, The Different Dynamics of the Biotechnology and ICT Sectors in Finland

Christopher Palmberg and Terttu Luukkonen focus their chapter on comparing the performance and assessing the future potential of two different technology sectors in Finland: ICT (information and communication technologies) and

XX

Introduction and Chapter Summaries

biotechnology. Since the mid-1990s, Finland has achieved impressive economic growth and ranks high in international competitiveness. Crucial for this Finnish model of success was its ICT performance, as expressed by the Finnish Nokia company, which progressed to a global player in ICT, and a global telecommunications giant. Palmberg and Luukkonen pose the following research question: Can Finnish biotechnology achieve a similar (international) breakthrough like that in Finnish ICT? To assess that question, the authors apply the concepts of technology systems and competence centers. A "technological system can be understood as a technology cluster, a network of actors, that structures how inventions, innovation, firms, and industries leverage technological opportunities. The competence bloc represents more specifically the factors that determine how, in the context of a technology system (technology cluster), technological opportunities are translated into viable businesses. A "complete" competence bloc finally should support and achieve endogenous and self-reinforcing growth. For assessing the dynamics of a competence bloc, the following actors should be taken into account, whose tasks subsequently interlink: inventors, "entrepreneurs (innovators), competent venture capitalists," exit markets, and industrialists. Biotechnology is more sciencedependent, and the commercialization of R&D takes significantly longer than in ICT. In their cross-sectoral analysis, Palmberg and Luukkonen arrive at the conclusion that the ICT sector in Finland developed to a higher degree of maturity than biotechnology. Finnish biotechnology is constrained by low levels of VC (venture capital) funding and the absence of a major domestic industrialist. The authors, therefore, do not expect, for the near future, an international breakthrough of Finnish biotechnology comparable to the success of ICT.

CHAPTER 10: Edgar Grande and Robert Kaiser, The Transformation of the German System of Innovation: The Case of Biotechnology

Edgar Grande and Robert Kaiser place their analysis of the German biotechnology industry in the conceptual context of innovation systems. Across different countries, variations of national innovation systems can be observed, and at the same time, established innovation patterns at nation-state levels express a tendency toward reproductive stability. However, the potential for change should not be underestimated. Innovation changes can result from: 1. technological paradigm shifts, which steer the directions of technical change of innovative organizations. Institutions support or constrain innovative organizations in their ambitions and interest in applying the new technological paradigm. Therefore, the institutional environment plays a crucial role for an innovation system. 2. Institutional reconfigurations, in which globalization and the internationalization of R&D, technology, and markets, on the one hand, and the European economic integration process, on the other hand, impacted the European innovation systems.

Introduction and Chapter Summaries

XXI

Grande and Kaiser analyze how changes in the German innovation system affected the German pharmaceutical biotechnology industry, which caught momentum in the 1990s and has unfolded, since then, in a competitive profile against the more mature biotech clusters in the United States and the United Kingdom. Traditionally, the German pharmaceutical industry was chemistrydriven, neglecting, at the beginning, the new biotechnological cycle, which was first fully leveraged in the United States. Thus, Germany's pharmaceutical biotechnology industry did not benefit Irom first-mover advantages. Referring to changes in the institutional environment of Germany's biotech industry, Grande and Kaiser distinguish between the following areas: legal norms and regulation, financial resources, human resources, and technical resources. For legislative regulation, the supranational EU was important, since the EU enacted more biotechnology-friendly legal norms and regulations than German}' and moreover seems to have an overall less cautious approach to biotechnology, somewhat neutralizing skeptical views about biotechnology in Germany. In the other areas, the German national system proved to be more influential, with public institutions (at federal and subfederal levels) and public policy as suppliers of crucial inputs. The authors set up the hypothesis that early public investments defined and embedded the geographical clusters, where, in Germany, commercially successful and competitive biotech businesses developed starting in the 1990s.

CHAPTER 1 1 : Susanne Biihrer, "Competence Centers" in Germany: How Can Policymakers Support the Improved Diffusion of Knowledge?

In her chapter, Susanne Biihrer focuses on a policy initiative of the Federal Ministry of Education and Science (BMBF) in Germany that has promoted, since the late 1990s, the creation of six competence centers for nanotechnology (CCN). This program represents a policy reply to challenges of increased global technology competition. Competence centers should be regarded as a new and complex promotional scheme, as a multiactor system, serving the following purposes: 1. Bringing together different actors (e.g., universities and firms), which then 2. interact on the basis ol~ network relationships; 3. in its final consequence, a competence center should be carried by a self-supporting self-dynamics that generates economic revenues, while 4. the role of politics (policy) focuses on stimulating and promoting the establishment of such centers; consequently, political actors are challenged how to identify promotable clusters at the start. Biihrer lists four crucial factors for the success of competence centers: organization and networking, science and technological performance of the involved actors, the quality of the embeddness of the innovation network in attractive framework conditions (locations), and a focus on knowledge and information

Introduction and Chapter Summaries

XXII

diffusion. Practically, the implementation of a CCN demands a competitive project application; successful applicants then receive public funding for a certain number of years. Two years after the setup of the six CCN a Erst evaluation was carried out, which focused on the dimensions of organization and networking and information and knowledge dissemination. Biihrer reports on the concepts, methods, objectives, and first results of that early-stage evaluation exercise.

CHAPTER 12: Kerstin Cuhls, Cooperation and Networking as a Side Effect of the German Delphi '98

In her contribution, Kerstin Cuhls focuses on the conceptual instrument and policy tool of foresight. Besides a review of general considerations and implications, Cuhls offers an in-depth account of the German Delphi Study '98, which clearly falls into the category of foresight studies. Institutionally, the German Delphi study was organized and funded by the Federal Ministry for Education and Research (BMBF), with pre-decessing early initiatives dating as far back as the first half of the 1990s. A major push w7as initiated in 1997, with the focused preparations for the Delphi '98. The ambitious task of managing this comprehensive foresight study was assigned to ISI, the Karlsruhe-based Fraunhofer Institute for Systems and Innovation Research. Main objectives of Delphi '98 were to supply answers to key questions about the future, following the interest of assessing where—in which innovation areas—major advances were expected during the next thirty years. From that central theme, different questions were developed, for example: f. 2. 3. 4.

What is the potential impact of such advances on economic performance? How long will it take to implement advances in profitable commercial applications? Which countries lead in which areas of innovation or research (R&D)? What are the solutions and scenarios for Germany to catch up in those areas where Germany is not leading?

Out of an interest in specific strategy remedies, the Delphi '98 focused on twelve individual technology (innovation) areas. Cuhls concludes that, in principle, the objectives of foresight studies can emphasize results and/or processes. An important process outcome may refer to and encourage a networking and further developing of the cooperative interaction of those institutions and firms that participate in the whole foresight exercise. Why, however, should organizations share their knowledge about the future and not try to leverage information saliency instead for advantages of competitiveness? Institutions and firms could be inclined to hide future-oriented knowledge. Therefore, it represents a crucial challenge for the dynamic processing of foresight activities to set explicit incentives which encourage an interactive knowledge sharing and networking among the participants.

Introduction and Chapter Summaries

XXIII

CHAPTER 13: Frederic Fontane, Patrice Houdayer, and Franck Vasseur, Certification and Knowledge Management: An Approach Applied to the Space Transport Industry

The authors Frederic Fontane, Patrice Houdayer, and Franck Vasseur observe major changes in the space transport industry. With a substantial increase in providers and companies engaged in more than ten launches per year, the levels of competitiveness increased. New demands put on space transport are that clients expect an overall service, a customized standardization, similar to largescale individual services in other industries. Service packages must combine competitive prices, timely delivery, quality, and reliability. According to the authors, space transport is currently challenged by the circumstance that several of the key players will retire in a few years. This leads to a need to pass on knowledge from one generation to the next generation of decision makers. Even though a lot of the knowledge is codified in space transport, learning linkages of tacit knowledge represent a particular concern. One approach to dealing with this challenge suggests developing systematic strategies of knowledge management that reflect the knowledge base of firms. To be successful, this demands that knowledge management represent a strategic top goal of the firm, fully supported by the leadership of the director of a company. Furthermore, the ramifications of knowledge management should cover and integrate the whole activity spectrum of a firm and should even be extended to a systematic coverage and monitoring of the clients, suppliers, and competitors. In practical decisionmaking terms it is being suggested that the implementation of an ISO 9000 certification should be understood as a strategy, for developing and advancing knowledge management. CHAPTER 14: Guy Ben-Ari, Innovation Policy in the Knowledge-Based Economy: The Israeli Case

Israel serves as an interesting case for a small-sized country, advancing as a hightechnology and globally connected economy and performing as a maturing innovation system. Guy Ben-Ari structures his analysis into four main sections: 1. An examination of the global evolution of innovation policy from post-1945 to the present. 2. A more specific account of the history of the Israeli S&T and innovation policies, which largely aligned with the international trends. 3. An assessment attempt of the Israeli policies, benchmarked by the actual performance of the country. 4. An overview of the new challenges for the Israeli innovation system. Ben-Ari underscores that a government policy capable of effectively leveraging scientific and technological R&D drives the dynamics of a knowledge-based economy and additionally enhances economic performance. Three areas are

XXIV

Introduction and Chapter Summaries

identified as posing special challenges for the Israeli innovation system of the early twenty-first century: first, formulating and implementing a long-term innovation policy, which is inclined to apply more systematic evaluations to the outcome of its programs and which is willing to pick winners; second, supporting a stronger linkage of the Israeli high-tech sectors with the medium- and low-tech sectors of the whole economy, and also with the regional economies of the countries that neighbor Israel; third, reassessing the premises of Israel's defense sector, which has been historically very prominent. A specific challenge seems to lie in dual-use technologies, implying that the R&D and technological portfolio of defense firms can cross the defense/civilian border of knowledge production. Assessed in a long-term perspective, a certain shift of knowledgerelated government policy can be observed, generally refocusing from a traditional S&T policy to a broader, more interdisciplinary innovation policy. Innovation policy should be inclined to support networking activities across different sectors and institutions, to reflect the whole national context within the context of globalization, and to be explicit about the framework for innovation. CHAPTER 15: Mark S. Hewitt, Using National Innovation Systems to Enhance S&T Policy: A Knowledge-Based Approach with Examples from Japan

Mark S. Hewitt focuses conceptually on the NIS (national innovation system), by presenting NIS as a crucial benchmark and frame of reference for government S&T policy. As a core definition for NIS, Hewitt states "the idea for how a (government) S&T policy can support technology creation and diffusion, through effectively tying together technological advancement, economic performance, and international competitiveness." Knowledge, both explicit (codified) and tacit, essentially feeds into a NIS, expressing implications for NIS-based government policy strategies that concentrate on knowledge diffusion across government-university-industry linkages. More particularly, the government S&T policy should be specific in addressing and supporting the national competitiveness in different technology fields and should decide how to network national institutions with these technology clusters. Conceptually this implies bridging NIS with the national innovative capacity. Comparisons of different NISs leverage two advantages: identifying a profile of competitive strengths (and weaknesses) of the NISs; and indicating possibilities of a learning process between the different NISs that finally should enhance the overall competitiveness of an economy or a NIS. Since, at the same time, technology shapes society and society shapes technology, this complicates an exact cross-country assessment of the policy rationales of the different NISs. NIS-based research is challenged by three sets of factors: a need for enhanced information about government-universityindustry research networks; improvement in the comparison of different NISs; and an assessment of the implications and ramifications of globalization in NIS. Empirically, Hewitt applies and tests the concept of a NIS for Japan. One

Introduction and Chapter Summaries

XXV

hypothesis that he presents for discussion is that the Japanese NIS of the 1980s expressed a higher degree of coherence than that of the 1990s. CHAPTER 16: Thomas Pfeffer, Virtualization of Research Universities: Raising the Right Questions to Address Key Functions of the Institution

The extended and intensified use of modern ICT (information and communication technology) represents one strategy for research universities' adapting to new demands, partially caused by globalization. Thomas Pfeffer develops a general overview of how traditional universities try to integrate ICT specifically, and how this impacts the organizational structure and mission of universities. More particularly, linkages and consequences of ICT with regard to three key areas of scholarly activity are being reviewed: research, publication, and education. Universities are being permanently challenged, to integrate, as an organization, these diverse functions and their social context. This, in fact, serves to test the underlying rationales and perhaps also the need for their reassessment and change in research, publication, and education. As a final benchmark, ICT has the potential of enhancing productivity in these three key areas and leveraging their better integration into the organizational framework of universities. Pfeffer concludes that the further evolution of universities depends crucially on successfully using ICT. ICT supports the connectivity of scholars worldwide and thus contributes to the research and innovation potential of academic communities. Universities can be interpreted as offering the organizational infrastructure to academic communities, to become connected—and in this sense ICT strengthens the position of universities by further enhancing and enriching their role as organizational infrastructures for knowledge creation, diffusion, and use. NOTES 1. World Economic Forum and Harvard CID. 2002. The Global Competitiveness Report 2001-2002. New York: Oxford University Press. 2. Toward e-Development in Asia and the Pacific: A Strategic Approach for Information and Communication Technology, ADB, June 2001. 3. Carl Dahlman and Jean Eric Aubert. 2001. China and the Knowledge Economy: Seizing the 21st Century. Washington, DC: World Bank Publications. 4. OECD. 2001. Innovative Clusters: Drivers of National Innovation Systems. Pans: OECD. 5. J. Matthew, 1996. Organizational Foundations of the Knowledge-Based Economy. Paris: OECD. 6. 2000. The Strategic Management of Technological Learning: Case Studies from Power Generation, Transportation, Pharmaceuticals, and Software US and European Firms. Boca Raton, FL: CRC Press. 7. David Dyker and Slavo Radosevic. 2000. Building the Knowledge-Based Economy in Countries in Transition: From Concepts to Policies. Working Paper, World Bank and EU DGXII TSER.

XXVI

Introduction and Chapter Summaries

8. Elias Carayannis and Max Zedtwitz. 2005. Architecting GloCal (global-local), realvirtual incubator networks (G-RVINs) as catalysts and accelerators of entrepreneurship in transitioning and developing economies: Eessons learned and best practices from current development and business incubation practice. International Journal of Technovation, 25. 9. Networking is important for understanding the dynamics of advanced and knowledge-based societies. Networking links together different modes of knowledge production and knowledge use and also connects (subnationally, nationally, and transnationally) different sectors or systems of society. Systems theory, as presented here, is flexible enough for integrating and reconciling systems and networks, thus creating conceptual synergies. 10. Elias G. Carayannis and Jeffrey Alexander. 2004. Strategy, structure and performance issues of pre-competitive R&D consortia: insights and lessons learned. IEEE Transactions of Engineering Management 52. 11. Elias Carayannis and Jeffrey Alexander. 1999. Winning by co-opeting in strategic government-university-mdustry (GLU) partnerships: The power of complex, dynamic knowledge networks. Journal of Technology Transfer 24, 197-210. Note: Awarded 1999 Lang-Rosen Award for Best Paper by the Technology Transfer Society.

1 "Mode 3" Meaning and Implications from a Knowledge Systems Perspective ELIAS G. CARAYANNIS DAVID F. J. CAMPBELL

U

nder the comprehensive umbrella term of Mode 3 our interest is to put a conceptual link between systems and systems theory on the one hand, and their application to knowledge on the other hand. Systems can be understood as being composed of elements, which are tied together by a self-rationale. For innovation, often innovation clusters and innovation networks are being regarded as important. Leveraging systems theory for innovation concepts, one can draw a referential line between the elements of a system and clusters (innovation clusters) and the self-rationale of a system and networks (innovation neLworks). One advantage of this approach is that it makes the tools of systems theory effectively available for research about innovation. Also from original research about the European Union the concept of a multilevel hierarchy promises conceptual opportunities. Further integrating systems theory, we can speak of multilevel systems of knowledge (following different levels of aggregation) and multilevel systems of innovation (also following different levels of aggregation). The popular and powerful concept of the national innovation system is being chronically challenged by ongoing processes of supranational and global integration. Conceptually unlocking the national innovation systems in favor of a broader multilevel logic implies further accepting the existence of national innovation systems, but, at the same time, emphasizing also their global embeddedness. Our suggested catch-phrase of Mode 3, therefore, integrates several considerations that want to relate systems theory, knowledge, and innovation more directly and should be understood as a contribution to the general discourse.

2

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

Favoring a conceptual point of departure, the analysis is carried by three conceptual research questions. First of all, elaborating an interface between concepts of systems and concepts of networks (or innovation networks) claims analogies between (1) elements (parts) of a system and clusters and (2) the self-rationale of a system and networks. Just as the self-rationale holds together the elements of a system, a network ties together different clusters (an innovation network thus links different innovation clusters). Second, an application of systems theory is encouraged to the world of knowledge, by speaking of knowledge systems. Following the logic of a "multilevel" architecture, knowledge should be regarded as an aggregated concept: While innovation represents a highly aggregated term, S&T (science and technology) is already less aggregated, and R&D (research and experimental development) is even less aggregated than S&T. This implies using the concept of multilevel systems of knowledge or, when an emphasis should be put on innovation, to apply the concept of multilevel systems of innovation. Through policy the political system tries to influence the economy (economic system) and the other systems of a society. One can seriously discuss to which extent a "more narrow" economic policy is being replaced by a "broader" innovation policy. Third, the term Mode 3 is being introduced, bridging systems theory and knowledge, thus emphasizing a knowledge systems perspective. The chapter is structured into four major sections. In the first section we shortly refer to a first introduction and relation of knowledge, systems, and systems theory, indicating opportunities of a mutual leveraging. The second section is devoted to a detailed discussion and review of systems theory. It focuses on how a system can be defined, referring to the concepts of elements and self-rationale of a system. Constructivist notions are emphasized, implying that social (societal) systems can not be understood independently of an observer, since they are not naturally predetermined but to a large extent socially constructed. A further emphasis is placed on designing a conceptual bridge between the elements/self-rationale of system clusters/networks. Clusters and networks (and networks of clusters and networks) express a crucial relevance for knowledge and innovation. Through bridging elements/self-rationale and clusters/networks, the application of systems theory and systemic notions to knowledge gains additional plausibility. In the third section, the application of systems and systems theory to knowledge and innovation is pursued in more concrete terms. Explicitly, the appropriateness of a multilevel hierarchy is being tested. The ramifications of such a multilevel design can follow the logic of either multilevel systems of knowledge or multilevel systems of innovation. In advanced policy terms, a political system—which operates for governing a society—aims to influence the economy not only through economic policy but also through innovation policy, which reflects the knowledge base of a society and its economy. An economic policy, perhaps, does not take the knowledge base that comprehensively into account. In the context of the conclusion, under the umbrella term of

"Mode 3"

3

Mode 3, we again summarize our lines of arguments, by setting up a list of short propositions. KNOWLEDGE AND SYSTEMS AND SYSTEMS THEORY

Currently a comprehensive spreading of an economic rationale is postulated. In that context, markets often are classified as an economic concept, integrating the principles of supply and demand. Furthermore, a tendency is manifested by increasingly applying the economic rationale (or rationales) to disciplines and fields, lying outside of the traditional realm of economics (economy) and business. For example, explaining competition between parties based on references to political markets (Downs, 1957) or, to state another case: comprehensive evaluation exercises—such as evaluations of university research or of science and technology—are being compared by introducing the principles of a market logic to academia and thus creating academic markets (Campbell, 2003, p. 109; Shapira and Kuhlmann, 2003). The breakdown of East European and Soviet communism, after 1989, amplified a global proliferation of market economy and capitalism (Held et ah, 1999, pp. 149-282; Yergin and Stamslaw, 2002). l In the world of ideas and concepts, however, one could propose that this dominating economic rationale is seriously challenged by the concept or paradigm of systems or systems theory. Perhaps this suggests a complex conceptual relationship and interaction between the economic rationale (economics) and systems theory. This relationship could be understood competitively, but also complementarity, when the economic rationale and systems theory are being regarded as analytical tools that can be applied in parallel. An economic perception of market dynamics emphasizes processes of supply and demand: More specifically, modern business theories are inclined to broaden the market-based view7 w7ith a resource-based view, which underscores the importance of resources (including knowledge resources) for successful firm strategies in a market context (Guttel, 2003, pp. 16-28, 69-83; see, for a general overview7, also Barney, 2002; Grant, 2002; Pettigrew, Thomas, and Whittington, 2002). The systems theoretical approach to markets may interpret the market as a system, operated by complex feedback mechanisms (coupling inputs and outputs), which, in an economic context, refer to the interaction of supply and demand. Such a simultaneously binary economic and systemic coding of markets might leverage important conceptual advantages. Therefore, an alternative hypothesis could stress benefits, should the supposed conceptualeconomization of the world be conceptually reframed as a spreading of systemic or systems theory notions. This points toward a polarizing question set: Economics (economic rationale) and/or systems (systems theory)? A conceptual reconciliation would propose for discussion the following equation: (economic) markets = a specific type of a system (?). Knowledge represents an area where the application of systemic concepts (systems theory) promises particularly explanatory benefits. Modern and advanced

4

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

societies and economies are being understood as knowledge-based. Knowledge is regarded as crucial for sustaining wealth and competitiveness. Global knowledge rankings of societies often correlate, at least to a certain extent, with wealth or competitiveness rankings (IMD, 1996, p. 12; 2003a; 2003b; 2004; World Bank, 2002). On the one hand, knowledge serves as an input or resource for advanced societies and economies, which increasingly depend on knowledge. On the other hand, knowledge production (knowledge creation) also generates knowledge as an output, which then is being fed back (recycled) as a knowledge input. Mature knowledge production emphasizes high-quality knowledge, produced and used efficiently and effectively. Despite this importance of knowledge for economic performance, it is equally necessary to underscore that not all ramifications of knowledge are purely economically oriented. Noneconomic aspects of knowledge stress that knowledge is crucial for enhancing a dynamic and high-quality democracy. Global freedom rankings of countries (democracies), as designed and measured by Freedom House (2003, 2004a, 2004b), display a limited correlation tendency with knowledge rankings. This implies regarding knowledge as a key input that helps transform the quantitative spreading of market economies and democracies into economic and political systems with a "high quality" (Campbell and Schaller, 2002; Campbell and Sukosd, 2002, 2003). The "quantitative success" of market economies and democracies creates an intensified demand for the production and use of knowledge. WHAT IS A SYSTEM?

Systemic thinking and the application of systems theory require the employment of a definition of what a system is. W. Ross Ashby (1965, p. 16), for instance, emphasizes that a system represents a set of variables, selected by an observer.2 Humberto R. Maturana and Francisco J. Varela (1979) interpret a system as a definable set of components. Heinz von Foerster (1979, p. 8) draws a distinction between "observed" and "observing" systems and was the first to introduce the term "second-order cybernetics" as a "cybernetics of cybernetics" (see also Krippendorff, 1979). Foerster classifies controlled systems as "first-order cybernetics," and autonomous systems as "second-order cybernetics" (Umpleby, 1990, p. 113). In his work on systems theory and cybernetics, Stuart A. Umpleby underscores the conceptual additionality when the perspective of the observer is included. Umpleby (1990, pp. 113, 119) suggests the following propositions for first-order cybernetics: "interaction among the variables in a system" and "theories of social systems"; and for second-order cybernetics: "interaction between observer and observed" and "theories of the interaction between ideas and society." Consistently, Umpleby (1997, p. 635) can claim, "Theories of social systems, when acted upon, change social systems." In addition to this conceptual innovation of first-order and second-order cybernetics, the systemic notions of self-organization and self-organizing systems are expressed

"Mode 3"

5

as a long-lasting impact (von Foerster and Zopf, 1962; Roth and Schwegler, 1981; Paslack, 1991, pp. 91-184). Self-organization is carried by several conceptual inputs: first-order/second-order cybernetics; observed/observing systems (Foerster, 1984a); autonomy; and "autopoiesis." Autopoiesis represents a system that self-produces the components of which the system is set up. Consequently, autopoiesis serves as a viable characterization of biologically living systems, for example, cells and organisms. The term allopoiesis, on the other hand, implies a system which does not reproduce itself but produces something else, for instance, an assembly line of industrial production (Maturana, 1975; Maturana, Varela, and Uribe, 1975; Maturana and Varela, 1979; see also Maturana 1985). German-speaking Niklas Luhmann imported the concept of autopoiesis into his design of systems theory and applied autopoiesis to the social sciences (Luhmann, 1988a, p. 295; also see Luhmann, 1988b; Gnpp-Hagelstange, 1995; Pfeffer, 2001). More formally approached, systems can be defined by referring to two important principles (Campbell, 2001, p. 426): 1. Elements: Systems consist of "elements" (parts) 2. Self-Rationale: and systems have a mode of operation, a self-rationale (logic, selflogic) that organizes the self-organization and reproduction of a system and the relationship between the elements within a system and, furthermore, the relationship between the system and the other systems. In systems theory the distinction between the system and its environment, quasi embedding the system, is essential: The other systems also define something like an environment for the specific system (Easton, 1965a, p. 24; Foerster, 1984b, p. 4; Luhmann, 1988a, p. 292; Willke, 1989, p. 121). Related questions, therefore, are: Do systems have boundaries, and where are they located? Can systems overlap, and if so, how should those areas of overlapping be interpreted? Do systems network? Referring back to the self-rationale of a system, it should be emphasized that every specific system proposes a specific set of elements and a specific self-rationale. Thus a self-rationale also distinguishes one system from the other systems and makes the borderlines more visible. At the same time, potential overlaps complicate the issue of exact borders of a system. Society can be understood—and can be ''constructed" (designed)—as being composed of different systems, and these subsystems of a society then define social (societal) systems. The political system and the economic system are examples for such social (societal) systems. Furthermore, geographically, there can be subnational (local), national, and transnational (supranational, international, and global) systems or societies. The focus of Lundvall on the knowledge concept of the "national systems of innovation" consequently requires Lundvall to elaborate what a system is. For a formal definition of a system, Lundvall (1992, p. 2) suggests, "Somewhat more specifically, a system is constituted by a number of elements and by the relationships between these elements." In that context, Lundvall also cites Bouldmg,

6

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

claiming that Boulding defines a system as the opposite of chaos. 3 Also emphasizing references to knowledge, Stefan Kuhlmann (2001, p. 955) offers the following conceptualization for a system: "As a system we understand a conglomeration of actors, institutions and processes all functionally bound together, whereby certain characteristic core functions of each form the demarcation criteria against other societal (sub)systems." Already back in the late 1980s, the Max-Planck-Institut fur Gesellschaftsforschung in Cologne, Germany, developed an interesting approach to systems theory, focusing on key issues: understanding society in a systemic context; looking for possible synergy effects between systems theory and action theory (decision making); analyzing the science system and technology; and assessing the possibilities of influencing or controlling developments of and in different systems (subsystems) of society (Mayntz et al., 1988; see, more specifically, Mayntz, 1988; Stichweh, 1988a, 1988b; Rosewitz and Schimank, 1988). A literature search and review quickly reveals that there exist very different definitions of systems, social systems, and suggested elements of a system. David Easton, for example, stresses as possible "units of analysis" action, decision, and function, and even systems can serve as units of analysis when a system is being understood as a part (element) of a larger system (Easton, 1965b, pp. 15-16). Easton was inclined to analyze politics under the premises of systems theory, and to frame political life (political activities) as a system of behavior. According to Easton a meaningful systems analysis, therefore, requires (1) a "system" (e.g., political life); (2) an "environment" in which the system is embedded; (3) "response," implying internal variations of the structures and processes of a system, in response to the environment and the "internal sources"; (4) and "feedback," by tendency information-based, that influences the decision making of actors (Easton, 1965a, pp. 23-25). Easton (1965c, p. 112) claims, when referring to his "simplified model of a pohtical system," that demands on and support for a political system function as inputs; decisions and actions of a political system function as outputs. Gabriel A. Almond (1956, pp. 393-394) interprets a political system as a system of "action." Thus, the smallest unit is the role, representing the pattern of participation of an actor in an interactive process. Consequently, a political system can be conceptualized as a set of interacting roles or as a structure of roles. While these (earlier) Anglo-American systems applications in the social sciences underscored action and behavior, the systems theory of the 1980s and 1990s in the German-language area emphasized an alternative focus. Niklas Luhmann (1988a, p. 299), for instance, defines "communication" as the most basic element of a social system. Helmut Willke (1989, p. 25) also underscores that communication (and not action) constitutes the basic element of the operation mode of social systems. This implies that society cannot be understood as an aggregation of individuals, and that a single individual represents a psychological system,but not a social system (Willke 1989, pp. 18, 21). Individuals, persons, are necessary preconditions, a necessary environmental (context)

"Mode 3"

7

condition for society, but not part of society (Luhmann, 1988a, p. 299; Willke, 1989, p. 24). Communication operates between individuals. Communication and action (behavior) are not being regarded as identical, but as different entities, with communication as the more comprehensive concept, since communication also reflects on actors and acting (Willke, 1989, pp. 24-25). In variance (and perhaps opposition) to Luhmann and Willke, Umpleby (1990, p. 115) states, "Social systems are composed of thinking participants whereas physical systems are not." Luhmann and Willke strongly emphasize a difference between communication (social systems) and action (e.g., Luhmann, 1988a, p. 299). Contrary to that, earlier Anglo-American system, thinkers such as David Easton and Gabriel Almond apply a more mtegrative approach by interpreting action (behavior) as the basic elements of a social system. These systems theoretical differences lead to the following hypothesis: There are no restrictions with regard to the possible design of a system and the specific configuration of its elements and self-rationale, as long as the systems design is not (self-)contradictory. In principle, every consistent design or concept of a system can claim a certain legitimacy. Consistency refers to the internal logical construction of systems as well as the empirical definition of the systems terms. While the conceptual production (creation) of systems is permissive during the ex-ante phase, there operate processes of conceptual selection in the ex-post altermath. Every systems design is exposed to a communicative discourse and to external assessment, evaluation, and criticism. Therefore, there are general expectations that the designers of a system can offer arguments that demonstrate the plausibility of their systemic approach. Specifically, this can imply: 1. To which extent can a concept of a system convince other observers, members and actors of a society? 2. How useful is a concept of a system, and what is its potential of application? 3. To which degree is a concept of a system open for learning, and to what degree can it be adapted and improved? The number of systems (How7 many systems?) and the internal and external configuration of social (societal) systems, as proposed by observers, are not "naturally" predetermined, but socially constructed. Different observers propose different (or similar) definitions of systems, about which then these different observers, and communities, debate. Under certain circumstances, some consensus (consensuses) may be established, for example, that it is useful to speak about a political, economic, education, research, science and technology (S&T), and innovation system. But to make a realistic judgment, one must also acknowledge that competition between concepts of systems and between observers represents a common situation. Dissent between observers (or members and actors of a society) can be very fundamental. We already referred to systems theories that emphasize the importance of the observer4 and distinguish between observed and observing systems. Restating

8

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

the communication-based systems theoretical approach of Luhmann and Willke, it appears definitely legitimate to design a social system using communication as its basic element, which also leverages analytical advantages. On the other hand, evaluated from our analytical point of departure, it is not legitimate to rule out (or to forbid) per se a systems theoretical design that prefers to introduce and to use basic elements other than communication. We suggested that the two essential components of a system are its elements and its self-rationale. We also proposed that every (or almost every) concept of a system, based on a specific definition of elements and of a self-rationale, is legitimate, as long as the systems theoretical design is consistent and not self-contradictory. What then counts is: How convincing and how useful is a specific systems theoretical design? A dynamically unfolding discussion of the usefulness can lead to several outcomes: Either one systems theoretical concept replaces another concept, or there operates a parallel coexistence (coevolution) of different systems theoretical concepts. The coexistence paradigm may imply, depending on the application needs, that systems theoretical concepts develop their specific profile of usefulness and/or convince different communities to a varying degree. Communication and communicative discourses play an important role for such evaluation processes of systemic concepts. Already Easton (1965a, p. 33) asserted that concepts are not necessarily right or wrong but more or less useful. Should a specific conceptualization of a system convince broader communities and constituencies, then such a systemic concept enters and structures mainstream thinking and perhaps can achieve the status of a relative consensus. Again referring to an argument made by Umpleby (1997, p. 635), the following distinction can be drawn between the natural and social sciences: Different theories (concepts) in the natural sciences change our perception and interpretation, but not the actual behavior of the natural world. Different theories (concepts) in the social sciences, however, can change social systems and societies, if that new theory (concept) convinces enough members or key actors of a society. Our proposed flexible designing of systems may also be more appropriate for dealing effectively with the complexities of society. Societal complexity may create ambiguities concerning the number, function (self-rationale), and boundaries of social systems. Overlaps between systems cause conceptual irritations for some systems theories that are inclined to develop exactness, despite the circumstance that in modern and advanced societies and economies such hybrid overlapping and networking within and between systems occurs quite frequently. For a flexible systems theoretical approach a potential overlap between systems does not really pose a problem, because the premise of constructing social systems already implies that systems boundaries can be volatile. An overlap between different systems either can be accepted as a "hybrid area" or can be solved by redefining the zone of overlapping to a new system. Networks and networking are not only important for explaining the dynamics of society (Mann and Mayntz, 1991; Sabatier and Jenkins-Smith, 1993; Sabatier, 1999), but they also support our understanding of knowledge. Mode 1 and Mode 2

"Mode 3"

9

(Gibbons et al., 1994; Nowotny, Scott, and Gibbons, 2003), Science One and Science Two (Umpleby, 2002), Triple Helix (Etzkowitz and Leydesdorff, 2000), and the Technology Life Cycles (Tassey, 2001) can be introduced as knowledge concepts that emphasize a network-style and network-based linkage of different modes of knowledge production (see also Geuna and Steinmueller, 2003). Innovation networks and clusters—and networks of innovation networks and clusters— represent further knowledge-oriented concepts that stress the importance o^ networks. This creates a demand for conceptually bridging systems and systems theory with networks and clusters. How can we define the conceptual relationship between clusters and networks on the one hand, and between the elements and the self-rationale of a system on the other? There are different possibilities. One way to look at this is: Clusters could be interpreted as an equivalent for the elements of a system, and networks as a (partial) equivalent for the relationship between the elements of one or of several systems. Networks may represent a specific, but crucial, subset of relationships. Through networking the clusters/elements of a system (of different systems) relate and interact (and communicate). A system, acting as a subsystem and being embedded in a larger system, could also be interpreted as an element or as a cluster of that metasystem. Such a perspective of further aggregation emphasizes that the borderlines between elements (clusters) and systems are perhaps more in flux than originally expected. Every element or cluster of a system could be tested for whether it qualifies as a microsystem (subsystem). The manifold possibilities for relations (linkages) between elements within a system or across different systems clearly underscore the dynamic capabilities of networks and networking. In Figure 1-1 we summarize our preliminary conclusions through conceptually integrating the axes of elements/self-rationale and clusters/networks. In the following we want to propose, for discussion, possible definitions for the self-rationale of the political, economic, and knowledge systems: 1. Self-rationale(s) of the political system: The political system has or should express a responsibility lor the overall performance o( a society. The governance of society can be defined as a sell-rationale ot politics: through policy (policymakmg) and legislation or—alternatively—steering,^ coordination, and communication the political system attempts to influence the dynamics o{ a society and economy and tries to support the performance (and self-rationales) of the other systems (Campbell, 2001, p. 428). In summary, the political system is interested in effectively stimulating and coordinating the performances of the other systems and thus enhancing a synergetic performance surplus. Policy objectives can and should target the implementation, support, and supervision^ of markets and market mechanisms. In fact, enhancing the buildup of (self-organizing) markets represents in advanced societies and economies an important policy application area for politics.' 2. Sell-rationale(s) of the economic system: Phrased simply, wealth creation defines a primary function of an economy. A more sophisticated approach would have to outline specific implications and ramifications, such as: What is the relationship between wealth and competitiveness? How can the economic system perform

10

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

Properties of a System

Elements of a System;

Self-Rationale of a System:

(Parts of a System).

Logic; Function; S elf- Organization; Self-Reproduction; Dynamics; Self-Dynamics; Internal Processes; (Functional) Relationship between the Elements of the System; Definition of the "Border" of the System; Mode of Influence of Other Systems; (Functional) Relationship with Other Systems and/or the Environment of the System.

Functional Equivalent of Innovation Clusters and Networks with the Properties of a System FIGURE 1-1. A Formalized Definition of Systems and of Innovation Clusters and Innovation Networks

"Mode 3"

11

without negatively impacting its environments? Is it possible to create wealth and to avoid, at the same time, (major") distribution inequalities of the surplus wealth? 3. Sclf-rationalc(s) of the knowledge system: One main function of the knowledge system is to produce (create) and to distribute knowledge. Partly, knowledge can be regarded as an input, as a resource, with the potential o[ enhancing processes. Understood systems theoretical knowledge, being produced by the knowledge system, has the potential of supporting and enhancing the performance oi the other systems of a society, which are increasingly knowledge-dependent. Here, some similar interests between the political and the knowledge systems may be stated. The political system enhances the performance of a society through the governance oi society: policymaking and legislation, coordination, and communication (.and the support o( market building). Complementarily, the knowledge system enhances the overall performance of a society by producing and distributing knowledge resources, which then are used by the other systems ot a society to support their processes and performances. If the innovation system is understood as a subsystem of the knowledge system, then the innovation system represents, for the advanced societies and economies, an interface through which politics and the economy (business) communicate and interact. The innovation system defines a crucial area for applying and testing hypotheses of modern political economy.

KNOWLEDGE AND KNOWLEDGE SYSTEMS

After having elaborated on systems and systems theory, we want to locus on linking this systems perspective with know-ledge. Knowledge—and consequently the knowledge system (or knowledge systems)—should be regarded as an aggregate term or concept. Referring analytically to "multilevel systems" can leverage important conceptual advantages and benefits. Conceptual multilevel architectures already are being used frequently for analyzing and evaluating the supranational structures of the European Union (EU). In fact, speaking about multilevel governance originates from research about the EU (Hooghe and Marks, 2001; Bomberg and Stubb, 2003, p. 9). One could also reflect about multilevel legislation of the EU.lS Obviously, this logic of multilevels may also be applied to other research areas and to alternative macropolitical entities with a federal structure, for example, the United States. This w7ould introduce interesting research designs, such as comparing multilevel EU and U.S. and to search for possible similarities and differences. Evaluated from an analytical perspective, it appears promising to import this EU-based research concept oi multilevels and to modify it specifically, so that it fits our research interests about knowledge. The logic of multilevels implies that there is one (or more than one) axis of further aggregation. Aggregation can be approached cither functionally (as a sequence of continuously more comprehensive concepts) and/or geographically (e.g., subnationally, nationally, supranationally, or transnational ly). If we are interested in displaying the knowledge system in the context of the architecture of multilevel systems, then we can propose two (functional) axes: an education axis (OECD, 2002, pp. 35-63; 2003b) and a research axis. In the specific institutional context of universities,

12

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

following the Humboldtian tradition of emphasizing research-based teaching, obviously education and research overlap considerably (Campbell and Felderer, 1997, pp. 56-57; Etzkowitz, 2003, p. 110). With the concept of corporate universities, also firms may establish and support institutions of tertiary education (Etzkowitz and Leydesdorff, 2000, pp. 117-118). The research axis, as proposed here, could aggregate from research, to science and technology (S&T), and to innovation (see Figure 1-2). A broader term for research is research and experimental development (R&D), where research again differentiates in basic research and applied research (OECD, 1994, p. 29). Basic research represents a primary competence area of universities, and experimental development, already closely market-oriented, is the primary competence of business firms in the economic markets (OECD, 2003a, Table 3; National Science Board, 2002, vol. 1, pp. 4-29, and vol. 2, pp. A4-7 to A4-34). Consequently, R&D, S&T, and innovation can be regarded either as specific knowledge systems or, alternatively, as subsystems of the aggregated knowledge system (the knowledge metasystem). Referring to innovation, there are two issues of further concern: (1) Is innovation research-oriented and biased in favor of research (and disfavoring education)?

FIGURE 1-2.

Multilevel Systems of Knowledge

"Mode 3"

13

On the one hand, we conventionally associate innovation more closely to R&D (S&T) than to education. However, this depends on the specific conceptual approach. Should innovation (and innovation policy) be understood broadly, then innovation clearly integrates symmetrically the two axes of research and education (see, for example, Kuhlmann, 2001, p. 954). g (2) Is there a knowledge concept more comprehensive than innovation? One could claim that innovation already represents the most comprehensive knowledge concept; therefore, the innovation and knowledge systems overlap completely and coincide. An alternative approach would suggest regarding the knowledge concept (and knowledge system) still as broader and more comprehensive than the innovation concept (innovation system). This offers the advantage of sustaining a conceptual difference between knowledge and innovation, acknowledging that innovation could be closer associated to research than to education. Without a conceptual difference, both concepts, knowledge and innovation, would collapse into interchangeable terms. Emphasizing the "research axis" of knowledge, we can apply more specifically the multilevel logic by referring to multilevel systems of innovation (Campbell, see Chapter 5, this volume) and propose two particular directions of further aggregation (see Figure 1-3). Functionally, the aggregation may move from R&D to S&T and finally to innovation; and geographically, the aggregation may take the direction from subnational (local) to national and transnational (supranational, global); for example, Kaiser and Prange (2004, pp. 395, 405-406) discuss multilevel systems of innovation under geographical premises. 10 The concept of the national systems of innovation, prominently promoted by Bengt-Ake Lundvall, interprets and places the innovation system in the context of the nation-state, focusing consequently on the nation-state level (Lundvall, 1992, pp. 2-3; compare with Lundvall et al., 2002; Lundvall and Tomlmson, 2002; Nelson, 1993; Laredo and Mustar, 2001; Mowery, 2001; Bozeman and Dietz, 2001). The existence and operation of national patterns obviously supports the plausibility of a concept, such as national innovation systems. But Lundvall (1992, pp. 3-4) also explicitly comments that the national innovation systems are exposed to and challenged by globalization and regionalization. In our opinion, Lundvall could be interpreted in two ways: First, innovation processes (and innovation systems) operate in parallel, locally, nationally, and globally; second, the national context (nation-state configuration) still represents an important reference for innovation systems. Therefore, the Lundvall inclination of explaining innovation, based on national innovation systems, can be comfortably incorporated into our proposed concept of multilevel systems of innovation. Lundvall (1992, p. 4) emphasizes an understanding of modern Western nation-states as ''engines of growth." Also Richard R. Nelson (1990, p. 193) paraphrases capitalism as an "engine of progress.11 Discussing the objectives of innovation policy, Lundvall (1992, p. 15) underscores the government goal of supporting economic growth. Kuhlmann (2001, p. 954) defines as the primary objective for a public innovation policy the enhancement of the competitiveness of an economy.

14

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

FIGURE 1-3.

Multilevel Systems of Innovation: Functional and Geographical

Aggregations Source: Campbell (2004).

The functional aggregation of the ^research axis" of knowledge allows for different options of b o w research (R&D), science and technology (S&T), and innovation could be related to each other (see Figure 1-4). Concerning the placement of innovation, it reflects a broad consensus to interpret innovation as the most comprehensive concept. Regarding more specifically the vis-a-vis placement of R&D and S&T, there is certainly r o o m for an interesting debate: 1. R&D, S&T, innovation:11 In a conventional understanding, R&D is less aggregative than S&T. This may be made plausible by referring to empirically based indicators. Expenditure on R&D and S&T can be expressed in percentage terms of the GDP

"Mode 3"

15

(gross domestic product). ICT (information and communication technology) certainly qualifies as a subcategory of S&T expenditure. A comparison of the advanced OECD countries clearly demonstrates that already ICT expenditure alone (only a subcategory of S&T) consumes a larger percentage of GDP than all of the R&D expenditure (OECD, 2003c, Table 2; World Bank, 2002). 2. R&D S&T, innovation: 2 This approach is inclined to speak of one integrated R&D and S&T system, and not to see R&D and S&T as two distinct and different systems. As a consequence, R&D and S&T could be, at least partially, mutually and conceptually "translated'"; for example, basic research and science as well as experimental development and technology overlap. But the comprehensiveness and "conceptual stretch11 of related R&D and S&T categories might deviate considerably. Phrased differently, R&D and S&T represent perhaps alternative sets of categories for retypologizing and reconceptualizing common knowledge structures and processes. 3. R&D (S&T), innovation:^ Here the emphasis focuses on the R&D and innovation systems, where the R&D system is embedded in the larger innovation system. S&T is not necessarily being designed as an independent system, and thus there arises no need for a specific or systemic placement of S&T versus R&D and innovation.

FIGURE 1-4. Aggregations

Multilevel Systems of Innovation: Different Functional

16

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

Those components of S&T that display a relevance to R&D, are incorporated as a subset (subsystem) into the R&D system. Consequently, leftover properties of S&T, which express no implications for R&D, are not configurated to an independent S&T system. Our proposed (and already earlier stated) flexibility for constructing social (societal) systems allows, in principle, for very different systems designs. In the following we want to suggest more specifically, how knowledge may relate to politics and the economy. This requires introducing a political and an economic system for the analysis of society and economic dynamics. Our interest in knowledge suggests furthermore a need for an R&D and/or S&T system and an education system. Thus we are facing an interaction, potentially, between the following systems: political system; economic system; R&D and/or S&T system; and the education system (see Figure 1-5). Obviously, this represents only a selective and knowledge-oriented design of social systems, not including other systems (such as the legal system). A broader and more comprehensive systems design of society would have to incorporate a considerably larger number of systems. In a next step, we may integrate the innovation system into this picture. We want to suggest, for discussion, a specific design, where the innovation system

FIGURE 1-5.

Different Societal Systems

"Mode 3"

17

displays the following properties and patterns of interaction with the other systems of society (see Figure 1-6): 1. The innovation system is more comprehensive than R&D and S&T; thus, the innovation system embodies the R&D and/or S&T systems. 2. The innovation system overlaps with the education system. 14 In addition, on the borderline of the innovation and education system—or the R&D (S&T) and education systems—the university system may be placed; this should reflect the dual research and teaching functionality of universities. 3. Furthermore, the innovation system also overlaps partially with the political and economic systems. Innovation policies may represent a common subset of the political and innovation systems. Since innovation policies emphasize particularly the enhancement of economic performance, innovation policies also can be regarded as a cross-cutting subset of the innovation and economic systems. The political system expresses an interest in enhancing the performance of the other systems of a society. Policies represent one possibility: h o w politics

FIGURE 1-6. System

Different Societal Systems: Placement of the Innovation

18

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

may want to leverage an influence on processes in different social (societal) systems (see Figure 1-7). Through economic policy the political system can impact the economic system directly; through education policy the education system; and through R&D policy the R&D system. Through innovation policy, however, which recognizes more specifically the conditions and ramifications of knowledge, the political system also projects an indirect and mediated, knowledge-tailored influence on the economic system. This understanding underscores the interpretation and valuation of the innovation system as an interface between politics and the economy. The concept of the knowledge-based economy and society even suggests that in many contexts an innovation policy may be

FIGURE 1-7.

Different Societal Systems: Lines of Political (Policy) Influence

"Mode 3"

19

more effective in s u p p o r t i n g economic performance than traditional economic policy. In advanced societies the indirect coupling of the political and economic systems, through the innovation system that overlaps with politics and the economy, gams considerably in importance. Discursively, this implies that for knowledgebased economies and societies the innovation system and the innovation policy might define a crucial area for analysis u n d e r the premises of political economy or international political economy (for a general overview of political economy, see: Balaam and Veseth, 2 0 0 1 ; Crane and Amawi, 1997; Frieden and Lake, 2000).

CONCLUSION: THE KNOWLEDGE SYSTEMS PERSPECTIVE OF MODE 3 Using systems theory and applying systems concepts on knowledge certainly represents an interesting approach. We want to conclude our analysis by suggesting— for discussion—propositions for a possible knowledge systems perspective, which we label ad hoc as Mode 3. Mode 3 focuses on linking systems theory and knowledge, and the analysis of knowledge: 1. Analyzing knowledge in the context ol systems and systems theory leverages conceptual advantages. 2. The knowledge-based economy, knowledge-based society, and knowledge-based democracy are concepts demonstrating how important knowledge is for understanding the dynamics of advanced societies. 3. Systems theory and the systemic approach represent a comprehensive paradigm, displaying a greater conceptual extension than a purely (primarily) economy-based rationale. 4. Markets can be integrated into systems theory, by interpreting markets (.economic markets) as a specific type of a system. The claimed economization of the world may be reinterpreted in the context of systems theory. i. Systems consist of elements and a self-rationale. Systems can be designed (constructed), to a maximum extent, flexible, as long as the design is consistent and not self-contradictory. Discussion then will focus on whether a systems design can convince other "observers,"' whether it can be usefully and practically applied, and whether it is capable of "conceptual learning." 6. Systems theory, in principle, is open to conceptually combining elements/self-rationale and clusters/networks. 7. There is a need for permanently testing the applicability of knowledge-based systems concepts. Through this application orientation, the theoretical development of knowledge systems concepts will be further enhanced. (.Systems theory, in general, should be application-oriented.) 8. In recent years the concept of the innovation system (national innovation system) experienced a serious proliferation and can be interpreted, de facto, as a successful application of systemic concepts. Innovation, innovation systems, and innovation policies are key terms.

20

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

9. Knowledge represents an aggregated term. Research, science and technology, and innovation are less aggregative. 10. Applying the logic of multilevel systems to knowledge and innovation appears promising. Consequently, one can speak of "multilevel systems of knowledge and/ or multilevel systems of innovation. 11. Networking is important for understanding the dynamics of advanced and knowledge-based societies. Networking links together different modes of knowledge production and knowledge use and also connects (subnationally, nationally, transnationally) different sectors or systems of society. Systems theory, as presented here, is flexible enough to integrate and reconcile systems and networks, thus creating conceptual synergies. NOTES 1. A "quantitative" spreading of democracy implies that "quality" issues of democracy, and their evaluation, crucially gain in importance (Campbell et al., 1996, p. 5). 2. For an interesting Web reference about systems theory, see: "ASC Glossary on Cybernetics and Systems Theory" (http://pespmcl.vub.ac.be/ASC/indexASC.html). 3. "According to Boulding (1985), the broadest possible definition of a system is 'anything that is not chaos'" (Lundvall, 1992, p. 2). 4. Can there be an observation, independent of the characteristics of the observer? 5. The systems theoretical equivalent for steering, in German, is Steuening (Willke, 1998, p. 2). For further readings, in that context, see also: Willke, 1997; and Kuhlmann, 1998. 6. Helmut Willke (1997) titled one of his books consequently Supervision des Staates ("supervision by the state"). 7. This is paralleled by a skepticism against "political control" of a society, reinforced by the collapse of the planned economy system of the communist regimes in Eastern Europe and the Soviet Union (Yergin and Stanislaw, 2002), and a reshuffling of the political-economic agenda in Western democracies since the 1980s, emphasizing more directly market concepts (Cooper, Kornberg, and Mishler, 1988). 8. Multilevel governance of the EU represents a more frequently cited research objective than focusing on the EU's multilevel legislation. 9. The direct Kuhlmann (2001, p. 954) quote is "In the meantime, national and increasingly also regional governments of all these countries pursue, more or less explicitly, 'innovation policies', understood here as the integral of all state initiatives regarding science, education, research, technology policy and industrial modernization, overlapping also with industrial, environmental, labour and social policies." 10. Interestingly, the aggregative scope of "region" (regions) is not convincingly standardized and depends on the specific discourse. In the context of the EU, a region clearly represents a subnational unit, otten coinciding with the local or locality. But in comparative or international affairs research, a region also could be defined as a nationstate-transcending cluster of several countries (for example, see Peters, 1998, pp. 18-19). 11. Typologized as Model A in Figure 1-4. 12. Typologized as Model B in Figure 1-4. 13. Typologized as Model C in Figure 1-4. 14. Alternatively, one could also suggest that the education system (as in the case of R&D and S&T) does not only overlap with innovation but is completely covered by the innovation system.

"Mode 3"

21

REFERENCES Almond, Gabriel A. 1956. Comparative political systems. Journal of Politics 18, 391-409. Ashby, W. Ross. 1965/1952. Design for a Brain. London: Chapman and Hall. Balaam, David N., and Veseth, Michael (Eds.). 2001. Introduction to Political Economy. Upper Saddle River, NJ: Prentice Hall. Barney, Jay B. 2002. Gaining and Sustaining Competitive Advantage. Upper Saddle River. NJ: Prentice Hall. Bomberg, Elizabeth, and Stubb, Alexander. 2003. Introduction, 3-18. in Elizabeth Bomberg and Alexander Stubb (Eds.), The European Union: How Does It Work? Oxford: Oxford University Press. Boulding, K. E. 1985. The World as a Total System. Beverly Hills, CA: Sage. Bozeman, Barry, and Dietz, James S. 2001. Research policy trends in the United States: Civilian technology programs, deiense technology and the deployment of the national laboratories, 47-78, in Phillipe Laredo and Phillipe Mustar (Eds.), Research and Innovation Policies in the New Global Econom:. An International Comparative Analysis. Cheltenham: Edward Elgar. Campbell, David F. J. 2001. Politische Steuerung uber offentliche Forderung universitarer Forschung? Systemtheoretische Uberlegungen zu Forschungs- und Technologiepolitik. Ostenvichischc Zcitschnft fur Pohtikwisscnschaft 30, 425-438. Campbell, David F. J. 2003. The evaluation of university research in the United Kingdom and the Netherlands, Germany and Austria, 98-131, in Philip Shapira and Stefan Kuhlmann (Eds.), Learning from Science and Technology Policy Evaluation. Experiences from the United States and Europe. Cheltenham: Edward Elgar. Campbell, David F. J., and Felderer, Bernhard. 1997. Evaluating Academic Research in Germany. Patterns and Policies. Vienna, Institute for Advanced Studies (1HS), Political Science Series No. 48, http://www.ihs.ac.at/publications/pol/pw_48.pdf. Campbell, David F. J., Liebhart, Karin, Martinsen, Renate, Schaller, Christian, and Schedler, Andreas. 1996. Vorwort, 5, in David F. J. Campbell, Karin Liebhart, Renate Martinsen, Christian Schaller, and Andreas Schedler (Eds.), Die Quahtat der osterreichischen Dcmokratic. Vcrsuche einer Anndherung. Vienna: Manz. Campbell, David F. J., and Schaller, Christian (Eds.). 2002. Dcmohraticquahtat in Osterreich. Zustand und Entwicklungspcrspcktivcn. Opladen, German)-: Leske 4- Budrich. Campbell, David F. J., and Sukosd, Miklos (Eds.). 2002. Feasibility Study for a Quality Ranking of Democracies. Vienna: Global Democracy Award, http://\v\vw.global-democracy-award.org/do\vnloads/feasibility_study.pdf. Campbell, David F. J., and Sukosd, Miklos (Eds.). 2003. Global Quality Ranking of Democracies: Pilot Ranking 2000. Vienna: Global Democracy Award, http://\v\v\v.globaldemocracy-award . org/downloads/folder_a4. pelf. Cooper, Barry, Kornberg, Allan, and Mishler, William (Eds.). 1988. The Resurgence of Conservatism in Anglo-American Democracies. Durham, NO Duke University Press. Crane, George T., and Amawi, Abla (Eds.). 1997. The Theoretical Evolution of International Political Economy. Oxford: Oxford University Press. Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper. Easton, David. 1965a. Political life as a system of behavior, 23-34, in David Easton (Ed.), A Framework for Political Analysis. Englewood Cliffs, NJ: Prentice-Hall.

22

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

Easton, David. 1965b. Theory and behavioral research, 1-22, in David Easton (Ed.), A Framework for Political Analysis. Englewood Cliffs, NJ: Prentice-Hall. Easton, David (Ed.). 1965c. A Framework for Political Analysis. Englewood Cliffs, NJ: Prentice-Hall. Etzkowitz, Henry. 2003. Research groups as "quasi-firms . The invention of the entrepreneurial university. Research Policy 32, 109-121. Etzkowitz, Henry, and Leydesdorff, Loet. 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. Freedom House. 2003. Freedom in the World 2003: The Annual Survey of Political Rights and Civil Liberties. Washington, DC, Freedom House, http://www.freedomhouse.org/ research/index, htm. Freedom House. 2004a. Freedom in the World 2004: Global Freedom Gains amid Terror, Uncertainty. Washington, DC, Freedom House, http://www.freedomhouse.org/ research/survey 2004. htm. Freedom House. 2004b. Freedom in the World Country Ratings (1972 through 2003). Washington, DC, Freedom House, http://www.freedomhouse.org/ratings/mdex .htm. Frieden, Jeffry A., and Lake, David A. (Eds.). 2000. International Political Economy. Perspectives on Global Power and Wealth. Boston: Bedford. Geuna, Aldo, and Steinmueller, W. Edward (Eds.). 2003. Science and Innovation. Rethinking the Rationales for Funding and Governance. Cheltenham: Edward Elgar. Gibbons, Michael, Limoges, Camille, Nowotny, Helga, Scfvwartzman, Simon, Scott, Peter, and Trow, Martin. 1994. The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. London: Sage. Grant, Robert M. 2002. Contemporary Strategy Analysis: Concepts, Techniques, Applications. Oxford: Blackwell. Gripp-Hagelstange, Helga. 1995. Niklas Luhmann: Fine Einfuhrung. Munchen: Wilhelm Fink. Guttel, Wolfgang H. 2003. Die Identifikation strategischer immaterieller Vermogenswcrte im Post-Merger-Integrationsprozess: Ressourcen- und Wissensmanagement hex Mergers-andAcquisitions. Munchen: Rainer Hampp. Held, David, McGrew, Anthony, Goldblatt, David, and Perraton, Jonathan. 1999. Global Transformations. Politics, Economics and Culture. Cambridge: Polity Press. Hooghe, Liesbet, Marks, Gary. 2001. Multilevel Governance and European Integration. Lanham: Rowman & Littlefield Publishers. IMD (International Institute for Management Development). 1996. The World Competitiveness Yearbook 1996. Lausanne: IMD. IMD (International Institute for Management Development). 2003a. The World Competitiveness Scoreboard 2003. Lausanne: IMD, http://wT\vw702.imd.ch/documents/wcy/ content/ranking.pdf. IMD (International Institute for Management Development). 2003b. IMD World Competitiveness Yearbook 2003. Overall Rankings (1999-2003). Lausanne: IMD, hup:// www02.imd.ch/documents/w r cy/content/pastrankmg.pdf. IMD (International Institute for Management Development). 2004. IMD World Competitiveness Yearbook 2004. Lausanne: IMD, http://www02.imd.ch/documents/wcy/wcy BookTour.pdf.

"Mode 3"

23

Kaiser, Robert, and Prange, Heiko. 2004. The reconfiguration of national innovation systems—The example of German biotechnology. Research Policy 33, 395-408. Krippendorff, Klaus (Ed.). 1979. Communication and Control in Society. New York: Gordon and Breach. Kuhlmann, Stefan. 1998. Politikmoderation. Evaluationsverfahren in dcr Forschungs- und Technologicpohtik. Baden-Baden: Nomos. Kuhlmann, Stefan. 2001. Future governance of innovation policy in Europe—Three scenarios. Research Policy 30, 953-976. Laredo, Phillipe, and Mustar, Phillipe (Eds.). 2001. Research and Innovation Policies in the New Global Economy: An International Comparative Analysis. Cheltenham: Edward Elgar. Luhmann, Niklas. 1988a. Neuere Entwicklungen in der Systemtheorie. Mcrkur 42, 292-300. Luhmann, Niklas. 1988b. Soziale Svstemc: Grundriss cincr allgcmcincn Theorie. Frankfurt am Mam: Suhrkamp. Lundvall, Bengt-Ake. 1992. National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. London: Pinter. Lundvall, Bengt-Ake, Johnson, Bjorn, Andersen, Esben Sloth, and Dalum, Bent. 2002. National systems of production, innovation and competence building. Research Policy 31, 213-231. Lundvall, Bengt-Ake, and Tomlinson, Mark. 2002. International benchmarking as a policy learning tool, 203-231, in Maria Joao Rodrigues (Ed.), The New Knowledge Economy in Europe: A Strategy for International Competitiveness and Social Cohesion. Cheltenham: Edward Elgar. Marin, Bernd, and Mayntz, Renate (Eds.). 1991. Policy Networks: Empirical Evidence and Theoretical Considerations. Boulder, CO: Westview Press. Maturana, Humberto R. 1975. The organization of the living: A theory ot the living organization. International Journal of Man-Machine Studies 7, 313-332. Maturana, Humberto R. 1985. Erkennen: Die Organisation und Vcrkorpcrung von Wirklichkeit. Braunschweig: Vieweg. Maturana, Humberto R., and Varela, Francisco J. 1979. Autopoiesis and Cognition: The Realization of the Living. Boston: D. Reiciel. Maturana, Humberto R., Varela, Francisco J., and Uribe, R. 1975. Autopoiesis: The organization of living systems, its characterization and a model. Biosvstcms 5. 187-196. Mayntz, Renate. 1988. Funktionelle Teilsysteme in der Theorie sozialer Differenzierung, 11-44, in Renate Mayntz, Bernd Rosewitz, Uwe Schimank, and Rudolf Stichweh (Eds.), Differenzierung und Versclbstandigung: Zur Entwicklung gcscllschaftlichcr Teilsysteme. Frankfurt am Main: Campus. Mayntz, Renate, Rosewitz, Bernd, Schimank, Uwe, and Stichweh, Rudolf (Eds.). 1988. Differenzierung und Versclbstandigung: Zur Entwicklung gcscllschaftlichcr Teilsysteme. Frankfurt am Main: Campus. Mowery, David C. 2001. The United States national innovation system after the Cold War, 15-46, in Phillipe Laredo and Phillipe Mustar (Eds.), Research and Innovation Policies in the New Global Economy. An International Comparative Analysis. Cheltenham: Edward Elgar. National Science Board. 2002. Science and Engineering Indicators 2002. Vols. 1 and 2. Arlington, VA: National Science Foundation.

24

INNOVATION NETWORKS AND KNOWLEDGE CLUSTERS

Nelson, Richard R. (1990). Capitalism as an engine of progress. Research Policy 19, 193-214. Nelson, Richard R. 1993. National Innovation Systems: A Comparative Analysis. Oxford: Oxford University Press. Nowotny, Helga, Scott, Peter, and Gibbons, Michael. 2001. Re-Thinking Science. Knowledge and the Public in an Age of Uncertainty. Cambridge: Polity Press. Nowotny, Helga, Scott, Peter, and Gibbons, Michael. 2003. Mode 2 revisited: The new production of knowledge. Minerva 41, 179-194. OECD. 1994. The Measurement of Scientific and Technological Activities. Proposed Standard Practice for Surveys of Research and Experimental Development. Frascati Manual 1993. Pans: OECD. OECD. 2002. Education Policy Analysis. Paris: OECD. OECD. 2003a. Basic Science and Technology Statistics. Paris: OECD. OECD. 2003b. Education at a Glance. OECD Indicators. Pans: OECD. OECD. 2003c. Main Science and Technology Indicators. Paris: OECD. Peters, B. Guy. 1998. Comparative Politics. Theory and Methods. London: Macmillan. Pettigrew, Andrew, Thomas, Howard, and Whittington, Richard (Eds.). 2002. Handbook of Strategy and Management. London: Sage. Pfeffer, Thomas. 2001. Das "zirkulcirc Fragen" als Forschungsmethode zur Luhmannschcn Systemthcorie. Heidelberg: Carl-Auer-Systeme Verlag. Rosewitz, Bernd, and Schimank, Uwe. 1988. Verselbstandigung und politische Steuerbarkeit gesellschaftlicher Teilsysteme, 295-329, in Renate Mayntz, Bernd Rosewitz, Uwe Schimank, and Rudolf Stichweh (Eds.), Differenzierung und Versclbstandigung: Zur Entwicklung gesellschaftlicher Teilsysteme. Frankfurt am Mam: Campus. Roth, Gerhard, Schwegler, Helmut (Eds.). 1981. Self-Organizing Systems: An Interdisciplinary Approach. Frankfurt am Main: Campus. Sabatier, Paul A. 1999. Theories of the Policy Process. Boulder, CO: Westview Press. Sabatier, Paul A., and Jenkins-Smith, Hank C. (Eds.). 1993. Policy Change and Learning: An Advocacy Coalition Approach. Boulder, CO: Westview