Understanding the Relationship Between Networks and Technology, Creativity and Innovation 9781781904909, 9781781904893

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UNDERSTANDING THE RELATIONSHIP BETWEEN NETWORKS AND TECHNOLOGY, CREATIVITY AND INNOVATION

TECHNOLOGY, INNOVATION, ENTREPRENEURSHIP AND COMPETITIVE STRATEGY Series Editor: Barak S. Aharonson Recent Volumes: Volume 1:

Newgames – Strategic Competition in the PC Revolution – Edited by J. Steffens

Volume 2:

Entrepreneurship: Perspectives on Theory Building – Edited by I. Bull, H. C. Thomas and G. Willard

Volume 3:

Dynamics of Competence-Based Competition – Edited by R. Sanchez, A. Heene and H. Thomas

Volume 4:

Corporate Strategy, Public Policy and New Technologies – Edited by X. Dai

Volume 5:

Drugs to Market – Edited by W. C. Bogner

Volume 6:

The Defense Industry in the Post-Cold War Era – Edited by G. I. Susman and S. O’Keefe

Volume 7:

The Strategic Management of High Technology Contracts: The Case of CERN – Edited by M. Nordberg

Volume 8:

Creating Value with Entrepreneurial Leadership and Skill-Based Strategies – Edited by W. C. Schulz and C. W. Hofer

Volume 9:

Silicon Valley North: A High-Tech Cluster of Innovation and Entrepreneurship – Edited by L. V. Shavinina

Volume 10:

Astute Competition: The Economics of Strategic Diversity – Edited by P. Johnson

Volume 11:

Constructing Industries and Markets – Edited by J. Porac and M. Ventresca

Volume 12:

The Take-off of Israeli High-Tech Entrepreneurship During the 1990s – Edited by A. Fiegenbaum

TECHNOLOGY, INNOVATION, ENTREPRENEURSHIP AND COMPETITIVE STRATEGY VOLUME 13

UNDERSTANDING THE RELATIONSHIP BETWEEN NETWORKS AND TECHNOLOGY, CREATIVITY AND INNOVATION EDITED BY BARAK S. AHARONSON Recanati Business School, Tel Aviv University, Israel

URIEL STETTNER Recanati Business School, Tel Aviv University, Israel

TERRY L. AMBURGEY Rotman School of Management, University of Toronto, Canada

SHMUEL ELLIS Recanati Business School, Tel Aviv University, Israel

ISRAEL DRORI Recanati Business School, Tel Aviv University, Israel

United Kingdom – North America – Japan India – Malaysia – China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2013 Copyright r 2013 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-78190-489-3 ISSN: 1479-067X (Series)

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CONTENTS DEDICATIONS

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LIST OF CONTRIBUTORS

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INTRODUCTION

xi PART I

CHAPTER 1 COEVOLUTIONARY PERSPECTIVE OF INDUSTRY–NETWORK DYNAMICS Leonid Bakman and Amalya L. Oliver CHAPTER 2 ISRAEL’S KNOWLEDGE-INTENSIVE SECTORS: INNOVATION, NETWORKS AND REGIONS Amalya L. Oliver and Noam Frank

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PART II CHAPTER 3 THE EVOLUTION OF RESEARCH COLLABORATION NETWORKS AND THEIR IMPACT ON FIRM INNOVATION OUTPUT Irem Demirkan and David L. Deeds

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CHAPTER 4 AN EXPLORATORY STUDY OF THE ROLE OF PUBLISHING INVENTORS IN NANOTECHNOLOGY Gino Cattani and Daniele Rotolo

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CHAPTER 5 THE INTERDEPENDENCIES OF FORMAL AND INFORMAL NETWORK STRUCTURE AND THE EXPLORATION OF NEW TECHNOLOGICAL OPPORTUNITIES AMONG GEOGRAPHICALLY DISPERSED FIRMS Daniel Tzabbar and Alex Vestal

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CHAPTER 6 THE DUALITY OF KNOWLEDGE NETWORKS: THE IMPACT OF PRODUCTION AND USAGE NETWORKS ON ACADEMIC CITATIONS Atul Nerkar and Nandini Lahiri

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CHAPTER 7 THE COSTS OF CREATING NETWORK RELATIONS AND THE IMPLICATIONS FOR FIRM PERFORMANCE – THE CASE OF HIGH TECHNOLOGY FIRMS Niron Hashai

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CHAPTER 8 REGIONAL NETWORKS, ALLIANCE PORTFOLIO CONFIGURATION, AND INNOVATION PERFORMANCE Suleika Bort, Marie Oehme and Florian Zock

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ABOUT THE EDITORS

257

ABOUT THE AUTHORS

259

To Mankind

LIST OF CONTRIBUTORS Barak S. Aharonson

Tel-Aviv University, Recanati Business School, Department of Strategy, Tel Aviv, Israel

Leonid Bakman

Israel Science Technology and Innovation Policy Institute, Israel

Suleika Bort

University of Mannheim, Department of Strategic and International Management, Mannheim, Germany

Gino Cattani

New York University, Stern School of Business, Department of Management & Organizations, New York, NY, USA

David L. Deeds

The University of St. Thomas, Opus College of Business, Morrison Center for Entrepreneurship, Minneapolis, MN, USA

Irem Demirkan

Suffolk University, Sawyer Business School, Boston, MA, USA

Noam Frank

The Hebrew University, Department of Sociology and Anthropology, Mt. Scopus, Jerusalem, Israel

Niron Hashai

Hebrew University, School of Business Administration, Mt. Scopus, Jerusalem, Israel

Nandini Lahiri

Fox School of Business, Temple University, Philadelphia, PA, USA

Atul Nerkar

University of North Carolina at Chapel Hill, Kehan-Flagler Business School, Chapel Hill, NC, USA

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LIST OF CONTRIBUTORS

Marie Oehme

University of Mannheim, Department of Strategic and International Management, Mannheim, Germany

Amalya L. Oliver

The Hebrew University, Department of Sociology and Anthropology, Mt. Scopus, Jerusalem, Israel

Daniele Rotolo

SPRU (Science and Technology Policy Research), University of Sussex, Sussex, UK

Uriel Stettner

Tel-Aviv University, Recanati Business School, Department of Strategy, Tel Aviv, Israel

Daniel Tzabbar

LeBow College of Business, Drexel University, Huntingdon Valley, PA, USA

Alex Vestal

LeBow College of Business, Drexel University, Huntingdon Valley, PA, USA

Florian Zock

University of Mannheim, Mannheim, Germany

INTRODUCTION In a world characterized by globalization and rapid social change, strategic management of innovation is crucial for both producers and consumers of new technology. Recent research highlights the ever-increasing importance of social networks for the generation of technological innovation and the conversion of knowledge into commercial products and services (e.g., Abrahamson & Rosenkopf, 1997; Ahuja, 2000; Amburgey, Al-Laham, Tzabbar, & Aharonson, 2008; Capaldo, 2007; Gilsing, Nooteboom, Vanhaverbeke, Duysters, & van den Oord, 2008; Guler & Nerkar 2012; Powell, Koput, & Smith-Doerr, 1996; Tsai, 2001). One important form of social networking is the web of interorganizational relationships that is created in many sectors of economic life. In some cases these networks are deliberately designed and in other cases they emerge from the collective action of many organizations pursuing individual interests. Networks foster creativity and innovation at the individual, group, and organization levels, and also enable the dissemination of knowledge, innovations, and technologies. The 13th volume of the series entitled ‘‘Technology, Innovation, Entrepreneurship and Competitive Strategy’’ is devoted to research aimed at understanding the implications of networks to creativity, innovation, and technology. The ability to cultivate creativity within an organization has long been at the focus of research as it is considered an important firm capability. Creativity is defined as the creation of original and appropriable ideas, innovations, products and processes (Amabile, 1979, 1983, 1996; Mumford, 2003). In order to be creative one must be both novel in her/his thinking and also come up with something practical and valuable. In the case of organizations the value depends on how these novel thoughts/ideas/products benefit the performance of the organization. For example, Mumford and Gustafson (1988) explained that creative thinking allows people to solve problems more effectively. Flach (1990) argued that creative thinking enhances flexibility of decisions. Creative individuals may thus be able to cope better with changing environments (Runco, 2004) and be more receptive to opportunities. Organizations facing a competitive environment rely xi

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on creative thinking for their survival and at times even enjoy a competitive edge. Moreover, other research has identified innovations and technologies as one type of output of the creative process. Such innovations provide operational or product improvements and can be categorized into radical versus incremental (Anderson & Tushman, 1990; Green, Gavin, & Smith, 1995), competence enhancing versus competence destroying (Tushman & Anderson, 1986) and architectural versus component innovation (Henderson & Clark, 1990). Regardless of their classification, technological innovations have been shown to provide a significant competitive advantage (Barney, 1995; Peteraf, 1993). An organizations’ intellectual property is a valuable asset and is one of the core determinants of firm survival. One of the key elements of creativity and innovation is knowledge. The greater the knowledge base and scope the more individuals and organizations can begin to think of newer ways to utilize this knowledge. In this regard knowledge diversity has been highlighted as having a significant impact on creative thinking (e.g., Liu, Chiu, & Chiu, 2010). Yet knowledge takes time to accumulate and is hard to store and recall at will. Social networks allow firms to access different types and ranges of knowledge. In fact, two ways can be identified by which networks can provide a support mechanism in the creative process. The first is the view of networks as a mechanism that serves the organization and the individuals to seek assistance, encouragement, and reinforcement. Accordingly, individuals may leverage their network ties within and beyond organizational boundaries to access such resources in attempt to not only improve the decision-making process but also to corroborate their past choices and maintain their course of action. Two main theories about creativity suggest that creativity is a social process (Amabile, 1983, 1985; Perry-Smith & Shalley, 2003; Woodman, Sawyer, & Griffin, 1993). Previous research indicates that a work environment with supportive supervisors and group interactions will encourage creativity in the work place to flourish. Employees who encounter other creative individuals or have creative role models are often praised for their creativity in the workplace (Simonton, 1984, 1997). Creativity is also thought to arise from contact with diverse array of other people (e.g., Amabile, 1996; Ford, 1996; Woodman et al., 1993). The second influence of networks is in the realm of knowledge accessibility. Knowledge and information are the basic building blocks of new ideas that may be circulated within and across organizations through social network ties. Granovetter (1973, 1983) in his seminal work on

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strength of ties explain that social networks facilitate the transfer of knowledge. Strong ties which entail high levels of trust can facilitate a greater flow of knowledge among the players of the network. However, as strong ties involve frequent interactions among the same players, such network ties often yield redundant information. Strong ties can foster similar thought patterns amidst actors and thus lead to a less propitious exchange of ideas, stifling what would otherwise be a creative process. Weak ties, on the other hand, are important for creativity as a lower frequency of interaction potentially contributes to the exchange of new information during the infrequent interactions. However, these infrequent interactions together imply lower levels of tryst and may yield less promising information which is ultimately worthless to the individual in his/her endeavors toward achieving a creative thought/product. In addition to studies of weak versus strong ties research on social networks examine network characteristics such as centrality, degree, betweenness, closeness, and eigenvector. In addition, this body of research has studied the antecedents and consequences of node embbededness, the different types of ties (e.g., bridging ties), the role of network components (e.g., cliques and clusters within the network), and overall network characteristics (e.g., cohesion, closed vs. open networks) on the distribution of knowledge and the creation of new technological innovations (e.g., PerrySmith, 2006) Understanding how knowledge flows within a network can help us understand better the role networks play in facilitating and contributing to individual and organizational creativity. Social networks have also been studied in the focus of adoption or diffusion of innovations and technologies (Burkhardt, 1994). The percolation process may differ through the technology life cycle, which is often described as an S-curve. At the beginning of the life cycle the technology is at its early stage, mostly unknown, and any standards and general values are not yet established. Later, as the technological value derivatives and standards are established, more innovations develop at a nearly exponential rate. The life cycle reaches a point where there is over crowding with new incremental innovations adding little value to the development of the technology. Researchers have argued that the adoption rate of the technology and innovation follow the same S-curve pattern (Abrahamson & Rosenkopf, 1993; Rogers, 2005). In so doing, they showed that understanding the network structure and characteristics as well as the connectivity attributes between the actors of the network can help facilitate increased understanding of the adoption magnitude and rate of technologies (Abrahamson & Rosenkopf, 1997).

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In summary, networks at both the individual and organizational levels play a significant role in determining the percolation of knowledge which fosters creativity and innovations. Accordingly, network entities play a significant role in the diffusion and adoption of technologies and innovations. The following six book chapters highlight the tremendous evolution and contribution of network theory, but also emphasize the need for more research that needs to be done in this field. The book is divided into two parts. Part I consists of two chapters that offer perspectives on the evolution of networks within geographic and industry boundaries. These two chapters deal with conceptual frameworks underlying networks of individuals and organizations alike. Part II contains six chapters that center on the performance implications of networks. Focusing on the interorganizational, organizational, and intra-organizational levels of analysis, these chapters offer insights on the significance of networks to firms’ innovation capacity, creative output, and ultimately financial performance. Each chapter takes a distinct and autonomous perspective on the relationship between networks and either innovation, technology, or creativity. Together they portrait the variety and complexity of the dimensions describing agent based networks. In so doing, these writings capture selected core components of contemporary network research with each offering not only a rigorous treatment of their topic but also directions for future research. Part I of the book deals with geographic and industry boundaries. In Chapter 1, Leonid Bakman and Amalya L. Oliver depict a ‘‘Coevolutionary Perspective of Industry–Network Dynamics.’’ In their theoretical treatment, the authors offer insights into how networks and industries coevolve. This chapter presents a theoretical framework interweaving interfirm network evolution to the evolution of an industry’s life cycle. More specifically, the authors employ both structural and relational network perspectives to questions some of the basic tenets inherent to the industry life-cycle model. In so doing, they challenge the reader to consider a more complex nexus in which industry specific evolutionary patterns impact the structure of network relations, which in turn lead to diversification in the sources of innovation and to variation in the patterns of industrial evolution. In Chapter 2, ‘‘Israel’s Knowledge-Intensive Sectors: Innovation, Networks and Regions,’’ Amalya L. Oliver and Noam Frank deal conceptualize the notion of ‘small country interregional homogeneity effect’. Focusing on the knowledge intensive entrepreneurial firms within the national boundaries of the State of Israel, the authors present a case study for examining

Introduction

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sector-based differences and ‘‘small country’’ regional patterns. This research sets of by illustrating similarities and differences between entrepreneurial firms operating in Life sciences, information technology and clean-tech sectors. Then, having identified firm level network structures, funding patterns and innovation proxies within these knowledge intensive sectors, the authors establish a link between geographical distribution of these firms across regions to different levels of knowledge concentration and organizational homogeneity. More specifically, the authors demonstrate strong similarities in the patterns of firm and network characteristics, yet associated sector-level differences with sector-specific attributes. Part II of the book aims at lining networks to performance. In chapter 3, Irem Demirkan and David L. Deeds explore how the evolution of inter-firm research collaboration networks impact firm level innovation performance. Focusing on the biotechnology industry, this chapter demonstrates on a sample of 482 biotechnology firms over a period of 17 years (1990–2006) how the structure and dynamics of these firms’ interorganizational research collaboration ego-networks change over time and how this evolution affects focal firms’ subsequent innovative output. The authors demonstrate positive innovation performance associated with increasing ego-network size, the growth of the ego-network, and the inclusion of new members in the egonetwork significant management challenges. In Chapter 4 ‘‘An Exploratory Study of the Role of Publishing Inventors in Nanotechnology,’’ Gino Cattani and Daniele Rotolo offer insight into the interaction between science and technology in the nanotechnology industry. Focusing on individuals who are central to bridging the collaborative networks between authors (coauthorship network) and inventors (coinvention network), the authors study how the structural position of such publishing inventors affects the quality of the inventions to which they contribute. This study posits important theoretical and managerial implications of retaining publishing inventors in that their absence would result in a fragmented network, disintegrated coauthorship, and coinvention networks, and inventions of lesser quality. In Chapter 5, ‘‘The Interdependencies of Formal and Informal Network Structure and the Exploration of New Technological Opportunities among Geographically Dispersed Firms,’’ Daniel Tzabbar and Alex Vestal address an inherent dilemma in extant research on interdependencies between formal and informal network structures in geographically dispersed R&D. The authors suggest that firms that seek to benefit from the decentralization associated with disperse R&D must align it with an informal structure that enhances organizational members’ motivation to share and assimilate their

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unique knowledge and skills. Focusing on 424 U.S. biotechnology firms between 1973 and 2003, this study demonstrates that the higher the social network density among R&D members, the more likely geographic disparity is to affect exploration, a condition that is negatively affected under increasing in power asymmetries. Chapter 6, ‘‘The Duality of Knowledge Networks: The Impact of Production and Usage Networks on Academic Citations,’’ Atul Nerkar and Nandini Lahiri argue that academic research can be analyzed by viewing the production or usage dimension of their associated knowledge networks. The study analyzes how the absolute and relative positioning of authors in such networks help the diffusion of their research. Looking at papers published in the top five management journals between 1993 and 1997, Atul Nerkar and Nandini Lahiri show that the position that authors occupy in the usage networks and production networks is a strong determinant of future citations received by a paper in these five journals. The authors discuss the direct impact on knowledge creation, dissemination, and recognition efforts of authors. Interestingly enough, the study suggests diseconomies of prominence in that authors who have gained prominence in their usage network, decrease their likelihood of future citations as they work on increasing their prominence in their production network. In contrast, diseconomies of prominence develop as authors gain significant prominence in their production network such that increases in prominence in the usage network dampen increase in future citations. In Chapter 7, ‘‘The Costs of Creating Network Relations and the Implications for Firm Performance – The Case of High Technology Firms,’’ Niron Hashai looks at the benefits of network relations for firms’ competitive advantage. The study focuses on the sunk costs associated with engaging in network specific relations. Adopting a cost-benefit approach this research shows that short term efforts in creating network relations may hamper performance. In contrast, in the long term, network relations enhance firm performance. The author argues that the costs of creating network relations may mask the benefits of such relations, suggesting that networks can be a competitive risk for firms in cases where network relations unexpectedly terminate. These negative performance implications of short term network participation increase as the technological intensity strengthens suggesting that high technology firms may be more exposed to risks associated with the allocation of resources to developing network specific relations. In the last Chapter 8, ‘‘Regional Networks, Alliance Portfolio Configuration, and Innovation Performance,’’ Suleika Bort, Marie Oehme, and

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Florian Zock investigate the role of regional networks within the context of alliance portfolio configuration. Focusing on the link between regional network density, alliance portfolio configuration, and its contribution to firm innovation performance, the authors examine how regional network density and alliance partner diversity influences firm level innovation output. Using an in depth analysis of 1,233 German biotechnology, they argue that regional network density and alliance partner diversity has an inverted U-shape effect on firm level innovation performance. However, overall network status as well as alliance partner diversity negatively moderates the link between regional network density and innovation output. Barak S. Aharonson Uriel Stettner Editors

REFERENCES Abrahamson, E., & Rosenkopf, L. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. The Academy of Management Review, 18(3), 487–517. Abrahamson, E., & Rosenkopf, L. (1997). Social network effects on the extent of innovation diffusion. A Computer Simulation Organization Science, 8, 289–309. Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455. Amabile, T. M. (1979). Effects of external evaluation on artistic creativity. Journal of Personality and Social Psychology, 37, 221–233. Amabile, T. M. (1983). The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45, 357–377. Amabile, T. M. (1985). Motivation and creativity: Effects of motivational orientation on creative writers. Journal of Personality and Social Psychology, 48, 393–399. Amabile, T. M. (1996). Creativity in context. Boulder, CO: Westview Press. Amburgey, T. L., Al-Laham, A., Tzabbar, D., & Aharonson, B. (2008). The structural evolution of multiplex organizational networks: Research and commerce in biotechnology. Advances in Strategic Management, 25, 171–209. Anderson, P., & Tushman, M. L. (1990). Technological discontinuities and dominant designs: A cyclical model of technological change. Administrative Science Quarterly, 35(4), 604–633. Barney, J. B. (1995). Looking inside for competitive advantage. Academy of Management Perspectives, 9(4), 49–61. Burkhardt, M. E. (1994). Social interaction effects following a technological change: A longitudinal investigation. Academy of Management Journal, 869–898. Capaldo, A. (2007). Network structure and innovation: The leveraging of a dual network as a distinctive relational capability. Strategic Management Journal, 28, 585–608.

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Flach, F. (1990). Disorders of the pathways involved in the creative process. Creativity Research Journal, 3, 158–165. Ford, C. M. (1996). A theory of individual creative action in multiple social domains. Academy of Management Review, 1112–1142. Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10), 1717–1731. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. Granovetter, M. S. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201–233. Green, S., Gavin, M., & Smith, L. (1995). Assessing a multidimensional measure of radical innovation. IEEE Trans. Engineering. Management, 42(3), 203–214. Guler, I., & Nerkar, A. (2012). The impact of global and local cohesion on innovation in the pharmaceutical industry. Strategic Management Journal, 33, 535–549. Henderson, R. M., & Clark, K. B. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9–30. Liu, C., Chiu, S., & Chiu, C. (2010). Intranetwork relationships, creativity, knowledge diversification, and network position. Social Behavior and Personality, 38(9), 1173–1190. Mumford, M., & Gustafson, S. (1988). Creativity syndrome: integration, application, and innovation. Psychological Bulletin, 103, 27–43. Mumford, M. D. (2003). Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15, 107–120. Perry-Smith, J. E. (2006). Social yet creative: the role of social relationships in facilitating individual creativity. Academy of Management Journal, 49(1), 85–101. Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. The Academy of Management Review, 28(1), 860–875. Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14, 179–191. Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–146. Rogers, E. M. (2005). Diffusion of innovations (5th ed.). New York, NY: Free Press. Runco, M. A. (2004). Creativity. Annual Review of Psychology, 55, 657–687. Simonton, D. K. (1984). Artistic creativity and interpersonal relationships across and within generations. Journal of Personality and Social Psychology, 46(6), 1273. Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104, 66–89. Tsai, W. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy of Management Journal, 44, 996–1004. Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environment. Administrative Science Quarterly, 31(3), 439–465. Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a theory of organizational creativity. Academy of Management Review, 293–321.

PART I

CHAPTER 1 COEVOLUTIONARY PERSPECTIVE OF INDUSTRY–NETWORK DYNAMICS Leonid Bakman and Amalya L. Oliver ABSTRACT The chapter presents a theoretical framework that deals with the basic question of how networks and industries coevolve. We draw upon the structural and relational perspectives of networks to theorize about changes occurring in interfirm networks over time and the coevolutionary linkage of these changes to the industry life cycle. We further extend the widely accepted industry life cycle model by claiming that industryspecific evolutionary patterns impact the structure of the network’s relations, which in turn lead to diversification in the sources of innovation and to variation in the patterns of industrial evolution. Keywords: Industry evolution; industry life cycle; network embeddedness; structural embeddedness; relational embeddedness

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 3–36 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013004

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INTRODUCTION The aim of the current chapter is to widen the theoretical underpinnings of research into the basic question of how networks and industries coevolve (Nohria, 1992; Madhavan, Koka, & Prescott, 1998). More specifically, the chapter concentrates on the sources of innovation which leads industrial evolution and its coevolutionary relations with supply network development. The term ‘‘supply network’’ refers to firms that collaborate in order to generate the supply side of the industry (Kogut, Shan, & Walker, 1992). Over the past two decades, ‘‘Organizational Network’’ research has broadened into a few different theoretical and analytical perspectives (Oliver & Ebers, 1998). Generally speaking, these different streams can be characterized as belonging to one of two analytical levels: the whole network or firm levels. The network perspective introduces issues that are related to network evolution (Aldrich, 1976, 1999), types of network relationships (Granovetter, 1973; Uzzi, 1996, 1997), and the forms and structures of networks (Burt, 1992; Bothner, 2003; Powell, 1990; Powell, White, Koput, & Owen-Smith, 2005). Research undertaken from a firm perspective concentrates mostly on networks as an external resource (Lavie, 2006; Pfeffer & Salancik, 1978) and focuses on trying to understand the impact of participation in exchange networks on the innovativeness of the firm and its performance (e.g., Ahuja, 2000; Baum, Calabrese, & Silverman, 2000; Brass, Galaskiewicz, Greve, & Tsai, 2004; Mizruchi, Stearns, & Marquis, 2006; Stuart, 2000). Despite the wide attention given in the literature to networks as an analytical level, there is still a need for comprehensive theories of network evolution and change (Knoke, 2001; Madhavan et al., 1998; Nohria, 1992; Parkhe, Wasserman, & Ralston, 2006). As suggested by Kilduff, Tsai, and Hanke (2006, p. 1032), ‘‘To look ahead, we see organizational networks as complex adaptive systems that exhibit both persistence and change.’’ To enhance understanding of networks as adaptive systems, this chapter aims to unfold their coevolutionary logic1 (de Rond & Bouchiki, 2004; Hoffmann, 2007; Kim, Oh, & Swaminathan, 2006; Koka, Madhavan, & Prescott, 2006; Koza & Lewin, 1998; Madhavan et al., 1998; Murmann, 2003; Walker, Kogut, & Shan, 1997). We argue that changes in the industrial settings of the industry life cycle (ILC) (Abernathy & Utterback, 1978; Klepper, 1996, 1997) change the sources of innovation and the pool of opportunities and objectives for interfirm collaboration. We draw upon the structural perspective (Wellman, 1982) and the relational perspective

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(Granovetter, 1973) of networks to theorize about the change that occurs to supply networks over time and the coevolutionary linkage of this change to the ILC. The chapter is organized as follows: In the next section, we explain the main argument of the chapter and provide some definitions for our theoretical framework. We then review the ILC model and demonstrate the specific competitive objectives of each of its stages. Next, we discuss industry–network coevolutionary dynamics, concentrating on the changing levels of the structural and relational embeddedness of the network, the capacity for change, and their impact on the ILC. We extend the ILC model by claiming that industry-specific evolutionary patterns impact the structure of relations within the supply network, and these changes may in turn lead to variation in the patterns of industrial evolution. The fifth section discusses the theoretical implications of our model and put forward a number of testable propositions. We conclude with a summary and a discussion of the strengths and weaknesses of our approach.

THEORETICAL BACKGROUND Networks are usually defined as ‘‘any collection of actors (NZ2) that pursue repeated, enduring exchange relations with one another’’ (Podolny & Page, 1998, p. 58). In this chapter, we narrow the scope of the research to what we call ‘‘supply networks,’’ which refers to networks based on the collaborations between firms that generate the supply side of the industry (Kogut et al., 1992). In general, networks are established to develop competitive advantage (Dyer & Singh, 1998; Gulati, Nohria, & Zaheer, 2000; Lavie, 2006). Through establishing networks of exchanges, the collaborating firms combine their resources and achieve both complementarities (Teece, 1986) and reduced time to market (Baum et al., 2000). In that sense, networks contribute to the achievement of organizational objectives that could not have been achieved individually (Human & Provan, 1997, Powell, Koput, & Smith-Doerr, 1996). Yet as suggested by Kogut, Shan, and Walker, ‘‘The make-or-cooperate decision is made in the context of a concrete network as opposed to an abstract market. The network is not, however, simply given, but is itself emergent over timey Although firms make boundary decisions as individual agents and in response to the information available, the availability of information is influenced by the cumulative patter of cooperation in the industry represented in the structure of the network’’ (1992, p. 349).

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Drawing on Koza and Lewin (1998), we suggest that industries and networks should be considered as interdependent structures that coevolve as an outcome of mutual influence (Baum & Singh, 1994; Kauffman, 1993; McKelvey, 1997; Murmann, 2003). Technological advances and increasing environmental complexity lead to a reality in which industrial breakthroughs (or paradigm shifts) are not solely a derivative of a linear innovation process or the result of individual scientific achievements or entrepreneurial initiatives; they result from a complex integration of factors such as scientific research, business entrepreneurship, national policies, and culture. Such interactions cross multiple levels of analysis and lead to a coevolution of industries and networks over time. Thus, supply networks do not evolve in isolation (Aldrich, 1976, 1999), but coevolve together with industrial evolution (Walker et al., 1997; de Rond & Bouchiki, 2004). Namely, changes in the ILC, as will be further discussed, form the scope of opportunities available to firms in terms of interfirm collaboration and the motivation for these collaborations.

INDUSTRY LIFE CYCLE Theories of industrial evolution suggest that any industry has a finite life span which ideally portrays the four evolutionary stages (fluid, growth, maturity, and decline) of the ILC (Abernathy & Utterback, 1978; Gort & Klepper, 1982; Klepper, 1996; Klepper & Graddy, 1990; Utterback, 1994). Each phase is characterized by its own distinctive paradigm of competition (Dosi, 1982; Nelson & Winter, 1982) and delineates a set of options and opportunities in the light of which the network-industry coevolutionary process takes place (McKelvey, 1997; Murmann, 2003). Obviously, the industry evolutionary path is not as deterministic as indicated by the model. Many fast-growing industries will not survive throughout the whole ILC, which may be discontinued at any stage of the cycle (McGahan, 2000, 2004) for a wide range of reasons.2 Yet, in the effort to link industrial and network dynamics, we find it essential to comprehend the dynamics of ILC through a basic evolutionary model, which offers a coherent and well-defined theoretical trajectory framework. As such, we base our model on the four common stages of the ILC that correspond to key changes in the characteristics of industrial innovation, market structure, and competition over time. In that sense, each stage in the ILC represents a specific contextual framework that coevolves with the supply network. The core of our argument is that each stage of

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the ILC is associated with specific competitive and structural settings which could be characterized as a typical source of innovation. These ideally lead to different network characteristics that, according to coevolutionary logic, influence the progress of the ILC itself. In order to comprehensively link the ILC to network dynamics, the next paragraphs provide a short description of each stage in the ILC. The fluid stage of the ILC involves the inception of a new competitive environment and usually starts with the launching of a new product or technology. Indeed, the literature indicates that most efforts to create new industries fail (Aldrich, 1999; Aldrich & Fiol, 1994; Audretsch, 1995). Yet, if adopted by the market, the new product or technology will lead to the emergence of a new industrial ecosystem in which new interests and capabilities, customer needs, technological practices, and business models will jointly evolve. At this point, the demand is embryonic and the sources of innovation are dispersed, the production systems are unsettled, and the technological and business concepts are dispersed and emerging (Abernathy & Utterback, 1978; Christensen & Rosenbloom, 1995). Experimentation with different technologies is extensive (March, 1991; Nooteboom, 2000) and improvements to technical efficiency are limited because firms opt to operate at low volumes and under unestablished operational standards. In such extreme explorative settings, where the main efforts are directed toward accumulation of a critical mass of capabilities, assets, and resources, development of new knowledge is intensive and the potential rents are entrepreneurial (McEvily & Zaheer, 1999; Schumpeter, 1934). The growth stage of the ILC follows on from the successful introduction of a new product (or technology), which opens the door to accelerated industrial growth. Gradually, the earlier exploration begins to converge around one (or a few) ‘‘dominant design(s)’’ (Abernathy & Utterback, 1978; Dosi, 1982; Klepper, 1996, 1997), common industrial practices and a shared, newly established, body of knowledge (Winter, 1984). Here, the innovative focus turns from product to process innovation (Abernathy & Utterback, 1978) and the locus of competition shifts from accumulation of a critical mass of resources and demand to establishment of a dominant market position within the evolving industrial boundaries. The mature stage of the ILC begins once the slope of the industrial growth curve decreases, and is characterized by the maturation of technological, conceptual, and operational paradigms. At this stage, the leadership of the industry is usually stabilized (Klepper, 1996), the explorative activities of the competing firms are reduced, and exploitative activities are dominant

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(March, 1991). Innovation becomes mostly incremental (Tushman & O’Reilly, 1996) and the competition shifts from product innovation to market diversification (Porter, 1985) and economies of scale and scope (Teece, 1986; Tushman & Anderson, 1986; Vernon, 1966). While, at the growth stage, firm expansion is accelerated exponentially by industrial growth, at the mature stage of the ILC, firms preserve their expansion by a combination of penetration to new segments of the market and mergers and acquisitions. The decline stage of the ILC begins when consumers shift their preferences to other products, which decreases sales volumes (Ghemawat & Nalebuff, 1985, 1990; Lieberman, 1990). At this stage, industrial renewal does not keep pace with new innovations or technological changes. Here, firms tend to maximize their exploitative efforts within the declining industry and explore alternative strategic directions that lead to turning of innovative focus toward alternative emerging areas of activity. In summary, the characteristics of the competitive settings change throughout the ILC. As suggested by Burt, ‘‘organizations are not the source of action so much as they are the vehicles for structurally induced action’’ (1992, p. 5). On the one hand, each stage of the ILC focuses on specific competitive objectives and collaborative structures, which in turn delineate the potential network characteristics. On the other hand, the development of the supply network plays a role in the evolutionary path of the industry. In the next section, we will elaborate on the industry–network coevolutionary dynamics.

THE INDUSTRY–NETWORK COEVOLUTIONARY DYNAMICS Evolutionary theory in organization science advocates developing a coevolutionary approach to study mutual development processes (Lewin, Long, & Carroll, 1999; McKelvey, 1997). As noted by Lewin and Volberda (1999, p. 521), ‘‘the coevolution lens has the potential for integrating microand macro-level evolution within a unifying framework, incorporating multiple levels of analyses and contingent effects, and leading to new insights, new theories, new empirical methods, and new understanding.’’ Murmann’s (2003) coevolutionary theory, for example, links industrial, technological, and institutional dynamics with organizational evolution. In his work, Murmann extends the common perspective of coevolution, which

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focuses on the parallel development of two entities, to the phenomenon of multiple parties jointly evolving. Applying coevolutionary logic to the area of strategic alliances, Koza and Lewin (1998) aimed at understanding the coevolution of the firm and its environment. Namely, industries and firms should be considered as interdependent structures that coevolve over time as an outcome of mutual influence. Interorganizational research within the organizational and sociological literature introduces issues that are related to the nature of network relationships (Granovetter, 1973; Uzzi, 1996, 1997) and the forms and structures of networks (Bothner, 2003; Burt, 1992; Powell, 1990; Powell et al., 2005). Combining these two perspectives, the concept of embeddedness refers to the process by which social relations shape economic actions and usually focuses on the network structure and the relations between its members.3 There is a consensus that the degree to which a firm is embedded in a network of collaboration is a significant factor in shaping its economic and innovative performance (Ahuja 2000; Hagedoorn, 1993; Owen-Smith & Powell, 2004; Powell et al., 1996; Rowley, Behrens, & Krackhardt, 2000; Tsai & Ghoshal, 1998; Walker et al., 1997). Empirical researchers have shown that factors such as the number of direct and indirect ties between firms (Ahuja, 2000) and the redundancy among these ties (Baum et al., 2000; McEvily & Zaheer, 1999) affect firms’ innovation performance. Among others, such an impact has been found in, for example, the textile (Uzzi, 1996), biotechnology (Baum et al., 2000; Powell et al., 1996), chemicals (Ahuja, 2000), semiconductors (Stuart, 2000), and computers (Hagedoorn & Duysters, 2002) industries. According to Granovetter, embeddedness refers to the ‘‘fact that economic action and outcomes, like all social action and outcomes, are affected by actors’ dyadic (pair wise) relations and by the structure of the overall network of relations’’ (1992, p. 33).4 Granovetter argues that relational and structural embeddedness are responsible for a great deal of the order and the disorder of industries and firms. Following Granovetter’s conceptualization of embeddedness, Nahapiet and Ghoshal (1998) differentiate between structural and relational embeddedness. Structural embeddedness has been defined as ‘‘the impersonal configuration of linkages between people or units’’ (Nahapiet & Ghoshal, 1998, p. 244) which refers to network ties, connectivity, centrality, and hierarchy. In contrast, the authors refer to relational embeddedness as the ‘‘personal relationships people have developed with each other through a history of interactions’’ (Nahapiet & Ghoshal, 1998, p. 244). Key factors of relational embeddedness include overlapping identities, solidarity, and trust. Whereas

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structural embeddedness shapes the amount and range of resources that a firm potentially could have acquired, relational embeddedness defines how much of this potential will be actualized. Namely, the characteristics of the social relations have an impact on which of the resources that are within reach will be exploited and to what extent (Moran, 2005). In this vein, Gulati (1998, p. 296) claims that ‘‘relational embeddednessy stress[es] the role of direct cohesive ties as a mechanism for gaining fine-grained informationy Structural embeddednessy go[es] beyond the immediate ties of firms and emphasize[es] the informational value of the structural position these partners occupy in the network.’’ Following the distinction between structural embeddedness and relational embeddedness (Granovetter, 1985; Gulati, 1998; Moran, 2005; Nahapiet & Ghoshal, 1998; Portes, 1998; Rowley et al., 2000), we build our model on each of them (see Table 1) as two complementary levels of analysis. One level is based on structural embeddedness and the other level concentrates on relational embeddedness (see Table 1). In the next section, we wish to draw a clear line of demarcation between these two network concepts and link them to industry–network coevolutionary dynamics.

Structural Embeddedness Structural embeddedness is defined as the extent to which two (or more) actors relate to the same others (Feld, 1997; Wellman, 1982). This concept follows Granovetter’s claim (1985) that the nature and extent of structural embeddedness is the context in which interactions occur. Feld (1981) suggests that structural embeddedness arises when one or more foci of activities are shared with others, thus building common relationships with others from those activities. Feld (1997) makes two further claims as to the important properties of structural embeddedness: (a) that structural embeddedness is less under the control of the individual than are other properties of relationships and (b) that it tends to be more stable than other relationship properties. In addition, structural embeddedness reflects the way in which the assembly of ties systematizes transfer of resource or information among organizations (Uzzi, 1996). As such, it focuses on the network social structure (Marsden & Friedkin, 1993) and defines how many network actors interact with one another, and how likely these actors are to talk about these interactions (Granovetter, 1985, 1992). In other words, higher structural embeddedness in a network is associated with higher availability of

Coevolutionary Perspective of Industry – Network Dynamics

Table 1.

Structural and Relational Embeddedness. Structural Embeddedness

Granovetter (1992)

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Relational Embeddedness

‘‘Structure of the overall network of relations’’ (p. 33) ‘‘Structures of relations also result from processes over time and can rarely be understood except as accretions of these processes’’ (p. 34)

‘‘Actors’ dyadic (pair wise) relations’’ (p. 33) ‘‘Relational embeddedness typically has quite effect on individual economic actionyby the kind of personal relationship they have, which is determined largely by a history of interactions’’ (pp. 34–35)

‘‘y To the extent that dyad’s mutual contacts are connected to one another, there is more efficient information spread about what members of the pair are doing, and thus better ability to shape that behavior. Such cohesive groups are better not only at spreading information, but also at generating normative, symbolic, and cultural structures that affect our behavior’’ (p. 35)

‘‘Not only particular relations may affect your behavior, but also the aggregated impact of all such relations. The mere fact of attachment to others may modify economic action’’ (p. 35)

Nahapiet and Ghoshal (1998)

‘‘Structural embeddedness concerns the properties of the social system and the network of relations as a whole. The term describes the impersonal configuration of linkages between people or units’’ (p. 244)

‘‘The term ‘relational embeddedness’ describes the personal relationships people have developed with each other through a history of interactionsy This concept focuses on the particular relationships people have, such as respect and friendship, that influence their behavior’’ (p. 244)

Gulati (1998)

‘‘Goes beyond the immediate ties of firms and emphasizes the informational value of the structural position these partners occupy in the network’’ (p. 296)

‘‘Stresses the role of direct cohesive ties as a mechanism for gaining fine-grained information’’ (p. 296)

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information about each actor to the other actors in the network (Gulati, 1998) and with higher constraints on each actor’s behavior (Burt, 1992). Structural embeddedness is essential for understanding the coordination patterns of social capital forms that allow exchanges in networks and serve as a channel for diffusion of behavioral norms and information about parties’ behavior. In general, there is a consensus that social capital can contribute to firms’ competitive advantage (e.g., Burt, 2005). Yet, two opposing arguments in the literature, Burt’s (1992) ‘‘structure holes’’ argument and Coleman’s (1988) ‘‘closure’’ argument, suggest that different forms of social capital are beneficial for firm advantage.5 Burt (1992, 2005), who advocates the significance of structural holes, suggests that actors that develop ties with otherwise unconnected actors will benefit from the ability to bridge structural holes and gain entrepreneurial rents. Moreover, occupation of structural holes economizes on the number of direct ties needed to obtain exclusive information. In that sense, development of networks rich with indirect ties is an effective way to enjoy the payback of large networks without paying the costs of sustaining direct ties (Ahuja, 2000; Gulati & Gargiulo, 1999). On the closure line of argument, Coleman (1990) claims that the preferable network structure involves dense networks in which all actors are connected through direct ties. Such a closed structure facilitates development of shared behavioral standards (Rowley, 1997) and efficient sanctioning (Coleman, 1988), which prevent opportunistic behavior (Axelrod, 1984; Podolny, 2001) and generate an effective coordination mechanism (Zaheer, McEvily, & Perrone, 1998). Burt (2005) further resolves the debate and argues that there is no contradiction between the two forms, but rather that the two forms play different roles for different populations or goals. The main question is about the optimal level of network closure and the presence of cohesive6 ties in promoting a normative environment that facilitates trust and cooperation between actors (Burt, 2005; Coleman, 1988, 1990; Gargiulo & Benassi, 2000). In that context, one may describe the evolution of network relations as ranging from a pure brokerage position of bridging structural holes (Burt, 1992) to the other pole of membership within a closed network of social capital (Coleman, 1990). Thus, the closure tendency of the supply networks highlights the idea that ‘‘different types of structural embeddedness can be beneficial’’ (Rowley et al., 2000, p. 370) in different competitive settings. We contend that there are not only differential benefits, but that differentiation in ILC stages also important for understanding network dynamics. We thus turn now to discuss the four ILC stages in the context of structural embeddedness.

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At the fluid stage of the ILC, the supply network is at an embryonic stage. Most of the actors in the industry are usually newly established entrepreneurial firms (Schumpeter, 1934) characterized by scattered network relations that originated from the previous activity of its founders and executives (Rowley et al., 2000). Following the logic that structural embeddedness is about the degree to which actors are connected directly and indirectly with each other as a group in comparison to their distance from non-group members (Wasserman & Faust, 1997), it can be claimed that the fluid stage is associated with an extremely low rate of structural embeddedness and dispersed sources of innovation. Moreover, at this stage, the competitive environment is emerging and the firm is characterized by highly explorative activity (March, 1991), which involves the development of new resources and new capabilities, entails trial and error with regard to industrial standards, and is executed in the light of high technological uncertainty (Kogut & Zander, 1992; Noda & Bower, 1996; Nooteboom, 2000; Rosenkopf & Nerkar, 2001; Tushman and O’Reilly, 1996; Tushman & Romanelli, 1985). Such industrial settings are usually associated with a multiplicity of structural holes and with innovative entrepreneurial firms occupying brokerage positions. The link between structural holes and a high rate of innovation has been strongly established in the literature. For example, Stuart and Podolny (1996), who studied semiconductor firms, suggest that a higher rate of innovation is expected in collaborations with firms outside their own technological domain. In addition, Koput and Powell (2000) and Baum et al. (2000) suggest similar results in American and Canadian biotechnology firms, respectively. While the evolving industrial settings provide the ground for entrepreneurial activity and obtainment of brokerage positions, the initial stages in the development of the supply network are crucial to future industrial growth. Considering the minimal number of network actors participating in the initial industrial activity, the impact of the removal of one node from the network (i.e., the exit of one firm from the network) will weaken the evolving network dramatically (Moody & White, 2003). At this stage, demand is embryonic and the future of the industry is highly dependent on the firms’ cumulative capability to pull attention toward the evolving industrial activity both in terms of demand and in terms of complementary assets and resources. Therefore, the initial development of the supply network’s structural embeddedness is a crucial factor in subsequent industrial growth. The successful introduction of a new product (or technology) to the market demarcates the industry growth stage. At this stage, both new and

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established firms seek to enter the emergent industry. In an effort to improve market position, new entrants attempt to leverage available network resources (Gulati, 1999; Gulati et al., 2000; Lavie, 2006) and strengthen their relations with a certain sub-network composed of a clique of tightly connected firms (Powell et al., 2005; Wasserman & Faust, 1997) with which the firm establishes first-order relations within the larger supply network. In that context, structural embeddedness within the industry sub-networks increases. Such interconnected firms benefit from shared opportunities coupled with shared risks (Powell et al., 1996). At this stage, in addition to the competition that takes place between the firms within the growing industry, there is also competition between the subnetworks in the industry. Relatively to the fluid stage, at the growth stage the sources of innovation are more internal and much more complementary (as a derivative of strengthening relations among the network partners). This results from both the industry structure and the characteristics of the network. On the industry level, during this phase, there is a critical mass of mature solutions with a high level of connectivity of complementing capabilities, knowledge, and technology. This is a reflection of the standardization process, where everyone shares the same language and technical framework. This brings us to the network characteristics associated with this stage. The industry firms at this stage are characterized by common interests, driving trustworthy collaborations and embeddedness among the sub-networks within the industry. One may argue that at this stage, the winning solution sub-networks is associated more with the higher systemic and collaborative efficiency rather than technological or innovative superiority. The focus of this competition is on the definition of the industry’s dominant design. Dominant design is a set of stable product standards, which are adopted by the majority of the industry producers. As such, establishing the industry’s dominant design represents a significant turning point in the evolution of the industry and a direct outcome of the collaborative networking capabilities of the firms. According to Abernathy and Utterback (1978), the emergence of the industry’s dominant design triggers a shakeout of producers, which leads to a dramatic decrease in the number of actors in the evolving industry network. In addition, the incremental transfer from product innovation to process innovation (Anderson & Tushman, 1990) opens opportunities to achieve economies of scale and scope. Klepper (1996), on the other hand, in his evolutionary model, links the turning point of the industry to economies of scale as the trigger for the shakeout, which consequently leads to the emergence of

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the dominant design. Groups of collaborating firms that opt to take advantage of the new dominant design increase the volume of sales and leverage the exponential growth of the industry. Firms that do not adapt to the dominant design or cannot enter the networks of customers and suppliers that are evolving around that design experience major competitive pressure and many exit the industry. Moreover, despite market expenditure, the entry of new firms slows down, while, at the same time, the rate of exits increases, reducing the overall number of firms in the industry. Consequently, the few surviving firms may prevent entry and benefit from relatively long-term stability in market share (Klepper, 2002). Thus, the formation of the industry’s dominant design, which opens the door to economies of scale and marks the transformation to a phase of massive production and maturity, is widely dependent on the evolution of the supply network and the ability of its members to develop and promote shared interests and goals. The beginning of the mature stage of the ILC is signaled by a decrease in the slope of the industrial growth curve. Here, attention shifts from explorative activity and intense product innovation to more exploitative efforts that emphasize operational improvement. The leadership of the industry is usually stabilized (Klepper, 1996), innovation becomes mostly incremental (Tushman & O’Reilly, 1996), and competitive efforts focus on market diversification (Porter, 1985) as well as on economies of scale and scope (Teece, 1986; Tushman & Anderson, 1986; Vernon, 1966). Such change involves not just a shift in the sources of the economic rents but also in the type of the knowledge generated within the network and the scope of enduring network relations. While the initial stages of network formation are characterized by bridging over structural holes, and a relatively low level of structural embeddedness, the closure position with a relatively high rate of structural embeddedness characterizes the industrial maturation stage. Such a closed network structure facilitates efficient cooperation and the attainment of economies of scale and scope. Moreover, during the last decade, aspects such as heterogeneity of technological solutions, shortening ILC timeframes, and growing crossindustrial interdependency and volatility are hampering the sustainability of industries and networks. In this scenario, both firms and networks of firms have to develop the capacity to internalize innovation from external sources (e.g., open innovation models; Chesbrough, 2003). While supporting increasing efficiency and specialization, high network embeddedness may lead to network inertia (Burt, 2005; Kim et al., 2006; Koka et al., 2006) which is associated with rigidity (Leonard-Barton, 1992) and a decrease in effective responsiveness to change (Christensen & Bower, 1996).

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Namely, at the stage when industrial renewal does not keep pace with external competition, the industry enters the decline stage of the ILC.7 Here, consumers shift their preferences to other products (Ghemawat & Nalebuff, 1985, 1990; Lieberman, 1990), sales volumes continuously decrease, and firms tend to maximize their exploitative efforts within the declining industry while exploring alternative directions. Here, incumbents’ sustainability depends on their ability to lead an efficient change in existing routines (Nelson & Winter, 1982), to perform major unlearning processes (Prahalad & Bettis, 1986; Starbuck, 1996) and to alter their sources of competitive advantage (Helfat et al., 2007; Teece, 2007; Teece, Pisano, & Shuen, 1997). These requirements demand penetration into new areas of activity and new network constellations, which leads to an incremental decrease in the number of interactions within the sub-network and a reduction in the frequency of ties among the collaborating members within the declining network compared to non-members. Due to a decreasing number of members in the industry, the structural embeddedness of the supply network is expected to decrease hand in hand with the industrial decline. In summary, the industry–network coevolutionary process is associated with changes in the structure of the network. During the fluid stage of the ILC, the network has a more dispersed structure and is characterized by a multiplicity of structural holes and low structural embeddedness. During the growth stage, new firms enter the industry at a high rate and structural embeddedness strengthens within the sub-networks. With maturation, the structural embeddedness of the supply network reaches its peak and starts to decrease as the industry repeatedly consolidates and gradually enters its decline. Based on the above discussion, and following the conventional model of the ILC, we develop our first proposition: Proposition 1. Over the course of the ILC, the degree of structural embeddedness will exhibit an inverted U shape. It will initially increase until reaching a peak at the mature stage and then gradually decrease throughout the decline stage of the industry. The coevolutionary view suggests that the interaction between the actors contributes to the emergence of structure. Yet, while embeddedness emerges from the internal structure, it reflects the relationship between the evolving network and the external environment. In other words, the more structural embeddedness there is in a network, the more constraints there are on each actor’s behavior (Burt, 1992). Yet, it is important to emphasize that the missing link is that the internal structure of a network has an impact on

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the external connections of the sub-network with the environment and with other sub-networks within it. For example, as expressed in the ‘‘complexity catastrophe’’ (Kauffman, 1993), the adaptability of a system depends on the connections between its parts. Without internal connections, the system is simply disorganized, but if the elements are over-connected, the system becomes immobilized and adaptability decreases (Coleman, 1990; Schumpeter, 1934). As stated, the fluid and growth stages of the ILC involve uncertain processes for new searches for business opportunities coupled with the development of new knowledge and capabilities (March, 1991; Rowley et al., 2000). These require diverse knowledge bases (Hagedoorn & Duysters, 2002; Rothaermel & Deeds, 2004) and relatively low structural embeddedness. These stages are best characterized as being rich with structural holes (Burt, 1992, 2005) and network actors with sparsely related network collaborations. In an embryonic industrial structure, and having a need to accumulate a critical mass of capabilities, assets, and resources, firms are expected to establish a wide range of network collaborations. These collaborations are characterized by an intense flow of novel and diverse knowledge. This diverse and open structure of collaborations is usually associated with a high capacity for change. Following industrial maturation, structural embeddedness increases and hampers the flow of novel knowledge into the network (Gulati, 1998; Uzzi, 1997). As suggested by Kilduff et al. (2006, p. 1041) ‘‘actors embedded in relatively open structures, with ties to several clusters, may become experienced facilitators of new knowledge flows, whereas actors in relatively closed structures may block incoming knowledge flows.’’ In that sense, high levels of closure can reduce awareness, on the part of the collaborating parties, to important external changes and thus lead to low adaptability (Uzzi, 1997). Burt (2005) argues that closure results in an equilibrium in which there is no endogenous change that may disrupt the status quo. Such a tendency, while contributing to short-term performance, hampers longterm sustainability and decreases the ability of a firm to adjust to its environment over time. Organizational routines, while supporting efficient reaction to previously experienced problems, may encourage the firm to react in ways unsuited to new and unfamiliar situations (Cohen & Bacdayan, 1994; Nelson & Winter, 1982). Kim et al. depict the constraints on network change as ‘‘network inertia’’ and suggest that ‘‘inertia is not a symptom of ‘bad management’; rather, it is the natural result of creating a well-tuned organizational architecture that exploits strategic advantage and synergy’’ (2006, p. 705). Such an approach

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highlights the fact that the network formation process is path dependent and that ‘‘firms are constrained in their future tie activity by their existing ego networks’’ (Koka et al., 2006, p. 726). In the same line of argument, but on the firm level, the structural inertia theory of organizations suggests that the inability to change in response to environmental modifications is a direct outcome of inertial pressures (Hannan & Freeman, 1984). Based on the above discussion, we develop two more propositions: Proposition 2a. High structural embeddedness contributes to the adaptation of the network and its member firms to incremental environmental changes. Proposition 2b. High structural embeddedness hampers the adaptability of the network and its member firms to radical environmental changes.

Relational Embeddedness The concept of relational embeddedness refers to the quality of ties between the network actors (Granovetter, 1985). According to Granovetter, tie strength is based on a ‘‘combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocity services which characterize the tie’’ (1973, p. 1361). Gulati (1998) suggests that relational embeddedness emphasizes the role of direct cohesive ties as a source of ‘‘fine-grained information.’’ As the strength of the ties represents the relational link between two actors, a high degree of relational embeddedness is associated with strong ties, greater consensus (Friedkin, 1984) and high standards (Collins, 1988) within groups, and with trust and cooperation (Coleman, 1990; Rowley et al., 2000). At the fluid stage of the ILC, the production systems are unsettled and the technological and the business concepts are dispersed and emerging, while behavioral norms and a common body of knowledge scarcely exist (Winter, 1984). The relationships among the actors in the new industrial settings are rare and usually random while no coordinating mechanisms are established. There is minimal closeness between the parties and the relative number of ties among subgroup members compared to non-members is low (Rowley et al., 2000). Consequently, the degree of relational embeddedness, if any, may be evaluated as minimal. Yet, the establishment of some level of relational embeddedness represents a critical phase in the evolution of the industry for two main

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reasons. First, due to the limited resources available to actors during the fluid phase, the emergence of the industry depends on the collaborative capabilities of its members and on their capacity to attract complementary assets (Teece, 1986). Second, actors’ collaborative capabilities are also crucial for generating initial market demands that may trigger further industrial development. Following the fluid stage of the ILC is the growth stage, which involves accelerated industrial expansion. Here, mutuality and frequency of ties among firms increases due to increasing collaboration. Parallel to the stabilization of the technological and operational standards of the industry, the sub-networks tend to form and synchronize co-dependently (Powell et al., 2005). Social embeddedness scholars suggest that an established network of relations reduces behavioral uncertainty and information asymmetry (Granovetter, 1985; Gulati & Gargiulo, 1999). Moreover, evolvement of a technological-dominant design, common behavioral norms and a shared body of knowledge within the sub-network, will lead to higher levels of process-based trust (Rowley et al., 2000; Van de Ven & Ring, 2005; Zucker, 1986) coupled with longer and more frequent collaborations between firms. Such increasing relational embeddedness within the sub-network involves the evolvement of shared vision, cognitions, and values. In this sense, participation in a network characterized by a high degree of relational embeddedness provides access to trustworthy information about the quality and reliability of potential partners (Rangan, 2000). Moreover, reliance on past collaborative experience may enhance trust between parties in future interactions (Gulati, 1995),8 which in turn reduces the transaction costs involved in network collaboration (Podolny, 2001). Therefore, it is possible to claim that increasing relational embeddedness indicates the transition from the growth stage, which is characterized by relatively high exploratory activity (March, 1991) and relatively radical product innovation requiring novelty and creativity (Tushman & Anderson, 1986), to the maturity stage, which is characterized by competitive foci on economies of scale and scope (Teece, 1986; Tushman & Anderson, 1986; Vernon, 1966) and demands for an efficient and low-cost collaborative environment. The maturation of the industry, as mentioned above, is associated with a down-swing in the industrial growth curve, while the incumbent firms are faced with the need for increasing efficiency and specialization. Due to the highly competitive environment, participation in a network with a high degree of relational embeddedness becomes crucial (Uzzi, 1996, 1997). Long-term interactions facilitate the building of commitment mechanisms

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and a strong group identity, which facilitate efficient network relations and the transfer of tacit and complex knowledge (Walker et al., 1997). Such a mechanism facilitates effective cooperation and reduces appropriation and monitoring costs (Williamson, 1985; Zaheer & Bell, 2005; Zaheer & Venkatraman, 1995). Moreover, a structure of high relational embeddedness emphasizes the role of direct sticky ties and repetitive relationships as a mechanism for complex knowledge transfer (Hansen, 1999; Kogut & Zander, 1995). In that line, Uzzi (1997) argues that while low degrees of relational embeddedness may cause problems in the flow of noncodified knowledge between firms, high degrees of relational embeddedness facilitate joint problem solving and the flow of fine-grained knowledge between firms. According to the ILC model, industry maturation ends when consumers change their preferences and sales volumes decrease (Ghemawat & Nalebuff, 1985, 1990; Klepper, 1996; Lieberman, 1990). While acting in a condition of incremental decrease in the number of firms operating in the decline industry, the remaining firms are expected to continue to strengthen their relational embeddedness as an outcome of lasting interaction. In summary, relational embeddedness refers to the quality of ties between the network actors and is expected to be present to different degrees depending on the point an industry has reached in its life cycle. During the evolution of the industry, the level of relational embeddedness will exhibit an inverted U-shape. During the fluid stage, the network will be dispersed as collaborations will only start to evolve. The rate of relational embeddedness in the industry will start increasing toward the late growth stage, and throughout the mature and the decline stages. Based on this discussion, and following the conventional model of the ILC, we develop our third proposition: Proposition 3. Over the course of the ILC, the degree of relational embeddedness will increase. Organizational theories suggest that concepts such as ‘‘the satisficing principle’’ (March & Simon, 1958), routinization (Cyert & March, 1963; Nelson & Winter, 1982), increasing confidence, and willingness to engage in future cooperation are possible outcomes of positive past experience (Gulati, 1995; Stan & Rowley, 2002). These variables may lead to repetitive network interaction and an increase in stickiness among the collaborating parties. As suggested by Kim et al., ‘‘once relationship-specific routinesy become institutionalized between two parties, it is unlikely the firms will replace their partners with new ones based solely on economic motivations’’

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(2006, p. 704). It is expected that, within highly relationally embedded networks, firms will tend to search for new knowledge and opportunities in their direct relationships rather than searching for knowledge from new partners (Helfat 1994; Stuart & Podolny, 1996). In this vein, Maurer and Ebers (2006) found that inertia is associated with embedded relations that turn the firm’s social capital into a liability. Thus, a high degree of relational embeddedness not only impacts the type of available knowledge and opportunities but also impacts the patterns and rate of their exploitation. Namely, network collaborations characterized by a high degree of relational embeddedness usually include repetitive and deep interactions that exceed the scope of formal contracts. This makes exploitation of new innovation more difficult than in collaborations with a low degree of embeddedness. As stated by Christensen and Rosenbloom (1995, p. 242), ‘‘while successful incumbents will become more cognizant of relevant information pertaining to the networks in which they compete, they will have greater difficulty acquiring and assessing information about others. The longer the firm has been in a given position, and the more successful it has been, the stronger these effects are likely to be.’’ Thus, during the mature stage and moreover during the decline stage of the industry’s evolution, the ability of the supply network members, both as individuals and as a collective, to cope with and adjust to relatively drastic changes in the competitive environment is hampered. Thus, while sticky network relations lead to specialization and high embeddedness, which facilitates the efficient interchange of resources and capabilities (Coleman, 1990; Powell, 1990; Williamson, 1985), they also incrementally lead to resistance to change (Greenwood & Hinings, 1996; Kilduff et al., 2006; Kim et al., 2006; Koka et al., 2006; Uzzi, 1999). The environment in which the industry operates is important here, as well. Some industries are characterized by a sequence of incremental environmental changes in which changes gradually modify the original conditions, while other industries operate within an environment characterized by radical changes which are punctuated by major shifts in the competitive conditions. Based on the arguments above, we suggest the following propositions: Proposition 4a. High relational embeddedness contributes to the adaptation of the network and its member firms to incremental environmental changes. Proposition 4b. High relational embeddedness hampers the adaptation of the network and its member firms to radical environmental changes.

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NETWORK EMBEDDEDNESS AND ALTERNATIVE PATTERNS OF INDUSTRIAL EVOLUTION The ILC model suggested here depicts the general pattern of industrial evolution (see Table 2). Yet, both the duration of each stage of the ILC and the specific sequence of the stages may vary among different industries. Therefore, industry characteristics and specific evolution patterns impact the structure of supply network relations, which in turn may lead to variation in industrial evolution. Initially, as suggested already, a critical level of structural and relational embeddedness is a major internal factor associated with transitions throughout the ILC. Leaving exogenous factors aside, one may claim that in a state in which structural and relational embeddedness do not reach the critical level, industrial growth is expected to be harmed. In addition, the degrees of structural and relational embeddedness compose various configurations in the industrial evolution. Although industrial growth is usually associated with increasing structural and relational embeddedness, these two factors are not necessarily correlated and mixed compositions may exist under certain conditions (see Table 3). The common evolution of network relations for firms can range from a pure brokerage position of bridging structural holes (Burt, 2005) to cohesive networks of social capital (Coleman, 1990). Such a dynamic is associated with moving from sparse networks (Cell A, Fig. 1) to dense sticky networks (Cell D), and with industrial evolution and the transformation from the early stages to the late stages of the ILC. Sparse networks (Cell A) are usually associated with the fluid stage of the ILC, due to the fact that the interactions within the evolving network are embryonic and unstable. In addition, information is distributed asymmetrically and the network’s structure is generally characterized by a multiplicity of structural halls. The interdependencies of the parties in the evolving network are limited, and the normative sanctions on inappropriate behavior are inefficient. In this state, trust among the parties is low and the transaction costs are high. Under such conditions, effective search for novel explicit and codified knowledge is facilitated by additional actors entering the system. The combination of low structural and relational embeddedness leads to the evolvement of new industrial settings in which new capabilities, technological practices, and business models emerge. Further, the ability of the parties to develop common shared knowledge and behavioral norms plays a central role in the sustainability of the industry and its transition to the growth stage of the ILC.

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Table 2.

Industry Life Cycle and Network Characteristics. Industrial Setting

Network Setting

Fluid stage

Main objectives: Accumulation of a critical mass of assets and capabilities Competitive characteristics: Production systems are unsettled and the business concepts are dispersed Demand: Embryonic

Main objectives: Occupation of structural holes and accumulation of a critical mass of collaborations with firms within a network Network structure: Extremely disperse Type of relations: Mainly weak ties among disconnected firms

Growth stage

Main objectives: Definition of dominant design and acquisition of market share Competitive characteristics: Converge around common industrial practices and business models Demand: Exponential growth

Main objectives: Leverage of available network resources and attainment of a strong position within a prospering sub-network Network structure: Increasing density and split into subnetworks Type of relations: Strengthening of ties and co-dependency within the sub-networks

Mature stage

Main objectives: Market diversification and economies of scale and scope Competitive characteristics: Growth by penetration to new segments, mergers and acquisitions, and consolidation Demand: Moderate growth

Main objectives: Acting under the shelter of norms of mutual trustworthiness, which facilitate free and efficient cooperation Network structure: Dense structure of several competing subnetworks Type of relations: Strong and densely connected ties within the sub-networks

Decline stage

Main objectives: Maximizing exploitation of current position vis-a-vis penetration to new industry Competitive characteristics: Extraction of the existing commercial potential Demand: Shift in consumer preferences and decreasing demand

Main objectives: Breaking network inertia, detaching from the existing network, and managing an unlearning process Network structure: Dispersing and shrinking network with decreasing density Type of relations: Proactive weakening of existing ties

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Table 3.

Variation in Structural and Relational Embeddedness. Relational Embeddedness Low

Structural embeddedness

High

Low

(A): Sparse network Information flow: highly limited Information type: highly novel and codified Capacity for change: very high

(C): Continuous exclusive dyadic network Information flow: low level, high redundancy Information type: low novelty, codified, tacit Capacity for change: low

High

(B): Dense shallow network Information flow: high level and redundancy Information type: novel, codified. Capacity for change: medium to high

(D): Dense sticky network Information flow: highly limited Information type: low novelty, codified, tacit Capacity for change: very low

(C) Continuous Exclusive Dyadic Network

(A) Sparse Network

(D) Dense Sticky Network

(B) Dense Shallow Network

Fig. 1.

Variation in Sequences of Network Compositions.

At the other extreme, a supply side characterized by dense sticky networks (Cell D) is usually associated with industrial maturation. Under such conditions, interactions and collaborations within the network are well settled and the information is distributed relatively effectively. The interdependency between the parties is high, the sanctions on inappropriate behavior are well established and effective, and the transaction costs are relatively low. This dense sticky structure contributes to effective exchanges

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of tacit knowledge, but becomes ineffective in importing novel knowledge into the system. In a state of high structural and high relational embeddedness, the system may operate efficiently, yet the dense sticky nature of the relationships hampers change and opportunities for industrial renewal are severely reduced. Thus, network dynamics, as expressed by the shift from a sparse network (Cell A) to a dense sticky network (Cell D), is expected to lead eventually to the decline stage of the ILC. Two additional potential combinations of relational and structural embeddedness, namely, a dense-shallow network (Cell B, Fig. 1) and a continuous exclusive dyadic network (Cell C), may be manifested in the coevolution of the industry and network. The dense-shallow network (Cell B) entails effective distribution of information, which allows for diffusion of behavioral norms and values within the network. In this context, the discrepancy between high structural and low relational embeddedness may occur in environments that, for operational or structural reasons, demand relatively limited interorganizational collaborations (such as industrial e-commerce platforms or industrial standard setting committees). This structure contributes to the effective exchange of relatively novel but codified knowledge. As a result, the network’s capacity for change is expected to be relatively high and there is also a good industrial capacity to cope with radical external change, which increases the chances of the industry entering an additional growth stage rather than a decline stage. This network-based conditional argument adds to the existing ILC literature in suggesting that the structure of networks can generate the needed conditions for industry renewal. Therefore, we can extend the basic ILC model to alternative patterns of evolution and replace the deterministic nature of the original ILC model. Finally, a continuous exclusive dyadic network (Cell C) based on continuous collaborations between dyadic members in the network is another possible combination. This context is associated with low structural embeddedness and high relational embeddedness and may evolve for several reasons. First, it may arise at a state of industrial decline, when firms exit the industry and a small number of collaborations are sustained. This is a possible transition from Cell D to Cell C. In addition, this combination may exist in a niche market, or when there are a small number of dominant actors, or even a monopoly. Due to the low structural embeddedness, the information distribution becomes asymmetric and fractured, while, at the same time, within collaborations, the process-based trust is high. This structure is associated with the effective exchange of tacit knowledge characterized by relatively high redundancy between the collaborating

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parties. Under such conditions, the network capacity for change is expected to be relatively low and the industrial capacity to adapt to radical environmental change is minimal. The common theoretical models of ILC and network evolution suggest a linear evolutionary trajectory between two extreme network structure positions. One is associated with sparse networks (Cell A) and the other is associated with dense sticky networks (Cell D). In contrast, the general claim of our argument calls for a complex and nonlinear evolution of the ILC and network structures (see Fig. 1). As a result, the ILC can have different sequences of network constellations that shape different industry trajectories. For example, one option of such variability is the very short life cycle associated with technological discontinuities and practically no industrial maturation as often happens in technology-oriented industries. Here, network dynamics are expected to shift from Cell A to Cell B. Another option is a mature industry with a dense sticky network which, following some radical environmental change (such as technological discontinuity), experiences industrial renewal, accompanied by a regrowth in demand and a new wave of firms entering the industry. This constellation is expected to be accompanied by a reduction in relational embeddedness and movement from Cell D to Cell B. In summary, industry network evolution is associated with the trajectory of the ILC. Since network relations are associated with a capacity for change, structures rich in inflowing information, such as sparse networks and dense shallow networks, provide an opportunity of industrial renewal. By contrast, sticky dense networks and continuous exclusive dyadic networks are expected to hamper industrial renewal and therefore eventually enter the decline stage of the ILC.

CONCLUSIONS AND FURTHER RESEARCH This chapter is based on an effort to offer an integrative coevolutionary approach to ILC and networks. Our central claim is that evolutionary theories have to consider both longitudinal aspects of industries and structural and relational aspects of networks as they mutually coevolve. While combining the industry and network levels of analysis is crucial for our theoretical understanding, it is not a simple task. Of course, this effort has its limitations, associated with the oversimplification of the suggested concepts and model. In addition, it is clear that the overgeneralizations

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found in the propositions fail to acknowledge the idiosyncratic aspects of the networks and industrial evolutionary processes and the equifinality of multiple scenarios. Yet, the suggested general propositions in this chapter are only a starting point for deciphering the complexities associated with coevolutionary processes. As argued above, the increase in environmental complexity is leading to changes in the sources and types of innovation throughout the ILC. As we move through the ILC stages, the natural structure of the networks becomes counter-productive for innovative processes. The networks tend to become inert and stable and this hampers the innovative potential of the industry. In order to maintain industry-level innovativeness, there is a need to generate innovative enhancing practices such as open innovation. In general, the chapter brings to the fore a few theoretical elements that did not receive sufficient emphasis in previous organizational and strategic approaches to networks. As suggested by Knoke (2001, p. 63), ‘‘Presently, diverse network approaches represent loosely connected sets of concepts, principles, and analysis methods rather than a rigorously deductive system.’’ Organizational network research has developed mainly into two different streams of research that are characterized by two units of analysis: networks (e.g., Aldrich, 1976, 1999; Bothner, 2003; Burt, 1992; Granovetter, 1973; Powell, 1990; Powell et al., 2005; Uzzi, 1996, 1997) and firms (e.g., Ahuja, 2000; Baum et al., 2000; Brass et al., 2004; Mizruchi et al., 2006; Stuart, 2000). Yet, with the exception of only a few works (e.g., Koka et al., 2006; Koza & Lewin, 1998; Madhavan et al., 1998), it seems to be missing the holistic perspective that links between networks and industries as they coevolve over time (Knoke, 2001; Parkhe et al., 2006). The current chapter contributes to this fundamental perspective by suggesting an industry– network coevolutionary framework. In doing so, we claim that changes in industrial settings shape the scope of opportunities available to firms in terms of the potential of their interfirm collaborations and outcomes. The main assumption of our model is that both network characteristics and environmental competitive aspects change throughout time. In an effort to obtain a clearer view of the change drivers and change patterns of networks (Koka et al., 2006), we suggest that the ILC perspective should highlight the temporal as well as the contextual aspects that shape the characteristics of the network. While each stage in the ILC represents different competitive settings, these stages are characterized by different network relations in terms of structural and relational embeddedness (Ahuja & Lampert, 2001; Hagedoorn & Duysters, 2002; Koza & Lewin, 1998; Nooteboom, 2000; Oliver, 2001; Rothaermel & Deeds, 2004; Rowley et al., 2000).

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Although relational and structural characteristics are not all alike, they are also not all idiosyncratic (McKelvey & Aldrich, 1983). Consequently, such a classification is an imperative act in theoretical development (Ulrich & McKelvey, 1990). This classification emphasizes the need to embed network level analysis in a dynamic perspective (Emirbayer & Goodwin, 1994), which will link the changing environmental conditions to the process of network evolution. Such perspective may contribute to the effort to understand the alleged contradictions between different research streams. For example, as previously mentioned, the debate between Burt’s (1992) structural hole and Coleman’s (1990) closure forms of social capital might be contextualized in the nature of the changing competitive settings (Rowley et al., 2000). Consequently, what seems to be a basic disagreement regarding the advantages of closure versus brokerage might coexist under the different contextual framework of the ILC. Theories of ILC (Abernathy & Utterback, 1978; Klepper, 1996), organizational ecology (Hannan & Freeman, 1984) and evolutionary theories (Nelson & Winter, 1982), agree that different phases of industry evolution impact differentially firms’ action and performance. Yet, the micromechanisms that set evolutionary patterns are not clearly formulated. The suggested perspective in this chapter aims to introduce a more comprehensive understanding of the underpinnings of the network and the industry development process. Consequently, the propositions formulated aim at assisting research on differential patterns of industrial life cycle evolution and its mutual relations with the network forms and characteristics. There are a few theoretical directions that we refer to in this chapter but were not develop and thus need further exploration. First, the understanding of the industry sub-networks at certain stages is underdeveloped in the literature. Such sub-networks may form and dissolve as both internal and external processes take place and this dynamic may have an explainable pattern that should be further explored. An additional stream of research that may enrich our understanding of industry and network evolution processes lies in the area of interindustrial relations and the impact of established networks in one industry on new and emerging neighboring industries and their supply networks. Finally, the model suggested here can be advantageous in terms of its ability to integrate what seem to be unrelated or even conflicting theoretical arguments. While previous research associates strong network positioning with greater firm competitiveness (Gulati & Higgins, 2003; Lavie, 2006; Powell et al., 2005), with few exceptions (Kim et al., 2006; Koka et al., 2006;

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Maurer & Ebers 2006), it usually neglects the negative aspects of such a position. Our model incorporates both the opportunities and the constraints that are contingently associated with various network structures. To this, we add the differential trajectory of the network structure’s argument that suggests that the duration of the ILC and the sequence of the stages it passes through may vary among different industries. That is, specific industry characteristics may impact the structure of the supply network, which in turn may lead to deviation from the traditional four stages of ILC model. Thus, our model should enable several research streams and network paradigms to be united within a single framework that provides a solid base for future research into the basic question of how networks and industries coevolve.

NOTES 1. By coevolution, we refer to the mutual influence the supply network and the industry exert on each other throughout their development. 2. While Bower and Christensen (1995) suggest that disruptive technology is very hard to predict as it usually comes from outsiders, Tushman and Anderson (1986), for example, suggest product (new product class, substitution, or fundamental improvement) and process (substitution or radical improvement) as two dimensions of disruption. 3. The term ‘‘social relations’’ was formerly used to express those relational resources that are rooted in cross-cutting personal ties. Recent research has applied this concept to a broader range of social phenomena describing interfirm relations (e.g., Coleman, 1990; Burt, 1992, Tsai & Ghoshal, 1998) and the firm–market interface (Baker, 1990). 4. One should not bind structural embeddedness to an actor’s direct relations, but can extend the concept of embeddedness in a cohesive group to the wider social network (Frank & Yasumoto, 1998; Moody & White, 2003). 5. Intuitively, one may be inclined to claim that the concept of ‘‘structural holes’’ corresponds to weak ties, while the concept of ‘‘closure of social capital’’ corresponds to strong ties. Yet, despite their close connection, the strength of the tie is not the source of redundancy. For instance, McEvily and Zaheer (1999) confront Granovetter’s claim that bridging ties are weak ties, claiming that strong ties may serve as a bridge, since the connection is non-redundant. By the same token, previous works demonstrate that variety of information sources is best reflected in the lack of overlap among actors rather than in the intensity of the interaction. As such, the concepts of structural holes and weak ties are, in fact, dissimilar (Reagans & McEvily, 2003). 6. The concept of cohesion refers to the degree of intensity of direct contacts among sub-group members. Such intensity is associated with strong internal group standards and distance of the groups from outsiders (Collins, 1988; Wasserman & Faust, 1997, pp. 250–253).

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CHAPTER 2 ISRAEL’S KNOWLEDGE-INTENSIVE SECTORS: INNOVATION, NETWORKS AND REGIONS Amalya L. Oliver and Noam Frank ABSTRACT Israel, characterized by various knowledge-intensive entrepreneurial firms, provides an interesting case study for examining sector-based differences and ‘‘small country’’ regional patterns. This chapter has a dual goal of exploring sector and regional differences of knowledge-intensive firms in Israel. The first goal is to depict similarities and differences between firms in three knowledge-intensive sectors: Life Sciences, information technology, and Cleantech. The second goal questions whether the geographical distribution of these firms across regions is associated with different levels of knowledge concentration and organizational homogeneity. Regional and sector-based differences were measured by firmlevel network structures, funding patterns, and innovation proxies. One way analysis of variance tests were conducted for attaining these research goals. The main findings show that while most regions exhibit similar patterns of firm and network characteristics, many differences exist on the sector level that are associated with sector-specific attributes. These

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 37–64 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013005

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findings support the notion of a ‘‘small country inter-regional homogeneity effect.’’ Keywords: Knowledge-intensive industries; regional clusters; innovation performance; homogeneity

INTRODUCTION The literature provides evidence that some countries or geographic regions gather the needed resources to become magnets for knowledge-intensive start-ups, innovative products, and attracting venture capital to support such innovation. Density of entrepreneurial innovative firms can be associated with or result from the availability of knowledge resources, for example, universities (Lerner, 2004; Liebeskind, Oliver, Zucker, & Brewer, 1996), governmental funding policies and private investments availability (Florida & Martin Kenney, 1988), access to knowledge exchange platforms and spill over from other firms or universities (Inkpen & Tsang, 2005), or a strong national or regional entrepreneurial culture (Lee & Peterson, 2001). Israel provides an interesting and illuminating case study for trying to understand regional issues related to innovation, networks, and knowledgeintensive sectors, since knowledge-based entrepreneurial activities in Israel are frequent and relatively successful, in comparison to many other countries. Since the 1970s, many knowledge-intensive start-up firms were founded in Israel, demonstrating a relatively high level of innovation and ingenuity. In the chapter, we focus on three knowledge-intensive sectors: Life Sciences, information technology (IT) and Cleantech. We aim to understand how network-related measures such as learning networks, interlocking management\board networks, and investor networks are associated with entrepreneurial innovation. The chapter will map and compare the entrepreneurial firms in Israel by sectors, regions, development stage, and network effects that are associated with innovation.

REGIONAL APPROACHES TO CLUSTERING The literature that intersects geography, organization studies, and economics argues that regional location of firms matters. Prior studies showed that firm agglomerations generate external economic opportunities

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and efficiencies that results from a stable market of labor experts with needed skills and technological capabilities (Krugman, 1991), as well as promoting international competitiveness (Saxenian, 1994). Furthermore, the agglomeration of firms focusing on similar technologies and knowledge characteristics may constitute an institutional environment that facilitates policy issues adhering to the local business and labor interests, as well as manufacturing needs (Piore & Sabel, 1984). Recent organizational and network literature suggests that the advantage of knowledge spill over opportunities and joint learning infrastructure in region clusters generate an added value for knowledge-intensive firms located in the region. In addition, when academic basic or applied research is needed for product development, expert recruiting, or consultation, the proximity to research universities is also important. This is depicted in the literature on university technology parks that assume that frequent face-toface interaction with university scientists as well as joint university–industry laboratory work lead to more innovative research.

Sector Homophily and Knowledge-Intensive Industries The relationship between similarities and connections are of great interest to organizational scholars on the micro and macro levels. There are several theoretical arguments that are associated with homophily and social processes. One of the early claims is presented by Blau (1977), who argued that homophily results primarily from the structure of opportunities. This argument can be further developed in different directions. For example, he demonstrated that in connecting environmental compositions with individual-level outcomes, the composition of an area regarding its occupational structure, income structure, industry composition, and education distribution had an impact on the homophily in marriages that took place there (Blau, Beeker, & Fitzpatrick, 1984). Another study focusing on economic exchange systems (Sorenson & Stuart, 2001) found that differences in the influence of homophily in economic exchange systems stem from variation across actors in their opportunities to trade. Their study investigates the social structure factors and the degree to which they determine variation or homophily with respect to geography and industry. While most studies of homophily follow the principle that ‘‘similarity breeds connections’’ (McPherson, Smith-Lovin, & Cook, 2001), focusing on the individual level tendency to establish ties to similar others, this study is

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different. The primary focus is on the organizational level rather than on the tie level and this is one difference. Another difference regards firm homogeneity, based on common sectorial, technological or regional characteristics. Here, our argument assumes that firms with similar characteristics located in close proximity will be at higher odds for forming collaborations. This conjuncture is not tested empirically in this study. Two main research goals are explored in this study. The first goal is to depict the similarities among firms within the same sector and differences between firms belonging to different knowledge-intensive sectors across characteristics such as network patterns, firm-level intellectual properties, and funding patterns. The second research goal refers to the geographical distribution of knowledge-intensive firms across the main regions and cities in Israel. This goal addresses questions about different levels of concentration of knowledge-intensive sectors within specific regions, and possible explanations for such concentrations.

Knowledge and Proximity/Similarity The interplay between the knowledge base of a firm and the exchange learning networks it establishes become an asset for a knowledge-intensive firm (Liebeskind et al., 1996; Powell, White, Koput, & Owen-Smith, 2005). Therefore, a valuable resulting question is, ‘‘What can increase interorganizational knowledge exchange and related learning?’’ Research found that the closeness of organizations within a geographic unit affects the transfer of knowledge between them, increasing the efficiency of knowledge transfer (Saxenian, 1994; Salomon & Martin, 2008). We also know from the literature, on the firm level, that the depth of the knowledge a firm has increases its motivation and ability to receive further knowledge as well as the efficacy of the knowledge transferred (Baum, Calabrese, & Silverman, 2000; Owen-Smith & Powell, 2004; Phelps, Heidl, & Wadhwa, 2012; Solomon & Martin, 2008). In addition, the depth of knowledge of a recipient organization generates a greater ability for recombination of the knowledge with greater know-how power (Solomon & Martin, 2008; Wong, Ho, & Lee, 2008). Therefore, we expect that organizations, whose founders typically have PhDs or academic appointments, are involved in high quality research, will have greater expertise power, and facilitating further knowledge transfer and collaborative research and learning. This will be stronger when the sector is more dependent on academic basic research as it is with the Life Sciences sector versus the more

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industry-based research in the Cleantech or IT sectors. Obviously, formal knowledge exchanges are only a part of the interorganizational learning process, yet informal networks are also an important mechanism that contributes to the learning capacity, range of organizational knowledge, and these parameters are not easily captured/measured (Liebeskind et al., 1996; Oliver, 2009). With regard to the conjuncture of geography and networks, the literature offers two theoretical perspectives (Gluckler, 2007): (1) proximity effects network formation and (2) place makes a difference. The first perspective originates from economic geography and claims that the physical proximity has a latent effect on economic processes. This is important especially when face-to-face interactions are an important mode of learning and communicating. Thus, proximity leads to interactions, leading to learning. The second perspective uses the notion of ‘‘resources bundle’’ from the theory of the firm (Penrose, 1959) and regards a place as a bundle of resources and opportunities. In a geographic region, we may have specific contextual resources that lead to enhanced economic development (Bathelt & Gluckler, 2005). Under this perspective, proximity is associated with similar resources and opportunities and they further enhance economic success.

Interorganizational Networks Interorganizational networks are considered a crucial resource for knowledge-intensive firms (Oliver, 2009; Powell, Koput, & Smith-Doerr, 1996; Powell et al., 2005). Organizations can be linked through many types of connections based on information, material, financial resources, services, and social support (Provan, Fish, & Sydow, 2007). Obviously, knowledgeintensive firms are no exception and are linked to other organizations within their organizational field through a variety of networks, in some of which knowledge creation is central. As Powell et al. (1996, p. 118) claim: ‘‘Knowledge creation occurs in the context of a community, one that is fluent and evolving, rather than rightly bound or static. The canonical formal organization with its bureaucratic rigidities is a poor vehicle for learning. Sources of innovation do not reside exclusively inside firms; instead they are commonly found in the interstices between firms, universities, research laboratories, suppliers and customers.’’ This is true, especially in fields where scientific or technological progress is developing at a fast pace, and the sources of knowledge are rapidly distributed; no firm has all the necessary skills to bring significant innovations to the market (Hagedoorn & Duysters,

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2002; Oliver, 2001, 2009; Powell et al., 1996). In that sense, networks can be the locus of innovation that creates new knowledge. Firms that engage in many kinds of interorganizational relations experience positive outcomes. Powell, Koput, Smith-Doerr, and Owen-Smith (1999) assert that a diversity of interorganizational ties has a positive influence on patent output rate. In this sense, the variables for this study were selected in order to measure a diversified set of boundary spanning relationships that individual organizations initiate with actors in the environment.1 Similarly, Oliver (2001) showed that firms in the biotechnology industry that did not develop collaborative network ties eventually became extinct.

Context: Innovation in Israel Some background information on Israel as an entrepreneurial nation is needed here. First, the nation of Israel was founded in 1948, with limited resources and capabilities based mainly on Jews migrating from Europe. In addition, resources were constrained due to the high level of security costs and immigration costs. It is hard to explain exactly how and why, but at an early stage, the local industries became highly entrepreneurial (Senor & Singer, 2009). Among the different knowledge-intensive sectors that emerged in Israel, three sectors play a significant role in innovation; yet, they have different characteristics as with regard to the source of knowledge, the product life cycle, and the funding and network features associated with firms. We identified three knowledge-intensive sectors: IT, Life Sciences, and Cleantech for intersector comparisons. The second important factor is that Israel is a very small country, both geographically and based on population size. As of 2011, the population of Israel is approximately 7.8 million. Israel is less than 400 kilometers long and 150 kilometers wide. There are currently only seven research universities: Hebrew University in Jerusalem, Tel Aviv University, the Technion in Haifa, Haifa University, Ben-Gurion University in Beer-Sheva, Bar-Ilan University in Ramat Gan, and the Weizmann Institute in Rehovot. In addition, there are about 20 colleges where relatively little research is conducted. The small geographic area of the country is an interesting context of our study, as it challenges the assumptions regarding the added value of geographical proximity to collaboration and learning spillover. The short distances between the large technological regions – Tel Aviv, Rehovot, Jerusalem, and Haifa – allow for manageable daily travel for conducting joint research, consulting or establishing strategic alliances with regional firms or with firms from different regions. The relatively small numbers of

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research universities, coupled with the small size of the population, facilitate close networks between scientists with many connecting actors. Essentially any scientist from academia or industry in Israel can easily contact and connect with any other scientists due to the intense and dense networks of encounters between Israeli scientists and entrepreneurs. Third, Israel represents a highly developed economy that emphasizes regional development with the help of industrial clusters (Frenkel, Shefer, Koschatzky, & Walter, 2001). Policy intervention in technological processes in Israel is based on the understanding of Israel’s geographic isolation. This regional geographical isolation drove the Israeli government to craft a national innovation policy in 1969 with the intention of linking Israel with the emerging global knowledge economy. According to Avnimelech, Rosiello, and Teubal (2010), this policy aimed to enhance innovation-based economic growth through the embedding of research and development (R&D) into the business sector, stimulated a 30-year evolutionary process, which led to the emergence of a Venture Capital (VC) market and an entrepreneurial information and communication technology (ICT) cluster during the second half of the 1990s. In this study, we will compare the Life Sciences, Cleantech, and the IT clusters as knowledge-intensive sectors. There are some major differences between these sectors concerning the role that basic research has in their activities. While the development of biotechnology is known to be strongly dependent on the advancement of basic scientific knowledge (Oliver, 2009, Powell et al., 1996, 2005), and on collaborations with universities, innovation in IT and Cleantech more often is based on applied sciences and less collaborations with universities (Rampersad, Quester, & Troshani, 2010). Thus, we ask two main questions in this study: (1) To what degree and over which variables are the three sub-industries similar or different? (2) To what degree and over which variables are the geographical regions similar or different? We ask these general questions rather than suggesting testable hypotheses due to the exploratory, early stage of this study. We have not encountered another study of this nature in Israel, and due to the small size of the country and the closeness between regions it is hard to apply findings from other countries on the study in the context of Israel.

SAMPLE AND METHODOLOGY Sample In order to answer the two main questions, a dataset composed of 1,010 Israeli based knowledge-intensive firms was compiled. While most studies

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on networks restrict themselves to one specific sector (Ahuja, 2000; Powell et al., 2005), we consider a wider sample spanning three sectors: Life Sciences (257 firms), IT (551 firms), and Cleantech (202 firms). These sectors are well suited for testing the research questions because they are composed of a heterogeneous population of firms, varying in age, size, subsector, development stage, innovation output, academic activity, and Initial Public Offering (IPO) status. Data were collected from various sources. Organizational data such as age, size, geographic and financial information, board data, and development stage were collected from the Israel Venture Capital Research Center (IVC). The IVC’s online database includes detailed listings of thousands of Israel-based knowledge-intensive companies, alongside information about venture capital and private equity funds, angels, and much more. Data concerning firm patent activity were collected from the United States Patent and Trademark Office (USPTO) and the European Patent Office (EPO) online databases. Data collection was carried out using ‘‘Harzing’s Publish or Perish’’ software. Data concerning academic publication activity were acquired through use of the Google Scholar database via ‘‘Harzing’s Publish or Perish’’ software. This study, similar to other network research, is not longitudinal, but provides a ‘‘snapshot’’ of different organizational networks, regarding knowledge-intensive companies in Israel. Data were collected in one single time frame, between February and April 2012. The IVC is the only data source for Israeli knowledge-intensive firms, yet it has some limitations that cause some variance.2 In order to further characterize the research context, we offer a brief description of each of the three sectors in the study3: Life Sciences, IT, and Cleantech. The Life Sciences sector includes biological, medical, and healthcare-related technologies. Companies developing products for the healthcare market can be found in this sector, along with companies performing biological and genetic research, and companies developing technologies, tools, and materials used in such research. Subsectors included in this sector are agro-biotech, bioinformatics, biologicals, diagnostics, healthcare – IT, industrial, medical devices, telemedicine, and therapeutics. The IT sector aggregates various software sub-sectors, with emphasis on IT systems for the enterprise market. Companies developing software products for enterprises and for business end-users are included here, as well as some companies developing software for the home market. This sector does not encompass all companies where software is the core technology, since companies with software expertise in specific market niches are

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sometimes included in other sectors (e.g., healthcare – IT). Subsectors included in this sector are business analytics, design and development tools, enterprise applications, enterprise infrastructure, and security. The Cleantech sector includes companies concerned with development of technologies that promote efficiency and economy in the use of resources, recycling technologies, recyclable materials, technologies for the prevention and treatment of pollution, and other eco-friendly technologies, products, processes, and services. Subsectors in this sector are agro technology, energy, environment, materials, and water technologies. It is known that organizations are not evenly dispersed across geographical space but tend to aggregate in local or regional clusters (Saxenian, 1994). Hence, data collection for this sample was done by way of clustered sampling by city. Israeli cities from each region, holding dense populations of relevant organizations, were selected for data collection, and whole populations of organizations situated in each city were included in the study. The ability to present sub-populations rather than samples is a great advantage to our study. Table 1 shows the distribution of sample firms by region. Table 1 concurs with the regional argument (Saxenian, 1994) that firms tend to be located in areas with an abundance of industry-related activity so that they will likely have greater access to resources such as qualified personnel, suitable laboratory space, and compatible technology that can give them an advantage over other firms. We see in Table 1 that Israeli firms are not evenly dispersed across geographical space. The densest overall regions are ‘‘Greater Tel Aviv’’ and Central with 43.8% and 33.8% (respectively) of all firms in the sample. This is not surprising, since these regions are considered the ‘‘technology center’’ of Israel, known for the abundance of high technology and knowledge-intensive firms and industries, residing in them. Table 1.

Life Sciences IT Cleantech Total

Distribution of Sample Firms by Sector and Region.

Count % within sector Count % within sector Count % within sector Count % within sector

North

Central

Greater Tel Aviv

Greater Jerusalem

South

Total

33 12.80 31 5.60 36 17.80 100 9.90

83 32.30 192 34.80 66 32.70 341 33.80

83 32.30 284 51.50 75 37.10 442 43.80

50 19.50 43 7.80 18 8.90 111 11.00

8 3.10 1 0.20 7 3.50 16 1.60

257 100.00 551 100.00 202 100.00 1,010 100.00

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In addition, firms were filtered by their ‘‘Development Stage.’’ The IVC segregates all knowledge-intensive firms into five development stage categories. The first category, ‘‘seed,’’ represents all firms in early stages of product development and fund raising. R&D stage includes firms that are in the midst of discovery and development of new knowledge about products, processes, technology, and services that can later be applied to market needs. The third stage category, initial revenue, includes firms that have yearly revenues that do not exceed 10 million dollars. Revenue growth is the fourth development stage category, including firms that have yearly revenue exceeding 10 million dollars, and a double digit yearly growth rate. The fifth category includes all firms that have ceased to operate, because they were either extinct or had gone through a merger or acquisition. As noted above, the aim of this study is to determine whether sectorial or regional differences exist in network activity and other parameters, regarding three knowledge-intensive sectors. In order to achieve this research objective, a sample rich in network ties was compiled. Since firms at early stages lack established networks of ties (Eisenhardt & Schoonhoven, 1996; Oliver, 2001), firms that are in the seed stage were not included in the sample. Dead and merged firms were also omitted from the sample due to the selection effect. According to Ahuja, Soda, and Zaheer (2012) and Oliver (2001), poorly positioned firms in a network may lack adequate resources to survive; thus, the network in a given industry may act as a selection mechanism, thinning out firms with weak or few partners. In this sense, including extinct organizations in the sample could cause difficulties in creating a sample abundant with network activity. For this reason, only firms that were classified as being in the R&D, initial revenue, revenue growth, and acquired stages were included in the sample. Table 2 presents information regarding the distribution of firms in the sample by development stage. This table shows that the majority of Life Sciences firms are in the R&D stage (45.5%), while the majority of IT and Cleantech firms are in the initial revenue stage (49.7% and 44.1%, respectively). The revenue growth stage has the least amount of firms, with 9.8% of firms in the overall sample. Of all firms in the sample, 20.7% are acquired, and IT has the largest amount of acquired firms. Methodology One Way Analysis of Variance In order to test our hypotheses, it is crucial to identify if existing regional and sectorial differences exist in the sample. To achieve this, one way

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Table 2.

Life Sciences IT Cleantech Total

Distribution of Sample Firms by Sector and Development Stage.

Count % within Count % within Count % within Count % within

sector sector sector sector

R&D

Initial Revenue

Revenue Growth

Acquired

Total

117 45.50 83 15.10 69 34.20 269 26.60

70 27.20 274 49.70 89 44.10 433 42.90

19 7.40 65 11.80 15 7.40 99 9.80

51 19.80 129 23.40 29 14.40 209 20.70

257 100.00 551 100.00 202 100.00 1,010 100.00

analysis of variance (ANOVA) tests were administered. Different variables measuring network ties, investor patterns, and innovation output were included in these tests. ANOVA tests are used to determine whether three or more groups have different means. In other words, this procedure tests the hypothesis that all group means are equal. This makes it an ideal procedure for testing differences in academic orientation for the three sectors included in the sample. An ANOVA test produces an F-statistic, or F-ratio, which compares the amount of systematic variance in the data to the amount of unsystematic variance. It can be calculated by dividing the model mean squares (MSm) by the residual mean squares (MSr), as shown below: F¼

MSm MSr

The F-statistic is in fact a ratio between the amounts of systematic and unsystematic variance in the data. A value less than 1 represents a nonsignificant effect, because the amount of unsystematic variance is greater than the systematic variance. Values greater than 1 are compared to the maximum F value expected to be obtained by chance in an F distribution with the same degrees of freedom. This is done to make sure that the obtained F value is large enough not to be a random result. If the value obtained exceeds the critical value, then it reflects an effect of the independent variable (Field, 2005). The ANOVA test is an omnibus test, meaning that it tests an overall mean difference effect, but it does not provide information as to which groups differ significantly from one another (Field, 2005). In order to examine

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specific differences between the sectors in question, post hoc tests must be utilized. Post hoc tests are used to compare all different combinations of test groups through pairwise comparisons. Bonferroni and Dunnett’s T3 post hoc tests were performed on the ANOVA models. The Bonferroni test was used when the homogeneity of variances assumption was possible. In condition where the homogeneity of variance assumption was violated, Dunnett’s T3 test was used. This post hoc test is specifically designed for conditions when the homogeneity of variance assumption is violated. Both post hoc tests are very conservative, and decrease the chance of making a type I error (Field, 2005). Variables Included in the One Way ANOVA tests Nine variables were included in the one way ANOVA tests. These variables measure different types of network activity and innovation output. Five variables representing four types of network ties were included in the study. Table 3 describes the nine measures used in this study. Table 3. Definition of Dependent Variables. No. 1

Type Interorganizational network ties

Title Academic ties

2

Technological ties

3

Board interlocks

4 5

Investors Returning investors

6 7

Innovation proxies

Academic publications Academic publication citation

8

Patents

9

Patent citation

Definition Number of outer firm collaborators for 5 most cited academic publications of firms founder/s Number of collaborating organizations written as assignees on the 7 most cited patents Number of board members that have a managerial position in another company Number of investors Number of investors who have invested in the same company at least twice Number of academic publications published by firm founder/s Number of citations for the 5 most cited academic publications of firm founder/s Number of registered European and US patents Number of citations for the 7 most cited patents

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Israel’s Knowledge-Intensive Sectors

A variety of network ties provide organizations with a range of resources ranging from funding, external capabilities, and knowledge to legitimacy and reputation. This study will measure network ties spanning four organizational levels. At the founder level, we measured the number of external collaborators on the five most cited academic papers published by firm founders. On the directorate level, we measured the number of board members holding a managerial position in another firm. Interorganizational ties were measured by the number of external organizations on the seven most cited patents assigned to the firm. Finally, investor networks were measured by two variables. The first variable gave a count of the number of investors. The second variable was designed to measure strength of investor’s trust in the firm and counted the number of returning investors. Innovation is measured on two levels: academic and technological. Academic innovation output is measured by sum of academic papers published by firm founders, while technological innovation output is measured by the number of registered US and European patents assigned to each firm. Innovation centrality, or brilliance of innovation, was measured by the number of citations for the five most cited academic publications and the seven most cited patents of the organization.

Data Description First, we present some data descriptive tables that show how the firms in the three knowledge-intensive sectors and regions and the firm development stage are distributed. Table 4 gives us the distribution information regarding Table 4. Three Regions with the Most Density for the Entire Sample and Each Sector. Sample – Regions with the most density: Greater Tel Aviv: 43.8% of all firms in the sample Central: 33.8% of the firms in the sample Jerusalem: 10.8% of the firms in the sample

Life Sciences – Regions with the most density: Greater Tel Aviv: 32.3% of all firms in the sector Central: 32.3% of the firms in the sector Jerusalem: 19.5% of the firms in the sector

IT – Regions with the most density: Greater Tel Aviv: 51.5% of all firms in the sector Central: 34.8% of the firms in the sector Jerusalem: 7.8% of the firms in the sector

Cleantech – Regions with the most density: Greater Tel Aviv: 37.1% of all firms in the sector Central: 32.7% of the firms in the sector North: 17.8% of the firms in the sector

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the three knowledge-intensive sectors and their regional distribution for the three densest regions. Greater Tel Aviv is the densest region, housing 43.8% of all firms on the sample. This region includes the following cities: Herzliya Pituach, Tel Aviv, Ramat Hasharon, Hod Hasharon, and Ramat Gan. The Central region holds 33.8% of all sample firms, making it quite dense as well. Cities included in this region are Raanana, Rosh-HaEin, Petach-Tikva, Kfar-Saba, Netanya, and Rehovot, where the Weizmann Institute and the Agriculture Faculty of the Hebrew University are located. The majority of the IT firms (50.4%) are located in the Greater Tel Aviv region. The Life Sciences industry is less densely concentrated and there are two main regions for this sector, Greater Tel Aviv and Central. These regions hold roughly two-thirds of all Life Sciences firms (64.6%). The Cleantech sector is concentrated mainly in the Greater Tel Aviv area (37.1) and Central region (32.7), and also has 17.8% of the firms in the North. This is the only sector that has a meaningful location outside of Tel Aviv and the Jerusalem area. In terms of the main regions, it is of interest that Jerusalem, being the capital and the location of the highly ranked Hebrew University, has a relatively low representation of Life Sciences firms (19.5%) and of IT firms (7.8%) with no significant density for Cleantech firms. In conclusion, the Greater Tel Aviv and Central regions are by far the densest regions for the sample as a whole and for each sector individually.

RESULTS One of the main goals of this study is to depict differences between firms belonging to different knowledge-intensive sectors. Table 5 shows results for one way ANOVA tests, conducted for the attainment of this research question.4 Differences between the three sectors were tested along three parameters that include the three measures of network patterns, the two measures of funding patterns, and the four measures of intellectual property. Network patterns were measured by way of three variables: academic ties, patent ties, and board interlocks. The F-statistic for both academic ties and technological ties is significant indicating that statistically significant differences exist between the sectors. The F-statistic for board interlocks is not significant indicating that no significant differences exist between the three sectors concerning board interlocks. Post hoc test results clearly show the existence of significant differences between all sectors, in regard to academic ties. The Life Sciences sector

10.218

91.236

8.41862 6.44768

F-statistic

Life Sciences IT Cleantech

5.997

Investors

6.44768 0.04282 1.97094 .04901

Cleantech Life Sciences IT

0.55798 1.72333

0.53770 0.81401

0.53770 0.27631

5.038

Returning investors

 The mean difference is significant at the 0.05 level.  The mean difference is significant at the 0.01 level.

0.59662 1.16536 0.27631 .51872 1.72333 .81401

8.41862 0.09183 0.07791 0.51872 1.97094 0.04901

0.07791 0.55798 0.59662 1.16536

2.489

Board interlocks

IT Life Sciences Cleantech

0.09183 0.04282

Patent ties

Academic ties

Funding Patterns

42.76330 4.00042

46.76372 4.00042

46.76372 42.76330

22.406

Academic publications

729.48907 63.82563

793.31470 63.82563

793.31470 729.48907

43.242

Academic publication citation

2.03164 3.08556

2.03164 1.05392

0.676

Patent citation

4.24600 1.05392 1.33434 3.08556

5.58035 1.33434

5.58035 4.24600

4.148

Patents

Innovation Proxies

Results for One Way Analysis of Variance by Sector.

Network Patterns

Table 5.

Israel’s Knowledge-Intensive Sectors 51

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exhibits the highest amount of average academic ties, with a significant mean difference of 8.41 and 6.44 in comparison to IT and Cleantech, respectively. Post hoc tests also indicate that the Cleantech sector has a significantly higher mean of academic ties in comparison to the IT sector, with a mean difference of 1.97. In conclusion, the Life Sciences sector has the highest average of academic ties, Cleantech is next in line, and the IT sector has the least amount of academic ties, relatively to the other sectors. The ANOVA test for patent ties also received a significant F score indicating the existence of significant differences between the sectors. The differences between sectors are less robust for patent ties as they are for academic ties. Post hoc tests show a single significant difference between the Life Sciences and IT sectors. The Life Sciences sector has significantly more patent ties on average than the IT sector, although this mean difference is quite small (0.09). No significant differences exist between IT and Cleantech, or between Life Sciences and Cleantech. The ANOVA test for board interlocks did not receive a significant F score. This indicates that no statistically significant differences exist in board interlocks for the three sectors. Regardless of sector, all firms in the sample have a similar average amount of board interlocks. Funding patterns were measured by way of two variables: investors and returning investors. The first variable measures quantity of ties, while the latter acts as a proxy for strength of investor ties for each focal firm. Both investor and returning investor ANOVA tests received a significant F score, meaning that significant differences exist between the sectors. Post hoc tests for both variables exhibit one significant difference between the IT and Cleantech sectors. The IT sector significantly has more investors and returning investors on average than the Cleantech sector, with a mean difference of 1.72 for investors and 0.81 for returning investors. The Life Sciences sector exhibited no significant differences with the other sectors, in regards to investors and returning investors. This sector does have a negative mean difference with the IT sector, indicating that firms situated in the IT sector have the largest average amount of investors and returning investors of all other sectors in the sample. Intellectual property was measured by four variables. The first variable in this category is academic publication and it received a significant F score, indicating that sector differences exist for academic publications of firm founders. Post hoc tests for this variable reveal that statistically significant differences exist between the Life Sciences sector and IT and Cleantech. Firm founders in the Life Sciences sector on average have more academic

Israel’s Knowledge-Intensive Sectors

53

publications then their peers in the IT and Cleantech sectors, with a significant mean difference of 46.7 with IT and 42.7 with Cleantech. A nonsignificant difference exists between the IT and Cleantech sectors. Cleantech firm founders on average will have four more academic publications than their counterparts in the IT sector. The second variable in the intellectual property category is academic publication citation, and the ANOVA test for this variable received a significant F score, indicating significant differences between sectors. Post hoc tests reveal a similar pattern of differences for this variable and the academic publication variable. The Life Sciences sector again has the highest amount of average academic publication citation, with significant positive differences with the other sectors. The mean differences are quite large, with a 793.3 mean difference between the Life Sciences and IT, and a 729.48 mean difference between the Life Sciences and Cleantech sectors. As with academic publications, no significant mean difference can be seen between IT and Cleantech, although Cleantech academic publications will receive an average of 63.8 citations more than the IT academic publications. The third and fourth variables are number of patents and patent citations. The ANOVA test for number of patents received a significant F score indicating the existence of significant differences between sectors. The only statistically significant difference, detected by the post hoc tests, is between the Cleantech and IT sectors. Cleantech companies have a significantly larger average of patents than the IT firms, with a mean difference of 1.33. No significant differences exist between the Life Sciences sector regarding IT and Cleantech, although it is worth mentioning that on average, Life Sciences firms have more patents than IT and Cleantech, with a mean difference of 5.58 with IT and 4.24 with Cleantech. The ANOVA test for patent citation received a nonsignificant F score indicating that no significant differences exist between the three sectors. However, on average IT patents receive 2.03 more citations than Life Sciences patents, and 3.08 more citations than Cleantech patents. Life Sciences and Cleantech patent citation averages are almost similar with a mean difference of 0.67. A second goal of this study is to depict differences between firms belonging to different regional areas of Israel. Table 6 shows results for one way ANOVA, administered for the attainment of this research question. Differences were calculated once again along three parameters: network patterns, funding patterns, and firm-level intellectual property. Out of all tests, only four variables received a significant F score: academic ties, investors, returning investors, and patent citation. This means

4.11046 1.01132 1.85714 8.08201

3.97155 7.07069 6.22487 8.08201

0.16582 1.68852 0.43338 0.19907 0.18068 0.40637 0.50000 1.81818

0.34649 1.28215 0.25270 0.60545 0.18068 0.40637 0.68068 1.41181

0.02000 0.33418 0.12966 0.02933 0.93338 2.01726 .04299 .68068 1.41181 0.07207 0.50000 1.81818

0.05207 0.04275 0.02909 0.07207

2.25331 0.02299 0.84582 0.01366 6.22487 0.02909 1.85714 0.04299

0.69242 1.74163 1.32473 1.36623

0.67381 0.37539 0.04150 1.36623

0.63230 0.41690 0.04150 1.32473

1.04920 0.41690 0.37539 1.74163

1.88760 0.60545 0.19907 2.01726

3.09913 0.00933 0.84582 0.01366 7.07069 0.04275 1.01132 0.02933 0.59919 0.25270 0.43338 0.93338

0.59919 1.88760 1.04920 0.34649 1.28215 0.63230 0.16582 1.68852 0.67381 0.33418 0.12966 0.69242

2.898

Returning investors

3.09913 0.00933 2.25331 0.02299 3.97155 0.05207 4.11046 0.02000

1.216

2.36

0.717

15.483

 Significant at the 0.05 level.  Significant at the 0.01 level.

F-Statistic North Central Greater Tel Aviv Greater Jerusalem South Central North Greater Tel Aviv Greater Jerusalem South Greater Tel Aviv North Central Greater Jerusalem South Greater Jerusalem North Central Greater Tel Aviv South South North Central Greater Tel Aviv Greater Jerusalem

Investors

Academic Patent ties Board ties interlocks

Funding Patterns

13.11224 0.51165 7.00922 23.74735

10.63511 24.25901 16.73814 23.74735

6.10303 7.52087 16.73814 7.00922

13.62390 7.52087 24.25901 0.51165

13.62390 6.10303 10.63511 13.11224

1.466

Academic publications

122.72449 123.10823 3.18785 325.93651

203.21202 202.82828 322.74866 325.93651

119.53664 119.92038 322.74866 3.18785

0.38374 119.92038 202.82828 123.10823

0.38374 119.53664 203.21202 122.72,449

1.753

Academic publication citation 2.791

Patent citation

0.45077 8.17358 2.52356 1.35577

7.72282 8.17358 5.65002 6.81782

0.82000 0.90500 2.76760 6.81782 0.58032 1.35577 0.42117 1.16779

0.39883 2.07279 3.18877 5.65002 0.15915 2.52356 0.42117 1.16779

0.23968 3.34791 0.15915 0.58032

3.58760 3.34791 3.18877 2.76760

3.58760 7.72282 0.23968 0.45077 0.39883 2.07279 0.82000 0.90500

0.953

Patents

Innovation Proxies

Results for One Way Analysis of Variance by Region.

Network Patterns

Table 6. 54 AMALYA L. OLIVER AND NOAM FRANK

Israel’s Knowledge-Intensive Sectors

55

that significant differences exist between regions for only these variables confirming the expectation for a high level of homogeneity across regions. Network pattern variables received a significant F score only for academic ties. Firms in the Greater Jerusalem region have the highest average number of academic ties (9.79) and exhibit significant mean differences with Greater Tel Aviv, Central, and Southern regions. One possible explanation for this phenomenon is the proximity of the firms in the Jerusalem region to the Hebrew University, which is one of the leading research institutes in the country. The northern region has two statistically significant differences with the Central and southern regions, with a mean difference of 3.09 and 4.11, respectively. Firms in this region have 5.82 academic ties on average. Firms in the southern region have the least average amount of academic ties. ‘‘Greater Tel Aviv’’ and the ‘‘Central’’ regions have the highest firm density in comparison to the other regions. Interestingly, they do not exhibit a significant difference between themselves, with regards to academic ties. The second variable measuring network patterns is patent ties. The ANOVA test for this variable did not receive a significant F score, indicating no significant differences between regions regarding patent ties, although one significant difference did occur. Post hoc tests uncovered a significant difference between the Central and southern regions, with a small mean difference of 0.029. The ANOVA test for board interlocks received an insignificant F score as well; thus, no significant differences exist between regions on this variable. This finding is similar to the comparison between sectors where no significant differences in board interlocks were found. Both funding pattern variables received a significant F score, indicating that significant differences exist between regions, regarding sum of investors and returning investors. In terms of investors, Greater Jerusalem exhibited no significant differences with all regions, while the Northern region exhibited significant differences with the Central and Greater Tel Aviv regions. The mean differences here are negative meaning that on average, firms in the north attract significantly less investors than the Central and Greater Tel Aviv regions. The Central and Greater Tel Aviv regions again show no significant differences between them. Firms located in the southern region of Israel have the least ability to attract returning investors, in comparison to all other regions. This region exhibits significant negative differences with all other regions, indicating that firms in the southern region have the lowest average amount of returning investors.

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Post hoc tests show a significant difference between the Central and northern regions, with a mean difference of 1.04, indicating that on average firms located in the Central region will attract more returning investors than their peers in the northern region. Once again no significant differences can be seen between the densest regions: Central and Greater Tel Aviv. The only variable in the intellectual property group that received a significant ANOVA score is patent citation. This is striking, because no significant post hoc scores were discovered for this variable. In addition although academic publication did not receive a significant ANOVA F score, post hoc tests reveal that a significant difference exists between the Greater Jerusalem and Central regions. The mean difference here is 24.25, indication that founders of firms located in the Greater Jerusalem region will publish an average of 24 more papers than their peers in the Central region. The Greater Jerusalem region is the leader of academic publications, holding positive mean differences with all other regions. Academic publication citation and number of patents did not receive significant F scores with regards to regions. This indicates that no significant difference exist between regions, regarding these variables. In contrast, the sector comparison of this variable was significant for the number of patents held by firms. As mentioned above, the two firm densest regions are Greater Tel Aviv and Central. No significant differences were found between these two regions. Although many significant differences exist at the sector level, these differences vanish when firms are located in the regions with the largest amount of firm density. There are also no differences between the northern and Greater Jerusalem regions.

DISCUSSION The goal of this study was to explore sector and region differences between three knowledge-intensive sectors in Israel. The choice of Israel offers an opportunity to explore such issues in a small country where there are relatively small commuting distances between all regions. This characteristic brings to the forefront, the question regarding regional effects. The main argument in our study, in terms of regional effects, is that ‘‘place makes a difference’’ (Gluckler, 2007, p. 621). This argument is twofold: place makes a difference and proximity affects network formation. The first argument borrows on the idea of ‘‘resources bundle’’ (Penrose, 1959) that a geographical region is conceived as an area with agglomeration

Israel’s Knowledge-Intensive Sectors

57

of resources and opportunities for firms. The place-specific resources provide the context in which the firms operate. The geographic locality of resources has an impact on network features such as on social capital of firms and on other resources such as materials, knowledge, and institutional resources to which firms have access. The second part of the argument is that geographical proximity matters only if there is a need for frequent ‘‘face-to-face’’ interaction as a main form of communication or if transportation is prohibitively expensive or taxing (Gluckler, 2007, pp. 621–622). In this context, we know that face-to-face interactions are important for learning and knowledge development (Nonaka, 1994); yet, the effect of a small country and short travel distance between regions and the proximity constraint becomes questionable and worthy of inquiring. This challenge was one of the two goals of our study. If the over-ruling effect of a small country on the need for regional proximity of knowledgeintensive industries is valid, we should observe homogeneity between clusters in different regions. These are indeed the main finding of our study. Generally, we found no significant differences between the five main regions across the measures of patent ties, board interlocks, number of academic publications of founders and the citations of the academic publications of the founders, and the firm number of patents. The significant differences across regions were found only for academic ties and number of investors and the number of returning investors and the rate of firm patent citations. Academic ties are mostly significant for firms located in the Jerusalem cluster and this includes mostly biotechnology firms, for which we know there is a great dependency on academic basic research and that many entrepreneurs emerge out of universities and conduct research prior to their entrepreneurial stage (Oliver, 2004). Similarly, for firm patent citations, this innovation proxy variable is especially associated with biotechnology that is located mainly in the Jerusalem area. The other significant finding is that the number of investors and the number of returning investors differentiate significantly between sectors and that the regions that has the highest values are Greater Tel Aviv and Central. This finding corresponds to the concept of ‘‘regional industrial identity’’ suggested by Romanelli and Khessina (2005). Their argument is that the concept serves as a social code that ‘‘(1) arises from the shared understandings of residents and external audiences about the suitability of a region for particular kinds of business activity and (2) influences decisions about where to locate investments’’ (2005, p. 344). The second part of the argument is evident in our study. The number of investors and the number of returning investors within a region depicts the perception of investors

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about the resources and legitimacy of firms within a regional concentration in comparison to other regions. We do not have detailed internal and procedural data on the decision-making process of the investors, but we embrace the argument by Romanelli and Khessina on metropolitan regions as sites of identities. These identities are ‘‘ybased on the degree to which both internal and external audiences develop shared understandings about the key features of life and work in a regiony. To the extent that observers agree about the key features of life and work in a region, they are likely to respond in similar waysy.Thus, regional identity influences the developmental capacities of regions through informing the understandings of observers and directing their geographic targets of investment.’’ (2005, p. 346). It seems that the regional identity of the Greater Tel Aviv and Central regions is associated with joint investments and returning investments, more than in any other region. Our findings also show that for the most dense and proximate regions, Central and Greater Tel Aviv regions, there were no significant differences on all measures, giving a strong confirmation to the homogeneity of proximate dense regions argument. It is of value to stress here that the two densest regions for all sectors are Greater Tel Aviv and Central and they are also the only two regions that no significant differences were found across all the variables in our study. Therefore, we can claim that intra-regional density for proximate regions has an additional homogeneity effect beyond intra-regional homogeneity. The second goal of this study is to compare the three knowledge-intensive sectors in order to detect the main differences between them. On this front, we found many more sector level differences. The sectors were significantly different for the variables that measured: academic ties, patent ties, investors and returning investors, number of academic publications of founders and academic publication citation, and number of firm patents. There were no significant differences between the three knowledge-intensive sectors for board interlocks and for the firm patent citation rate. From the literature on the Life Sciences sector, we know that there is a high dependency on academic basic research and on the protection of IP through patenting, and that most of the early entrepreneurs in this industry come from academia (Oliver, 2009). These insights help explain why the variables that estimated the contact with academia were significantly higher for the firms in the Life Sciences sector. Product development in the Life Sciences entails high costs and requires great financial investments and the product life cycle can be longer than 10 years (Powell et al., 1996); this may explain why there are more investors and more returning investors in this sector, in comparison with other sectors.

Israel’s Knowledge-Intensive Sectors

59

Our exploratory study allows us to offer some generalized propositions that can be further tested in a large-scale, hypotheses testing study. Based on our initial arguments and findings, we can offer the following propositions: H1. Geographical proximity is associated with similarity in resources that are available and needed for knowledge-intensive firms. The similarity in the availability of the resources is associated with similarity of appropriation patterns of these resources by knowledge-intensive firms. These similarities are expected to be strong despite sector-based differences. The second proposition follows the geographical proximity argument, in the context of a small geographical area: H2. Geographical proximity for knowledge-intensive firms is needed in order to allow for frequent interactions and learning-based encounters. Localized cluster intensity of interactions is dominant, where there are no other proximate clusters, but when there are short geographical distances between clusters, the local cluster distinguishing effect is reduced. This will result with more homogeneity between firms in proximate clusters. Finally, our study showed some similarities and some differences between the knowledge-intensive sectors. In light of these findings, we can propose that H3. Despite regional similarities between the three knowledge-intensive sectors, there are sector-based idiosyncrasies that remain and these are associated with the dependency on university-based knowledge. Life Sciences are expected to be dependent upon collaborative ties with universities more than other knowledge-intensive sectors, regardless of the geographical area of the cluster to which they belong. Aside from these propositions, we want to emphasize one final topic of interest in the context of a small country effect. The lack of significant differences between the three sectors for the board interlock measure is, in our view, the sign of the small country homophily pattern. The network structure of board interlocks is similar for all three sectors probably due to the fact that in a small country context, the social capital of actors in terms of ‘‘who knows who’’ is of value to board members (Maman, 2000). This is because directors, who are active in different organizations and connect between them, become more powerful and can also enhance the firm’s accessibility to resources in other firms. In Israel, dense board interlocking directorates, especially among business groups, is a common phenomenon (Maman, 2000). Thus, we can assume

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that the similarity among the three sectors on this measure reflects the equal diffusion of the political-economy norms to the three sectors, regardless of the product and process differences between them, as were depicted by the other variables. In summary, our findings support the notion of a ‘‘small country interregional homogeneity effect.’’ Our findings show that while most regions show similar patterns of firm and networking characteristics, there is large differentiation between the three sectors.

NOTES 1. A summary of the dependent variables used for the linear regression models is reported in Table 4. 2. All data compiled from the IVC data source are labeled by date of last update. Although data were collected during a single period, firms exhibit minor differences regarding the date of last update, causing a small variance. The mean year of last update for all firms in the sample is 2010, with a standard deviation of 1.04 years. The minimum and maximum years of last update are 2006 and 2012, respectively. By contrast, data regarding patent and academic publication activity are up to date. 3. All information regarding these sectors is taken from the IVC database: http:// www.ivc-online.com/ 4. Tables with mean and standard deviation statistics for sectors and regions can be found in Appendices A and B.

ACKNOWLEDGMENTS This research was funded by the Israeli Science Foundation (to Amalya L. Oliver) and the Shaine Center for Research in Social Sciences, Department of Sociology and Anthropology, Hebrew University (to Noam Frank). The paper was written while being hosted by the Institute of Advanced Studies at the Hebrew University, 2012–2013.

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Oliver, A. L. (2004). Biotechnology entrepreneurial scientists and their collaborations. Research Policy, 33(4), 583–597. Oliver, A. L. (2009). Networks for learning and knowledge-creation in biotechnology. Cambridge: Cambridge University Press. Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5–21. Penrose, E. T. (1959). The theory of the growth of the firm. New York: John Wiley. Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knowledge networks: A review and research agenda. Journal of Management, 38(4), 1115–1166. Piore, M. J., & Sabel, C. F. (1984). The second industrial divide: Possibilities for prosperity. New York: Basic Books. Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–145. Powell, W. W., Koput, K. W., Smith-Doerr, L., & Owen-Smith, J. (1999). Network position and firm performance: Organizational returns to collaboration in the biotechnology industry. In S. Andrews & D. Knoke (Eds.), Research in the sociology of organizations (pp. 129–159). Greenwich, CT: JAI Press. Powell, W. W., White, D. R., Koput, K. W., & Owen-Smith, J. (2005). Network dynamics and field evolution: The growth of inter organizational collaboration in the life sciences. American Journal of Sociology, 110(4), 1132–1205. Provan, K. G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of Management, 33(3), 479–516. Rampersad, G., Quester, P., & Troshani, I. (2010). Managing innovation networks: Exploratory evidence from ICT, biotechnology and nanotechnology networks. Industrial Marketing Management, 39(5), 793–805. Romanelli, E., & Khessina, O. M. (2005). Regional industrial identity: Cluster configurations and economic development. Organization Science, 16(4), 344–358. Salomon, R., & Martin, X. (2008). Learning, knowledge transfer, and technology implementation performance: A study of time-to-build in the global semiconductor industry. Management Science, 54(7), 1266–1280. Saxenian, A. (1994). Regional advantage: Culture and competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Senor, D., & Singer, S. (2009). Start-up nation. New York, NY: Grand Central Publishing. Sorenson, O., & Stuart, T. E. (2001). Syndication networks and the spatial distribution of venture capital investments. American Journal of Sociology, 106, 1546–1588. Wong, S. S., Ho, V. T., & Lee, C. H. (2008). A power perspective to interunit knowledge transfer: Linking knowledge attributes to unit power and the transfer of knowledge. Journal of Management, 34(1), 127–150.

Cleantech

IT

Life Sciences

Whole sample

Mean SD Mean SD Mean SD Mean SD

Statistic

4.1702 8.87766 10.0643 13.05432 1.6456 4.28911 3.6166 8.20684

Academic ties

0.0386 0.27713 0.0973 0.40778 0.0054 .07365 0.0545 0.38891

Patent ties

Investor Ties

2.3346 2.21375 2.4832 2.42622 2.4053 2.16837 1.8866 1.96255

4.7016 4.69176 4.6613 5.39152 5.2193 4.62080 3.4959 3.34216

1.4789 2.60002 1.2678 2.61429 1.8055 2.54676 0.9915 2.62119

19.7533 95.42196 53.9076 173.65948 7.1439 39.99170 11.1443 34.09207

Board Investors Returning Academic interlocks investors publications

Network Ties

299.8853 1,188.32816 880.8911 2,126.35687 87.5764 438.34076 151.4021 539.30774

2.8030 25.80144 6.6965 50.50550 1.1162 3.15956 2.4505 6.19742

6.3723 35.30136 5.4747 25.49158 7.5064 43.18631 4.4208 17.78551

Academic Number of Patent patents citation publication citation

Innovation Proxies

APPENDIX A: MEANS AND STANDARD DEVIATION STATISTICS FOR SECTORS

Israel’s Knowledge-Intensive Sectors 63

Greater Tel Aviv Greater Jerusalem South

Central

North

Mean SD Mean SD Mean SD Mean SD Mean SD

Statistic

Investor Ties

Innovation Proxies

5.8247 9.82133 2.7256 6.27390 3.5714 7.43071 9.7963 15.72649 1.7143 3.09910

0.0200 0.14071 0.0293 0.16897 0.0430 0.35693 0.0721 0.29269 0.0000 0.00000

1.9592 1.59373 2.5584 2.39321 2.3057 2.22683 2.1250 2.01974 1.6250 1.68502

3.3115 2.53995 5.1991 5.37974 4.5936 4.48489 5.0000 4.68432 3.1818 1.60114

0.7833 1.62701 1.8325 3.02803 1.4156 2.36977 1.4571 2.69560 0.0909 0.30151

26.0408 73.46265 12.4169 37.65427 19.9378 128.57341 36.6759 83.82579 12.9286 19.93630

332.0102 1,336.81779 332.3939 1,170.51847 212.4736 1,086.99826 535.2222 1,502.54614 209.2857 301.06003

1.4300 2.76067 5.0176 43.75829 1.6697 5.90433 1.8288 4.06062 2.2500 3.62399

3.7200 18.57232 11.4428 50.94865 3.2692 24.99174 5.7928 21.37250 4.6250 8.91347

Academic Patent ties Board Investors Returning Academic Academic Number of Patent patents citation ties interlocks investors publications publication citation

Network Ties

APPENDIX B: MEANS AND STANDARD DEVIATIONS FOR REGIONS

64 AMALYA L. OLIVER AND NOAM FRANK

PART II

CHAPTER 3 THE EVOLUTION OF RESEARCH COLLABORATION NETWORKS AND THEIR IMPACT ON FIRM INNOVATION OUTPUT Irem Demirkan and David L. Deeds ABSTRACT How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the egonetwork on the innovation output of biotechnology firms. Our results also

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 67–95 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013006

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suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges. Keywords: Network structure; research collaboration networks; ego-networks; biotechnology industry; innovation

INTRODUCTION A significant number of studies have recently provided evidence that networks and relationship-specific assets enable organizations to access resources that may not be available through market exchanges (Gulati, 1999; Gulati, Nohria, & Zaheer, 2000). Accordingly, during the last decade, researchers have attempted to look into the various aspects of networks such as the evolution of networks (Gulati & Gargiulo, 1999; Madhavan, Koka, & Prescott, 1998; Powell, White, Koput, & Owen-Smith, 2005). An in-depth understanding of how networks evolve and change over time (Nohria, 1992) is important because such an understanding enables managers to further build effective interorganizational networks (Koka, Madhavan, & Prescott, 2006) and eventually to benefit from them. Accordingly, this article addresses the crucial questions: (1) How does a firms’ ego-network change? (2) How does such a change affect firms’ innovation output? Firms’ networks emerge, develop, and sometimes disappear. Firms’ subsequent development will affect and be affected by the interactions that have taken place in their networks (Ford & Redwood, 2005). Yet, the majority of current research on such dynamic processes of network change has been relatively static in nature (Powell et al., 2005). Most studies analyze organizations and their networks at a particular point in time, disregarding the dynamics and change in their networks. Although Powell et al. (2005) note that significant structural changes occur in the network as organizations enter and exit the network and as their relationships deepen and expand, Powell et al. (2005) do not move beyond this observation. Similarly, Hite and Hesterly (2001) try to understand the dynamics of networks of organizations by studying cross-sections of firms and their networks at different stages of the firm’s life cycle rather than directly examining the change in individual firms’ networks across time. Moreover, there have been very few studies that employ a longer time perspective to analyze networks (Burt, 2000; McPherson, Smith-Lovin, & Cook, 2001). Although these studies open up the discussion of the need to look into the dynamics of

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network change and the importance of this process, in particular, network dynamics and the evolution of networks have both been under-researched. In addition to the need for more research on the evolutionary dynamics of networks, the literature still lacks an understanding that addresses the specific effects of the dynamics of the firm’s ego-network structure on organizational performance and the implications of changes in ego-network structure on the demand on a firm’s management. While some changes in firms’ ego-networks may enhance performance, some may destroy value. However, the literature does not provide a clear picture of how such egonetworks differ from one another. There are a few studies that look into the effects of different elements of ego-network structure on organizational performance, yet the existing studies have not explained how the changing characteristics of ego-networks affect firms’ innovative output. A rare example is Ahuja (2000), who assesses the effects of network characteristics such as direct ties, indirect ties, and structural holes on a firm’s subsequent innovation output. To our knowledge, there has been no study that directly links changes in the firms’ ego-networks over time to changes in firm performance over time, nor are there studies that directly examine potential limits to ego-network growth such as the considerations of the costs of managing an additional node in firms’ ego-networks. The purpose of this study is to develop and empirically test an integrative model that directly links changes in the structure of the firms’ ego-network across time to firm-specific outcomes by considering both the benefits and potential costs of changes in the firms’ ego-network. We build our model of firms’ ego-networks by focusing on the research collaboration ego-networks through co-authorship, that is, the firms’ co-authorship networks. From this standpoint, the biotechnology industry offers a well-defined research setting. The biotechnology industry is characterized by the dominance of social networks of academic scientists (Liebeskind, Oliver, Zucker, & Brewer, 1996), who predominantly work and publish together and show norms of trustworthy behavior in information exchanges. Therefore, firms’ coauthorship networks represent a quantifiable social interaction where the co-authorship decision is made entirely by the authors and hence the decision making is performed at the individual level and are closer to a prototypical evolving networks (Barabasi et al., 2002, p. 592). Social interaction is important since knowledge diffusion occurs through interaction, and the structure over which organizations interact influences not only the scope but also the impact of the diffusion of knowledge. In this study, we rely on the co-authorships between biotechnology firms, universities, research institutions, and pharmaceutical firms as the primary source of

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ego-networks. Co-authors on one chapter have, by necessity, been required to interact socially in some form; this interaction is not only in accordance with the definition of ego-networks (Powell, 1990), but it is also a more direct way to measure knowledge diffusion than most of the previous studies that have mainly focused on organizational level ties, such as strategic alliances, between firms (Kogut, Shan, & Walker, 1992; Powell & Brantley, 1992; Pisano, 1990). Overall, we contribute to the current literature on interorganizational networks not only by focusing on the characteristics of ego-network change and its effects on organizational outputs but also by offering a novel context that brings out the social aspect of network-based relationships.

THEORY AND HYPOTHESES It is crucial to understand ego-network changes over time in order to look into the effects of ego-networks on firm performance. One important reflection of firm performance is a firm’s innovativeness. Existing research in the innovation literature supports the notion that highly innovative firms perform better than less innovative ones (Dosi, 1988; Mansfield, 1968; Wolfe, 1994). It has been widely acknowledged that the search for novel knowledge is at the center of the research on innovation (Coombs, Deeds, & Ireland, 2009). Organizations rely on interfirm linkages to foster innovation, since focusing only on internal research and development (R&D) is not sufficient in creation of significant scientific breakthroughs (Rothaermel & Hess, 2007; Su, Tsang, & Peng, 2009). Although evolving networks display a high degree of complexity (Powell et al., 2005), we aim to open up this complex structural relationship through the use of a longitudinal perspective. Network ties generally act as conduits of and access to knowledge, and play a key role in the information gathering process of firms. However, managing an ego-network is unlikely to be costless and will therefore add to a firm’s management burden, and the performance implications of a firm’s ego-network are likely to vary depending on the changing structure of the ego-network. We argue that while ego-network structure is likely to influence firm performance in the context of knowledge transfer and use, its effects may be contingent on the properties of this ego-network structure and its evolution over time. In order to describe the evolutionary effects of ego-network change, from a structural view, we focus on the impact of four main ego-network variables on the firm’s innovative outcome. These four variables are (1) the size of

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the firm’s ego-network, (2) its growth rate, (3) the number of new egonetwork members, and (4) the diversity of the ego-network. While these variables are highlighted in previous studies, they have rarely been examined collectively. For example, Powell et al. (2005) mention that the relationships of member organizations as well as the structure of the network change as organizations enter and exit a certain field and this dynamic entry and exit process would most likely affect the size and rate of growth in the network. Our focus on the new ties created and changed in the composition of the ego-network (ego-network diversity) is based on prior research. Madhavan et al. (1998) and Goerzen (2007) have asserted that the composition of a firm’s ego-network may change in response to a change in industry conditions. Therefore, focusing on the new ties created and changed in the composition of the ego-network (ego-network diversity) also allows us to look into how a firm’s ego-network changes its portfolio, and also eventually enables us to form predicted patterns of ego-network change (Koka et al., 2006). We propose that these four variables of (1) ego-network size, (2) growth rate, (3) new tie formation, and (4) ego-network diversity are basic units of analysis that would enable us to track empirically the effects of ego-network evolution. We posit that focusing on these variables is one way to assess the effects of ego-network change. Then, we examine these dynamics of change on firms’ innovativeness; these relationships are depicted in Fig. 1.

Ego-Network Size Interorganizational relationships enable firms to access critical resources and create competitive advantages (Dyer & Singh, 1998). Powell, Koput, and Smith-Doerr (1996) assert that firms operating in industries where know-how is critical must develop skills necessary for both in-house research and cooperative research with external partners. In the biotechnology industry, particularly with close relationships in co-authorships, research collaboration ego-networks facilitate the flow of knowledge among organizations (Kogut, 1988; Mowery, Oxley, & Silverman, 1996). These ego-networks also offer the firm the benefit of extended know-how (Su et al., 2009; Teece, 1992) as well as increasing constituents’ perceptions of the quality and reliability of the firm (Stuart, Hoang, & Hybels, 1999). Through such collaborative research ego-networks, organizations also understand the need for further exploration of novel ideas and information in order to exploit their findings in a commercial context (Powell et al., 1996).

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H1 (+)

Ego-Network Size

H2 (inv. U) H3 (+)

Ego-Network Growth H4 (–)

Firm Size

Firm Age H7 (–)

New Members

H5 (–)

Innovation Output H8 (–)

H6 (+) H9 (+)

Ego-Network Diversity

Fig. 1.

H10 (inv. U)

Conceptual Model.

However, most of the time, firms do not know from which collaboration partners they can most benefit depending on particular situations and changing research contexts (Peng & Wang, 2000; Zucker, Darby, & Brewer, 1998). We assert that it is beneficial for the focal firm to keep its ego-network as large as possible, thereby increasing the probability of partners that are valuable for the new knowledge creation challenges faced by the firm. There is a higher possibility that a firms’ network would include weaker ties (Granovetter, 1973) within a larger network; for instance, Peng and Zhou (2005) point out the importance of having both strong and weak ties within one’s network. Exposure to new ideas also comes from interaction with those to whom we are weakly tied, since such individuals might be included in many different circles and have access to information and resources that the focal firm may not. Therefore, H1. A firm’s innovative output increases with the size of its ego-network. Although we argue that it is desirable to have a large ego-network, there are some complexities of interdependencies between network actors, which

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often make it difficult, if not impossible, for the firm to effectively manage its network (Hakansson & Johanson, 1992; Rothaermel & Deeds, 2006). Increasing the number of direct partners in a network boosts the amount of information, ideas, and resources available through it, but it also increases the challenges of managing the ego-network. Additional members in the ego-network increase the potential for goal and value conflicts, the amount of coordination required between members, and the difficulty of negotiating agreement among the members (McFadyen & Cannella, 2004). Therefore, there will be a limit to the number of productive relationships that a firm can maintain over time. In addition, the larger the total ego-network of the firm, the more the resources and time are devoted to managing these relationships and the less effort researchers devote to innovative activities, which eventually affects the innovative output of the firm. Too few members in the ego-network, on the contrary, may limit novel knowledge creation and innovative performance because there will be an insufficient flow of ideas, information, and resources. Accordingly, we argue that: H2. The size of the firm’s ego-network has an inverted U-shaped relationship with its innovative output.

Ego-Network Growth Ego-network growth is defined as the rate of increase or decrease in the number of participants in the firms’ ego-network. Inclusion or exclusion of some members within specific time intervals reflects real changes in the egonetwork over time and captures an important aspect of the evolution of the firms’ ego-network. The primary motivation for enlarging a firms’ egonetwork is to access new knowledge that aids in the generation of novel product or service ideas for these companies. The motivations for shrinking firms’ ego-networks are not as clear; however, they most likely reflect the failure of the prior connection to generate valuable new knowledge, or the full exploitation of any novelty available from the inclusion of that organization in the firm’s ego-network. We believe that there are three main reasons why growth of the egonetwork is important. First, as Powell et al. (2005, p. 1137) claims, ‘‘Biotechnology firms had to both make news and be in the news.’’ As such, it is important for a biotechnology firm to ensure visibility in the whole industry. Enlarging networks through research collaborations indicates that the specific biotechnology firm is in action and it is searching for novel ideas.

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Growth in a firm’s ego-network in that sense increases visibility. Second, through growth in their ego-network, firms increase their opportunity to create valuable, novel combinations of knowledge by adding other organizations’ knowledge to their ego-network. From this standpoint, prior research has asserted that in general, network growth is characterized by the appearance of new nodes (Gay & Dousset, 2005), which in turn brings in new, non-redundant knowledge. Third, firms expand or renew their egonetworks through the addition of network members, and by doing so they maintain their position in the industry network. This also helps to increase the legitimacy of the focal firm by mitigating the risks and disadvantages of being in an otherwise static ego-network. Such a position also benefits the firm in terms of increased opportunity to work and publish with other firms within the industry network. By publishing more and expanding their egonetwork, firms enhance their access to leading-edge knowledge and information, which should increase the innovative output of the firm. Ego-network growth enhances the firm’s ability to generate innovations by expanding their access to novel knowledge and information and enhancing their visibility within the industry. Therefore, H3. A firm’s innovative output increases with the growth in its egonetwork. As scholars, we recognize the benefits to having a large co-authorship network; however, most of us have also experienced the resulting challenge of productively managing the larger number of co-authors and projects. Some of us may have experienced the point of diminishing returns to work with another co-author and recognized our personal limits. We should expect firms to experience similar demands and constraints. In any egonetwork, there are complexities caused by the interdependencies between network actors that often make it difficult, if not impossible, for the individual or the firm to effectively manage large ego-networks (Hakansson & Johanson, 1992). As the number of direct partners in a network expands, the amount of information, ideas, and resources available through it rises. However, it also heightens the managerial demands placed on the firm by the ego-network. Growth in the ego-network raises the potential for goal and value conflicts, the amount of coordination required between members, and the difficulty of negotiating agreement among the members (McFadyen & Cannella, 2004). While growth in firms’ ego-network is desirable for a firm to gather new ideas and increase its innovativeness, more ego-network partners at the same time mean more direct relationships to be managed and more time,

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energy, and resources that must be devoted to such relationships. Management issues in an ego-network context can be exceedingly complicated due to the embedded and reciprocal character of relationships (Ford & McDowell, 1999; Hakansson & Ford, 2002). These issues might become even more complicated with ego-network growth due to the increasing number of transactions and relationships. Given the difficulties associated with the management of a growing number of relationships, we suggest that such management is particularly difficult for small companies. It is established in the literature that due to the liability of smallness (Hannan & Freeman, 1984), small companies have various difficulties compared to larger ones, such as difficulty in raising capital and in dealing with governmental regulations, as well as problems gathering skilled labor experienced in managing extensive collaborative relationships (Singh & Lumsden, 1990, p. 176). Higher administrative costs are already incurred by these small firms, impeding the channeling of resources for the management of these growing collaborative relationships. Firm size is a primary indicator of firm resources, and most of the time larger firms are associated with an ample amount of both tangible and intangible resources (Dobrev, 2001; Levinthal, 1991; Mitchell, 1994). Therefore, we suggest that larger firms would more easily be able to manage the extensive relationships associated with ego-network growth. H4. Firm size will moderate the relationship between ego-network growth and the innovative output of the firm: the positive relationship between ego-network growth and the innovative output of the firm will be stronger for larger firms. Closely related to the concept of ‘‘smallness’’ is the liability of newness (Stinchcombe, 1965). Prior research has noted that young or new firms face particular difficulties due to immature routines, unstable relationships with constituents, and the simple task of learning how to work efficiently together. Younger, less experienced firms will be less likely to have developed the managerial skills and processes needed to effectively meet the demands of rapidly growing and increasingly complex ego-networks. Younger firms are already stretched simply trying to deal with the challenges of institutionalizing their existence in the broader industry, which will limit their ability to invest the resources required to effectively manage growth in their ego-network. Thus, younger firms will be less able to reap the benefits of growth in their ego-network, in comparison to older and more established firms. With their experienced managerial resources, established roles, and

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more standardized routines, older firms will have a greater capacity to manage their ego-network. This enables them to more effectively manage growth in their ego-network and capture greater benefits from this growth, embedding those benefits in innovative outputs.1 Therefore, H5. Firm age will moderate the relationship between ego-network growth and the innovative output of the firm: the positive relationship between ego-network growth and the innovative output of the firm will be stronger for older firms.

New Members Powell et al. (2005) argue that the evolution of networks is due not only to the entry and exit of organizations into the network but also the formation of new ties. Looking into new ties within a network is fundamental to understanding the structure and dynamics of interorganizational networks (Baum, Rowley, Shipilov, & Chuang, 2005). Similarly, research has found that with time the size of the network increases due to arrival of new members to the network (Barabasi et al., 2002). As noted earlier, the search for novel knowledge is at the center of the innovation process (Deeds, Coombs, & Ireland, 2004). The addition of new members into the network enables the firm to learn about new practices, new technologies, new ideas, and new information (Kogut, 1988; Powell et al., 1996). In addition, these new relationships allow the firm to break free from the constraints of their old ties, breaking their frame of reference and encouraging them to take a fresh approach (Burt, 1982; Gargiulo, 1993). New ties within firm’s egonetwork furnish the firm with unique information that their existing egonetwork partners do not possess and create opportunities to broker resource and information flows across unconnected partners (Ahuja, 2000; Burt, 1992; Rowley, Behrens, & Krackhardt, 2000). In our context, new ties are important in the sense that they enable the firm to create new ideas, bring in novel knowledge, bring knowledge diversity into the ego-network, and challenge their current frame of reference (Burt, 1982; Gargiulo, 1993). Therefore, H6. A firm’s innovative output increases with the addition of new members to the ego-network. The suggested relationship may not hold for all firms. We argue that network management capabilities will vary with firm size and firm age.

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Different firms are subject to different organizational experiences and capabilities, such as their capability to manage their network relationships. Large and established firms, in general, have the resources necessary to maintain extensive relations, and such firms may possess organizational slack, which is ‘‘a cushion of excess resources available to an organization that will either solve many organizational problems or facilitate the pursuit of goals outside the realm of those dictated by the optimization principles’’ (Bourgeois, 1981, p. 29). Organizational slack possessed by these firms can become a resource, which can solve many problems (Tan & Peng, 2003), including the overall management of the firms’ ego-network. Given such an ample resource supply, larger and older firms are better able to pursue the benefits of managing new relationships coming into the ego-network. Conversely, smaller firms may have resource constraints that prevent them from managing new relationships because they suffer not from only the liability of newness (Stinchcombe, 1965) but also the liability of smallness (Baum, 1996). This inhibits them in accumulating further slack resources and management capability. The inclusion of new members in the ego-network might benefit larger and older firms more than the smaller firms. H7. Firm size will moderate the relationship between the addition of new members to the ego-network and the innovative output of the firm: the positive relationship between the addition of new members to the egonetwork and the innovative output of the firm will be stronger for larger firms. H8. Firm age will moderate the relationship between the addition of new members to the ego-network and the innovative output of the firm: the positive relationship between the addition of new members to the egonetwork and the innovative output of the firm will be stronger for older firms.

Ego-Network Diversity Another important contribution of this research is the inclusion of the overall composition of the ego-network as one important network evolution variable. By overall composition, we refer to the similarity or diversity of firms within the ego-network. The overall composition of the ego-network, such as its size and growth, are subject to change throughout the life of the ego-network and this might have significant contributions to the innovative

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performance of the associated firms. For example, the composition of the firm’s alliance network usually changes with the changes in industry conditions due to a need to reduce uncertainty (Goerzen, 2007). From a network theory perspective, factors such as redundancy in the network ties may have a negative effect in the innovative output of the firm, since repeated relationships might suffer from similarity of ideas and might hinder the development of new knowledge. While non-redundant ties (Burt, 1992, 2000) are advantageous in explorative learning and the inclusion of new ideas into the network, redundant ties may not contribute as much because networks composed of redundant ties usually only provide access to same information over and over (Burt, 1992). Without diversity in ego-network membership, there is a danger that as firms in the ego-network become increasingly alike through imitation, the ego-network may be less effective at generating new knowledge (Dyer & Nobeoka, 2000). Pisano (2006, p. 76) also suggests that the biotechnology industry requires the ‘‘right mix’’ of people from different scientific and functional backgrounds in order to collaborate and exchange information. Therefore, ego-network growth that ignores diversity in ego-network members can decrease the level of information that is valuable to the firm and thereby the firm’s innovative performance. Thus, H9. A firm’s innovative output increases with the diversity of its egonetwork. The arguments above suggest that although there may be good reasons to believe that diversity in a firm’s ego-network facilitates the development of new knowledge within the firm, more diversification is not without costs. First, a diversified ego-network is more complex and more difficult to manage. Firms also need to develop the capability to manage such diversified ego-networks. While a firm might benefit from moderate levels of diversity within the ego-network in terms of valuable knowledge acquired from network partners, at higher levels of ego-network diversity such benefits may no longer be possible because of the costs associated with managing such a network. Similarly, in a study of 580 multinational enterprises (MNEs), Goerzen and Beamish (2005) find that on average MNEs with more diverse alliance networks experience lower economic performance than those with less diverse networks. These arguments then suggest the following hypothesis: H10. The diversity of the firm’s ego-network has an inverted U-shaped relationship with its innovative output.

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DATA AND METHODOLOGY Sample Characteristics In order to test the hypotheses linking the evolutionary characteristics of ego-networks with the innovative performance of firms, we chose the biotechnology industry in the United States as our research setting. Accordingly, we identified a sample of publicly traded biotechnology firms that are listed in Recombinant Capital (ReCap), because complete financial data are needed to validate their performance index. BioScan and ReCap are the two most comprehensive databases that document a variety of activities in the global biotechnology industry and both sources are fairly consistent (Hoang & Rothaermel, 2005). We developed our sample firms ego-network based on who these firms’ co-authored research chapters. Specifically, we studied the co-authorship networks of biotechnology firms in our sample. The ego-network of our firms consisted of its set of direct, dyadic ties and the relationships between these ties, with the firm at the center of the network as the focal actor (Hite & Hesterly, 2001; Powell & Smith-Doerr, 1994; Wasserman & Faust, 1994) captured by who the firm co-authored journal articles. Accordingly, each network consisted of a focal biotechnology firm and a set of alters, that is, research institutions, universities, or pharmaceutical firms connected to the focal firm (Wasserman & Faust, 1994). Using the ISI-Social Citation Index (SCI), for each year and each biotechnology firm in our sample we identified the organizations that the researchers from a specific biotechnology firm had co-authored with a scientific article. In our ego-network setting, the focal biotechnology firm and its alters are linked if their affiliated researchers wrote a chapter together. Using SCI, we tracked the co-authorships from each biotechnology company in our sample for a period of 17 years (from 1990 to 2006). This allowed us to construct a map of which organizations a biotechnology firm had collaborated with each year, and we observed changes in the coauthorship networks of these firms over the specified years longitudinally. This method compares favorably with previous samples used in Rothaermel and Hess (2007). Scientific developments such as genetic engineering, which enabled the formation of the biotechnology industry, were accomplished during the mid1970s in university labs. The industry experienced the founding of hundreds of small science-based biotechnology firms in the 1980s, and reached its maturity stage in the 1990s with the commercialization of new drugs. Since the evolving structure of collaborative ego-networks is the focus of this

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study, we started data collection from the mature stage of the biotechnology industry. Subsequently, our study covers publicly traded biotechnology firms between the years 1990 and 2006 inclusively. Analyzing only publicly traded biotechnology firms has both pros and cons. Studying publicly traded firms gives us access to data (slack resources, size, etc.) that are not generally available on a consistent basis for private firms. However, publicly traded companies have by definition achieved a substantial success and are generally better funded, larger, older, and more productive than private firms. So in interpreting our results, readers are cautioned to consider the potential biases and impacts of our choice of sample on the interpretations and generalizability of the results. We obtained yearly patent counts, co-authorship network data, and firmattribute data for the firms in our sample. The panel used for the analysis includes specific variables for the period 1990–2006. Due to some missing variables as well as two-year lagged independent variables, an observation number of 3,349 remained in the sample with 482 firms. The panel used in the regression analysis is unbalanced.2

Variables Dependent Variable A biotechnology firm’s innovative performance is the dependent variable of Hypotheses 1 through 6. It is measured by the number of patents issued by the US Patent and Trademark Office (USPTO) in a given year. Patents are an important and widely used measure of innovation output (Ahuja, 2000; Rothaermel & Hess, 2007; Shan & Walker, 1994). Patents not only represent a measure of technological novelty (Griliches, 1990) but also have an economic significance (Zaheer & Bell, 2005). The patent data are taken from the Thomson Scientific’s Delphion database and verified using the Technology Profile Report of USPTO. Independent Variables We measured a biotechnology firm’s ego-network size for a given year t as the firm’s total number of ego-network partners (Bae & Gargiulo, 2004). For example, for year t if the firm’s researchers had collaborated with researchers from 10 organizations, then the ego-network size of that specific firm is coded as 10, which is a count variable.

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Growth in a biotechnology firm’s ego-network, ego-network growth, is proxied as the percentage change in a firm’s total organizational ties from one year to the next. New ego-network members are operationalized as the ratio of new ties within a given year t to the overall ego-network size in the same year. Egonetwork members are considered to be ‘‘new’’ if they had not appeared in the firm’s ego-network within the past three years. In measuring the diversity in the ego-network (ego-network diversity), we followed the methodology developed by Baum, Calabrese, and Silverman (2000). Diversity in the ego-network is based on the Hirschman–Herfindahl index and computes diversity as one minus the sum of the squared proportions of a firm’s number of collaborations with a specific partner in year t, divided by its total number of co-authorship collaborations. EgoP network diversity is measured as NDij ¼ ½ ij ðPCij Þ2 =TCi , where PCij is the proportion of a firm i’s number of collaborations with a specific partner j, and TCi is the firm i’s total number of co-authorship collaborations. For example, a firm that has total co-authorship collaborations of six, five with organization A and one with organization B, would score [1((5/6)2+ (1/6)2)]/6=0.046. In our sample, ego-network diversity ranges between 0 and 0.9805 with values closer to 0 showing less diversification and values closer to 1 showing more diversification in the ego-network. Control Variables Firm size is an important control variable when examining the effects of network variables on innovative output (Cohen & Levin, 1989). We controlled the firm size by using the number of employees as a proxy. In the biotechnology industry, using the number of employees as a measure of firm size is acceptable, since most biotechnology firms do not yet have positive revenues, nor do they have many tangible assets (Rothaermel & Deeds, 2006). We also controlled for the firm age. Another important variable to be controlled is that of the R&D intensity. It is shown that R&D expenditures are a significant determinant of firm innovativeness (Ahuja, 2000). We collected R&D data from Compustat and computed R&D intensity as the R&D expenditures over total sales. We also controlled for the profitability and liquidity of the focal firms. Profitability was captured by the return on equity variable (the ratio of net income to total equity) and liquidity was captured by the current ratio (ratio of current assets to current liabilities) of the firm. Over time, there might be differences in the innovative performance of all firms; therefore, we also

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controlled for such time variant effects by including dummies for every year from 1990 to 2006. In general, it is also necessary to control for the firm effects; however, since our data is longitudinal panel data, firm effects are captured with the data. All independent and control variables are lagged for two years.

Data Analysis Since our dependent variable, the number of patents issued to the firm, is a non-negative integer with a limited range, we applied a negative binomial model for our analysis. A non-negative dependent variable with an overdispersion (Beckman, Haunschild, & Phillips, 2004; Park, Chen, & Gallagher, 2002) violates the assumptions of ordinary least squares (OLS) regression. Under these conditions, Poisson or negative binomial models are appropriate; negative binomial models correct for overdispersion and have been used in other studies of overdispersed dependent variables (Beckman et al., 2004; Haunschild & Sullivan, 2002; Marquis, 2003; Park et al., 2002). Our dependent variable is strongly skewed to the right, and therefore OLS regression would be inappropriate. The variance of our dependent variable is 23.4 while the mean is 3.19; the variance is significantly larger than the mean. The distribution of our dependent variable is displaying signs of overdispersion, or greater variance than might be expected in a Poisson distribution; therefore, we preferred the generalized negative binomial model with HuberWhite procedure (Petersen, 2007).3

RESULTS Table 1 presents the descriptive statistics. Table 2 presents generalized negative binomial regression results with a dummy codification for years. Model 1 is the base-line model including only the control variables; independent variables ego-network size, ego-network growth, new members, and ego-network diversity are entered in the second model. Model 2 tests Hypotheses 1, 3, 6, and 9, which concern the direct relationships between ego-network size, ego-network growth, new members, and ego-network diversity, respectively, and also the firm’s innovative output. Accordingly, in Model 2, we find that ego-network size and ego-network diversity are both positive and significant at the 0.005 level. This shows that Hypotheses 1 and 9 are supported. In contrast to our expectations, Hypothesis 2 is not

Innovative output Ego-network size Ego-network growth New members Ego-network diversity Firm size Firm age Firm R&D Firm profitability Firm liquidity

1 2 3 4 5 6 7 8 9 10

 po0.01 (one tailed).

Variable

No.

8194 7174 7174 7174 7174 4380 7417 8194 8192 4360

Obs

3.19 10.71 0.01 4.57 0.33 1.52 9.54 9.96 0.20 2.01

Mean

Table 1.

23.42 34.20 0.75 17.07 0.38 0.77 9.07 130.35 8.38 10.20

SD 1.00 0.26 0.07 0.17 0.11 0.19 0.09 0.00 0.00 0.00

1

1.00 0.17 0.66 0.35 0.35 0.13 0.00 0.00 0.00

2

1.00 0.07 0.08 0.08 0.01 0.00 0.00 0.00

3

1.00 0.31 0.22 0.12 0.00 0.00 0.00

4

1.00 0.27 0.25 0.03 0.01 0.04

5

6

1.00 0.23 0.01 0.03 0.07

Descriptive Statistics and Correlation Matrix.

1.00 0.02 0.01 0.01

7

1.00 0.00 0.00

8

1.00 0.00

9

1.00

10

The Evolution of Research Collaboration Networks and Innovation Output 83

3,841 1,192.57

(0.00) (0.00) (0.03) (0.00) (0.03) (0.00) (0.01)

3,349 1,722.27

0.00 0.00 0.02 0.01 0.19 0.00 1.01

0.01 (0.00) 0.93 (0.05)

0.01 (0.00) 1.36 (0.05)

0.00 (0.00) 0.00 (0.00)w 0.01 (0.01)

Model 2

Model 1

Note: Standard errors are in parentheses. w po0.10; po0.05; po0.01; po0.005.

Firm age Firm size Firm R&D Firm profitability Firm liquidity Ego-network size Ego-network growth New members Ego-network diversity Ego-network size2 Ego-network diversity2 Ego-Network growth  firm size Ego-network growth  firm age New members  firm size New members  firm age N Wald w2

Variables (0.00) (0.05) (0.00) (0.00) (0.02) (0.00) (0.03) (0.00) (0.11)

w

3,349 1,799.57

0.00 (0.00)

0.00 0.86 0.00 0.00 0.02 0.02 0.15 0.00 0.75

Model 3 (0.00) (0.05) (0.00) (0.00) (0.03) (0.00) (0.03) (0.00) (0.38)

3,349 1,828.35

0.00 (0.00) 2.74 (0.44)

0.00 0.85 0.00 0.00 0.02 0.02 0.09 0.00 2.95

Model 4

3,349 1,843.45

3,349 1,876.95

0.00 (0.00) 0.00 (0.00)w

3,349 1,835.56

0.01 (0.00)

0.00 (0.00)

3,349 1,890.65

0.00 (0.00)

0.11 (0.06)w 0.00 (0.00)

(0.00) (0.05) (0.00) (0.00) (0.03) (0.00) (0.11) (0.01) (0.38) 0.00 (0.00) 2.54 (0.45)

0.00 0.82 0.00 0.00 0.03 0.02 0.05 0.01 2.78

Model 8

0.11 (0.06)

(0.00) (0.05) (0.00) (0.00) (0.03) (0.00) (0.11) (0.00) (0.38) 0.00 (0.00) 2.69 (0.44)

0.00 0.84 0.00 0.00 0.02 0.02 0.06 0.00 2.90

w

0.11 (0.06)

(0.00) (0.05) (0.00) (0.00) (0.03) (0.00) (0.11) (0.00) (0.37)

Model 7

0.17 (0.06)

0.01 0.84 0.00 0.00 0.03 0.02 0.05 0.00 2.90

w

0.00 (0.00) 2.67 (0.44)

(0.00) (0.05) (0.00) (0.00) (0.03) (0.00) (0.11) (0.00) (0.37)

Model 6

0.00 (0.00) 2.71 (0.44)

0.00 0.84 0.00 0.00 0.02 0.00 0.07 0.00 2.93

Model 5

Table 2. Generalized Negative Binomial Models for Effects of Network Evolution on Innovation Output.

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supported. Even though our analyses reveal that ego-network growth is a significant variable in determining changes in the overall ego-network, the direction of the relationship is negative. This aspect of ego-network growth warrants further exploration. The effect of new members on a firm’s innovation, Hypothesis 6, does not receive any support. In Model 8 (full model) in Table 3, the coefficient of ego-network size is positive and that for the squared term of ego-network size is negative and significant, supporting the inverted U-shaped relationship between egonetwork size and the innovation output of the firm, Hypothesis 2. Hypothesis 10 proposes a curvilinear relationship between ego-network diversity and firm innovation. The positive coefficient of ego-network diversity and negative and significant coefficient of its squared term in Model 8 also lend support to Hypothesis 10. Hypotheses 4 and 5 indicate that firm size and firm age moderate the relationship between ego-network growth and the innovative output of the firm. In Model 8, the interaction term between ego-network growth and firm size is significant at the 0.10 level, and the interaction term between the egonetwork growth and firm age is significant at the 0.05 level, strongly supporting our hypotheses. This relationship suggests that ego-network growth benefits the firm’s innovative performance if growth is pursued by larger and older firms. Figs. 2 and 3 clarify these relationships. Hypothesis 7 states that the relationship between the addition of new members to the ego-network and innovative performance is moderated by the firm size. The interaction of firm size and new members in Model 8 is significant at the 0.05 level. Hypothesis 8 tests the moderating relationship between the additions of new members to the ego-network; this hypothesis is

Table 3. Summary of Hypotheses Tested. Hypotheses

Models

1 2 3 4 5 6 7 8 9 10

Model Model Model Model Model Model Model Model Model Model

2 8 2 8 8 2 8 8 2 8

Results Supported Supported Not supported Supported Supported Not supported Supported Supported Supported Supported

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IREM DEMIRKAN AND DAVID L. DEEDS 5 Small Firms

4.5

Large Firms

Innovation Output

4 3.5 3 2.5 2 1.5 1 Low

High Network Growth

Fig. 2.

The Interaction of Ego-Network Growth and Firm Size.

5 Young Firms

4.5

Old Firms

Innovation Output

4 3.5 3 2.5 2 1.5 1 Low

High Network Growth

Fig. 3. The Interaction of Ego-Network Growth and Firm Age.

supported at the 0.001 level. These findings hold up our interaction hypotheses in such a way that larger and older firms benefit more from the addition of new members to their ego-network. Figs. 4 and 5 clarify these relationships. These results are summarized in Table 3.

The Evolution of Research Collaboration Networks and Innovation Output 5 4.5

Small Firms Large Firms

Innovation Output

4 3.5 3 2.5 2 1.5 1 Low

High New Members

Fig. 4.

The Interaction of New Members and Firm Size.

5 4.5

Young Firms Old Firms

Innovation Output

4 3.5 3 2.5 2 1.5 1 Low

High New Members

Fig. 5.

The Interaction of New Members and Firm Age.

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DISCUSSION The dynamics and evolution of a firm’s ego-network is an important, yet underexplored, phenomenon. The main contribution of our study is the identification and empirical investigation of key ego-network variables that have an impact on firm outcomes through the changes in a firms’ co-authorship ego-network. In addressing the evolution of firms’ egonetworks, we have argued that the structural characteristics of the egonetwork, namely, the firms’ ego-network size, ego-network growth, new ego-network members, and the diversity of the ego-network, are more likely to change with subsequent changes in firm’s initial ego-network. In this regard, our analyses have supported that over time, the key ego-network variables identified in this chapter have a significant impact on firms’ innovative outcome. The size of the ego-network in our analyses has proved to be a significant indicator of the evolution of ego-networks, enabling us to observe important structural changes in the firms’ ego-network. While our analyses show that the larger the ego-network, the better the firm innovation, there are limits. These limits support our arguments on the complexities and dynamics of interdependencies between ego-network actors, which often make it difficult for the firm to effectively manage its network (Hakansson & Johanson, 1992). Increase or decrease in the number of ego-network participants is another aspect of the evolutionary dynamics of co-authorship networks in the biotechnology industry. Our analyses revealed that inclusion or exclusion of some members within specific time intervals reflects real change in the egonetwork over time, which in turn affects the performance of the focal firm, and captures an important aspect of the evolution of the firms’ ego-network. However, our results suggest that never ending ego-network growth by itself can be detrimental to the innovative performance of firms. This aspect of ego-network growth is interesting and suggests that increasing number of transactions consume more energy and resources. Our findings suggest that firms benefit from the rate of growth as long as they have the necessary capabilities. Our results suggest that the addition of new members to the ego-network is also a significant indicator of firm performance; however, this aspect becomes significant for a firm’s innovation especially when we consider the boundary conditions. A study by Lechner, Dowling, and Welpe (2006) also supports our results that adding new and different relationships can enhance firm performance. In addition to these findings, our results suggest that

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a firm can benefit from such relationships as long as it can effectively manage the new members coming into the network. While literature also points out the need to bring in new members to have positive performance outcomes, we assert that not all the firms might have the capability to do so. Similar to our hypothesis on ego-network size, we found strong support for the curvilinear relationship effect for the ego-network diversity. Our findings suggest that although maintaining ego-network diversity through the evolution of the firms’ co-authorship network is beneficial for the firm, there are limits to this diversity. While past literature has argued that networks benefit the firm when there is a greater diversity because it enables variety generation (Kogut, 2000) and differentiation within the network (Gulati & Lawrence, 1999), our results reveal that greater diversification in the ego-network is not without a cost. The suggested framework supports that there are declining returns for more diversification in the ego-network. After this threshold, point diversity in the egonetwork is detrimental to the firm’s performance in terms of its innovative output. The central implication of our model of ego-network evolution, which also opens up a new discussion for future research, is that network management by itself is an important capability that firms need to acquire in order to effectively exploit their network. The fact that Hypotheses 4 and 5 receive strong support indicates that more established, older and larger firms are better prepared to reap the benefits of ego-network growth. We believe this is directly attributable to overcoming the liability of newness and their prior experience in network management. The fact that older firms are more successful in managing the growth in their ego-network indicates that network management is a firm-level capability, and that growth in the egonetwork places greater demands on this capability. For a firm to maximize the benefits from its networks, it invests in developing the skills required to effectively manage its networks. The improved research output due to better ego-network management indicates the potential for these capabilities to be an important source of competitive advantage. Given our results, more detailed examinations of the firm-specific capabilities that enhance network management are an area that demands further study. It is no longer appropriate to simply assume that more ego-network ties are better and that all firms will benefit equally from similar ego-networks. Further analyses and hypotheses support the importance of network management and the costs as well as benefits of expanding a firm’s egonetwork. Our findings also support the idea that adding a large proportion of new members to the ego-network aggravates the complexities and

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uncertainties associated with the management of the network. In findings similar to our results for more established firms, larger firms are also more effective at managing their relationships with the addition of new members to the ego-network, also highlighting differences between firms in terms of their network management capabilities. Overall, our findings contribute that a firm’s network management capability significantly impacts the ability of a firm to benefit from its egonetwork ties as the firms’ ego-network evolves over time. Within the evolving dynamics of the network, those firms that are able to manage growth, the addition of new members, and diversity in their ego-networks were able to achieve higher performance in terms of their innovative output.

Limitations and Future Research It is important to conclude with a cautionary remark that this study might suffer from the issue of generalizability. This research relies upon the coauthorship networks of a sample of firms drawn from a single industry with very distinctive characteristics, the biotechnology industry. As a result, the generalizability of our findings to other industries should be considered cautiously, since the high development costs, high potential payoff, strict regulatory environment, and strong dependence on basic science make the biotechnology industry unique. However, there are no specific reasons to expect the impact of network dynamics in the biotechnology setting to be so distinctive as to have no lessons for companies engaging research networks in their development process. Our findings are also limited by the type of ego-network we studied, a research collaboration network. Sales or finance-based ego-networks or egonetworks based on strategic alliances may be very different types of networks, and might be characterized by different evolutionary dynamics. Our findings are also limited by our choice of patent counts as a dependent variable. Patent counts are subject to problems with endogeneity, behavioral assumptions, and unobserved heterogeneity (Gittelman, 2008). These issues make patent counts noisy and potentially problematic. Readers should consider these issues in interpreting and generalizing the results, and future research should also consider using forward citation measures as potential robustness check on our results. However, patent counts are an established measure of research productivity and other measures such as forward citations also present significant issues. In general, the measurement of research productivity is difficult and subject to substantial errors;

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however, focusing on a single industry provides some level of control over these issues. This study is purely a structural evaluation of firms’ co-authorship network, which contains no qualitative assessment of the content of relationships and of their change over time. Such an assessment will require further in-depth study of the networks in future studies. Future research could consider questions such as follows: Is there a specific pattern in these evolving networks? What are the characteristics of firms that are consistently in the network, if any? What is the nature of knowledge shared in these networks, and how do these qualitative assessments of the network affect firms’ performance outcomes? It is also worthwhile to explore the management aspects of network growth in future studies. Our results specifically indicate that firms might be able to create a competitive advantage and superior performance based on their network management capabilities. However, we are still in the dark as to what kind of management yields superior outcome for the firm. Future research could further look into the role of managing networks, as well as how the knowledge flowing in these networks is managed.

CONCLUSION While there have been recent studies that examined the various aspects of network evolution (Koka et al., 2006; Powell et al., 2005), this chapter is the first one that directly tests the effects of ego-network evolution and changes in the ego-network on a firm’s innovative output. We extend the literature on network evolution by showing that ego-network size, ego-network growth, and new members are among the most important dynamics of egonetwork evolution; we also show that firms benefit differently from changing ego-network structure depending on their resources and management capabilities. Finally, our chapter opens up a new discussion in network research by questioning the validity of the notion of networks as a pure good. As a result, firms should be cautioned when expanding their research networks and consider the boundary conditions established in this chapter.

NOTES 1. Given the nature of our study, our theoretical development is industry specific. What distinguishes biotechnology firms from the incumbents of the pharmaceutical companies is their technology focus (Pisano, 2006).

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2. If there are no missing values in the data, the data set is called a balanced panel, but if some values are missing, the data set is referred to as an unbalanced panel. 3. This method produces white standard errors, which are robust to within cluster (i.e., within firm) correlation. Data are clustered by a firm identifier and year dummies are also created. These standard errors then allow observations in the same firm to be correlated, but would assume that observations in the same firm with different years are uncorrelated.

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CHAPTER 4 AN EXPLORATORY STUDY OF THE ROLE OF PUBLISHING INVENTORS IN NANOTECHNOLOGY Gino Cattani and Daniele Rotolo ABSTRACT Social network theory and analytic tools have been increasingly used to examine the interaction between science and technology. Recently, researchers have paid attention to the role of publishing inventors, that is, individuals bridging the collaborative networks between authors (coauthorship network) and inventors (co-invention network). Building on this research, we study how publishing inventors’ structural position in the joint co-authorship and co-invention network affects the quality of the inventions to which they contribute. Specifically, we identify publishing inventors who play a pivotal role in holding the two networks together: their removal not only increases the network fragmentation but also disconnects the joint co-authorship and co-invention network. We define these publishing inventors as cutpoints and find them to contribute to inventions of greater quality. We situate the analysis within the context of the emerging field of nanotechnology. The theoretical and managerial implications of the results are discussed. Keywords: Co-authorship/co-invention networks; Nanotechnology; invention quality; knowledge transfer Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 97–122 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013007

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INTRODUCTION The relationship between science and technology has been a long-standing theme in innovation research (Martin, 2012). On the premise that connections to the scientific community support innovation by providing access to basic research, several studies have looked more closely at the relationship between scientists and inventors as critical conduits for the transfer of knowledge between science and technology, and its impact on the volume and quality of innovation (e.g., Gittelman & Kogut, 2003; Owen-Smith & Powell, 2004; Powell, Koput, & Smith-Doerr, 1996). Using a social network lens, some researchers have also begun to probe the collaboration networks between authors (co-authorship network) and inventors (co-invention network) by focusing on those individuals who are instrumental in connecting the two networks (e.g., Bonaccorsi & Thoma, 2007; Breschi & Catalini, 2010; Lissoni, 2010). The interest in co-authorship and co-invention networks stems from the realization that their structural characteristics are critical for understanding how effectively scientists and inventors interact and exchange knowledge. Specifically, these studies draw attention to the role of publishing inventors in enhancing the connectivity between the co-authorship and co-invention networks. Building on this growing body of research, we examine how publishing inventors’ position within the joint network of authors and inventors affects the quality of the inventions to which they contribute. Given their structural position, publishing inventors can indeed access, integrate, and leverage different knowledge bases (scientific and technological). But are all publishing inventors equally consequential or can their structural position be further differentiated? And if so, what are the implications for the quality of the inventions with which they are involved? To address these questions, we start with the recognition that while acting as connectors between the coauthorship and co-invention networks, not all publishing inventors play the same pivotal role in holding the two networks together. Specifically, only those publishing inventors who connect otherwise disconnected subgroups of the network do act as cutpoints (Scott, 2000; Wassermann & Faust, 1994). If cutpoints are removed, the fragmentation of the joint co-authorship and co-invention network will increase because links between authors and inventors are also eliminated, thus jeopardizing inventors’ ability to (re)combine different knowledge bases. We conduct our analysis in the emerging field of nanotechnology, which we traced since its inception in 1980 until 2009, the year we stopped our data collection. We collected all patents in nanotechnology during the period

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under investigation and looked at their quality as measured by the number of future citations received from others’ patents. We also collected data on all publications in major nanoscience journals and matched authors’ names with inventors’ names. For all individuals in our final dataset, we know whether and when they published an article and/or patented an invention. We examine publishing inventors’ role in bridging the co-authorship and coinvention networks, and how that impacts the quality of the inventions in which they are involved. Specifically, we analyze the influence of publishing inventors’ structural position by identifying those acting as cutpoints between the two networks. As they increase the connectivity of the joint coauthorship and co-invention network, cutpoints are critical conduits for the transfer of knowledge between the two networks and the generation of new creative syntheses. By focusing on individuals spanning two distinct networks and their influence on the quality of the inventions to which they contribute, our study seeks to respond to a recent call to examine ‘‘how variables at different levels of analysis interact in determining the extent and type of resulting innovation’’ (Gupta, Tesluk, & Taylor, 2007, p. 885). In the next sections, we briefly review extant research on the relationship between science and technology. We then present the theoretical arguments. Next, we describe the empirical setting, the data, and the variables of interest. After describing the results of the analysis, we conclude with the theoretical and managerial implications of our findings.

THEORY The idea that science and technology co-evolve and interact in complex ways has replaced the old linear model in which new technological developments were considered to be relatively independent of scientific advancements (e.g., Etzkowitz & Leydesdorff, 2000; Kline & Rosenberg, 1986; Nightingale, 1998). Early work by Griliches (1979), Jaffe (1986), and Adams (1990) showed the positive contribution of public science to industrial innovations and economic growth. For instance, Mansfield (1995) revealed that 11% of product innovations and 9% of process innovations would not have been developed in the absence of scientific research. Accordingly, patents and publications have been used extensively to examine knowledge transfer between science and technology. By analyzing citation linkages between USbased patents and scientific research publications, Narin, Hamilton, and Olivastro (1997) found a threefold increase in the number of citations of academic literature in US-based patents through the mid-1990s, reporting

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that 73% of the cited chapters were authored by academic, governmental, and other public institutions. In a subsequent study, Salter and Martin (2001) proposed a critical review of the economic benefits of publicly funded basic research, revealing that the rates of return generally are estimated between 20% and 60%. Referencing a 1994 Carnegie Mellon Survey, Cohen, Nelson, and Walsh (2002) found that American firms consider publishing and patenting by universities among the most important knowledge sources for innovating. Dependence on public science for new technological advancements is further confirmed by patents’ references to non-patent-based prior art. Indeed, science can be conceived as a ‘‘map’’ of the technological landscape, guiding private research toward the most promising technological directions (Fleming & Sorenson, 2004). Over the years, research on the relationship between science and technology has shifted from a macro (i.e., organizational and institutional) to a micro level of analysis by focusing on individuals who act as bridges between the two domains. Increasingly, patents and publications have been treated as sources of relational data when examining collaboration between inventors and scientists. Several studies (e.g., Gittelman & Kogut, 2003; Powell et al., 1996) have shown how connections to the scientific community support technological innovations by providing access to basic research. Closer interactions between scientists and inventors foster the transfer and recombination of knowledge, especially when this knowledge is complex, tacit, and socially embedded. For instance, Gittelman and Kogut (2003) confirmed the importance to biotechnology firms of maintaining ties with the scientific community via boundary-spanning gatekeepers who facilitate access to socially embedded knowledge. Drawing from a large body of work showing how brokers mediate the flow of information and knowledge between actors who are not directly linked (Burt, 1992), researchers have begun to examine explicitly the role of publishing inventors. Since they span scientific and technological networks, publishing inventors enjoy a structural advantage relative to inventors who are located only in the technological network. Publishing inventors’ unique structural position is highly consequential for the quality of the inventive outcomes to which they contribute (Bonaccorsi & Thoma, 2007). For instance, Breschi and Catalini (2010) found publishing inventors to be critical in bridging the boundaries between science and technology in the three science-intensive technological fields of lasers, semiconductors, and biotechnology. Building on the brokerage taxonomy advanced by Gould and Fernandez (1989), Lissoni (2010) found that academic inventors acting as brokers between otherwise unrelated co-inventors from academia and/or

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industry usually produce a large number of patents, display a strong publication record, and have strong ties with both university and industry co-inventors. Yet these studies do not look at the extent to which publishing inventors contribute to holding the co-authorship and co-invention networks together, thereby serving as the actual gatekeepers between the two. To establish this, one must consider publishing inventors’ structural position beyond the ego-network (Borgatti, 2006). Moving beyond the egonetwork is important for advancing a more general framework to assess the validity of previous research and expounding on publishing inventors’ structural features. In social network theory, actors who play a pivotal role in holding a network together are known as cutpoints (Scott, 2000). Cutpoints are essentially brokers connecting otherwise disconnected groups, thus ensuring higher levels of connectivity between them. For example, in a communications network, a cutpoint is critical because ‘‘if that actor is removed from the network, the remaining network has two subsets of actors, between whom no communication can travel’’ (Wassermann & Faust, 1994, p. 113). As shown in Fig. 1, nodes A and B are both publishing inventors but only A is a cutpoint because its removal increases the number of separate components from 1 (a) to 3 (b), thus augmenting the fragmentation of the joint co-authorship and co-invention network. On the contrary, the removal of publishing inventor B (c) does not increase the number of separated components. While cutpoints are brokers, not all brokers are cutpoints because not all brokers contribute equally to holding the overall network together. The cutpoint approach in fact measures explicitly the contribution of a set of actors to the cohesion of a network. While removing a broker that is not a cutpoint causes pairs of nodes to become more distantly connected, removing a broker that is also a cutpoint causes pairs of nodes to become fully disconnected, thus de facto eliminating the only channel through which information and knowledge was flowing among them (for an illustration of this key difference, see Fig. 1 in Borgatti, 2006, p. 23). To identify publishing inventors acting as cutpoints, one has to consider the larger web of relationships in which individual actors are embedded, including their direct and indirect ties. By encompassing both types of ties, it is possible to establish whether publishing inventors are one of several bridges or the only bridge connecting otherwise isolated subgroups within scientific and technological networks. Prior research has looked at the different performance implications of moving information or knowledge between individual actors to whom one is

Fig. 1.

Removing Publishing Inventor B (non-cutpoint)

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Co-authorship Network [(t-5) - (t-1) period]

Co-authorship Network [(t-5) - (t-1) period]

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Co-invention network [(t-5) - (t-1) period]

Co-invention network [(t-5) - (t-1) period]

The Role of Publishing Inventors Acting as Cutpoints between the Co-authorship and Co-invention Networks.

Co-authorship Network [(t-5) - (t-1) period]

B

A

Removing Publishing Inventor A (cutpoint)

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connected directly or indirectly (e.g., Burt, 2007; Everett & Borgatti, 2005; Owen-Smith & Powell, 2004). In a study on network performance in three different populations (supply chain managers, investment bankers, and analysts), Burt (2007) distinguished direct brokerage (moving information between direct contacts) from secondhand brokerage (moving information between friends of friends) and found that ‘‘secondhand brokerage— moving information between people to whom one is only connected indirectly—often has little or no value’’ (p. 119). Brokerage benefits appear to be concentrated in the immediate network around a person. If information flow is difficult beyond direct contacts, or more valuable between direct contacts, the logical conclusion – Burt argues – is that ‘‘it makes sense to limit brokerage models to direct contactsy’’ (2007, p. 122). This emphasis on the benefits accruing to a brokerage position through direct contacts, however, tends to overlook individual actors’ role in keeping the overall network together. Even as the focal actor’s local network might be rich in terms of structural holes (Burt, 1992), removal of that actor may not be as consequential if network connectivity remains unaffected (Reagans & Zuckerman, 2001). For instance, betweenness centrality is often used to identify nodes acting as local brokers. Typically, high values of betweenness centrality are highly correlated with having many structural holes (Everett & Borgatti, 2005). But nodes that have the highest values of betweenness centrality do not necessarily act as cutpoints and so their removal does not split the network into separate components (Borgatti, 2006).1 With respect to the science-technology linkage, publishing inventors acting as cutpoints span the co-authorship and co-invention networks. If such actors are removed, existing connections between groups within the coauthorship and co-invention networks also are eliminated, thus increasing the fragmentation of the overall network. Although scientific knowledge becomes publicly available when published – for example, in a chapter, a patent, or other document – cutpoints contribute to linking otherwise disconnected authors and inventors, thus fulfilling the crucial function of information and knowledge conduits between the two domains. Given their unique structural position, cutpoints can control knowledge flows between different groups (e.g., Borgatti, 2006; de Nooy, Mrvar, & Batagelj, 2005). As a result, they can combine in more distinctive and original ways the knowledge – including tacit knowledge – that resides in otherwise separate clusters of the joint co-authorship and co-invention network. By leveraging their gate-keeping position, cutpoints can adopt others’ new combinations and enhance the inventive creativity of the research and development (R&D) team in which they partake. We thus expect invention quality to be

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positively related to the number of publishing inventors acting as cutpoints between the co-authorship and co-invention networks.

EMPIRICAL ANALYSIS Research Setting We situate the analysis in the emerging nanotechnology field. Nanotechnology can be defined as the ‘‘understanding and control of matter at dimensions of roughly 1 to 100 nm, where unique phenomena enable novel applications [y] At the nanoscale, the physical, chemical, and biological properties of materials differ in fundamental and valuable ways from the properties of individual atoms and molecules or bulk matter’’ (US National Nanotechnology Initiative). Yet nanoscale science and technology did not begin in earnest until the 1980s. In 1982, Ernst Ruska, Gerd Binnig, and Heinrich Rohrer developed the scanning tunneling microscope (STM) at the IBM Research Laboratory in Zurich (Gerber & Lang, 2006). They provided an idea of the potential application of the STM by spelling the letters IBM using individual xenon atoms deposited on a nickel surface. At the time, the primary driving force for miniaturization came from the electronics industry, which was attempting to develop tools to create smaller (faster and more complex) electronic devices on silicon chips. In the following years, increased funding opportunities and initiatives (such as the US National Nanotechnology Initiative, with an annual budget of $1.3 billion) spurred a wave of scientific discoveries and technological innovations that were reported in several scientometric studies (e.g., Braun, Schubert, & Zsindely, 1997; Hullmann & Meyer, 2003). The field of nanotechnology is a suitable setting for our study. First, patents represent a viable source for investigating innovation processes in this field (Lemley, 2005). Second, due to the nascent and science-based nature of this field, interactions between science and technology are critical for supporting the translation of new scientific discoveries into commercialized products and processes (e.g., Lavie & Drori, 2011).

Data We created a database containing bibliographic patent data in nanotechnology. We collected patent data by querying the United States Patent and

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Trademark Office (USPTO) database and selecting all patents with the ‘‘977’’ current US technology classification. This class tags all patents concerning inventions related to nanotechnology. The initial sample consisted of 6,011 ‘‘nano’’ patents from 1975 to 2009. By analyzing patent bibliographic data, we identified 18,171 inventor-patent pairs during the observation period. For these inventor-patent pairs, we dealt with homonymy issues by developing an algorithm that identified all potential matches among inventors using last name, first name, and middle name (if any). We considered two inventors to be the same person when at least one of the following fields for each inventor overlapped: city, zip code, assignee, technological subclasses, patent citations, and collaborating inventors (Singh, 2005). The remaining potentially true matches were resolved through an extensive manual check using several sources, including the Internet, personal websites, or websites of universities and companies. This produced a final sample of 10,374 distinct inventors. Given our interest in inventors who act as bridges between science and technology, we then identified individuals publishing in nanoscience. We collected publications by querying the Science Citation Index Expanded (SCI-EXPANDED) database of ISI Web of Knowledge and selecting all publications in journals assigned to the ‘‘NANOSCIENCE & NANOTECHNOLOGY’’ subject category. We obtained an initial sample of 111,401 publications from 1966 to 2009. We then filtered this sample and included only those records that referred to journal articles,2 excluding all records for which some relevant information (e.g., authors’ name) was missing. This resulted in a new sample of 70,578 publications. As with the sample of inventors, we adopted an algorithm matching authors’ information to address homonymy issues in the 300,114 author-publication pairs in the sample of publications. Since SCI-EXPANDED records only report authors’ last name and initials, we defined a three-step procedure that identified all the potential matches among authors. First, starting with the 300,114 authorpublication pairs, the algorithm created a profile for each record that included the author’s last name, initials (first name and, if any, middle name), co-authors, and assigned subject categories. Second, we created a list of potential matches based on the assumption that two authors could be the same person if the last name, initials, and at least one co-author and one subject category matched. Finally, we vetted this list of potential matches through an extensive manual check that relied on several sources, including the Internet, personal websites, or websites of universities, companies, and journals. We preferred to be conservative and thus categorized two authors as the same person only when highly confident. This resulted in a sample of

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121,045 distinct authors operating in nanoscience. The last step was to identify publishing inventors by matching inventors with authors. As before, an author and an inventor were considered potentially the same person when the last name and the initials were identical. We subsequently used the information collected in previous steps to address problematic matches. This final procedure led to a sample of 1,695 publishing inventors bridging the nanoscience and nanotechnology domains. In collecting data on both patents and publications, we adopted a conservative search strategy aimed at identifying only those patents and publications that strictly adhere to nanotechnology and nanoscience, respectively. Our sample selection procedure differs from those adopted in previous empirical studies on the role of publishing inventors because those studies typically used a keywords search strategy (e.g., Bonaccorsi & Thoma, 2007). We believe that our approach assures a higher degree of coherence with the phenomenon under investigation. By limiting our sample to patents assigned to a very specific technological class and articles published in specialized journals, all sample patents and articles share some affinity in terms of topic (Leydesdorff, 2006) – in addition to undergoing an analogous examination and review process (Murray & Stern, 2007). A keywords search strategy also presents two main drawbacks. First, a number of patents and chapters that do not clearly fall into the nano-field might be included in the sample, allowing for greater unobserved heterogeneity. This is of special concern since scientists may label their scientific and technological discoveries with the prefix ‘‘nano’’ to gain support from policy makers, even though their discoveries do not actually reflect studies in the field (Huang, Notten, & Rasters, 2011). Second, the nano-field is evolving rapidly, making keywords obsolete and thus limiting both the effectiveness of the search strategy and the reliability of the results. Our more conservative approach minimizes these concerns and increases confidence in our results. By using co-authorship and co-invention data, we constructed three different longitudinal networks: the co-authorship network based on publications only, the co-invention network based on patents only, and the joint network resulting from the combination of the other two networks (publications and patents). We constructed the networks adopting a five-year moving window, which assumes that scientific and technological collaboration ties older than five years become weaker and even dissolve. We also used other time windows (e.g., three- and sevenyear windows), but the results were not affected. Given the high fragmentation level of the nanotechnology co-authorship and co-invention networks in earlier years (there are only a few nodes and ties between them),

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we used network data starting with the 1980–1984 time window. Our decision was motivated by the fact that development of the STM in the 1980s represents the birth of nanotechnology (Gerber & Lang, 2006). We used Pajek 2.00 to construct the networks and compute our measures as it is well suited for analyzing large networks – even those consisting of several thousand nodes, as in our case (de Nooy et al., 2005). To reduce simultaneity problems, we lagged the network measures: if the dependent variable was evaluated at year t, we estimated our network measures for the previous five-year period (i.e., from t-5 to t-1).

Dependent Variable Our dependent variable measures invention quality as captured by the number of citations a focal patent received in subsequent years. Especially in R&Dintensive industries, the number of citations a patent receives is a more precise measure of technological performance and a better estimate of the focal patent’s true value (e.g., Griliches, 1981; Trajtenberg, 1990). Patent citations positively correlate with firm sales, profits, and stock prices (e.g., Bierly & Chakrabarti, 1996; Hall, 2000; Hall, Jaffe, & Trajtenberg, 2001). As patents filed earlier had a longer period during which they could be cited by subsequent patents, we computed future citations over the same five-year time window (we also used a six- and a seven-year time window but the results were consistent with those reported here). So, future citations for all patents reflect the same number of years, regardless of when they were filed. We relied on the USPTO database to evaluate the patent citations from those patents filed from 1985 (the first observation year of the five-year time window used to define the networks) to 2002. This reduced the sample to 4,201 nanotechnology patents. We also compared patents only to those filed during the same year – that is, patents belonging to the same cohort. While self-citations measure the extent to which the assignee of a patent builds on its previous R&D efforts, citations from others’ patents more objectively estimate the actual relevance of a specific patent. We computed the dependent variable as an index of weighted patent citation counts (Trajtenberg, 1990).

Explanatory Variables To identify individuals acting as cutpoints, we first had to identify publishing inventors – those individuals involved in the bridging of science

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and technology. Since publications are the most important output indicator in science, scientists ‘‘working in research institutes and universities show their current state of research and disseminate the results’’ (Hullmann & Meyer, 2003, p. 511). We thus looked at whether inventors responsible for the creation of a patent also published scientific chapters in nanoscience prior to the date the patent was filed.3 Specifically, we created a variable measuring the number of publishing inventors working on a given patent. To reduce simultaneity problems, we entered this variable into the model with a one-year lag. Next, we identified publishing inventors acting as cutpoints, that is, those nodes ‘‘whose removal would increase the number of components by dividing the sub-graph into two or more separate sub-sets between which there are no connections’’ (Scott, 2000, p. 170). More formally, a node i is a cutpoint if ‘‘the number of components in the graph that contains i is fewer than the number of components in the subgraph that results from deleting i from the graph’’ (Wassermann & Faust, 1994, pp. 112–113). In the presence of a cutpoint, any act of communication among the members of a network or its sub-set is dependent upon one particular member (i.e., the cutpoint). In the absence of cutpoints, there are alternative paths of communication between nodes, and so the network is both flexible and unstratified. We identified cutpoints using Pajek 2.00 (de Nooy et al., 2005). We created the previous variables using a five-year time window (varying the time window does not affect the main results of the analysis).

Control Variables We included in the final model specification several control variables at individual, team, and patent level to rule out possible competing interpretations of the results. Since learning from cumulative patenting experience may influence the quality of developed inventions (Mowery, Sampat, & Ziedonis, 2002), we accounted for the quality of an inventor’s previous patents. We created two distinct variables for non-publishing inventors and publishing inventors – the inventors patenting quality and publishing inventors patenting quality, respectively. These variables measure the average number of citations an inventor’s (or publishing inventor’s) nanotechnology patents received over a five-year time window. We considered patents granted to an inventor (or publishing inventor) in the five years prior to a patent’s filing date (varying the time window did not affect the main results of the analysis). When patents were assigned to

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a team of inventors (or involved more than one publishing inventor), we included the average of the inventors’ (or publishing inventors’) patenting quality. The results did not change when we controlled for the standard deviation or included the average patenting quality of the inventor (or publishing inventor) that produced the highest quality patents. Because the quality of publishing inventors’ scientific articles may influence the quality of the inventions to which they contribute, we created the variable publishing inventors publishing quality, which measures the number of future citations received by a publishing inventor’s articles in nanoscience. We considered articles published during the five-year time window prior to the filing date of a given patent. For inventing teams with more than one publishing inventor, we considered the average value of publishing quality. Again, the results did not change if we controlled for the standard deviation of publishing quality or included the average publishing quality of the publishing inventor who produced the highest quality scientific articles. For those patents that included publishing inventors on the inventing team, we controlled for whether the patents were developed by building on the publishing inventors’ scientific works. Specifically, we included a variable, publishing inventors scientific knowledge, which measures the number of nanoscience publications by publishing inventors cited in a given patent.4 The creation of a new patent might result from individual inventors’ inventive efforts or the efforts of several inventors working together as a research team. Singh and Fleming (2010) found inventors working alone, especially those without affiliation to organizations, to be less likely to make breakthrough inventions and more likely to make inventions of lower quality. We thus controlled for the number of inventors in an R&D team (inventors team size). The results do not change when we included a dummy that is equal to 1 if the team consisted of a single inventor, 0 otherwise. In our context, in 79% of the cases, a patent was filed by two or more inventors, while in 21% of the cases by an individual inventor. The claims in the patent specification delineate property rights protected by the patent. Although the patentee has an incentive to claim as much as possible in the application, the patent examiner may require that the claims be narrowed before granting the patent. Various researchers (e.g., Lanjouw & Schankerman, 2004) have found that the number of claims a patent makes is related to the value of the patent. Thus, we included the variable patent claims to account for the number of claims that define the legal boundaries of a given patent. A patent can be classified in one or several technological classes. Researchers have used the number of classes in which a patent is classified to measure its breadth. As patent breadth has been

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found to have a positive effect on a patent’s future impact, we included the variable patent scope to account for the number of distinct IPC 4-digit classes assigned to a given patent (Lerner, 1994). We obtained similar results when we measured patent scope using the number of IPC 8-digit classes. A patent usually cites one or more previous patents, indicating the prior art the patent is building upon. Backward citations have been found to affect a patent’s future impact. Patents that cite more prior art may belong to more crowded technological areas of nanotechnology and thus have differential influence relative to other patents. We controlled for these possible effects by including the variable patent technological references, which measures the ratio of the number of backward citations to patents with a different assignee to the total number of backward citations. To account for publishing inventors’ tendency to cite their own publications at a higher frequency than the general population of patents, we measured the number of scientific non-patent references each focal patent cites with the variable patent scientific references. The number of non-patent backward citations can be used to evaluate the extent to which a patent builds on existing scientific knowledge (Narin et al., 1997). We also controlled for those patents in which the assignee was an individual or a team of inventors. More precisely, we included a dummy – inventors patent – that is equal to 1 when a patent assignee was an individual, 0 otherwise. We also created the variable time to grant to control for the number of years between its filing and issue dates because the complexity of a patent is likely to affect the length of time elapsing before it is granted. Rather than being granted to a single inventor, firm, or other organization, a patent might be the outcome of interorganizational collaboration. We thus included a dummy, joint patent, which is equal to 1 when the number of co-assignees is greater than one, 0 otherwise. To control for differences in patenting across technological areas, we included dummy variables for each of the main classes a patent was assigned to. Specifically, we relied on the eight sections (1-digit) characterizing the IPC. Finally, we included fixed-year effects to control for differences in the forward citations patterns and other possible factors affecting patent quality over time.

ANALYSIS Our data are characterized by a nested structure, since distinct teams of inventors can be nested within the same organization (i.e., patent’s

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assignee). We identified 1,133 distinct assignees in the full sample of nanotechnology patents (N=4,201) and 288 distinct assignees for the sample of nanotechnology patents involving at least one publishing inventor (N=779). Such data structure creates non-independence across observations because errors will be more likely correlated for teams of inventors working for the same assignee. This would produce parameter estimates with biased standard errors. To address this issue, we estimated a multi-level mixed-effects model. This estimation model has the advantage of including simultaneously both fixed and random effects. Because the dependent variable invention quality can take on only nonnegative integer values, we adopted a multi-level Poisson mixed-effects specification. Following Rabe-Hesketh and Skrondal (2005), we controlled for overdispersion featuring in patent citations data by defining the observations themselves as the lowest level of the multi-level model. Tables 1 and 2 present the descriptive statistics and the correlation values for all variables, respectively. We first examined the correlations among all independent and control variables, and found no evidence of

Table 1.

Descriptive Statistics.

Variables

Mean

S.D.

Min.

Max.

Invention quality Year dummies (stratifying variable) IPC dummies (stratifying variable) Joint patent (dummy) Time to grant Inventors patent (dummy) Patent scientific references Patent technological references Patent scope Patent claims Inventors team size Inventors patenting quality Publishing inventors scientific knowledge Publishing inventors Publishing inventors publishing quality Publishing inventors patenting quality Publishing inventors cutpoints (dummy)

5.800 – – – 2.489 – 11.355 0.809 2.246 21.343 2.974 10.453 0.024 0.353 17.934 12.759 –

9.113 – – – 1.315 – 26.681 0.332 1.316 17.835 1.878 20.598 0.228 0.696 42.738 20.798 –

1 1985 A 0 0 0 0 0 1 1 1 0 0 0 0 0 0

124 2002 H 1 19 1 358 1 13 240 15 326 3 5 499 118 1

Note: Descriptive statistics refer to the sample of 4,201 observations except for the variables related to publishing inventors, which refer to the reduced sample of 779 observations.

– 0.055 0.093 0.068 0.037 0.01 0.100 0.035

8

Variables

Patent claims Inventors team size Inventors patenting quality Publishing inventors scientific knowledge Publishing inventors Publishing inventors publishing quality Publishing inventors patenting quality Publishing inventors cutpoints

– 0.001 0.212 0.015 0.038 0.065 0.071 0.077 0.001 0.180 0.015 0.022 0.016 0.055 0.113

Invention quality Joint patent Time to grant Inventors patent Patent scientific references Patent technological references Patent scope Patent claims Inventors team size Inventors patenting quality Publishing inventors scientific knowledge Publishing inventors Publishing inventors publishing quality Publishing inventors patenting quality Publishing inventors cutpoints

1

– 0.120 0.009 0.299 0.049 0.173 0.068

9

– 0.028 0.034 0.009 0.008 0.035 0.017 0.069 0.014 0.035 0.078 0.045 0.013 0.069

2

– 0.001 0.090 0.087 0.553 0.146

10

– 0.012 0.208 0.187 0.011 0.115 0.084 0.028 0.056 0.079 0.065 0.127 0.160

3

– 0.164 0.197 0.137 0.034

11

– 0.022 0.051 0.027 0.01 0.109 0.045 0.02 0.029 0.013 0.061 0.005

4

– 0.134 0.079 0.253

12

– 0.030 0.092 0.133 0.065 0.077 0.108 0.005 0.005 0.136 0.006

5

– 0.248 0.078

13

– 0.060 0.039 0.056 0.038 0.046 0.052 0.039 0.079 0.056

6

– 0.088

14

– 0.055 0.028 0.062 0.063 0.057 0.055 0.049 0.003

7

Notes: Correlations refer to the sample of 4,201 observations except for variables related to publishing inventors, which refer to the reduced sample of 779 observations 5% (po0.05).

8 9 10 11 12 13 14 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Variables

Table 2. Correlation Matrix.

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multicollinearity. In fact, the highest correlation is between inventors patenting quality and publishing inventors patenting quality (0.553, po0.05). Table 3 reports the estimates for the mixed-effects Poisson model. Model 1 is the baseline model with all controls. Model 2 reports the coefficient estimates after we included the variable measuring the number of publishing inventors involved in the development of a patent. Model 3 includes the effect of publishing inventors acting as cutpoints between coauthorship and co-invention networks on the quality of an invention. We further checked for multicollinearity in each regression model by evaluating the variance inflation factor (VIF). VIFs were below the recommended threshold of 10. The four models are nested, and the number of observations declines moving from Model 1 through Model 3 because the analysis focused on a different subset of patents – that is, those for which the variable of theoretical interest is not equal to 0 merely due to the way it was constructed (Model 2). In considering the impact of publishing inventors acting as cutpoints (Model 3), we discarded patents where there were no publishing inventors. Indeed, the variable publishing inventor cutpoints is theoretically meaningless for those inventions that did not involve at least one publishing inventor in the team. The baseline Model 1 shows the coefficient estimates of the variables patent technological references (b=0.337, po0.001), patent scope (b=0.089, po0.001), patent claims (b=0.010, po0.001), inventors team size (b=0.020, po0.1), and inventors patenting quality (b=0.007, po0.001), which all positively affect the quality of nanotechnology patents. On average, a patent is more likely to be cited by others’ future patents when it builds on a large technological knowledge stock, crosses several technological areas, legally covers several novel functions, and involves a larger number of inventors that previously produced high-quality patents. By contrast, the time required to grant a patent has a negative impact on the quality of a patent (b=0.508 po0.001). The other controls (joint patent, inventors patent, and patent scientific references) are not significant. Model 2 reports a positive effect (b=0.072, po0.01) of the presence of publishing inventors in the team of inventors. Specifically, the quality of a patent is positively related to the number of publishing inventors involved in developing a new technology. Ceteris paribus, the presence of one publishing inventor increases the number of future citations from others by 7.50% (exp(0.0721)=1.075). This suggests that individuals acting as both authors and inventors play a fundamental role in generating more significant innovation as they are likely to have unique access to relevant (scientific) knowledge. We further characterized the role of publishing inventors in Model 3 by estimating

(0.099) (0.021) (0.264) (0.001) (0.077) (0.017) (0.001) (0.012) (0.001)

33 10,735.56 1,551.90 4,201

(0.450)

Included Included 0.035 0.508 0.174 0.001 0.337 0.089 0.010 0.020w 0.007

34 10,733.18 1,556.97 4,201

Included Included 0.044 0.508 0.180 0.001 0.334 0.089 0.010 0.013 0.007 0.072

1.128

(0.099) (0.021) (0.264) (0.001) (0.077) (0.017) (0.001) (0.012) (0.001) (0.023)

(0.450)

SE

Coefficient

SE

Coefficient 1.100

Model 2 (Publishing Inventors)

Model 1 (Baseline)

(0.243) (0.001) (0.003) (0.110)

0.050 0.000 0.005 0.319 36 1,833.63 346.69 779

(0.182) (0.052) (0.381) (0.003) (0.201) (0.044) (0.003) (0.026) (0.003)

(0.800)

SE

0.156 Included Included 0.032 0.556 0.360 0.001 0.586 0.070 0.016 0.032 0.005w

Coefficient

Model 3 (Publishing Inventor Cutpoints)

Notes: All models account for a multi-level model specification at patent’s assignee level (1,133 and 288 distinct assignees for the sample of 4,210 and 779 patents, respectively) w 10% (po0.10); 5% (po0.05); 1% (po0.01); 0.1% (po0.001).

Df Log-likelihood Wald w2 Observations

Intercept Year dummies IPC dummies Joint patent Time to grant Inventors patent Patent scientific references Patent technological references Patent scope Patent claims Inventors team size Inventors patenting quality Publishing inventors Publishing inventors scientific knowledge Publishing inventors publishing quality Publishing inventors patenting quality Publishing inventors cutpoints

Dependent Variable: Invention Quality

Table 3. Mixed-Effects Poisson Regression on Invention Quality. 114 GINO CATTANI AND DANIELE ROTOLO

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the impact of those individuals acting as cutpoints between the coauthorship and co-invention networks. The coefficient for the variable publishing inventor cutpoints is significant and in the expected direction (b=0.319, po0.01). The presence of at least one publishing inventor acting as a cutpoint increases a patent’s number of future citations by 37.60% (exp(0.3191)=1.376). These results confirm that not all publishing inventors are critical in holding the joint co-authorship and co-invention network together, thus providing support for our conjectures.

Robustness Checks We conducted several additional analyses to probe the robustness of our results to measurement issues and alternative model specifications. First, the size of an inventor’s technological ego-network may positively affect the number of future citations a patent receives. Being connected to a high number of peer inventors may enhance the likelihood of receiving citations in these peers’ future patents. We thus checked for the effect of the number of direct relationships a patenting team has with other individuals in the coinvention network. This variable was not significant, thus providing support for our findings. Second, the quality of patents created with the involvement of publishing inventors acting as cutpoints may simply reflect their status as influential scientists, which could be reflected, for instance, in the quality of their scientific publications. We looked into this possibility by creating a variable measuring the number of publishing inventors’ publications in top nanoscience journals based on the impact factor scores reported in the ISI Web of Knowledge’s Journal Citation Report. Specifically, we selected the 10 journals with the highest impact factor scores (looking at the top 15 or 20 made no difference). Although the variable turned out to be negative and significant, all the variables of theoretical interest remained significant, suggesting that they have a distinct influence on the dependent variable. This result is consistent with the work of Gittelman and Kogut (2003, p. 376), who surmised that ‘‘the production of high quality publications actually detracts from the innovation effort y successful patents and successful chapters follow different selection logics, and y these logics are opposing.’’ Finally, our findings may reflect the unique feature of the emerging field of nanotechnology, where the low connectivity of the industry compared to other more mature industries – for example, semiconductors, telecommunications, and the like – might partly explain the relatively large number of nodes acting as cutpoints. In contexts characterized by higher connectivity

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levels, in fact, one would expect to see fewer actors holding the entire network together. We thus controlled for the number of different components within the technological and scientific networks, but the two variables turned out to be nonsignificant.

DISCUSSION AND CONCLUSIONS A growing body of research has recognized the importance of looking at inventors’ structural position in the co-authorship and co-invention networks to further advance understanding of the relationship between science and technology. The structural characteristics of these two networks are critical for understanding how effectively scientists and inventors interact and exchange knowledge, including tacit knowledge. Using a social network lens, our chapter has probed the role that publishing inventors play in bridging these two networks and shaping the quality of the inventions teams in which they are involved. We analyzed the influence of publishing inventors’ structural position by identifying those acting as cutpoints between the two networks. Cutpoints are critical conduits for the transfer of knowledge between authors and inventors and its recombination into new creative syntheses. Publishing inventors’ structural position is highly consequential for the quality of the inventions to which they contribute, particularly when they are cutpoints between the two networks. The current findings have several theoretical implications. Previous research has overlooked the importance of relationships with actors outside one’s network or community in generating creativity and innovation (e.g., Dahlander & Frederiksen, 2011; Perry-Smith & Shalley, 2003). By focusing on publishing inventors, especially those acting as cutpoints, our chapter shows how the ‘‘presence or absence of particular actors (nodes) that help span the boundaries in a network’’ (Doreian & Woodard, 1994, p. 288) might explain differences in the quality of the inventions to which those inventors contributed. Accounting for relationships with external constituents is important because some people tend to be cosmopolitans who choose to be (or end up being) part of multiple communities (Gouldner, 1958). Individuals in multiple communities can transfer, translate, and transform experiences from one community to another (Bechky, 2003; Dougherty, 1992), including their tacit knowledge, which cannot be captured by publications but is essential for generating inventions of greater future impact. Our chapter also adds to boundary-spanning literature (Allen, 1977; Ancona & Caldwell, 1992; Tushman & Scanlan, 1981) by identifying those

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individuals (cutpoints) who are the actual gatekeepers between the communities they are spanning. Publishing inventors are cosmopolitans because they choose to be active in distinct but interdependent networks of authors and inventors. Our chapter thus seeks to respond to a recent call for studying innovation at multiple levels by conducting a joint analysis of the co-authorship and co-invention networks and exploring publishing inventors’ role in fostering interaction between the two communities, and its impact on the quality of an invention. In this sense, it differs from most studies on innovation that ‘‘focus at one level of analysis, and it is rare that their contributions operate at different levels or are considered in combination’’ (Gupta et al., 2007, p. 885). Unlike previous research that concentrates on the benefits accruing to publishing inventors’ ego-network (e.g., Burt, 2007; Lissoni, 2010), our analysis embraces the overall network that publishing inventors inhabit. Moving beyond the ego-network is critical when identifying publishing inventors who act as cutpoints and are pivotal in increasing joint network connectivity (Borgatti, 2006). Distinguishing between actors who are actual gate-keepers and actors who span structural holes in their local network – but do not affect the connectivity of the joint network – is important to determine the vulnerability of the network should those actors be taken out. As mentioned before, betweenness centrality is often used to identify nodes acting as local brokers (Everett & Borgatti, 2005). But the use of betweenness is not entirely suitable for identifying nodes whose removal would most disconnect the network. Although the cutpoint approach has the advantage of recognizing such nodes, it is also important to bear in mind that ‘‘cutpoints are a kind of discrete nominal classification rather than a measurement of the extent to which removing a node fragments a network’’ (Borgatti, 2006, p. 24). Specifically, the implications of removing a cutpoint vary with the size of components created by its removal. Cutpoints whose removal only isolates single nodes cannot be seen as true key players (Borgatti, 2006). In the analysis, we accounted for this effect by considering different values of a cutpoint’s degree centrality – that is, the number of different nodes to which a cutpoint is directly connected.5 A correct application of the cutpoint approach, therefore, should not simply look at the increase in the number of components but also the size of those components. The results of the analysis further suggest that a critical source of team level heterogeneity is the social capital of its members. As Reagans and Zuckerman (2001) pointed out, the type of relations that individual members bring to a team is likely to affect the degree of this heterogeneity

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and cause team performance to vary. Demographic research has focused mainly on individual level attributes (e.g., age, gender, tenure, and education) that do not directly account for the type of relations (social capital) individuals contribute to their teams. Given the increasingly collaborative nature of the interaction between science and technology, as indicated by the sharp increase in interdisciplinary and collaborative work (Guimera`, Uzzi, Spiro, & Nunes Amaral, 2005), incorporating social network theory and analytic tools promises to shed additional light on this important, yet still underexplored, source of heterogeneity (Cattani & Ferriani, 2008) – particularly, the conditions under which those relations represent unique sources of knowledge advantages. This study has several limitations, which also represent opportunities for future research. First, many chapters on nanotechnology are published in journals classified under other subject categories and are not included in the list of journals in the Web of Science’s subject category. This means that the co-authorship network is incomplete. Also, by focusing on co-authorship and co-invention, we cannot trace informal social relations and other indirect forms of knowledge transmission such as reading from publications, attending conferences, and so on (Huang et al., 2011; Laudel, 2002). Our results are conservative as they capture only some of the information and knowledge authors and inventors could exchange by relying on a wider range of channels. Second, our findings may be industry specific and reflect unique features of the emerging field of nanotechnology, where the low connectivity of the industry compared to other more mature industries – for example, semiconductors, telecommunications, and the like – might partly explain the relatively large number of nodes acting as cutpoints. In a context with higher connectivity levels, one would expect to see fewer actors holding the entire network together. Third, even though publishing inventors have access to heterogeneous knowledge (i.e., scientific and technological knowledge), unlike other studies (e.g., Rodan & Galunic, 2004), we do not measure directly the degree of heterogeneity of that knowledge. A different research design (e.g., in-depth case study) and data collection strategy (e.g., interviews or a survey) would be needed to operationalize the heterogeneity of the knowledge accessible through social ties. Finally, as is the case in most studies using a social network lens, we infer knowledge exchange by considering only interactions observed through co-publication and copatenting activities; this clearly underestimates the relevance of other forms of interactions as equally viable sources of knowledge. Addressing this issue would require collecting finer-grained qualitative evidence and data (e.g., through interviews with publishing inventors). Despite these limitations, our

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study extends extant research on the relationship between science and technology by probing more deeply the collaboration networks between authors and inventors and further expounding on the structural characteristics of those actors who connect the two networks. Studying these characteristics adds to our understanding of how effectively scientists and inventors interact and exchange relevant knowledge.

ACKNOWLEDGMENT Daniele Rotolo acknowledges the support of the UK Economic and Social Research Council (award RES-360-25-0076 - Mapping the Dynamics of Emergent Technologies).

NOTES 1. Not surprisingly, the low correlation (0.233) in our data between the cutpoint and betweenness measures in the joint co-authorship and co-invention network further confirms this important distinction. 2. We selected the ‘‘Document Type: Articles’’ option in SCI-EXPANDED database. 3. It is worth noting that applying for a patent might delay a publication or even prevent it as the authors do not wish to disclose their invention to the scientific community, at least not until the patent is applied for or issued. This may limit our ability to track relevant publications back in time (though the same holds true for most of the studies cited in this chapter); however, if significant results are observed, our analysis is conservative. 4. We also tried a different approach by creating a dummy that is equal to 1 when the team was composed of only publishing inventors, 0 otherwise. Although not reported here, the results showed that teams of all publishing inventors tend to file patents of greater future impact. 5. Specifically, we tested the sensitivity of the results to the exclusion of cutpoints for different values of degree centrality. The results did not change significantly when excluding cutpoints having degree centrality lower than four – that is, the threshold after which cutpoints become key players and hence their exclusion is consequential for the outcome variable of interest.

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Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of Management Review, 28(1), 89–106. Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–145. Rabe-Hesketh, S., & Skrondal, A. (2005). Multilevel and longitudinal modeling using stata. College Station, TX: Stata Press. Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization Science, 12(4), 502–517. Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25(6), 541–562. Salter, A. J., & Martin, B. R. (2001). The economic benefits of publicly funded basic research: A critical review. Research Policy, 30(3), 509–532. Scott, J. (2000). Social network analysis: A handbook. London: SAGE Publications. Singh, J. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5), 756–770. Singh, J., & Fleming, L. (2010). Lone inventors as sources of breakthroughs: Myth or reality? Management Science, 56(1), 41–56. Trajtenberg, M. (1990). A penny for your quotes: Patent citations and the value of innovations. RAND Journal of Economics, 21(1), 172–187. Tushman, M. L., & Scanlan, T. J. (1981). Characteristics and external orientations of boundary spanning individuals. Academy of Management Journal, 24(1), 83–98. Wassermann, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

CHAPTER 5 THE INTERDEPENDENCIES OF FORMAL AND INFORMAL NETWORK STRUCTURE AND THE EXPLORATION OF NEW TECHNOLOGICAL OPPORTUNITIES AMONG GEOGRAPHICALLY DISPERSED FIRMS Daniel Tzabbar and Alex Vestal ABSTRACT To resolve an inherent dilemma in extant research on geographically dispersed research and development (R&D), this study explores interdependencies between formal and informal network structures. Firms that seek to benefit from the decentralization associated with disperse R&D must align it with an informal structure that enhances organizational members’ motivation to share and assimilate their unique knowledge and skills. On the basis of an investigation among 424 US biotechnology firms between 1973 and 2003, this study reveals the

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 123–163 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013008

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moderating effect of the firm’s informal social structure on the effect that geographically dispersed R&D personnel have on the exploration of new technological opportunities. Specifically, the higher the social network density among R&D members, the more likely geographic disparity is to affect exploration; however, this likelihood decreases with an increase in power asymmetries. These results offer insights into the conditions in which the appropriate management of geographically dispersed R&D varies. Keywords: Formal and informal structure; geographic dispersion; technological exploration; biotechnology; event history analysis

INTRODUCTION Aligning firm’s research and development (R&D) strategy with its formal structure has long been argued to influence the development of firm technological knowledge (Argyres & Silverman, 2004; Galbraith, 1973; Nadler & Tushman, 1997). Yet, insights regarding the interaction between formal and informal structures on the firm’s ability to explore new technological opportunities continue to go largely underexplored (Gulati & Puranam, 2009; Kleinbaum & Tushamn, 2007). Such insights are especially important to resolve an inherent dilemma in extant research on economic geography that examines the integration of scientific knowledge across sources in multiple locations (Leiponen & Helfat, 2011; Singh, 2008). Proponents of the virtues of dispersed geographic locations argue that accessing various knowledge and skills embedded in different geographical regions and countries may enable the firm to identify new technological opportunities (Nelson, 1993; Porter, 1990). Yet, the very distance and embeddedness of knowledge, which makes it valuable, also creates difficulties with regard to its acquisition and absorption (Aharonson, Baum, & Feldman, 2007; Zaheer, 1995). Not surprisingly, empirical studies that link geographic dispersion and innovative performance report mixed results, such that different researchers find positive (Leiponen & Helfat, 2011; Penner-Hahn & Shaver, 2005), negative (Singh, 2008), and curvilinear (Phene, Fladmoe-Lindquist, & Marsch, 2006) relationships. Despite these differences though, researchers generally agree that multiple locations imply some degree of structural decentralization (Ghosal & Nohria, 1989; Gupta & Govindarajan, 2000) and that firms’ ability to

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recognize and assimilate inherently distinct knowledge hinges on their integrative capabilities (Kogut & Zander, 1993). For example, Singh (2008) finds that innovations resulting from the integration of crossregional knowledge have significant positive associations with the value of innovation. Similarly, Penner-Hahn and Shaver (2005) and Phene et al. (2006) invoke absorptive capacity literature to explain a firm’s ability to assimilate diverse knowledge and skills associated with dispersed R&D. Notwithstanding these theoretical links, extant literature on dispersed R&D typically has examined the innovative consequences of knowledge integration, without directly investigating the organizational mechanisms that facilitate such integration. We extend this stream of literature by focusing on the micromechanism that enhances or hinders knowledge absorption. That is, consistent with literature on integrative capabilities (Henderson & Cockburn, 1994; Kogut & Zander, 1992) and absorptive capacity (Zahra & George, 2002), we argue that the assimilation of (geographically and technologically) distant knowledge depends on the firm’s informal social structure. Accordingly our main research question asks: Are some firms better organized socially to assimilate the unique knowledge and skills of their geographically dispersed R&D members and explore new technological opportunities? Drawing on the knowledge-based view of the firm, we propose that firms that seek to benefit from the decentralization associated with dispersed R&D need to align their efforts with a social structure that enhances the motivation of the organization’s members to share and assimilate their unique knowledge and skills. The social structure within which innovation efforts take place influences scientists’ ability and willingness to recombine their knowledge, which implies that it will also affect the likelihood of a successful transformation of a firm’s technological capabilities (Henderson & Cockburn, 1994; Kogut & Zander, 1992; Zahra & George, 2002). These views are consistent with long-standing arguments that suggest that the informal organization augments the formal organization by enabling tasks that formal dictates can only partially specify (Barnard, 1938; Kleinbaum & Tushamn, 2007; Simon, 1957). We follow these leads and explore the effects of two social mechanisms on knowledge assimilation: social network density and power asymmetries among firm members. Strong interpersonal network ties that span R&D members increase the motivation of dispersed scientists to share their knowledge and increase their efforts to resolve transfer-related issues. We therefore expect that greater collaboration makes the firm more likely to transfer and integrate the various knowledge and skills of its members but

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less likely to be negatively affected by transfer-related issues and the coordination costs associated with geographic dispersion. However, when a firm’s existing technological knowledge results from one or a few powerful ‘‘star’’ scientists, the likelihood that it will enter a new technological field decreases, as a result of the restricted autonomy and knowledge flows that come from power asymmetries among members. More generally, we posit that aligning a formal structure aimed at accessing nonlocal knowledge with an appropriate informal social structure may be key to realizing a firm’s potential. To test our hypotheses, we develop a unique longitudinal dataset of 424 independent (i.e., not subsidiaries) and dedicated biotechnology firms between the years 1973 and 2003. These firms undertake 834 entries into new technological sectors, which we refer to as technological exploration. The data generally support the hypotheses and help extend and redirect literature pertaining to the relationship between geographically dispersed R&D and technological exploration in several ways. First, organizational theory and economic research have focused on the impact of the organization’s formal structure (e.g., centralization vs. decentralization) (Argyres & Silverman, 2004; Nadler & Tushman, 1997) on the development of technological capabilities; we consider the interdependencies of its formal and informal structures. Excluding the firm’s informal social structure, with a singular focus on formal structure, likely has produced inconclusive prior findings. Access to nonlocal knowledge gained through a decentralized R&D structure may be insufficient if the firm cannot bundle its formal structure with the social mechanisms that will enable it to exploit new technological opportunities. We expand the resource-bundling argument to include a decentralized structure together with social mechanisms that increase communication and coordination among members (Tzabbar, Aharonson, Amburgey, & Al-Laham, 2008). We also add to the notion that an informal organization can enhance the effectiveness of the formal organization by either supplementing it – that is, connecting the formal organization to employee action – or compensating for it through behaviors that are valuable to the success of the formal structure (Gulati & Puranam, 2009; Kleinbaum & Tushamn, 2007; Nickerson & Zenger, 2002). Second, whereas prior research on dispersed R&D has focused on the origin of knowledge (Almeida, 1996; Frost, 2001) and the innovative performance of dispersed R&D (Leiponen and Helfat, 2011; Singh, 2008), the empirical question of whether R&D dispersion actually results in strategic entries to a new technological domain remains unclear

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(de Figueiredo & Silverman, 2007; Silverman, 1999). Drawing from prior research, we argue and demonstrate that dispersed R&D exhibits an inverted U-shaped relationship with technological exploration, such that it increases exploration at low levels of geographic dispersion but decreases at very high levels. Third, to highlight the importance of a contingency approach to geographic dispersion, we argue that accessing different geographies increases the potential for exploring new technologies, yet its impact depends on the firm’s informal social structure. That is, a high degree of collaboration positively moderates the likelihood that a firm with dispersed R&D will enter a new technological sector, whereas power asymmetries among members negatively moderate this potential. By introducing a contingency approach into the dispersion–exploration relationship, we help clarify the micromechanisms that facilitate the assimilation of diverse knowledge and add to the scarce evidence pertaining to the relationship between potential and realized absorptive capacity (Zahra & George, 2002). Fourth, our study improves on previously used methods in three specific dimensions. The common use of case studies and cross-sectional models introduce generalizability and sample selection bias problems (for review, see Frost, 2001; Singh, 2008), whereas our unique longitudinal database features geographically dispersed R&D and thus enables us to account for potential selection bias and endogeneity. Moreover, to measure various innovative outcomes, most research relies on patent citation data, which provide meaningful measures of only intermediate innovation output for a relatively small number of industries (Griliches, 1990). In contrast, we use entry to a new technological sector as our measure of technological exploration. In the following sections, we first review relevant literature, and then develop three testable hypotheses. After we describe the data, we outline our empirical methodology and conclude with the results.

THEORETICAL BACKGROUND Geographically Distributed R&D (Decentralization) In an environment in which rapid technological innovation often renders current research obsolete, such as biotechnology, the exploration of new

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technologies constitutes an important component of any competitive strategy (Calabrese, Baum, & Silverman, 2000). Powell, Koput, and Smith-Doerr (1996) suggest that in the biotechnology industry, with its regime of rapid technological development, no single firm has all the internal capabilities necessary for innovation success. Similarly, Shan and Song (1997) assert that the competitive advantage of firms in such industries erodes if they rely solely on internally existing capabilities. The myopic learning associated with the exploitation of existing technologies can increase the gap between organizational competencies and environmental demands over time (Sorensen & Stuart, 2000). Therefore, biotechnology firms build competitive advantages and capabilities in their attempt to penetrate new technological sectors (Amburgey, Dacin, & Singh, 1996). The exploration of new technological opportunities requires that the firms have access to appropriate knowledge and skills, which may be located in specific geographic settings (Almeida & Kogut, 1999). Assembling R&D membership from multiple geographic locations offers access to various knowledge networks and can overcome the constraints of knowledge assimilation (Phene & Almeida, 2008). Specifically, firms with distributed R&D should enjoy a knowledge security advantage over alliances and joint ventures, with their great potential for knowledge leakage (Gulati & Singh, 1999), as well as over subsidiaries in multiple locations, which suffer from internal boundaries that can impede their knowledge flows (Nachum & Zaheer, 2005). From a transaction cost perspective, access to different regions and international locations requires some degree of knowledge transfer, which implies an increase in ex post opportunism, so the firm should prefer to internalize its innovation mechanisms, despite potentially higher coordination costs (Teece, 1980; Williamson, 1991). The knowledgebased view offers similar advice, yet attributes such motive to the fact that knowledge assimilation requires rich mechanisms for knowledge transfer, such as personal relationships across multiple locations (Kogut & Zander, 1993). Because organizations consist of communities of practice that are more likely to have shared languages and understandings, firms are superior to markets in their knowledge transfer capabilities (Grant, 1996; Kogut & Zander, 1992). While there is strong theoretical reasoning to use geographically decentralized structures to integrate external knowledge, empirical evidence about the effects of this usage is conflicting. Two schools of thought summarize this conflict; we examine their implications separately, and then offer a new theoretical frame for the debate that we use to develop our conceptual model.

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Positivistic View of Developing Dispersed R&D (Decentralized R&D Structure) Consistent with the knowledge-based view and learning theories, multiple R&D locations may offer key benefits. Literature pertaining to national innovation systems (Bartholomew, 1997; Cantwell, 1989) and economic geography (Almeida & Kogut, 1999; Jaffe, Henderson, & Trajtenberg, 1993) indicates that countries and regions develop distinct patterns in their technological specialization and capabilities that create ‘‘pockets’’ of expertise and advanced technology (Bartlett & Ghoshal, 1989; Nelson, 1993). Although these arguments often pertain to international R&D, they also apply to different regions of the same country (Furman, Kyle, Cockburn, & Henderson, 2006; Leiponen & Helfat, 2011). Specialized local knowledge that is useful in innovation may originate from many different sources, including universities, research institutes, suppliers, customers, and competitors (Von Hippel, 1988). These different national and regional systems affect not only the type of knowledge and the research orientation but also firm-level managerial capabilities, so they determine how firms respond to local stimuli and generate their own innovations (Adner & Helfat, 2003; Frost, Birkinshaw, & Ensign, 2002). Together with the effect of national and regional context, these capabilities have specific implications for innovation, including the type of knowledge created and the manner of creating it. A distinct system of knowledge generally evolves in each geographic location, even if it pertains to the same technological area (Phene et al., 2006). The specialization of technology and the tendency of knowledge to localize create the potential for nonoverlapping knowledge bases (Kogut, 1991). Therefore, accessing various geographic locations may provide the firm with novel information. Firms that use multiple R&D locations in their pursuit of knowledge spillovers should access more and more diverse knowledge sources than firms that remain confined to a single location. This expanded knowledge improves the likelihood that they gain new perspectives on existing technologies and their ability to develop absorptive capacities for new technologies (Adner & Helfat, 2003). If multiple locations yield a wider range of knowledge on which to build and recombine, the firm’s resultant innovations should span a wider range of applications as well (e.g., product and process innovations). Finally, because it provides access to various knowledge networks and skills, geographically dispersed R&D should enable firms to develop distinct capabilities, avoid ‘‘groupthink’’ and commitment to the status quo, and achieve more novel combinations (Nobel & Birkinshaw, 1998).

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Pessimistic View on Developing Dispersed R&D (Decentralized R&D Structure) The second school of thought emphasizes the coordination and social costs of decentralized innovative activity. The embeddedness of the knowledge makes it valuable but also creates difficulties for its acquisition and absorption (Bartholomew, 1997). The embeddedness perspective therefore highlights the potential ‘‘liability of foreignness,’’ which may impede overseas subsidiaries’ ability to assimilate local knowledge (Zaheer, 1995). Zaheer and Mosakowski (1997) report that foreign subsidiaries face formidable social and cultural barriers that block their effective participation in local knowledge-sharing communities. Norms of reciprocity also differ across regional and national boundaries (Bianchi & Bellini, 1991). In addition to the disadvantages associated with different cultures and the difficulties of transferring tacit knowledge, theories of foreign direct investment highlight the challenge of coordinating business activities across locations (Dunning, 1977; Penner-Hahn & Shaver, 2005), contesting the very notion that firms can tap into sources of knowledge and technology in foreign locations (see Frost, 2001). Similarly, extant research in organizational economics indicates that decentralization or dispersed R&D may improve external knowledge sourcing, but it raises the costs of communication and coordination (Chacar & Lieberman, 2003). According to this perspective, centralized R&D activities improve the alignment between a manager’s incentive and the firm’s innovative strategy (Argyres & Silverman, 2004; Williamson, 1991), as well as efficient knowledge transfer through scope economies (Henderson & Cockburn, 1994). The coordination costs associated with dispersed R&D might even negatively influence innovative performance; Furman et al. (2006) find that the number of R&D locations of pharmaceutical firms is negatively associated with patent counts, when they control for the therapeutic class. Singh (2008) also reveals that geographic dispersion results in decreased innovative impact. Finally, Leiponen and Helfat (2011) analyze survey data on the dispersion of R&D by Finnish firms and similarly find that the benefits do not extend to novel innovation but rather apply only to imitative innovations. Meso-organizational behavior research highlights yet another concern prompted by geographic dispersion, namely, poor social dynamics. Empirical evidence pertaining to distributed teams indicates that they experience more conflict and function less effectively than do colocated

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teams (Cramton & Hinds, 2005; Polzer, Crisp, Jarvenpaa, & Kim, 2006). Polzer et al. (2006) posit that team members establish better relations with colocated than with distant peers; Van den Bulte and Moenaert (1998) demonstrate that reducing the physical distance among R&D engineers enhances their communication. Even in virtual team environments, Nemiro (2002) finds that when dispersed team members conduct project development electronically, they reserve their idea generation efforts for their face-to-face contacts. Accessing geographically dispersed knowledge stocks may increase the potential for nonlocal search but not always grant the firm the ability to exploit that potential (Zahra & George, 2002).

Reframing the Debate Using Social Network Theory and Combinative Capabilities This debate about whether geographic dispersion supports or hinders innovation reveals striking parallels with literature on social network theory. For example, the idea that geographically colocated members develop mutual knowledge and a shared context that facilitates communication and coordination (Cramton & Hinds, 2005; Polzer et al., 2006) is consistent with the ‘‘closed-network’’ perspective, which suggests colocated members are more productive because their network density facilitates internal knowledge flows, faster learning, and trust (Coleman, 1988). A more optimistic view of geographically dispersed membership is consistent with the ‘‘structural holes’’ or open network view (Burt, 1992), which suggests that because geographic dispersion increases the links across geographies, it should generate novel recombinations. That is, geographically dispersed firm members should allocate more of their time to cross-geography or cross-knowledge interactions that facilitate their learning and creativity (Reagans & Zuckerman, 2001). By framing these two views of geographic dispersion in network terms, we may be able to resolve their apparent contradiction. A firm can have both physical and knowledge-based structural holes but simultaneously encourage dense or ‘‘close’’ relationships among its geographically dispersed members. This perspective refocuses the debate to highlight the importance of the firm’s informal social structure for determining the potential success of its formal structure, which attempts to access appropriate knowledge and skills. An organizational design that encourages both structural holes and closeness in the relationships among members can enable the firm to

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overcome its initiation–implementation dilemma (Kleinbaum & Tushamn, 2007; Szulanski, 1996). That is, structural holes created by firm members when they tap different knowledge sources can provide an engine to discover and initiate the exploration of new technologies. Furthermore, the closeness associated with dense social networks can facilitate the implementation of these novel ideas. Consistent with Kogut and Zander’s (1992) view of organizations, we argue that successful technological transfers among firm scientists depend on their mutual adaptation, which highlights the critical transformation of personal and group knowledge during the process of recombination. Organizational members must be able and motivated to share their knowledge and promote mutual understanding of nonlocal knowledge (Szulanski, 1996; Zahra & George, 2002). In their seminal work on firms’ combinative capabilities, Kogut and Zander (1992) also assert that increased ability and motivation to share knowledge depends on the informal social structure for individual and functional expertise, which in turn determines the firm’s ability to integrate existing and technologically distant knowledge. Similarly, Henderson and Cockburn (1994) posit that the capability to absorb external knowledge links into social relations is critical, such that its existing structures and processes determine the knowledge that a firm can assimilate and thus which strategies are feasible. The ability to integrate and utilize external knowledge becomes an architectural competence of the organization with positive impacts on research productivity (Henderson & Cockburn, 1994, 1996). Similarly, in their comprehensive review of firm absorptive capacity, Zahra and George (2002) argue that empirical studies ‘‘do not always capture y the multidimensionality of the absorptive capacity construct’’ and that the development of absorptive capacity hinges on multiple factors, such as the firm’s social structure. A unifying theme among these views is that the integration of external knowledge depends on the social structure within which innovation efforts take place, which influences scientists’ ability and willingness to recombine their knowledge and thereby affects the likelihood of a successful transformation of the firm’s technological capabilities. We extend this literature by focusing on the micromechanism that facilitates or hinders knowledge absorption. Access to nonlocal knowledge through decentralized R&D is likely insufficient if the firm cannot bundle its formal structure with an informal social structure that integrates the unique knowledge and skills of dispersed members to exploit new technological opportunities.

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HYPOTHESES Geographically Dispersed R&D and Technological Exploration The existence of diversity of knowledge across regions and countries has been substantiated in the biotechnology industry (Alcacer & Chung, 2007). The interdisciplinary and combinatorial forms of knowledge gathered across different biotechnology disciplines necessitate knowledge inputs from diverse sources and the ability to integrate them (Christensen, 2003). Multi-location firms gain access to a broader array of novel ideas and opportunities than do firms restricted to a single geographic location (Phene et al., 2006). Because organizational members located in different geographic locations are embedded in different networks than their counterparts, their boundary-spanning activities should offer access to unique information by bridging knowledge holes (Burt, 1992; Reagans & Zuckerman, 2001). Exposure to a broader set of ideas that are new to the firm allows the firm to avoid the familiarity trap and provides the basis for experimentation with new ideas (Ahuja & Lampert, 2001; Phene et al., 2006). Furthermore, interactions among geographically dispersed firm members, with their different contacts, skills, and information, should improve creative problem solving compared with that achieved by singlelocation firms (Reagans & Zuckerman, 2001). Given the informational advantages resulting from geographic dispersion, firms with scientists situated in diverse networks should be better positioned to exploit potential opportunities based on the unique knowledge their members bring to the firm. However, the positive effect of multi-location firms on the exploration of new technological opportunities may diminish at high levels of dispersion. First, a firm is likely to face diminishing returns to the number of locations simply due to the minimum efficient scale required for cost-effective operations of a stand-alone R&D unit (Leiponen & Helfat, 2011). Second, multiple sources of information can increase the likelihood of information overload, leading to suboptimal search behavior (March & Simon, 1958) and a reduced impact of additional information accessed by scientists in remote geographic locations. Williams and O’Reilly (1998, p. 88) posit that ‘‘it is reasonable to presume that the effect of increasing information availability has a curvilinear effect such that some initial diversity has more value than subsequent increments.’’ The knowledge-based view of the firm similarly suggests that certain knowledge combinations require higher marginal costs for knowledge transfers (Nickerson & Zenger, 2004).

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The potentially increased complexity of knowledge associated with greater geographic dispersion may also reduce the effectiveness of knowledge transfers and integration, due to causal ambiguity (Lippman & Rumelt, 1982) and uncodifiability (Zander & Kogut, 1995). Such differences increase the potential for conflict and miscommunication, which in turn reduce the firm’s ability to process the unique information of its dispersed members effectively (Cramton & Hinds, 2005; Lane, Salk, & Lyles, 2001). Consistent with these arguments, we posit that geographical dispersion has a curvilinear relationship with the likelihood of exploring new technologies: Hypothesis 1. The relationship between geographical dispersion and the firm’s technological exploration is curvilinear, such that the relationship is generally positive but decreases as geographic dispersion increases.

Moderating Effect of Social Structure Extant research on the knowledge-based view suggests that the firm’s ability to exploit the unique information that resides with its dispersed R&D members depends not only on formal organizational structures (R&D decentralization) but also on the informal social systems and routines that influence knowledge flows. Specifically, the informal organization may enhance the effectiveness of the formal organization by either supplementing or compensating for it (Gulati & Puranam, 2009; Nickerson & Zenger, 2002). Existing research on knowledge integration highlights two features of the firm’s informal structure that may influence mutual adaptation among firm members: social network density (Reagans & McEvily, 2003; Szulanski, 1996; Zahra & George, 2002) and power relationships (Bruke, Fournier, & Prasad, 2007; Tzabbar, 2009). Consistent with these arguments, we posit that the organization’s social structure, as reflected in the degree of social network density and power asymmetries among members, influences firm members’ ability and willingness to recombine their diverse knowledge and thereby moderates the relationship between geographic dispersion and technological exploration. Social Network Density For geographic dispersion to result in technological exploration, the unique knowledge and expertise of the firm’s scientists must be integrated. High degrees of personal collaboration among scientists, as reflected in their social network density, increase communication and knowledge integration

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in at least three ways. First, frequent collaboration establishes a rich communication channel (Dutton & Starbuck, 1978), resulting in more effective communication through relationship-specific heuristics (Uzzi, 1997), unique languages, and codes (Kogut & Zander, 1992), which also economize the communication flow over time (Arrow, 1974). This increase in efficiency and effectiveness should facilitate information flows and enable firm scientists to tap firm-wide knowledge bases to generate their novel ideas (Tzabbar, Silverman, & Aharonson, 2007). Second, a high degree of collaboration increases scientists’ motivation to share and accept information (Zahra & George, 2002). Closeness produces strong ties that motivate members to provide access and assistance, which may stem from social considerations, such as the desire to reciprocate (Granovetter, 1973), or be rooted in psychological considerations, such as the desire to maintain balanced relationships (Heider, 1958). Close relationships also can increase efforts to resolve transfer-related problems (Szulanski, 2000), because when two members are closely involved, they are willing to exert more time and effort on the other’s behalf, including sharing and integrating knowledge (Eisenhardt & Tabrizi, 1995; Tzabbar et al., 2007). Consequently, increased collaboration facilitates a firm’s ability to aggregate and make available individual, unique knowledge and skills for the design of new products or technologies (Rowley, Behrens, & Krackhardt, 2000). Third, collaboration among members reportedly reduces political conflict and opportunistic behavior by increasing the willingness of both the source and the recipient to share and accept knowledge (Szulanski, 1996). Strengthening the links among members of the R&D staff also reduces the likelihood of conflict about goals and implementation methods (Rindfleisch & Moorman, 2001). By generating social cohesion, connectedness aligns incumbent scientists and helps ensure a unified R&D effort (Sheremata, 2000), along with the pursuit of common initiatives (Reagans & Zuckerman, 2001). In relation to the diffusion of technological knowledge within a firm, Frost and Zhou (2005) find that the pursuit of ‘‘R&D copractices’’ – which they define as formal collaborations among individuals from disparate organizational subunits in a multinational corporation – relates positively to the extent to which those units draw on one another’s knowledge in their subsequent research projects. Because a high degree of collaboration decreases political conflict and self-interested behavior and increases knowledge flow among members, collaboration should moderate the relationship between geographic dispersion and exploration, strengthening the positive effect at lower levels of

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dispersion and weakening the negative effect of high levels of dispersion. The initial increasing portion of the inverted U becomes more steeply positive, and the peak of the curve moves to the left. The declining portion of the curve will be less steep when collaboration is high; much higher levels of geographic dispersion are required to dampen exploration. In summary, we propose that Hypothesis 2. The curvilinear relationship between geographic dispersion and technological exploration is positively moderated by social network density among firm members, such that it strengthens the positive effect at low levels of dispersion and weakens the negative effect of high levels of dispersion.

Power Asymmetries In research environments marked by power asymmetries between members, we expect a decrease in the positive effect of geographic dispersion on technological exploration and an increase in the negative effect, due to the reduced autonomy and information flows among members. In knowledge-based industries, variance in scientists’ innovative productivity tends to create power hierarchies based on skills and expertise (Tzabbar, 2009). Furthermore, the scarcity, complexity, and tacitness of knowledge provide highly prolific scientists with a significant source of expert power (Astley & Sachdeva, 1984). Powerful individuals have a strong influence over the behavioral and cultural norms of others (Bandura, 1977). Consistent with Amabile (1983), we argue that a more balanced distribution of innovative productivity should encourage broader involvement and enhance knowledge sharing and assimilation. Conversely, in innovative environments marked by power asymmetries, the firm’s ability to benefit from the diversity of knowledge and skills of geographically dispersed scientists suffers. First, highly prolific, star scientists provide ‘‘seeds’’ around which crystals form (Zucker, Darby, & Torero, 2002) and thereby attract and control key organizational resources. These star scientists tend to be self-interested and self-absorbed, which can introduce agency problems with other researchers. Moreover, by absorbing a greater proportion of the organization’s scarce resources, star scientists gain the power to determine the technological direction of the firm’s research, limiting the opportunities of other scientists or research programs (Huckman & Pisano, 2006; Zucker & Darby, 2001).

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Bruke et al. (2007) find that the presence of star scientists increases the probability that non-star scientists adopt and use the star’s knowledge, but not the other way around. These star scientists thus not only influence innovative performance but also control behavioral and cultural norms, as well as the innovative output and behaviors of other incumbent scientists (Groysberg & Lee, 2008; Lacetera, Cockburn, & Henderson, 2004). Second, when firm innovative productivity depends on star scientists who control the research agenda, the diffusion of knowledge among other firm members may be limited (Ibarra, 1993). Empirical research generally supports the notion of restricted knowledge flows due to power hierarchies (Daft, 1978; Pfeffer, 1981). The star scientist refracts new information through a particular lens and may weaken the potential and unique contributions of others (Eisenhardt & Bourgeois, 1988). When the dominant scientist nullifies others’ contributions, these other scientists tend to avoid providing information that runs counter to that preferred by the dominant party (Amabile, 1983). These restricted knowledge flows likely decrease the benefits of diverse ideas from geographically dispersed scientists, and the problem becomes compounded in a knowledge-based industry in which it is already difficult for the firm to verify appropriate behaviors, especially with regard to knowledge sharing and collaboration (Eisenhardt, 1989). Because power asymmetries reduce knowledge flows among members, they also may moderate the relationship between geographic dispersion and exploration, such that they weaken the positive effect of knowledge diversity at lower levels and further increase resistance to change, and thus the negative effect, at high levels. Accordingly, we expect that the curvilinear relationship between geographic dispersion and technological exploration shifts at differing levels of power asymmetries among scientists. The initial increasing portion of the inverted U becomes less steeply positive, and the peak of the curve moves to the right. The declining portion of the curve will be steeper when power asymmetry is high; much lower levels of geographic dispersion are required to dampen exploration. In summary, we propose that Hypothesis 3. The curvilinear relationship between geographic dispersion and technological exploration is moderated by the power asymmetries among firm members, such that it weakens the positive effect at low levels of dispersion and strengthens the negative effect at high levels of dispersion.

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METHOD Sample The sample of firms for this study comes from the biotechnology industry, where a technological race motivates biotechnology firms to explore technologies outside of their current boundaries, so access to different geographic and social knowledge networks constitutes an important component of any competitive strategy (Amburgey et al., 1996; Powell et al., 1996). We examined the life histories of all dedicated and independent US biotechnology firms founded between 1973 (the year of the Cohen-Boyer breakthrough involving recombinant DNA, often called the birth of modern biotechnology) and 2003. Unlike prior work that has focused on multinational corporations and their subsidiaries, we focus on firms with interdependent laboratories, which provide inputs into a centrally defined and coordinated R&D program but are not necessarily connected to the host country’s production operations. As opposed to local and international adaptor laboratories, independent laboratories concentrate on R&D rather than improvement or adaptation efforts. The firm-level data come from two main sources, BioScan and Knowledge-Express, which provide the firm name, address, titles and names of the top management team (TMT), number of employees, technological sectors, and other firm characteristics. We cross-checked these data with information from the US companies database (also called Bioworld), compiled by the North Carolina Biotechnology Center. These data sources and a similar selection procedure have been used frequently in studies of the biotechnology industry (Stuart, Hoang, & Hybels, 1999). This effort yields a sample of 424 US biotechnology firms that were not subsidiaries of other entities. We augment these data sources with two other complimentary sources: the NBER patent for patents granted during 1963–1999 (Hall, Jaffe, & Trajtenberg, 2001) and the US Patent and Trademark Office (USPTO) for 2000–2006. Thus, we can identify patents applied for before December 31, 2003, but granted later.1 Both sources provide information about all inventors associated with a patent and their geographic location. Specifically, patents specify the city, state (if in the United States), and country of the inventors, along with the other patent information. On the basis of the 2007 standards and Census 2000 data,2 we also identified collocated members registered in a different state but belonging to the same major metropolitan area, such as cities in New Jersey that are practically part of New York City. Similarly, we expended effort to identify

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organizational members that appeared collocated but actually were dispersed, in that they demanded more than a three-hour commute (e.g., cities in Northern and Southern California). These instances were rare, but we eliminated potential misidentifications through this process and thus reduced the number of firms with geographically dispersed membership.

Measures Technological Exploration We focus on the strategic and technological implications of a geographically dispersed R&D structure by examining the likelihood that dispersed firms will enter a new technological sector. Dedicated biotechnology firms can be categorized into six major sectors: human therapeutics, human diagnostics, agriculture, veterinary, food/brewing industry, and other (e.g., manufacturers of specialized equipment, waste management) (Amburgey et al., 1996). Using information provided by BioScan, Knowledge Express, and Lexis-Nexis, we define and operationalize a strategic exploration as an event during which the firm enters a market sector in which it has not been involved in previously. Our 424 firms with geographic dispersion undertook 834 strategic exploration events during the study period. We use the marginal intensity function of exploration as a dependent variable to analyze the probability of technological exploration. The intensity function, l(t), thus represents the limit of the probability that an exploration event occurs at time t (date of entry into a new sector), given some observation in the past. The rate of technological exploration equals a logarithmic linear function of the independent variables, l(t)=exp(bXt), where Xt represents the values of the vector of parameters that summarizes the effects of the independent variables on the rate at time t. The independent variables then are factors that affect this conditional probability. More formally, the dependent variable is lj ðtÞ ¼ lim

Dt!0

Prj ðt  Tot þ DtjT  tÞ Dt

where Prj is the discrete probability of a type j event occurring in the time interval between t and t+Dt, given that the organization is at risk for the event at time t. As the innovation cycle shortens and quickens, the time to explore a new technological sector becomes a critical issue for biotechnology firms. Therefore, time to enter a new technological sector is an appropriate,

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interesting, and important innovative output measure for this research setting. Distribution of Geographic Location Theoretically, knowledge diversity should be associated with the various geographies in which firm members reside, not the physical distance among members. Accordingly, we identify firm members’ geographic locations from the patent data to determine the degree to which members of firm i concentrate in one or few locations. We use an inverted Herfindahl index, because we seek to measure dispersion instead of concentration, with a special effort to account for the number of countries where R&D is sourced:  !  s  X Gis 2 Ci he ¼ 1 Gi C i 1 s¼2 where Gis refers to the geographic location with the most members, Gi is the total number of firm members in each location, and Ci represents the number of countries R&D members are sourced from. When firm members are sourced from only one country, we dropped the latter part of the formula to prevent the possibility of considering a nationally dispersed R&D as colocated R&D. The score therefore reflects both the number of locations and the proportion of members in the same location, while giving a higher weight to firms that are dispersed across a number of countries. This also enables us to address the possibility that a concentration of firm members in one location will have greater impact on the research agenda than will individual members in various locations (Polzer et al., 2006). Moderators To test Hypotheses 2 and 3, we consider the interactions of two variables that may influence communication among firm members. Because variance in biotechnology scientists’ innovative productivity tends to create power asymmetries (Tzabbar, 2009), we operationalize power asymmetries as the degree to which the innovative productivity of firm y centers in one or a few key scientists. We use a weighted index of inventors’ patenting activity and correct for the possibility that the number of inventors might bias the measure. That is, s represents the number of inventors, indexed by s=1, y, s, and each patent can be assigned to a different inventor. Therefore, Nis

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denotes the number of patents that the most prolific inventor holds for firm i at time t. The power asymmetries variable then can be calculated as:  !  s  X N is 2 si he ¼ Ni si 1 s¼1 This estimator increases the value of the Herfindahl index for firms with fewer scientists (Hall, 2002). We used a five-year rolling window and updated the power asymmetries with every new patent application; a higher result indicates a high degree of power asymmetries. Using Reagans & Zuckerman’s (2001) measure of network density, we consider collaboration among scientists as well. Our measure uses the frequency of collaboration to indicate relational strength among the members of the R&D department (Reagans & McEvily, 2003). From the list of inventors on each of a firm’s patents granted through time t, we compute density as the average level of co-invention frequency among scientists, Ns P Ns P

Social network densitys ¼

zijs = maxðzijs Þ

i¼1 j¼1

N s ðN s 1Þ

; jai

where zijs is the frequency with which firm member i co-invents with firm member j, max (zis) is the largest of i’s reported ties to anyone on the department, and Ns is the number of members in the firm. Density therefore varies from 0 (no co-invention) to 1, which represents a complete network with all possible ties among incumbent scientists. A dense network contains many links and co-inventions; a low-density network implies the firm engages in a limited number of co-invention activities. We update network density after every new patent application; higher results indicate a denser social network. We interact this term with geographic dispersion and, as noted in Hypothesis 2, expect the linear and squared interaction terms to be positive. Controls Our two sets of control variables pertain to firm features and R&D members. With regard to the firm-based controls, we note that publicly held firms may differ from privately held firms with respect to their openness to new ideas and ability to fund exhaustive searches (Baum & Oliver, 1991). Therefore, we include a public firm variable, equal to 1 if firm y is publicly traded and 0 otherwise. Firm age is the number of years since the firm incorporated, derived from Lexis-Nexis or firm data; it may increase search

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in the neighborhood of existing knowledge and knowledge obsolescence (Sorensen & Stuart, 2000) and reduce the firm’s ability and motivation to explore novel recombinations. Because firm size may increase the ability to explore new opportunities, we control for it using the logarithm of the number of employees, as reported in BioScan. Broader firm technologies also may increase the likelihood of identifying novel recombinations (Tzabbar, 2009), so we measure the firm’s distribution of technological classes using an unbiased Herfindahl index of the technologies in which the firm patents, as follows: ! XN ij 2  P  ¼ P1 Ni where Ni is the number of technologies associated with the patents, and Nij denotes the number of patents that the firm holds in category j. This estimator assigns a higher value to firms with fewer patents to correct for possible bias. However, the concentration index also might reflect real technological homogeneity by a firm with fewer patents, which would bias the results in the adjusted diversification index downward. The adjusted Herfindahl index therefore establishes a lower bound on the effect of technological breadth on technological repositioning, which can range between 0 and 2. A smaller index indicates the firm has undertaken a narrower search. Because an innovative orientation may affect future innovative searches (Amburgey, Kelly & Barnett, 1993), we control for the degree of exploitation, operationalized as the sum of self-citations divided by the number of citations: X Self-citations Ratio of exploitation ¼ No: of citations Two other factors might influence both the composition of firm members and search behavior: the inflow of external knowledge through R&D collaborations with other firms and the hiring of novice scientists. Accordingly, we include the number of in-progress R&D alliances and the number of scientists’ recruitment events, using a three-year rolling window, as controls. Furthermore, top-tier executives generally take responsibility for the decision to initiate strategic changes (Child, 1972), so we controlled for outsider CEO succession and the number and tenure heterogeneity of TMT members. Using BioScan reports about key personnel, hiring

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announcements in Lexis-Nexis, and firm contacts, we coded CEO succession according to whether a firm had appointed someone who was not previously part of the firm’s TMT as its new CEO. Following prior research on CEO succession (Shen & Cannella, 2002), we included a three-year lagged measure of CEO succession to allow sufficient time for new CEOs to implement their staffing and technological strategies. Larger TMTs also encounter greater coordination and control problems, which increases the likelihood of goal asymmetries and conflicts (Smith et al., 1994). Accordingly, we controlled for top management team size. Furthermore, heterogeneity in team tenure affects the likelihood that a TMT will initiate new searches (Bantel & Jackson, 1989). As recommended by Harrison and Klein (2007), we used the coefficient of variation to account for team tenure heterogeneity. A high score indicates a team of members with a long history of working together. Finally, we include several controls pertaining to the composition of the firm members. Those working in different time zones suffer coordination and communication challenges, so we include coefficients of variance time zones, based on Greenwich mean time (Polzer et al., 2006). Because heterogeneity in firm tenure among R&D members may affect their likelihood to innovate (Bantel & Jackson, 1989), we use the coefficient of variation to account for tenure heterogeneity and determine the first year an inventor joined the firm according to the year he or she applied for a first patent with that firm. If scientists do not patent with an organization, we have no record of their employment, which creates obvious limitations. However, the limited information available about inventors has made patent applications a common proxy for technology innovation research (Rosenkopf & Almeida, 2003). Finally, we account for heterogeneity in each member’s social network, according to a Blau index. That is, we note the number of unique collaborations in which each R&D member engages prior to collaborating on the focal patent. Larger numbers indicate more opportunities to access various knowledge sources (Reagans & McEvily, 2003).

Analysis Because at any point in time, a biotechnology firm might enter a new technological sector, replace a CEO, hire a scientist, or ally with other firms (i.e., repeatable point events), we use a multivariate point-process model instead of conventional event-history methods (Amburgey, 1986). We meancentered all the interaction terms to reduce multicollinearity and to facilitate

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the interpretation of our results (Echambadi & Hess, 2007; Kam & Franzese, 2007). Further, by including a Gamma mixture in our exponential hazard rate models, we check for endogeneity and then estimate the models using Heckman (1979) methods. This mixture distribution might aid the search for robust parameter estimates but does not provide an easy solution for problems generated by unobserved heterogeneity (Blossfeld & Rohwer, 2002). To deal with the limitations of the Gamma mixture specification and as a robustness check, we employ Heckman’s (1979) two-stage probit model. After estimating a probit regression to predict the likelihood that a firm will have geographically dispersed members, we used the residuals from the regression to create a selection instrument that provides a control variable we call the selection variable.

RESULTS In Table 1, we provide the means, standard deviations, and correlations for all variables. Some variables are correlated, but no critically collinear (ro0.6) results emerge. To ensure that multicollinearity does not bias our results, we calculate variance inflation factors (VIFs) for all variables in our analysis; all are less than 10, which is the conventional threshold (mean VIF=3.89). To identify potential model estimation issues, we add key independent variables one at a time and check for any instability in the coefficients or standard errors. No significant variance emerges, so multicollinearity does not appear to introduce material modeling problems. We provide the results of the event history analysis for the consequences of geographically distributed R&D members in Table 2. Model 1 includes the controls and main effects of the interaction terms; Model 2 adds the linear and square term of geographic dispersion; Model 3 and 4 add each interaction term independently; and Model 5 represents the fully specified model for technological exploration. The sign and significance of all coefficients remain consistent, but the likelihood ratio tests indicate that Model 5 offers the best fit. In Table 2, the linear term between geographic dispersion and technological exploration is positive and significant, and the squared term is negative and significant in all of the models, in strong and robust support for Hypothesis 1. To derive the percentage of change in the likelihood of technological exploration, we used (exp[b]–1)  100. The hazard rate associated with the linear term of geographical dispersion is 1.80 (1.80=e[0.59]),

0.01 0.10 0.98 0.31 0.31 0.12 0.48 5.73 1.35 0.18 1.09 1.66 0.02 4.62 0.41

2

3

4

5

6

7

8

9

10

11

12

0.03 0.02

13

0.09

14

0.02 0.02 0.56 0.03 0.01 0.02 0.02 0.00 0.03 0.64 0.13 0.01 0.01 0.11 0.14 0.01 0.00 0.03 0.06 0.08 0.12 0.01 0.05 0.05 0.15 0.08 0.08 0.31 0.08 0.02 0.08 0.12 0.11 0.17 0.42 0.54 0.25 0.00 0.00 0.08 0.12 0.17 0.26 0.23 0.26 0.05 0.06 0.01 0.00 0.01 0.02 0.02 0.04 0.01 0.11 0.11 0.08 0.04 0.05 0.03 0.01 0.25 0.22 0.11 0.10 0.08 0.02 0.02 0.01 0.00 0.03 0.03 0.06 0.09 0.02 0.08 0.02 0.08 0.04 0.02 0.03 0.00 0.04 0.00 0.28 0.24 0.12 0.09 0.03 0.16 0.02 0.01 0.00 0.01 0.08 0.05 0.23 0.24 0.18 0.08 0.19 0.08

Variables

0.01 0.03 0.18 0.33 0.19 0.22 0.65 7.42 5.09 0.34 2.32 2.34 0.00 7.21 0.29

1

Top management team size Top management team tenure

Technological exploration Degree geographic dispersion Variance in time zones R&D team tenure variance Power asymmetries Social network density Public firm Firm age (years) Firm sizea Firm degree of tech. concentration In progress R&D Scientist recruitment CEO succession Top management team size Top management team tenure

Mean SD

Notes: Correlations greater than 0.01 are significant at po0.05. SD, standard deviation. a Natural log transformation.

14. 15.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

Variables

Table 1. Means, Standard Deviations, and Correlations.

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Table 2.

DANIEL TZABBAR AND ALEX VESTAL

Event History Analysis of Firm Likelihood of Technological Exploration.

Variables

Model 1

Hypotheses H1: Degree of geographic dispersion (GD) H1: GD squared

Model 2

Model 3

Model 4

Model 5

0.53 (.14) .96 (.14)

0.57 (.14) .99 (.12) .59 (.09) .03 (.01)

0.599 (.14) .99 (.12)

.18 (.08) .03 (.01)

0.59 (.13) .95 (.12) .56 (.08) .04 (.01) .21 (.09) .02 (.01)

H2: GD  social density H2: GD squared  density H3: GD  power asymmetries H3: GD squared  power asymmetries Main effect for interaction Social network density Power asymmetries R&D team controls Variance in time zones R&D team tenure variance Firm controls Public firm Firm age (years) Firm sizea Firm degree of tech. concentration In progress R&D Scientist recruitment CEO succession Top management team size Top management team tenure

.28 (.08) .20 (.07)

.30 (.08) .21 (.07)

.30 (.08) .25 (.06)

.30 (.08) .21 (.06)

.30 (.08) .21 (.06)

.04 (.00) .11 (.01)

.04 (.00) .11 (.01)

.04 (.00) .11 (.01)

.04 (.00) .11 (.01)

.04 (.00) .11 (.01)

.18 (.05) .21 (.07) .18 (.01) .61 (.02) .21 (.02) .78 (.11) .28 (.03) .03 (.01) .18 (.07)

.18 (.05) .21 (.07) .18 (.01) .61 (.02) .21 (.02) .78 (.11) .28 (.03) .03 (.01) .18 (.07)

.18 (.05) .21 (.07) .18 (.01) .61 (.02) .21 (.02) .78 (.11) .28 (.03) .03 (.01) .18 (.07)

.18 (.05) .21 (.07) .18 (.01) .61 (.02) .21 (.02) .78 (.11) .28 (.03) .03 (.01) .18 (.07)

.18 (.05) .21 (.07) .18 (.01) .61 (.02) .21 (.02) .78 (.11) .28 (.03) .03 (.01) .18 (.07)

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Table 2. (Continued ) Variables

Model 1

Model 2

Model 3

Model 4

Model 5

In_Theta

3.85 (.21) 13.6 (.61)

3.85 (.21) 13.8 (.61)

3.90 (.21) 13.7 (.61)

3.83 (.21) 13.7 (.61)

3.89 (.21) 13.7 (.61)

3505.7 834

3417.3 834

3337.9 834

3301.8 834

3201.1 834

Constant Log pseudo-likelihood Number of events Notes: Standard errors in parentheses. a Natural logarithm.  po0.05.  po0.01.

which reflects an increase in the rate of technological exploration of 80% (80=[1.80–1]  100). The squared term of geographical dispersion is 0.39 (0.39=e[0.3]), a decrease in the rate of technological exploration of 61% (61=[0.39–1]  100). The hazard rate associated with the mean of geographic dispersion is 0.70 (0.70=e0.59–0.95), which reflects a 30% decrease in the rate of significant technological repositioning (30=[0.70–1]  100). A one standard deviation increase in the geographic dispersion correlates with a 5% decrease in the rate of technological exploration. From Model 5, we calculate the effect size as the exponent of the mean and standard deviation change in the explanatory variable, multiplied by the coefficient of geographic dispersion (e([0.590.95][0.03+0.10])=0.95–1  100=5%). As we show in Fig. 1, geographic dispersion generally increases the likelihood of technological exploration, but after a certain point, additional increases significantly decrease this likelihood. We also find support for our claim in Hypothesis 2 that the effect of geographic dispersion increases with an increase in the degree of collaboration among scientists (e0.56) and weakens the negative impact at high levels of dispersion (e0.04). To assess the economic significance, we compare the effect of geographic dispersion at different levels of collaboration. The mean value for collaboration is 0.22, and the standard deviation is 0.12, which means that the interaction at the mean value of collaboration decreases the likelihood of technological exploration by 23% (23=[e(0.590.95)+((0.56+0.04)0.22)]–1  100); at one standard deviation above the mean, this dispersion decreases the likelihood of technological exploration by 17% (17=e((0.590.95)+((0.56+0.04)(0.22+0.12)))–1  100). We plot the interaction terms between the linear and squared terms of

DANIEL TZABBAR AND ALEX VESTAL

Technological exploration

148

30% 25% 20% 15% 10% 5% 0% 0

0.1 0.2 0.3

0.4

0.5

0.6

0.7

Geographic dispersion

Fig. 1.

0.8

0.9

1

The Effect of Geographical Dispersion on Technological Exploration.

geographic dispersion and collaboration among scientists in Fig. 2. As we predicted, the higher the degree of collaboration, the weaker is the negative impact of a high degree of dispersion, whereas the positive effect is stronger at low to moderate levels of dispersion. As we predicted in Hypothesis 3, the effect of geographic dispersion decreases with the degree of power asymmetries among scientists (e0.21) and strengthens the negative impact at high levels of dispersion (e0.02). To assess the overall impact of this interaction on a firm’s likelihood to reposition its technology, we calculate the cumulative effect of both geographic dispersion and power asymmetries. The positive effect of geographic dispersion decreases with an increase in power asymmetries, from 30% to 45%, which represents a 66% decline (45= [e0.590.95–0.21]–1  100). We again compare the effect of geographic dispersion at different levels of power asymmetries to discern the economic impact. The mean value for power asymmetries is 0.19, and the standard deviation is 0.31. Therefore, at the mean value of power asymmetries, the likelihood of technological exploration declines to 35% (35= [e0.590.95+(0.21(0.19))]–1  100), and at one standard deviation above the mean, power asymmetries decrease the likelihood of technological exploration to 39% (39=e(0.590.95)+(0.21(0.19+0.31))–1  100). The interaction terms between the linear and squared terms of geographic dispersion and power asymmetries among scientists in Fig. 3 show, as we predicted, that

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Techological exploration

120% 100% 80%

40%

1 0.8 0.6 0.4

20% 0% 0

0.1 0.2 0.3 0.4

0.2 0.5

0.6

0.7

0.8

0.9

Geographic dispersion

0 1

Social network density

60%

The Joint Effect of Geographical Dispersion and Social Network Density on Technological Exploration.

Fig. 2.

30%

20% 15% 10% 0.9

5%

0.6

0% –5% 0 –10%

0.3 0.1 0.2 0.3

0.4

0.5

0 0.6

0.7

–15% –20%

0.8

0.9

1

Degree of power asymmetry

Technological exploration

25%

Geographic dispersion

Fig. 3.

The Joint Effect of Geographical Dispersion and Power Asymmetries on Technological Exploration.

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the higher the degree of power asymmetries, the stronger is the negative impact of a high degree of geographic dispersion, whereas the positive effect is weaker at low to moderate levels of dispersion. Furthermore, as we show in Table 2, all the main effects of the moderating variables are significant in all models. Firms with narrow distributions of innovative productivity are approximately 20% less likely to explore new technology (18=[e0.21]–1  100), whereas collaboration increases the likelihood of technological exploration by 35% (35=[e0.30]– 1  100). The joint impact of these main effects and geographic dispersion indicate a high degree of collaboration can compensate for the decreased likelihood of technological exploration caused by power asymmetries. That is, these two social structures should be considered complimentary in nature.

Other Effects Several results related to the control variables are worth noting. On average, public firms are less likely to explore new technologies than are private firms, perhaps due to power asymmetries among scientists in universities. Similarly, as firms age, they are less likely to explore new technologies, whereas larger firms have an increased likelihood of doing so. Furthermore, the more narrow the technological base of the firm, the less likely it is to explore new knowledge. The flow of new knowledge and strategies that rely on R&D alliances, scientist recruitment, and CEO succession increases the likelihood of technological exploration. Finally, large TMTs with high tenure heterogeneity increase the likelihood of technological exploration.

Additional Analysis Endogeneity As noted previously, we employ a Gamma mixture model to examine alternative explanations for our results. The Gamma coefficient (In_Theta) is negative and significant across all models in Table 2, which indicates that unobserved firm-level factors affect the likelihood that firms will explore new technological sectors. To assess the importance of accounting for endogeneity, we run our fully specified model without the Gamma mixture,3 but doing so creates an upward bias (coefficients are significantly higher than in Model 5). These results support the credibility of our theoretical and

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empirical model and results. Controlling for endogeneity thus is important in any examination of the impact of geographic dispersion on organizational outcomes. Because firms that develop geographically disperse R&D may differ in many unobservable ways from those that do not, we divide our sample into firms that build or do not build geographically dispersed membership. We use Heckman’s (1979) method to correct for selection bias (Hamilton & Nickerson, 2003). As described in Table 3, we obtain an inverse Mills ratio from a first-stage probit regression, with geographically dispersed membership as the (dummy) dependent variable, coded as 1 when at least one R&D member is not located in the same geography as the others, and 0 otherwise. The results of the first-stage model appear in Table 3; in Table 4, we report the results of the second-stage probit model for technological exploration. The Mills ratio coefficient is positive and significant across all regression models, highlights the importance of accounting for endogeneity when studying geographically dispersed R&D membership, and provides additional predictive and convergent validity to support our theory and findings; the reported models are highly consistent with our reported event history analysis. Despite the variance in analytical techniques, data structures, and measures, our theory and model remain consistent.

Table 3.

First-Stage Probit Selection Model for Antecedents of Building Geographically Disperse R&D Teams.

Environment Number of new firmsa Firm performance Ratio of forward citations Sum of venture capital raised Firm characteristics Technological breadth (Blau index) Constant Wald w2 Notes: Standard deviations in parentheses. po0.01. a Natural log transformation.

.21 (.08) .28 (.11) .57 (.21) .78 (.21) 21.69 1220.22

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Table 4. Second-Stage Probit Regression of Firm Likelihood of Strategic Exploration. Variables

Model 1 Model 2 Model 3 Model 4 Model 5

Hypotheses H1: Degree of geographic dispersion (GD)

.69 (.18) .86 (.10)

H1: GD squared H2: GD  social density H2: GD squared  social density

.60 (.18) .88 (.10) .56 (.06) .06 (.01)

H3: GD  power asymmetries H3: GD squared  power asymmetries Main effect for interaction Social network density

R&D team tenure variance Firm controls Public firm Firm age (years) Firm sizea Firm degree of tech. concentration In progress R&D Scientist recruitment CEO succession Top management team size Top management team tenure

.26 (.10) .03 (.01)

.61 (.18) .90 (.10) .58 (.06) .06 (.01) .21 (.09) .03 (.01)

.42 (.11) .17 (.05)

.42 (.11) .19 (.05)

.42 (.11) .19 (.05)

.42 (.11) .19 (.05)

.06 (.01) .09 (.01)

.07 (.01) .09 (.01)

.07 (.01) .09 (.01)

.07 (.01) .09 (.01

.07 (.01) .09 (.01)

.11 (.03) .18 (.02) .32 (.08) .68 (.18) .16 (.06) .56 (.21) .33 (.09) .07 (.02) .23 (.09)

.11 (.03) .16 (.02) .32 (.08) .72 (.21) .16 (.06) .56 (.21) .33 (.09) .07 (.02) .23 (.09)

.11 (.03) .16 (.02) .32 (.08) .72 (.21) .16 (.06) .56 (.21) .33 (.09) .07 (.02) .23 (.09)

.11 (.03) .16 (.02) .32 (.08) .72 (.21) .16 (.06) .56 (.21) .33 (.09) .07 (.02) .23 (.09)

.12 (.03) .15 (.02) .32 (.08) .72 (.21) .16 (.06) .56 (.21) .33 (.09) .07 (.02) .23 (.09)

Power asymmetries R&D team controls Variance in time zones

.62 (.18) .88 (.10)

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Table 4. (Continued ) Variables

Model 1 Model 2 Model 3 Model 4 Model 5

Mills ratio

1.69 1.69 1.69 1.69 1.69 (.11) (.11) (.11) (.11) (.11) 16.4 16.4 16.4 16.4 16.4 (.58) (.58) (.58) (.58) (.58) 1983. 2 2216.1 2718.2 2521.6 2878.9 834 834 834 834 834

Constant Wald w2 Number of events Notes: Standard errors in parentheses. a Natural logarithm.  po0.05.  po0.01.

Three-Way Interaction A natural extension to our theory and hypothesis would consider the joint moderating effect of high degrees of collaboration and power asymmetries on exploration. Such a three-way interaction is very hard to interpret when all three independent variables are continuous. Therefore, we arbitrarily dichotomized the degree of collaboration and power asymmetries into two categories, on the basis of one standard deviation above the mean (see also Tzabbar, 2009). The three-way interaction term is not significant, which we tentatively attribute to the very few firms with such a social structure in our data.

DISCUSSION Motivated by lacunae in prior work, we investigate conditions in which the effects of geographically dispersed R&D on entry into a new technological sector vary. Our results provide strong support for the idea that accessing geographically dispersed knowledge has a curvilinear relationship with the likelihood of exploring new technologies: The likelihood increases at moderate levels of dispersion and decreases at high levels. This positive effect also is moderated by the firm’s informal social structure, such that firms with more collaboration among their members enjoy a stronger positive relationship, whereas firms marked by power asymmetries among members suffer declines of the effects of dispersed membership on technological exploration. Our arguments and findings are consistent with the knowledge-based view, in that firms’ informal structures and processes

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can constrain or enhance knowledge flows for assimilation and application (Henderson & Clark, 1990; Henderson & Cockburn, 1994; Kogut & Zander, 1992). To realize their firm’s potential, managers must align the formal structure (R&D decentralization) with the firm’s informal social structure (Lane, Koka, & Pathak, 2006; Zahra & George, 2002). This study advances understanding of the effect of geographic dispersion on technological search in several ways. First, following recent calls to examine the interdependencies between firms’ formal and informal structures (Argyres & Silverman, 2004; Gulati & Puranam, 2009), our focus on the interdependencies between these structures sheds additional light on the debate about the impact of decentralization on the development of technological capabilities. By excluding firms’ informal social structures, prior studies may have achieved an incomplete understanding of the complex relationship between organizational structures and technological development (Argyres & Silverman, 2004). We demonstrate that to benefit fully from decentralized R&D activities, firms must bundle their formal structure with social mechanisms that facilitate communication and knowledge integration among members. Second, our results are congruent with both Kogut and Zander (1992) and Henderson and Cockburn (1994) and highlight the importance of the social structure for understanding the firm’s ability and motivation to integrate knowledge. Furthermore, the results add to scarce evidence pertaining to the relationship between potential and realized absorptive capacity (Lane et al., 2006; Tzabbar, 2009; Zahra & George, 2002). By accessing different knowledge networks, a firm can increase its potential to exploit new technologies, but bundling geographic dispersion with the firm’s informal social structure influences its ability to realize this potential. As the knowledge-based view of the firm suggests, firms must access different knowledge bases and capabilities to develop new knowledge, then bundle them with internal mechanisms, structures, and cultures (Kogut & Zander, 1992; Teece, Pisano, & Shuen, 1997). Particular social structures thus might complement particular personnel compositions. This approach is consistent with broader research on resource complementarities (Teece, 1986) and emerging work on resource bundling (Sirmon, Hitt, & Ireland, 2007). By developing a contingency approach to dispersion–exploration relationships, we advance understanding of the conditions in which the effects of geographic dispersion on technological exploration vary, which helps bridge literature streams pertaining to knowledge diversity and combinative capabilities. Third, we contribute to the long-standing debate about network structure (Ahuja, 2000; Burt, 1992; Granovetter, 1982; Hansen, 1999) by arguing that

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instead of viewing open and closed network arguments as contradictory, researchers should consider them complements. That is, developing bridging ties (open) by accessing the diverse knowledge and skills of multi-location firms increases their potential to explore new opportunities, but strong ties (i.e., high degree of collaboration) enable firms with geographically dispersed members to exploit their potential. Accordingly, managers should design the geographic composition of their R&D members to create moderate levels of external structural holes and internal closeness. Fourth, consistent with the concept of learning by hiring, we find that accessing distant knowledge enables firms to explore new opportunities (Rosenkopf & Almeida, 2003; Tzabbar, 2009). Strategic hiring may offer a practical means to access external competencies, mitigate institutional disadvantages, and overcome resource constraints. By hiring geographically dispersed members, firms can develop heterogeneous knowledge bases (Kogut & Zander, 1992); in this sense, our study extends research derived from the knowledge-based view. Fifth, our methodological and analytical approaches surmount some limitations of prior research, particularly those related to measurement, sample selection bias, and generalizability issues. Most recent evidence relies on patent citation data to examine innovative outcomes, which limit understanding of the technological developments that result in strategic outcomes. Other studies dichotomize their measures of dispersed R&D (e.g., Penner-Hahn & Shaver, 2005), resulting in range restrictions and a failure to account for the potential explanatory power of the number of locations and the geographic distance between locations (Singh, 2008). By examining small, independent, dedicated biotechnology firms, we also extend the potential generalizability of theory about dispersed teams, which previously has focused mainly on large corporations. However, as does any study, this work contains several limitations. First, similar to many other studies in this field, we rely on data from a knowledgebased industry, so our findings might not generalize to other industries. We recommend replications and elaborations of our research in other settings (Leiponen & Helfat, 2011). Second, many of our variables rely on information provided by the USPTO, which is a reliable source of information. However, using patent data to construct all three of our independent variables exposes our results to potential common method bias. This potential bias is very limited, relative to that in survey studies, but we still recommend caution (Tzabbar, 2009). Relying on patent data also introduces objections about the full representation of social processes and knowledge (Tzabbar et al., 2008). However, biotechnology firms likely

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patent any patentable technologies they develop, in response to their strong appropriability regime. In addition to addressing these limitations, further research might examine how the geographic composition of R&D personnel affects the firm’s overall absorptive capacity and performance (Cohen & Levinthal, 1990). From an organizational structural perspective, it might be helpful to consider how social structures influence firm potential and absorptive capacity. If the social structure enhances or restricts the transformation and exploitation of externally acquired knowledge, does it also reduce knowledge spillovers? If accessing varied knowledge from geographically dispersed members enhances the firm’s ability to generate a new technological sector, research should examine the influence of this increased ability on the firm’s status and value in its social network (Amburgey, Aharonson, & Tzabbar, 2009). Finally, from a practical standpoint, firms interested in exploring new technologies should hire R&D personnel with varied knowledge and skills, though simply hiring dispersed firm members with access to different knowledge networks is not sufficient. Human capital in general and R&D membership in particular should align with the firm’s existing resources and structures to facilitate knowledge sharing and mutual understanding. That is, to exploit the knowledge and skills of their geographically dispersed members, firms must motivate members to share their unique knowledge, without allowing them to suffer undue influence from power hierarchies.

NOTES 1. During this period, the USPTO did not publish patent applications unless and until they were successful. Thus, we only have information about applications that ultimately resulted in a granted patent. More than 90% of ultimately granted patents are granted within four years of application (Griliches, 1990), so this sample is virtually complete. 2. See www.census.gov. 3. These results are available upon request.

ACKNOWLEDGMENTS We are indebted to Terry Amburgey, Joel Baum, Raj Echambadi, Jasjit Singh, and MB Sarkar for their constructive comments. We also thank Aija

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Leiponen and Connie Helfat for sharing their forthcoming paper with us. Rusihi Tao provided assistance with the data collection and coding.

REFERENCES Adner, R., & Helfat, C. E. (2003). Corporate effects and dynamic managerial capability. Strategic Management Journal, 24(10), 1011–1025. Aharonson, B. S., Baum, J. A. C., & Feldman, M. P. (2007). Desperately seeking spillover? Increasing returns, industrial organization and the location of new entrants in geographic and technological space. In Industrial & Corporate Change, 16(1), 89–130. Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: a longitudinal study. Administrative Science Quarterly, 45, 425–455. Ahuja, G., & Lampert, C. M. (2001). Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal, 22(6-7), 521–543. Alcacer, J., & Chung, W. (2007). Location strategies and knowledge spillovers. Management Science, 53(5), 760–776. Almeida, P. (1996). Knowledge sourcing by foreign multinationals: patent citation analysis in the US semiconductor industry. Strategic Management Journal, 17, 155–165. Almeida, P., & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45, 905–917. Amabile, T. M. (1983). The social psychology of creativity. New York, NY: Springer-Verlag. Amburgey, T. L. (1986). Multivariate point process models in social research. Social Science Research, 15, 190–206. Amburgey, T. L, Aharonson, B. S., & Tzabbar, D. (2009). Heterophily in inter-organizational network ties. In 25th EGOS Colloquium: European Group of Organizational Studies, Barcelona, Spain. Amburgey, T. L., Dacin, T., & Singh, J. V. (1996). Learning races, patent races, and capital races: strategic interaction and embeddedness within organizational fields. In J. Dutton & J. A. C. Baum (Eds.), Advances in strategic management (pp. 303–322). Greenwich: JAI Press. Amburgey, T. L., Kelly, D., & Barnett, W. P. (1993). Resetting the clock: the dynamics of organizational change and failure. Administrative Science Quarterly, 38, 51–73. Argyres, N. S., & Silverman, B. S. (2004). R&D, organization structure, and the development of corporate technological knowledge. Strategic Management Journal, 25(8-9), 929–958. Arrow, K. J. (1974). Limited knowledge and economic analysis. The American Economic Review, 64(1), 1–10. Astley, W. G, & Sachdeva, P. S. (1984). Structural source of intra-organizational power: a theoretical synthesis. Academy of Management Review, 9, 104–113. Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall. Bantel, K. A., & Jackson, S. E. (1989). Top management and innovations in management: does the composition of the top team make a difference. Strategic Management Journal, 10, 107–124. Barnard, C. (1938). The Functions of the Executive. New York: Wiley.

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CHAPTER 6 THE DUALITY OF KNOWLEDGE NETWORKS: THE IMPACT OF PRODUCTION AND USAGE NETWORKS ON ACADEMIC CITATIONS Atul Nerkar and Nandini Lahiri ABSTRACT This chapter offers a complementary view to the ‘‘quality of knowledge’’ perspective whereby citations to academic articles are a result of efficient market processes. The chapter suggests that any academic research can be seen through the prism of two types of knowledge networks – production and usage. Author(s) of papers are located in these two networks and their absolute and relative position in these networks can help the diffusion of the focal research. The hypotheses are tested on a dataset of 1,085 papers published in the top five management journals between 1993 and 1997. Results suggest that controlling for attributes of a paper, the position occupied by author(s) in the usage networks and production networks contributes substantially to future citations received by a paper in these five journals. However, under conditions of extreme prominence in the usage network, increases in prominence in the

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 165–197 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013009

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production network dampen increase in future citations. Similarly under conditions of extreme prominence in the production network, increases in prominence in the usage network dampen increase in future citations. Implications of these results are discussed in the context of knowledge creation, dissemination, and recognition efforts of authors. Keywords: Academic research; agent prominence; innovation performance Why is some research more creative than others? Creativity and novelty in research more often than not drives greater citations. The question of ‘‘what drives academic citations?’’ has been explored by scholars interested in the sociology of science for many years (Cronin, 1984; Merton, 1957). Academic careers have been built and lost on the basis of the number of papers published in top tier journals and the citations received by these papers (Park & Gordon, 1996). Promotion and tenure decisions are evaluated on a range of diverse characteristics, many of which cannot be easily quantified or compared. Such characteristics of the author may include, but are not limited to, innate ability, knowledge acquired in the area, and work attitudes. While these attributes may be judged over time, uncertainty over the judgment never vanishes. Thus, decision makers at universities focus on observables that are regarded as immutable signals of the quality of the author. An important and universally accepted signal is the number of citations that the author’s papers receive (S. Cole & Cole, 1968; Diamond, 1986; Lawani, 1977). Given the importance of citations in research and its influence in shaping the career of researchers, our aim is to understand what makes some papers more creative than others. At the essence of creativity lies the ability of the researcher to use unseen and unobservable inputs like intellect and incorporate unique and effective twists toward making the paper stand out among his peers (Perry-Smith & Shalley, 2003). While early work on creativity had primarily approached it as an individual trait (Barron & Harrington, 1981), more recent work such as Simonton (1983) argues that creativity is better understood within a network of interpersonal relationships. Consistent with this stream of work, we approach our research in understanding what makes a paper creative from the perspective of knowledge markets, with players in the markets identified based on their position relative to others. In doing so, we build on the tradition of research in creativity from a relational perspective, and not one that is entirely individualistic.

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Considerable research has been done on the underlying drivers of citations (Durand, 1974; Rynes, 2007). While early work (de Solla Price, 1965) established differences across papers in citations received, there is a lack of consensus in the literature about what drives such heterogeneity in citations. Subsequent empirical work in recent years has focused on two approaches to academic citations: universalism and particularism (Blau, 1962; Boyd, Finkelstein, & Gove, 2005). Research taking the approach of universalism presupposes that the allocation of rewards and resources is a result of the author’s contribution to the field. Factors determining what is said in the paper and how it is said are considered universalistic characteristics of the paper. In contrast, factors affecting who says it belong to the particularistic category. Thus, characteristics of the paper and characteristics of the authors are identified as the primary drivers of the citations received by a paper. Empirical evidence on the dominance of one or the other group has been fairly mixed. Recent findings indicate that universalistic characteristics, such as the intellectual content of the paper (Baldi, 1998; Johnson, 1997; Judge, Cable, Colbert & Rynes 2007), journal quality (Bjarnason & Sigfusdottir, 2002; Johnson, 1997; Judge et al., 2007; Nerur, Sikora, Mangalraj, & Balijepally, 2005), the domain of research, and the extent to which it builds on previous research, have a positive impact on academic citations (Bergh, Perry, & Hanke, 2006; Judge et al., 2007; Stremersch, Verniers, & Verhoef, 2007). Consistent with the notion of how research is presented, prior work has found that the citations that a paper receives depends on the carefulness of theory construction and writing (Judge et al., 2007), the type of methodological approach chosen (Bergh et al., 2006), and the presentation of the idea (Stremersch et al., 2007). The findings on the particularistic factors have, however, been mixed. Prior research finds evidence that the reputation of the author (Johnson, 1997) and the recognition of prior publications (Bergh et al., 2006) does figure positively in determining the number of academic citations that a paper receives. On the other hand, studies have found that the prominence of the authors (Baldi, 1998), prior productivity (Rynes, McNatt, & Bretz, 1999), have no impact on the academic citations that a paper receives. Finally, Stremersch et al. (2007) find that central authors in the field of marketing may produce lower impact work than non-central authors in the field. Our line of argument in this chapter is based on the premise that the knowledge creation and dissemination process involves scholars who are driven by motivations of publication and consequent recognition

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(Dasgupta & David, 1994; Pelz & Andrews, 1966). In doing so, we depart from previous research in the following ways. First, we argue that the process of academic citations must be seen from the perspective both of production of knowledge and of consumption of knowledge, or what we call, use of knowledge. Second, we argue that a network theoretic lens lends itself to the use of heuristics, which are important indicators in this case, as the use of knowledge and the citation process associated with it is an ‘‘experience good.’’ Finally, we argue that it is important to understand the interactions of production and usage sides of the market for knowledge flows for a better understanding of the process. Our results, based on the 1,085 articles published in the top five management journals and citations to these articles within these journals, suggest that articles published by authors who are part of the knowledge (production or usage) network have 45 percent more of a chance of being cited as compared to articles published by authors who are not part of the knowledge network. In contributing to the substantial literature on academic citations, our research is, we believe, the first to consider both the production and usage aspect of knowledge networks. .

THEORY AND HYPOTHESES In summary, the current understanding of why academic citations vary across papers is, at best, mixed (Ilgen, 2007). In our attempt to identify the underlying mechanism of academic citations we conceptualize academic scholars as authors who are engaged in the pursuit of scientific research that involves creation, dissemination, and recognition (Merton, 1957). Creation of new knowledge by building on the efforts of other authors is the primary endeavor of authors engaged in scholarly research (Dasgupta & David, 1994). That said, creation of knowledge that is never published or otherwise disseminated – that is, a tree falls in a forest but no one heard or saw it fall – is not considered useful. Consequently, most authors pursue publication of their knowledge in journals that can best help to disseminate their knowledge. Finally, authors are rewarded for the recognition their published work receives in these journals. The question then remains – what causes an author to cite a focal paper? In ideally functioning knowledge markets, papers with the ‘‘best’’ knowledge would be cited in subsequent work. The underlying assumption is that these knowledge markets are efficient and that there exists a normative process whereby the characteristics of papers influence subsequent

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citation. Previous studies on academic citations have provided mixed evidence in of support of a normative approach to citations. This inconsistency implies that there exist imperfections in the market for knowledge. The primary imperfection of knowledge markets is driven by the fact that academic citations are ‘‘experience goods’’ as opposed to being ‘‘search goods’’ (Nelson, 1970). Building on Nelson’s ideas, Milgrom and Roberts (1986) state that in the case of search goods, the relevant characteristics of the good are evident on inspection, while in the context of experience goods, crucial aspects of the product’s quality (fitness for use) are impossible to verify except through the use of the product. This theory, applied to our context, suggests that authors will find it difficult to judge the usefulness of a paper prior to its actual use in their own research. Authors would like to build, ideally, on knowledge that will help them to get their papers published and disseminated. In their quest for publication, they are likely to cite prior research that, first, is relevant to the topic; second, will help them get published; and finally, will be useful in gaining recognition. There exists no prescription for determining which research papers to cite in order to achieve the objective (Shadish, Tolliver, Gray, & Sengupta, 1995). The decision to cite or not cite literature is made while a paper is written and/or during the review process; but, citing prior research in a paper to send out for review and possible publication and subsequent impact is a lengthy, uncertain means to recognition. Hence, author(s) are likely to use other heuristics in addition to, but independent of, the actual content of the paper in making a decision on citation.

Network Approach We use an economic sociology approach to understand the drivers of citations. The process of creation, dissemination, and recognition of knowledge has features of both markets and networks (Powell, 1990). From an economics perspective, there exists a knowledge market that consists of authors who are producers of knowledge and another group of authors who are consumers or users (Hansen & Haas, 2001). However, as there is no price mechanism that determines equilibrium, we suggest that consuming (citing) authors use a set of heuristics based on the position of the sellers (producers) in a knowledge network to decide which knowledge to use (Baldi, 1998). We suggest that the market or knowledge network has a duality, that is, the network of producers, and the networks of users. Building on this idea of knowledge markets, although imperfect, we term

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the two networks production network and usage network, respectively. This duality in the network approach to academic citations has not been examined in prior research and provides an important framework for understanding the underlying mechanism that drives the academic citation process. Fig. 1 shows a simple schematic of the two networks and their connection to future citations. The schematic is divided into three parts. The central section shows a set of papers (in future, called ‘‘focal’’ papers) published in year t. These papers are published by a set of authors drawn from a population of authors a1 to an, either alone or jointly. Each focal author is part of a production network composed of co-authors and other authors engaged in scholarly research in the time period t1 to ti. Simultaneously, some authors in the production network may also be part of a usage network composed of other authors who read their work and use their work Production Network

Focal Papers

Each line representsa co-authorship tie

Each box represents a paper with associated co-authors

a2 a1

a9

a3

a10

a18

a10

a4

Paper 1

a5

a2

a13 a14

a7

a8

a6

a6

a1 Paper 2

Usage Network

a3 a7 a2

Paper 3

Each line representsa co-authorship tie

a7 a4

a6

a14

a12

a15 a13

a9

a6 a5

a11

Paper 4

Paper 5

a3

Section 1 - Positions of authors and users between time period t-1 to t-i

Fig. 1.

a7

a8

Section 2 - Published Papers and Authors in year t

Section 3 - Citations by future authors in time period t +1 to t +j

Production Markets, Usage Markets, and Future Citations.

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to create new work in the corresponding time period t1 to ti. This is shown in the first part of the schematic. Finally, the third section of the schematic shows a set of authors drawn from the same population who are in time period t+1 to t+j. It is important to note that not all authors of the population appear in the different sections because of the uncertain nature of research activities. The authors in time period t+1 to t+j are in the process of creating new knowledge by building on earlier research. The dilemma they face is: which paper should they cite? Prominence in Networks Consider author a18 in Section 3 of the schematic. This is a first-time author who appears neither in the production network, nor in the usage network. She is working on a research idea and wants to maximize her chances of creating new knowledge and getting the research published. Given that this individual is a potential first-time author, how is she going to determine which papers to cite as provided in Section 2? We can assume that the papers represented in Section 2 are the relevant papers in the particular domain of research and that her research training has helped her filter the nonrelevant research (Long, Bowers, Barnett, & White, 1998). For instance, it is unlikely that a researcher, in studying the phenomena of organizational performance, would consider knowledge published in the Journal of Neurology. In order to select the appropriate papers, a18 must decide which of the five papers she must choose.1 Given the ‘‘experience good’’ nature of knowledge citation, the ideal experimental process for author a18 is to write different papers with different combinations of citations, that is, build on different ideas and see which is successful. That said, few authors have the time and patience to try this ideal process in practice. However, success in research assumes that authors are able to publish their work and subsequently have impact. The process of research is as much a social process as it is a market-led process (Merton, 1957). The scholarly community, while engaged in the production of research, also determines which research is published and is cited subsequently in its journals (Beyer, Chanove, & Fox, 1995). Author a18, being motivated by these considerations, is therefore likely to consider the attributes of the authors producing the papers while deciding whether to cite the papers. Let us assume that a18 has narrowed the set to two papers (2 and 5). The first of these papers is co-authored by a1, a6, and a13. The second paper is co-authored by a6 and a7. Prior research has suggested that actors with prestige arising from network positions are more likely to influence decisions made by other actors (Brass, Galaskiewicz,

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Greve, & Tsai, 2004; Tsai, 2001). We define prominence as the characteristic of being worthy of note in the context of the knowledge networks (Cerulo, 1990). Burt (1982) states that, ‘‘A scientist occupies a prominent position to the extent that he is acknowledged as a source of ideas and advice, which in turn is a function of his publications.’’ Therefore, prominence can arise from being part of the production and/or usage network. We suggest that the heuristic used by a18 would be associated with the prominence of the authors. In the next section, we develop specific hypotheses based on prominence that arises from positions held in the production or usage network. More specifically, we connect the likelihood of citation by author a18 and the prominence of authors of the papers 2 and 5 both from the perspective of the production as well as from the usage network.

Hypotheses Production Network The top portion of Section 1 depicts actors in the production network. Each bubble in the production network figure represents an author while links between bubbles represent co-authorship ties. Prominence in the production network arises from demonstrated productivity. The size of each bubble represents the prominence that an individual author enjoys by virtue of past productivity. There is considerable heterogeneity in the prominence of these authors. For example, a5 and a6 have demonstrated greater past productivity than a10, who has in turn been more productive than a1. Thus, in evaluating author attributes (which ultimately are connected to the focal paper), a18 determines that for papers 2 and 5, the latter has been written by more a prominent author(s) and is more likely to choose the papers written by this author(s), for the following reasons. First, prominent authors who have expertise as evidenced by prior productivity in the topic are more likely to be chosen as reviewers and editorial board members of journals (Ireland, 2008). These are people who play the critical role of gatekeepers in the dissemination of new knowledge (Ketchen, 2008; Trevino, 2008). For a paper to get accepted into a journal, it must meet the standards of acceptance as are determined by reviewers and editorial board members (Miller, 2006). New knowledge is considered to have been created only when it is published. The review process is often double blind. Hence prospective authors, such as a18, are more likely to cite prominent authors, as they are potential gatekeepers in the publication process.

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Second, prominence in the production network has long been considered an indicator of acceptability and prestige in the profession (Long, 1978). By building on the work of prominent authors, it is more likely that a18 will increase the chances of general awareness of her research, of being associated with prestigious work, and of being cited. Thus, we hypothesize: H1a. The greater the prominence of the focal author(s) of a paper in the production network, the greater the likelihood that the paper written by the particular set of focal authors will be cited.

Usage Network In contrast to the attainment of prominence in the production network, prominence in the usage network arises from recognition by authors in the scholarly community (J. Cole & Cole, 1973). The usage network is different from the production network. Authors who may not have published in the recent past but have received recent citations to prior work would be members of the usage network, but not of the production network. An example of such a difference is author a15, who is in the usage network, but not in the production network. Similarly, authors who have published in the recent past but have no recent citations to their work would belong to the production network, but not the usage network. An example of this is author a1. Finally, there exist authors, such as a11, who belong to the group of focal authors, but neither to the production, nor to the usage, network. Thus, the actors of the production network and usage network are nonidentical. This is an important point because it allows for the possibility that an author with high prominence in the production network may have low prominence in the usage network, and vice versa. The usage network is conceptualized as the pool of authors who have received recognition (citations) based on prior work. The size of each bubble determines the prominence based on recent citations to past work. In the usage network figure, the size of the bubble indicates that author a13 has higher prominence than has author a6. The impact of prominence in the usage network as a predictor of likelihood of academic citation follows Merton’s law (Merton, 1957). Merton states that the Matthew effect takes place when what gets ahead tends to continue to get ahead. Nobel laureates are cited for work subsequent to their receiving recognition because they are Nobel laureates, and authors want their work to be viewed in a comparative light. In our example, author a18 would choose to cite paper 2, as

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the prominence of the authors in the production network is more than that of the authors of paper 5. Thus, we hypothesize that: H1b. The greater the prominence of the focal author(s) in the usage network, the greater the likelihood of future citation to the focal paper. Derived Prominence in Networks Prior research has shown that actors may not only derive prominence from their own actions but also from association with other actors who are prominent because of their actions. For instance, Benjamin and Podolny (1999) show that firms in the California wine industry with high-status affiliations benefit from such associations as compared to firms without such affiliations, while Hsu (2004) shows that firms pay a premium to be associated with high-status venture capitalists. Similar, halo effects have been found in technology licensing by Sine, Shane, and Gregorio (2003), who find that universities with improved prestige tend to do better than what their past licensing performance would suggest. In the context of our chapter, we suggest that the prominence of focal authors not only arises from their own productivity or recognition, but also is derived from their association with other prominent researchers. Derived Prominence in Production Networks A second feature of the production network shown in Section 1 of our schematic is the number of linkages between the different bubbles. Linkages between actors in the production network are established when there are coauthorship ties between individuals. While there may be several mechanisms that forge ties between authors, we focus on the linkages established as a result of co-authorship ties. Such ties are established as a result of interactions between author pairs over a period of time. The outcome of such co-authorship ties is the establishment of an ongoing relation, which may serve as a conduit for flow of ideas (Acedo, Barroso, Casanueva, & Galan, 2006). Focal authors tied to other prominent authors in the production network are more likely to be asked to play a gatekeeper role than focal authors who are not tied to prominent authors. This is similar to the effect that mentors have on placement of their doctoral students (Long, Allison, & McGinnis, 1979). Given the ties that the focal author has to prominent authors in the knowledge network, the reach of the focal author is greater than if there were no such ties. Such ties enhance the reach of the focal author within

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the production network and facilitate the awareness and, hence, the recognition of the focal paper (Oh, Chung, & Labianca, 2004). In our schematic, paper 5 has a team of authors who have higher derived prominence than that of paper 2. Hence, we hypothesize H2a. The greater the derived prominence of the focal author(s) of the paper, the greater the likelihood that the paper will be cited. Derived Prominence in Usage Networks The lower portion of Section 1 of our schematic depicts the usage network with linkages between authors. A linkage between any two authors appears when they are co-cited together in work published in the time period between t1 and ti. Derived prominence in the usage network is by virtue of association with a prominent actor in the usage network. For example, a4 is not prominent (size of bubble), but can derive prominence by virtue of association with a15. Derived prominence in the usage network is the prominence enjoyed by an author as determined by the degree to which s/he is associated (even if there is no active effort on the part of the individual to be involved in such associations) with other prominent authors. Such derived prominence of the focal author(s) may help future authors to consider the focal work to be as important, relevant, and appropriate (Coleman, Katz, & Menzel, 1957). In a knowledge market context, Hudson (2007) finds that papers published in an issue that has a highly cited paper have more citations than papers published in issues that do not have such highly cited papers, that is, have low visibility. Continuing with our example of author a18, she is more likely to choose paper 2 over paper 5, as the focal authors in paper 2 have greater derived prominence in the usage network. Thus, we hypothesize, H2b. The greater the derived prominence of the author(s) of the focal paper, the greater the likelihood of future citation for the focal research. Our premise so far is that each focal author is evaluated in terms of direct or derived prominence in each of the production and usage networks. However, as is evidenced from our example of author a18, the decision to choose between paper 2 and paper 5 differs based on prominence in the usage network and production network. This observation suggests that these heuristics may complement or compete in helping the decision maker. Our next step is to determine whether the impact of prominence/derived prominence in one network on academic citation, is conditional on the prominence/derived prominence of the focal authors in a different

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network. Accordingly, we generate a set of four hypotheses, summarized here. Interaction of Prominence in Production and Prominence in Usage Network We hypothesized that the prominence and derived prominence in the production and usage network can form the basis for an author’s decision in citing previous work. When an author(s) cites previous work, given the uncertainty that surrounds the creation and recognition process, both prominence and derived prominence in the usage network are important heuristics that enable an author(s) to overcome uncertainty underlying the creation process. Heuristics drawn from prominence in the production network help to resolve uncertainty about the dissemination process, as prominent authors are likely to enjoy gate-keeping positions that have considerable influence in the decision making that underlies the publication process. The influence the authors’ positions in the usage and production networks have on each other in their association with future citations remains unexplored, that is, how do the two network positions interact in their effect on the academic impact of an article? Recall that we treat the prominence arising from the network position of the author(s) of an article to resolve the uncertainty that future authors face in citing their work. To the extent to which these heuristics help resolve the same uncertainty, they would be substitutes for each other. That is, the relative importance of one heuristic matters less in the presence of the other. For example, in considering the impact both of prominence in usage and of prominence in production networks, increasing prominence in the usage network will matter directly in reducing uncertainty in the creation process. With increasing likelihood of creation of strong ideas, the importance of citing gatekeepers (those likely to have high prominence in the production network) will matter less in the creation, dissemination, and recognition of the authors’ work. Hence, we hypothesize that H3a. The greater the prominence of the focal author(s) of a paper in the production network, the lower the impact of prominence of the same focal author(s) in the usage network on academic citations; H3b. The greater the derived prominence of the focal author(s) of a paper in the production network, the lower the impact of prominence of the same focal author(s) in the usage network on academic citations;

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H3c. The greater the prominence of the focal author(s) of a paper in the production network, the lower the impact of derived prominence of the same focal author(s) in the usage network on academic citations; H3d. The greater the derived prominence of the focal author(s) of a paper in the production network, the lower the impact of derived prominence of the same focal author(s) in the usage network on academic citations.

DATA AND METHODS Our focus in this chapter is on a social network approach to citations. To empirically test our theory we decided to focus on those journals considered to be the top five journals in the area of management: Administrative Science Quarterly, Academy of Management Journal, Academy of Management Review, Organization Science, and Strategic Management Journal. We chose not to include many other top journals, as we feel our choice of journals does not affect the internal validity of our findings. We do feel that these journals represent the top tier journals in the area of management, that is, most management departments in universities would consider publication in these journals as important and relevant for tenure decisions. As most prior studies on research in management include these five journals, we are more likely committing an error of omission than of commission, which at the most may limit the generalizability of our findings beyond these journals (Judge et al., 2007). Further, our sample is consistent with our networkbased theory, which suggests network positions in these journals matter for subsequent citations in these journals. To construct the various measures associated with our theory, we built a sample of all articles published in these five journals over the period of 1993– 1997. Our choice of years was driven by two considerations. First, we wanted to achieve a reasonable window to measure our dependent variable, the academic impact of the focal article. Second, we needed a statistically significant number of articles in order to provide an adequate representation of the spectrum of research published in the area of management. Dependent Variable Research that examines academic citation as a dependent variable has, in general, used the Social Science Citation Index (SSCI) as a primary source

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of citations (Stremersch et al., 2007). The number reported in the SSCI is a cumulative count of the number of citations received from articles published in the journals represented by the SSCI subsequent to publication of the focal article (Bergh et al., 2006). While this measure is useful, it proved less representative for the theory that we propose in our chapter, that is, a network analytic approach to citations in the focal journals. The SSCI includes citations from journals that are not in the network of the five journals. We chose, therefore, to operationalize our measure of academic impact as the number of citations received by a focal article from articles published in the sample list of journals. In other words, the dependent variable is the number of citations received in the five journals. We term this measure ‘‘focal paper citations.’’ This refers to the citations to the focal article that appear from the time of its publication until 2003. We closed our citation window at 2003 as our data collection efforts were limited to the end of that year.

Independent Variables Our independent variables are based on the publication records of authors in the five journals in the ten years preceding the publication of the focal article. We use a ten-year window to construct our network as most universities have a 6–10 years tenure clock, that is, time before faculty are reviewed for permanent tenure. Also, there could be a potential drop in productivity after tenure. By choosing a ten-year window we believe our data is capturing most of the productivity of the members of the production and usage networks. Our theory suggests that authors are simultaneously part of both production and usage networks, with a distinct possibility that their prominence levels within these two networks may differ. We construct the prominence and derived prominence of each author in the production and usage network. As the analysis is at the level of the paper, we generate an average prominence/derived prominence measure in each of the networks for the focal authors on the particular paper. Prominence in production network: We measure the prominence of focal authors in the production network as the average number of first-authored papers produced by the focal authors in the ten years preceding the publication of the focal paper (J. Cole & Cole, 1973). Prominence in usage network: We measure the prominence of focal authors in the usage network as the average number of citations received by the focal

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authors as first author in the ten years preceding the publication of the focal paper. Derived prominence in production network: We measure the derived prominence of focal authors in the production network as the average number of papers produced by all co-authors of the author of a focal paper in the ten years preceding the publication of the focal paper. Derived prominence in usage network: We measure the derived prominence of focal authors in the usage network as the average citations of the authors co-cited with the focal authors in the ten years preceding the publication of the focal paper (Burt, 1982; Cerulo, 1990).

Controls Size of production network: We measure size of production network by the number of unique co-authors of the focal authors in the ten years preceding the publication of the focal paper. We include this control as the greater the size of the production network, the greater the likelihood of diffusion of the focal paper through that network. Size of Usage network: We take as a measure of the size of the usage network the number of unique authors co-cited with the focal authors in the ten years preceding the publication of the focal paper. The size of the network controls for network effects that are independent of our hypothesized effects. However, we need to include controls at the paper level, as not all papers have the same attributes. Some of these attributes may have an influence on subsequent citation. These controls include: Length of focal paper: Editors are likely to allow important papers or papers that they believe make a greater contribution greater length in a journal. We control for this by including a variable that measures the length of the paper (Judge et al., 2007; Stremersch et al., 2007). Reliability of knowledge used in the focal paper: Reliability of knowledge used in the paper is measured by the median number of citations received by the cited authors in the bibliography of the focal paper, ten years preceding the publication of the paper. Age of knowledge used in the focal paper: Age is measured by the median age of references cited in the focal paper (Bergh et al., 2006; Stremersch et al., 2007).

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Network Focus of focal paper: Papers that cite work from outside the domain of the five journals may be different from papers that build within these five journals. To control for this effect we include a variable that measures the percentage of references cited in the focal paper that are within the focal group of journals. Team size on focal paper: Team size is measured by the number of coauthors on the focal paper (Acedo et al., 2006). Potential self-citations of focal paper: The importance or creativity of a paper should be judged independent of the number of times it has been used by author(s) in their own work. Since this measure is difficult to compute we used a measure that is highly correlated with self-citation. For a small subset of papers we calculated the correlation between the number of papers published by the author(s) of the focal paper and the number of selfcitations in the focal paper. The correlation was greater than 0.8 and therefore we control for the potential for self-citation is measured by the number of papers produced by the focal authors in the years succeeding the publication of the focal paper (Hudson, 2007). This control is not included in any of the citation studies that we examined and also represents the experience that the author(s) of a focal paper have prior to the paper being published.

Methods The dependent variable, impact in focal journals, is an integer variable that takes any value that is equal to or greater than zero. Past research suggests that such variables follow a Poisson distribution and that the appropriate technique to analyze such a variable is by Poisson regression, which is part of a larger group of techniques known as count models (Hausman, Hall, & Griliches, 1984). The specification for the Poisson regression is as follows (Allison, 1999): Let variable y be impact measured as future citations that can only have non-negative integer values. The probability that y is equal to some number r is given by r l Prðy ¼ rÞ l e ; r ¼ 0; 1; 2 . . . r!

where l is the expected value (mean) of y and r!=r(r  1)(r  2)(r  3)y. Although y can take only integer values, l can be any positive number. This

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leads to the following specification, where l is a function of the explanatory variables (in our case, knowledge recombination): log li ¼ b0 þ b1 xi1 þ b2 xi2 þ    þ bk xik However, for Poisson regression to work, the mean and variance of the distribution need to be equal. If this assumption is violated through overdispersion of data, such over-dispersion can produce severe underestimates of standard errors and overestimates of test statistics (Cameron & Trivedi, 1986). In most studies involving citation, data problems of over-dispersion persist and need to be corrected by means of a specification known as negative binomial regression, which is a generalization of the Poisson model. The above equation is modified to include a disturbance term that accounts for the over-dispersion leading to the unconditional distribution of yi as a negative binomial one: log li ¼ b0 þ b1 xi1 þ b2 xi2 þ    þ bk xik þ s i We include fixed-year effects to control for any differences in quality of publications across years. Finally, as seen from Table 1, the 1,085 articles published in the focal journals are distributed unevenly across the journals, and the nature of publications may vary across journals. To correct for this clustering, we use a generalized estimating equations (GEE) approach (Liang & Zeger, 1986) that allows for the standard errors to be corrected based on journals (White, 1980).

RESULTS Table 1 reports the distribution of the articles across journals and over the five-year period between 1993 and 1997. Table 2 reports simple descriptive statistics for the articles in our research study. The average number of citations received by the focal articles in future articles published in the focal journals is 10.58. Table 3 reports the bi-variate correlation matrix of the various constructs. All the correlation coefficients between variables of interest are below 0.4, reducing concerns of multicollinearity. There is only one coefficient above 0.4 (rproduction prominence  usage prominence=0.42). We checked for multicollinearity by dropping one variable from each of these pairs in the analysis. Neither the coefficient estimates, nor the standard errors, changed

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Table 1. Data Distribution. Journal and Year Academy of Management Journal Academy of Management Review Administrative Science Quarterly Organization Science Strategic Management Journal Total

1993

1994

1995

1996

1997

Total

66 23 22 32 58

71 24 22 39 62

73 31 21 42 50

64 40 28 40 67

54 27 24 40 65

328 145 117 193 302

201

218

217

239

210

1085

Table 2. Summary Statistics.

(1) (4) (2) (5) (3) (6) (7) (8) (9) (10) (11) (12) (13)

Variable Description

Mean

SD

Min.

Max.

Focal paper citations Prominence in production network Prominence in usage network Derived prominence in production network Derived prominence in usage network Size of production network Size of usage network Length of focal paper Reliability of knowledge used in focal paper Age of knowledge used in focal paper Network focus of focal paper Team size on focal paper Potential self-citations of focal paper

10.58 0.40 26.59 0.94 35.15 0.69 6.24 21.26 18.37 8.58 0.17 2.00 3.18

15.55 1.31 67.57 1.44 35.98 0.39 9.20 8.66 17.66 3.25 0.12 1.20 3.95

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00

126.00 12.00 886.00 10.33 382.00 5.50 56.67 55.00 175.00 33.00 0.72 23.00 27.00

substantially, thereby reducing concerns that multicollinearity limits the validity of the results (Table 4, Models 2 and 4). Table 4 presents the results of the negative binomial regression analysis of the dependent variable, impact, on the various independent variables. Model 1 includes the various controls and serves as a baseline model against which subsequent models can be compared. Most of the controls are significant and reflect the direction suggested by prior research. For instance, as the importance of the paper increases, there is a corresponding increase in the log likelihood of impact. Similarly, the use of reliable knowledge in the bibliography leads to an increase in future citations. Models 2 through 5 add each of the explanatory variables: prominence in production network; prominence in usage network; derived prominence in production network; and derived prominence in usage network. The

(2)

(3)

(4)

(5)

Correlation Matrix. (6)

(7)

(8)

(9)

(10)

(11) (12)

Focal paper citations 1.00 Prominence in production network 0.20 1.00 Prominence in usage network 0.18 0.42 1.00 Derived prominence in production network 0.09 0.23 0.26 1.00 Derived prominence in usage network 0.16 0.17 0.19 0.25 1.00 Size of production network 0.18 0.22 0.11 0.07 0.23 1.00 Size of usage network 0.01 0.02 0.09 0.16 0.12 0.02 1.00 Length of focal paper 0.18 0.01 0.12 0.05 0.02 0.10 0.05 1.00 Reliability of knowledge used in focal paper 0.17 0.12 0.22 0.14 0.20 0.13 0.01 0.00 1.00 Age of knowledge used in focal paper 0.08 0.01 0.03 0.04 0.01 0.04 0.00 0.06 0.15 1.00 Network focus of focal paper 0.17 0.11 0.10 0.14 0.13 0.14 0.01 0.07 0.52 0.03 1.00 Team size on focal paper 0.04 0.16 0.03 0.01 0.03 0.05 0.05 0.02 0.00 0.01 0.02 1.00 Potential for self-citations of focal paper 0.26 0.00 0.12 0.26 0.17 0.18 0.11 0.11 0.20 0.02 0.28 0.21

(1)

N=1085 All coefficients greater than |0.05| are significant at po0.05.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

Variable

Table 3.

The Duality of Knowledge Networks 183

Network focus of focal paper

Reliability of knowledge used in focal paper Age of knowledge used in focal paper

Length of focal paper

Size of usage network

Size of production network

Derived prominence in production network Derived prominence in usage network

Prominence in usage network

0.4518 (0.2074) 0.0073 (0.0027) 0.0466 (0.0084) 0.0141 (0.0012) 0.0554 (0.0146) 0.2611 (0.3403)

0.2499 (0.4129)

Intercept

Prominence in production network

Model 1

0.2953 (0.1815) 0.0077 (0.0026) 0.0496 (0.0077) 0.0128 (0.0015) 0.0536 (0.0135) 0.1665 (0.3272)

0.5187 (0.4029) 0.1265 (0.0281)

Model 2

0.406 (0.2248) 0.0039 (0.0025) 0.0503 (0.0087) 0.0124 (0.0011) 0.0533 (0.0132) 0.2943 (0.2930)

0.0028 (0.0005)

0.4392 (0.4126)

Model 3

0.4492 (0.2092) 0.0065 (0.0024) 0.0468 (0.0084) 0.0139 (0.0012) 0.0549 (0.0145) 0.2562 (0.3429)

0.0303 (0.0180)

0.2709 (0.4048)

Model 4

0.0039 (0.0008) 0.3604 (0.1903) 0.0052 (0.0025) 0.047 (0.0089) 0.0128 (0.0013) 0.0546 (0.0136) 0.2883 (0.3537)

0.4005 (0.3725)

Model 5

GEE Negative Binomial Regressions of Focal Paper Citations Main Effects.

Variable Description

Table 4.

0.6609 (0.3708) 0.0903 (0.0452) 0.002 (0.0008) 0.0218 (0.0246) 0.0028 (0.0008) 0.2437 (0.1744) 0.0042 (0.0021) 0.0512 (0.0087) 0.0113 (0.0011) 0.0523 (0.0116) 0.2484 (0.2873)

Model 6

184 ATUL NERKAR AND NANDINI LAHIRI

1.4085 22446.53

0.1169 (0.0228) 0.0665 (0.0117) 0.2372 (0.1409) 0.0355 (0.1157) 0.2024 (0.1282) 0.2168 (0.0959)

po0.05; po0.01; po0.001 (all one-tailed tests).

Dispersion Log likelihood Improvement in log likelihood Comparison model

Publication year 1994

Publication year 1995

Publication year 1996

Publication year 1997

Potential for self-citations of focal paper

Team size on focal paper

1.3788 22456.23 9.7 1

0.0935 (0.0202) 0.069 (0.0127) 0.2277 (0.1443) 0.0268 (0.1146) 0.2179 (0.1461) 0.1884 (0.1208) 1.3731 22458.72 12.19 1

0.1103 (0.0226) 0.0626 (0.0116) 0.2187 (0.1553) 0.0423 (0.1013) 0.1988 (0.1570) 0.1654 (0.0968) 1.4078 22446.82 0.29 1

0.115 (0.0237) 0.0637 (0.0129) 0.2461 (0.1471) 0.0321 (0.1197) 0.2085 (0.1356) 0.2148 (0.0953) 1.3943 22451.31 4.78 1

0.1094 (0.0221) 0.0619 (0.0110) 0.2899 (0.1468) 0.0125 (0.1144) 0.2503 (0.1275) 0.2514 (0.0751) 1.3533 22465.15 18.62 1

0.0929 (0.0221) 0.0642 (0.0145) 0.2525 (0.1651) 0.0044 (0.1130) 0.2444 (0.1609) 0.1877 (0.0968)

The Duality of Knowledge Networks 185

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parameter coefficient of the ‘‘prominence in production network’’ construct is positive and significant, as is seen in Model 2. This suggests that the greater the prominence of the focal authors, the greater the likelihood of impact (bprominence  production network=0.1265, po0.001), leading to support for H1a. Model 3 shows that the independent effect of ‘‘prominence in usage network’’ is positive and significant (bderived prominence=0.0028, po0.001), offering support for H1b. Similarly, Model 4 offers support for H2a (bderived prominence in production network=0.0303, po0.01). Model 5 offers support for H2b (bderived prominence in usage network=0.0039, po0.001). Model 6 is the full model, and the parameter estimates from this model offer support for all direct effect hypotheses. Table 5, Models 7 through 10, add each of the interaction terms: prominence in usage network with prominence in production network (H3a); prominence in usage network with derived prominence in production network (H3b); derived prominence in usage network with prominence in production network (H3c); and derived prominence in usage network with derived prominence in production network (H3d). The parameter coefficient of the interaction effect of prominence in usage network with prominence in production network is negative and significant, as is seen in Model 7. This suggests that the greater the prominence of the focal authors in the production network, the lower the impact of prominence in usage network in increasing the likelihood of impact (bprominence usage network=0.0005, po0.001), leading to support for H3a. The parameter coefficient of the interaction effect of prominence in usage network with prominence in production network is negative and significant, as is seen in Model 8. This suggests that the greater the prominence of the focal authors in the production network, the lower the impact of prominence in usage network in increasing the likelihood of impact (bprominence usage network=0.001, po0.01), leading to support for H3b. However, while Models 9 and 10 do not offer support for H3c and H3d, Model 11 includes all direct and interaction effects. The support for H3a and H3b continues in this model.

DISCUSSION A summary of the results from the above analysis are mixed. First there is strong support for three of the four hypothesized direct effects in the full model (H1a, H1b, H2a). These results are displayed in Table 4, Models 2–5. On the interaction variables, there is support for the substitution effects of

Reliability of knowledge recombined in focal paper Age of knowledge recombined in focal paper Network focus of focal paper

Size of production network

Prominence in usage  prominence in production network Prominence in usage  derived prominence in production network Derived prominence in usage  prominence in production network Derived prominence in usage  derived prominence in production network Size of usage network

Derived prominence in production network Derived prominence in usage network

Prominence in usage network

0.0034 (0.0021) 0.1854 (0.1571) 0.0108 (0.0015) 0.0511 (0.0115) 0.2927 (0.2714)

(0.3606) 0.1031 (0.0456) 0.0042 (0.0009) 0.0099 (0.0281) 0.0025 (0.0007)

(0.3557) 0.1651 (0.0642) 0.0035 (0.0009) 0.0259 (0.0299) 0.0024 (0.0007) 0.0005 (0.0003)

Intercept

0.0026 (0.0020) 0.217 (0.1773) 0.011 (0.0012) 0.0534 (0.0109) 0.2895 (0.2746)

0.001 (0.0001)

0.6883

0.7512

Prominence in production network

Model 8

Model 7

0.0041 (0.0021) 0.2261 (0.1602) 0.0113 (0.0011) 0.0521 (0.0120) 0.2785 (0.3044)

0.0011 (0.0009)

(0.3724) 0.1511 (0.0963) 0.002 (0.0008) 0.0217 (0.0255) 0.0033 (0.0009)

0.7205

Model 9

0.0005 (0.0007) 0.004 (0.0020) 0.2417 (0.1746) 0.0113 (0.0010) 0.0524 (0.0116) 0.2647 (0.2735)

(0.3663) 0.0914 (0.0470) 0.002 (0.0008) 0.0014 (0.0382) 0.0031 (0.0009)

0.6736

Model 10 0.7963 (0.3551) 0.2013 (0.1000) 0.0049 (0.0008) 0.0048 (0.0413) 0.0027 (0.0009) 0.0004 (0.0002) 0.0008 (0.0002) 0.0008 (0.0010) 0.0001 (0.0007) 0.0022 (0.0021) 0.1665 (0.1553) 0.0107 (0.0015) 0.0521 (0.0111) 0.3343 (0.2758)

Model 11

GEE Negative Binomial Regressions of Focal Paper Citations Interaction Effects.

Variable Description

Table 5.

The Duality of Knowledge Networks 187

0.0506 (0.0084) 0.0942 (0.0226) 0.065 (0.0140) 0.2633 (0.1687) 0.0027 (0.1116) 0.2276 (0.1529) 0.1643 (0.0965) 1.3405 22469.82 4.67

(0.0083) 0.0892 (0.0214) 0.0637 (0.0151) 0.2518 (0.1717) 0.0198 (0.1151) 0.2527 (0.1688) 0.1641 (0.1049) 1.3377 22470.58 5.4314

Model 8

0.0513

Model 7

po0.05; po0.01; po0.001 (all one-tailed tests).

Dispersion Log likelihood

Publication year 1994

Publication year 1995

Publication year 1996

Publication year 1997

Potential for self-citations of focal paper

Team size on focal paper

Length of focal paper

Variable Description

Table 5. (Continued )

(0.0084) 0.0932 (0.0217) 0.0635 (0.0147) 0.2356 (0.1747) 0.0273 (0.1237) 0.2269 (0.1696) 0.1703 (0.1103) 1.3522 22465.56 0.41

0.0516

Model 9

(0.0087) 0.0936 (0.0228) 0.064 (0.0147) 0.2483 (0.1634) 0.0023 (0.1135) 0.241 (0.1583) 0.187 (0.0944) 1.3528 22465.36 0.21

0.0512

Model 10 0.0512 (0.0080) 0.0911 (0.0221) 0.0638 (0.0148) 0.2451 (0.1808) 0.024 (0.1223) 0.2239 (0.1677) 0.14 (0.1134) 1.3305 22473.21 8.06

Model 11

188 ATUL NERKAR AND NANDINI LAHIRI

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prominence in usage network with prominence and derived prominence in production networks, respectively (H3a, H3b), with little evidence to support the other two interaction terms (H3c and H3d). Results are displayed in Table 5, Models 7–10. Analysis of the significance of results from Model 6 provides an insight into the practical significance of our results. Keeping all other independent variables at the mean and increasing the each hypothesized independent variable by 1 standard deviation has the following effect on log likelihood of citations: prominence in usage network increases the likelihood of focal citations by 14 percent; prominence in production network increases likelihood of focal citations by 12 percent; and finally, derived prominence in the usage network increases the likelihood of academic citations by 10 percent. These results indicate that among all the heuristics that future authors use in their citation decisions, prominence in the usage network appears to have the most impact. Accordingly, it is not surprising that in the presence of increasing prominence in the usage network, we find that increased prominence/derived prominence in the production network has a significantly lower impact in determining academic citations. Fig. 2 depicts the interaction effects between prominence in production network and prominence in the usage network. Fig. 2a indicates that with low levels of prominence in the production network, increasing prominence in the usage network results in increasing impact in focal journals. On the other hand, at high levels of prominence in the production network, increasing prominence in the usage network results in decreasing impact in focal journals. The inflexion point at which the two curves cross corresponds to a prominence in usage network value of 330. Fig. 3 depicts the interaction effects between derived prominence in production network and prominence in the usage network. Fig. 3a indicates that with low levels of derived prominence in the production network, increasing prominence in the usage network results in increasing impact in focal journals. On the other hand, at high levels of derived prominence in the production network, increasing prominence in the usage network results in decreased impact in focal journals. The inflexion point at which the two curves cross corresponds to a prominence in usage network value of 10, which is lower than the sample mean of 26.59 (Table 6). As a robustness check, we ran the analysis with a dummy that reflects the focal authors’ participation in networks. A value of 1 indicates that authors do exist in the network, whereas a value of 0 indicates that the authors had no prior network existence. The variable captured two different scenarios. In one case, the variable was coded 1 when the focal author appeared on

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(a) 6

Academic impact

5

4

Author prominence in production (low) = 1

3 Author prominence in production (high) = 10 2

1

0

0

50

100 150 200 250 300 350 400 450

Prominence in usage network

(b) 6

Academic impact

5 4 Prominence in usage network (low) = 25

3

Prominence in usage network (high) = 350

2 1 0 0

1

2

3 4 5 6 7 8 9 Prominence in production network

10

Fig. 2. (a) Interactive Effect of Author Prominence in Usage Network on Academic Impact. (b) Interactive Effect of Author Prominence in Production Network on Academic Impact.

both the production and the usage network. In a second case, we coded the network existence dummy 1 when the focal author belonged to either the production or the usage network. Economic significance indicates that in cases when the focal author(s) belonged both to the production and to the usage network, the likelihood of academic citations went up by 45 percent. On the other hand, when the focal authors belonged either to the production or to the usage network, the likelihood of academic citations went up by 24

191

The Duality of Knowledge Networks (a) 1.2

Academic impact

1 0.8 0.6

Derived prominence in production (low) = 1

0.4

Derived prominence in production (high) = 9

0.2 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 Prominence in usage network

(b) 1.4

Academic impact

1.2 1 0.8

Prominence in usage network (low) = 5

0.6 Prominence in usage network (high) = 50

0.4 0.2 0 0 0.51 1.52 2.53 3.54 4.55 5.56 6.57 7.58 8.59 Derived prominence in production network

Fig 3. (a) Interactive Effect of Author Prominence in Usage Network Academic Impact. (b) Interactive Effect of Derived Author Prominence in Production Network on Academic Impact.

percent. This indicates that the network theoretic approach that we have taken in this chapter, using heuristics in understanding a market-based mechanism, is well justified. It is important to compare our results with Baldi (1998), the first paper to use a network analytic approach to understand the process of academic citations. It is noteworthy that this chapter found no impact of networkbased measures in its study. There may be several reasons why our results are significantly different. First, the fact that Baldi (1998) is a study of

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Table 6. GEE Negative Binomial Regressions of Focal Paper Citations Exploring Production and Usage Network Effects. Variable

Model 12

Model 13

Model 14

Intercept

0.4846 (0.3538)

0.8388 (0.2921) 0.4472 (0.1558)

0.557 (0.3363)

Authors on focal paper are part of both production and usage network (dummy variable=l) Authors on focal paper are part of either production or usage network (dummy variable=l) Reliability of knowledge recombined in focal paper Age of knowledge recombined in focal paper Network focus of focal paper Length of focal paper Team size on focal paper Potential for self-citations of focal paper Publication year 1997 Publication year 1996 Publication year 1995 Publication year 1994 Dispersion Log likelihood Improvement in log likelihood

0.2442 (0.1195) 0.0149 (0.0012) 0.0534 (0.0144) 0.3019 (0.3342) 0.0463 (0.0083) 0.1361 (0.0232) 0.0741 (0.0127) 0.2424 (0.1072) 0.0387 (0.1266) 0.2474 (0.1159) 0.2269 (0.0853) 1.4085 (0.0681) 22434.73

0.0139 (0.0012) 0.0519 (0.0137) 0.3751 (0.3739) 0.0469 (0.0079) 0.1605 (0.0350) 0.0682 (0.0116) 0.2303 (0.1188) 0.0178 (0.1303) 0.2336 (0.1164) 0.2038 (0.0822)

1.4351 (0.0691) 22440.16 5.436

0.0143 (0.0011) 0.0537 (0.0145) 0.2813 (0.3578) 0.0459 (0.0087) 0.1554 (0.0326) 0.0663 (0.0130) 0.2655 (0.1190) 0.0184 (0.1221) 0.2499 (0.1143) 0.2347 (0.0741)

1.4351 (0.0691) 22437.57 2.8437

po0.05; po0.01; po0.001 (all one-tailed tests).

articles not in management, but in astrophysics, may reflect a difference in underlying mechanisms within divergent fields that drive why papers get cited. Second, the difference in results may be a broader reflection of academic impact between social and natural sciences. For instance, research in the natural sciences has fewer citations than research in social sciences.

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This chapter contributes to our understanding of the process of academic citations in several ways. First, we use a network-based approach to understand the role of heuristics in a market for knowledge that is imperfect. Our network theoretic and analytic approach provides significant evidence that such heuristics may be an important driver of a process that has received attention but is not fully understood. Second, our contribution lies in the idea of generating a role for production and usage of knowledge. Creating a separate role for networks, on the generation as well as on the usage side in the flow of new ideas, provides a new framework in the use of heuristics when market-based forces have their limitations. The results indicate that indeed knowledge networks are not perfect and that heuristics can be a useful means by which to identify the underlying mechanism in knowledge flows from the source to the recipient. This chapter is the first to offer a theoretical test of unique constructs. By using prominence/derived prominence of the focal (authors) in each of the usage and production networks, we have been able to provide an insight into the underlying mechanisms of the academic citation process, which has not been seen in earlier research. While previous work has focused on the universalism versus particularism debate as drivers of academic citations, our paper controls for the universalistic approach and uses the particularistic features of the academic citations to understand the underlying mechanism. Our results indicate that focal authors who enjoy higher prominence are not only likely to see a higher likelihood of academic impact of future research, but are also less likely to need other heuristics (such as those based in the production network) in their impact. These findings confirm the Matthew effect. That is, individuals who enjoy higher prominence are more likely to gain greater prominence in their futures. Our results also offer fresh ground on the use of derived prominence in the knowledge for ideas. While derived prominence is a relatively unused construct in this area of work, our results indicate that derived prominence, especially in the usage network, is a significant driver of academic citations. There are several practical implications to this research that may be relevant for promotion and tenure decisions. This research finds that belonging to both the usage and production networks will increase the likelihood of academic impact by 45 percent. This implies that a published dissertation, which is in many cases the first single-authored paper by a scholar, is half as likely to get cited as a paper with ‘‘established’’ focal authors. Thus, a first-time publication, if also a sole-authored publication, needs to be weighted accordingly, that is, keeping in mind that there exist

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enormous discrepancies in citations to work by first-time and established authors. Some of this inherent disadvantage to gaining recognition may be overcome with the help of co-authors who are active already in the production and/or usage networks.

Caveats and Limitations There are limitations to this research. The first limitation is the choice of journals. We have focused on the five journals that are generally accepted as ‘‘A’’ journals’’ in the ‘‘management area’’ across most schools. We excluded Management Science because of the multi-disciplinary nature of research being published in the journal. Second, we have chosen the limit of a sixyear window for measuring the dependent variable: focal citations. Papers may sometimes take longer to be fully understood and become relevant. That said, there is no agreed-upon window for measuring impact in this literature. While there has been a recent increase in the study of academic research (Bergh et al., 2006; Judge et al., 2007; Stremersch et al., 2007), this chapter makes a first attempt to understand the underlying process of academic citations. By focusing on production and usage networks independently, we attempt to disentangle the different forces of diffusion of knowledge. Our work contributes to a better understanding of the underlying mechanisms that cause papers to be cited.

NOTE 1. Author a18 could cite all of the five papers. Given a journal’s page limitations, however, comprehensive citation might hamper the chance for publication of the paper, so the author would prefer to cite fewer papers.

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Rynes, S. L. (2007). Academy of Management Journal editors’ forum on citations: Editor’s foreword. Academy of Management Journal, 50, 489–490. Rynes, S. L., McNatt, D. B., & Bretz, R. D. (1999). Academic research inside organizations: Inputs, processes and outcomes. Personnel Psychology, 52, 869–898. Shadish, W. R., Tolliver, D., Gray, M., & Sengupta, S. K. (1995). Author judgements about works they cite: Three studies from psychology journals. Social Studies of Science, 25, 477–498. Simonton, D. K. (1983). Formal education, eminence and dogmatism: The curvilinear relationship. Journal of Creative Behavior, 17(3), 149–162. Sine, W. D., Shane, S., & Gregorio, D. D. (2003). The halo effect and technology licensing: The influence of institutional prestige on the licensing of university inventions. Management Science, 49, 478–496. Stremersch, S., Verniers, I., & Verhoef, P. C. (2007). The quest for citations: Drivers of article impact. Journal of Marketing Research, 71. Trevino, L. K. (2008). Why review? Because reviewing is a professional responsibility. Academy of Management Review, 33, 670–684. Tsai, W. P. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy of Management Journal, 44, 996–1004. White, H. (1980). A heteroskedasticity-consistent covariance-matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.

CHAPTER 7 THE COSTS OF CREATING NETWORK RELATIONS AND THE IMPLICATIONS FOR FIRM PERFORMANCE – THE CASE OF HIGH TECHNOLOGY FIRMS Niron Hashai ABSTRACT The benefits of network relations for firms’ competitive advantage are increasingly acknowledged in the strategic management literature. Yet, the cost implications of engaging in network-specific relations, stemming from the irreversibility of sunk costs invested in creating network relations, are largely ignored. Such costs tend to be especially pronounced in high technology firms. It follows that the costs of creating network relations may mask the benefits of such relations, suggesting that networks can be a competitive risk for firms in cases where network relations unexpectedly terminate. This chapter adopts a cost-benefit approach to an empirical analysis showing that while in the long term, network relations enhance high technology firms’ performance, short-term efforts in creating network relations may hamper their performance.

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 199–227 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013010

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Furthermore, we show that greater technological intensity intensifies the negative performance implications of short term network participation and the positive performance implications of long term network participation. Keywords: Network relations; high technology firms; specificity; technological intensity; irreversibility; sunk costs

INTRODUCTION The fact that inter-organizational relations significantly contribute to firm performance is widely held in the extant literature (Dyer, 1996; Dyer & Singh, 1998; Gulati, 1999; Kale, Dyer, & Singh, 2002; Lavie, 2006). The stream of literature highlighting the benefits of network relations for creating and sustaining competitive advantage has further acknowledged that idiosyncratic network-specific relations are likely to yield abnormal returns (Dyer, 1996; Dyer & Singh, 1998). On the other hand, transactions costs theory, one of the major streams of organizational economics, has highlighted the role of resource specificity and its associated high opportunity costs for deployed resources (Buckley & Casson, 1976; Klein, Crawford, & Alchian, 1978; Teece, 1986; Williamson, 1985). This chapter integrates the organizational capability literature with the organizational economics literature to argue that network relations not only yield benefits for firms but also impose specific costs stemming from the efforts invested in creating network relations, and their irreversibility. The chapter adopts a cost-benefit approach to argue that although in the long term network relations enhance performance, firms’ short-term efforts in creating network relations hamper their performance. We propose that in the early stages of network engagement the costs derived from the managerial time and efforts invested in generating network relations may outweigh the benefits of network participation. Furthermore, since such costs are often highly network specific and irreversible, the migration of the benefits stemming from participation in one particular network to other networks is unlikely to occur without a lengthy and costly process of building additional network-specific relations. Over time, the organizational benefits of being engaged in a network increase. Less managerial time and effort are needed to maintain existing network ties, leading to a positive long-term impact of network relations on performance. We further argue

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that greater technological innovation at the firm level intensifies the negative performance implications of short term network participation and the positive performance implications of long term network participation, thus making technology central to the performance implications of network participation. The chapter has implications for the determination of firm boundaries in the short and long term with a particular emphasis to the significant role of technology plays in shaping such boundaries. It points managers toward an appreciation of the costs involved in engaging in different networks, implying that short-term participation in a particular network as well as unexpected termination of network participation may often bear negative performance outcomes. The engagement in network relations results with creation of multiple ties (Gilsing & Nooteboom, 2005) whose strength is based on a combination of the amount of time, emotional intensity, intimacy (mutual confiding), and reciprocity Granovetter (1973, p. 1361). Strong ties are associated with the exchange of high-quality information, tacit, and complex knowledge (Dyer & Nobeoka, 2000; Larson, 1992; Rowley, Behrens, & Krackhardt, 2000; Uzzi, 1997). Moreover, strong ties facilitate effective cooperation and reduce opportunism and monitoring costs as they create trust at the dyadic level as a governance mechanism (Rowley et al., 2000; Zaheer & Venkatraman, 1995). Nevertheless, the creation and development of strong ties further implies costs and risks of developing, maintaining, and ending relationships, including, in the last instance, the associated switching costs or exit barriers that arise from investment in specific relationships. The costs of creating and maintaining network relationships are likely to be linear with the number of times partners are in contact within a given time period and the range of issues they cover (Capaldo, 2007; Gilsing & Nooteboom, 2005; Rowley et al., 2000). In this respect the chapter adds to the literature highlighting other components of network-related costs, such as those involved in the internationalization of networks (Lavie & Miller, 2008), network diversity (Goerzen & Beamish, 2005), and network embeddedness (Uzzi, 1997). Next, we present a conceptual framework that highlights the extant view on the impact of network involvement on firm performance. This defines the costs of committing firm-specific resources to specialized tasks and provides testable hypotheses on the long term and short term performance implications of network participation in high technology firms. We then present our data, measures and methods, and our results. Finally, we discuss the results and their theoretical and practical implications.

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CONCEPTUAL FRAMEWORK Network Involvement and Firm Performance The Resource-Based View (RBV) of the firm emphasizes the importance of resources and capabilities in creating value for firms and their role in guiding firm action, directing firm development, and becoming a source for sustained competitive advantage (Barney, 1991; Dierickx & Cool, 1989; Wernerfelt, 1984). The RBV has adopted an inward-looking view, conceptualizing firms as heterogeneous entities consisting of bundles of idiosyncratic resources that create value. The RBV proposes that value-creating resources are confined by the firm’s boundaries. This leads to the assertion that firms secure rents by imposing resource-position barriers that safeguard their proprietary resources (Amit & Schoemaker, 1993; Barney, 1991; Wernerfelt, 1984). Nevertheless, more outward-looking views suggest that value creating resources may also exist outside the firm’s boundaries (e.g., Dyer, 1996; Dyer & Singh, 1998; Gulati, 1999; Gulati, Nohria, & Zaheer, 2000). Dyer and Singh (1998) claim that a firm’s critical resources may span its boundaries, being embedded in inter-firm routines and processes. This implies that the competitive advantage of firms is often linked to the networks in which they participate. Thus, idiosyncratic inter-firm linkages may be a source of ‘‘relational rents,’’ defined as: ‘‘a supernormal profit jointly generated in an exchange relationship that cannot be generated by either firm in isolation and can only be created through the joint idiosyncratic contributions of the specific alliance partners’’ (ibid. 662). Relational rents are generated through relation-specific investments and the firm benefits through lower total costs, greater product differentiation, fewer defects and faster product development cycle, among other things (Dyer & Singh, 1998). The view that firms do not have to fully own rent yielding resources in order to enjoy their associated benefits, and may extract abnormal value from shared resources is further supported by Kogut (2000), Kale, Dyer, and Singh (2002), Lavie (2006), Zaheer and Bell (2005) and many others.

The Costs of Network-Related Administrative Resource Commitment Transaction Cost (TC) theory provides an economic rationale for the existence of corporations by referring to such organizations as a hierarchical response to market imperfections (Buckley & Casson, 1976; Coase, 1937;

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Williamson, 1975, 1985). TC theory essentially argues that firms choose whether to integrate operations within their boundaries or disintegrate them by evaluating the economic costs of different transactions and selecting the mode that minimizes overall costs. While external transactions are advocated when markets are perfectly competitive, in the case of market failure the costs of such transactions are likely to increase substantially, leading to the integration of operations within firm boundaries. When TCs exceed the costs of integration, which include the opportunity costs of deployed resources, the sunk cost of dedicated investments and the bureaucratic costs of monitoring (Hill, Hwang, & Kim, 1990; Hill & Kim, 1988; Williamson, 1975, 1985), integration of operations is likely to occur, implying the commitment of internal firm resources to designated markets (Anderson & Gatignon, 1986; Hill et al., 1990; Williamson, 1985). Essentially we argue below that the same mechanisms relating to internal resource commitment are also in operation when the firm commits resources to its networks, thus resource commitment constitute an important cost factor related to engagement in network relations. The costs associated with ‘‘resource commitment’’ are defined by Hill et al. (1990, p. 118) as: ‘‘dedicated assets that cannot be redeployed to alternative uses without cost (lost value).’’ The irreversibility of deployed resources is often the consequence of asset specificity (Klein et al., 1978; Teece, 1986; Williamson, 1985) which implies high opportunity costs for resources dedicated to specific markets. Asset specificity means that invested resources cannot be fully redeployed without sacrifice of productive value if contracts are interrupted or prematurely terminated (Williamson, 1985). The allocation of resources to specific markets is therefore not only associated with increased control but with the risk of greater costs of resource commitment stemming from high switching cost due to potential exit barriers and decreased strategic flexibility (Anderson & Gatignon, 1986; Hill et al., 1990). Resource commitment thus increases the possibility of losses due to unexpected changes as firms become unwilling to absorb the losses of sunken investments when revenues fail to materialize as planned. Overall, this stream of literature makes a straightforward linkage between the integration of operations and the resource commitment firms make to given markets where the degree of ownership reflects resource commitment, that is, the greater the degree of ownership, the larger the resource commitment to designated markets (Gatignon & Anderson, 1988; Kim & Hwang, 1992). Combining the RBV and TC theory therefore implies that while specialized firm-specific resource investments, such as technological knowledge, are a source of abnormal rents they often also bear irreversible costs

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associated with resource commitment (Madhok, 1997; Zajac & Olsen, 1993). This suggests the existence of a tradeoff between the risks of resource commitment and its returns. More importantly, it is not only ‘‘internal’’ resource investments that bear the costs and risks stemming from resource commitment. Resource investments in the generation and maintenance of network relations may also create irreversibility in committed resources. These are often human resources (managerial time and effort) that have long been argued to be a scarce resource (Juster & Stafford, 1991).The creation of network relations requires firms to commit administrative resources for developing partnerspecific learning capabilities as a way of safeguarding against opportunism (for instance, building trust and reputation, see Zaheer, McEvily, & Perrone, 1998), mounting the ability to value the complementary resources of network partners, and creating a position in specific networks (Dyer & Singh, 1998; Larson, 1992; Levinthal & Fichman, 1988; Powell, 1990; Uzzi, 1997). Such ‘‘administrative resources commitment’’ does not create value by itself, but has important cost implications. Since the commitment of managerial time and effort to the creation and management of networks is to a large extent relationship specific, redeploying such committed resources to alternative uses is limited where the migration of such resource investments to other relationships is unlikely without a lengthy and costly process of building additional network-specific relations. Thus, the specificity of network relations is not only a source of abnormal value creation (Dyer, 1996; Dyer & Singh, 1998) but may also be a source of costs and risks. When administrative resources are committed to specific relationships they cannot be redeployed without loss of value. The greater the magnitude of investment in the creation of network relations and the greater the irreversibility and specificity of administrative resource commitment to given networks, the greater is their cost to the firm. Admittedly, such costs are not likely to be constant across different types of networks and also depend on tie properties, as well as the relative bargaining power between the firm and its network partners. For instance, sales alliances may have immediate benefits with a minimal investment in the appreciation of, whereas R&D alliances may likely yield only longterm benefits which will only occur after a substantial administrative resource commitment to learn and evaluate the alliance partners’ complementary resources. Yet, overall, we conclude that engagement in network relations may not only complement internal resources as a source for competitive advantage, but may also represent additional costs and risks for firms, stemming from the investments required for their

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generation and the irreversibility of resource commitment they confer. In firms relying on tacit and complex technological knowledge such cost and risks are in particular likely to be pronounced. Extant literature virtually ignores the costs and risks associated with the creation, and development of network relations and thus neglects an important factor that affects firms’ competitive position in the market (Adler & Kwon, 2002; Hansen, Podolny, & Pfeffer, 2001). In the next section we illustrate the importance of taking into account the costs of administrative resource commitment to networks by examining the short and long-term effects of generating network relations.

Short-Term and Long-Term Effects of Network Relations In essence, we argue that while in the long term the benefits of network relations exceed their costs, in the short term the costs of network relations creation and development exceed their benefits. As depicted in Fig. 1, the costs of creating network relations and building network positions are likely to exceed the benefits of network relations in the short term. This is not only due to greater administrative resource commitment required for the creation of network relations but also due to the fact that relational rents are not available at the outset of engaging in network relations, but are often the outcome of collaboration which takes time to emerge in the Costs/benefits

Short term

Long term

The benefits of network relations

The costs of network relations

Time of engagement in network relations

Fig. 1.

The Costs and Benefits of Network Relations in the Short and Long Term.

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course of network participation. Over time, network-related, administrative resource commitment costs are likely to reduce as firms have already developed network relations and are mostly engaged in their maintenance, while the benefits arising from relational rents are likely to come to fruition. When firms are at the outset of creating network relations, the benefits derived from network relations are likely to be low. Relational rents may take time to emerge where the outcome of complementary resources often emerge over time (for instance, joint R&D development) and where it takes time to develop organizational capabilities through inter-organizational relations. New network partners need time to effectively learn about each other’s needs, resources, and strategies and to improve their network position in terms of the benefits these positions imply (Dyer & Singh, 1998; Lavie, 2006). At early stages of network formation firms are unlikely to build strong individual ties and personal relationships (Granovetter, 1973; Nahapiet & Ghoshal, 1998). This, in turn, may hamper the benefits arising from joint problem solving capability and the flow of fine-grained knowledge between firms (Uzzi, 1997). Yet, such firms are often required to invest substantial managerial time and effort in order to become embedded in emerging or established networks. Once a new relationship is established, the nascent organizational routines probably require high monitoring costs given the partners’ lack of trust and unfamiliarity with each others’ processes, systems, and routines. The aforementioned process of creating network positions is a timely and resource consuming process subject to various network characteristics. The broader the scope of relationships (involving a greater diversity of issues with every partner) the greater the managerial time and effort that a given firm is likely to devote to the generation of such relationships (Blankenburg Holm et al., 1999; Dyer & Singh, 1998; Larson, 1992). The investments in building network relations are further intensified when more dissimilar relationships (e.g., in different value chain activities) are sought, involving a more diverse pull of people and units from the firm interacting with network partners. This is so because more dissimilar relationships require the training and guidance of more people within the organization. Likewise, as the number and diversity of ties a firm wishes to develop increases, more substantial investments of managerial time and effort are required (Goerzen & Beamish, 2005; Hansen et al., 2001; Larson, 1992). It follows that, on average, firms engaged in the creation of network relations are likely to extract low benefits from its network engagement,

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where they did not have time to effectively build strong individual ties and personal relationships and to learn how to leverage their network partners complementary resources. At the same time such firms bear the high costs of committing substantial managerial time and effort to nurture network ties, leads us to expect negative performance outcomes of short-term engagement in network relations. We thus hypothesize that: Hypothesis 1. In the short term, engagement in network relations is negatively associated with firm performance. Over time, network partners gradually learn about each other’s needs, resources, and strategies and improve their network positions (Dyer & Singh, 1998; Kale et al., 2002; Lavie, 2006). This process involves developing the ability to recognize and assimilate valuable knowledge from particular alliance partners, building trust and reputation as means to avoid potential opportunism in addition to identifying potential partners and valuing their complementary resources (Dyer & Singh, 1998; Larson, 1992; Powell, 1990, Uzzi, 1997). Long-term interactions facilitate the building of a strong group identity, which enhances the benefits arising from the transfer of tacit and complex knowledge (Uzzi, 1997; Walker, Kogut, & Shan, 1997; Zander & Kogut, 1995). Over time the outcome of complementary resources is likely to be realized and rent-yielding network-specific capabilities are likely to emerge (Dyer, 1996; Dyer & Singh, 1998; Gulati, 1999; Kale et al., 2002) resulting with increased benefits from network relations. In addition, once firms have created network relations, their ongoing investments of managerial time and effort in maintaining these relations are likely to reduce. Once a broad scope of relationships has been established and a diverse pull of ties has been created the costs of maintaining or even gradually developing network-specific relations and positions is likely to be fairly moderate relative to the costs of generating novel network relations. Furthermore, long-term interactions facilitate effective cooperation and reduce appropriation and monitoring costs because they create trust at the dyadic level as an efficient governance mechanism (Gulati & Nickerson, 2008; Rowley, Behrens, & Krackhardt, 2000; Zaheer & Bell, 2005; Zaheer & Venkatraman, 1995). Taken together, in the long term, we expect the benefits of engaging in network relations to exceed their costs and lead to positive performance outcomes. We therefore hypothesize that: Hypothesis 2. In the long term, engagement in network relations is positively associated with firm performance.

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The Impact of Technological Innovation and Prior Network Experience Importantly, we expect the technological innovation of firms to affect the hypothesized relationships between short-term and long-term network engagement and performance. Technological innovation essentially reflects the extent to which firms are engaged in the creation of novel technological knowledge. Greater technological innovation often implies greater complexity of development, production, and marketing processes reflected by greater knowledge transfer costs (Kogut & Zander, 1992; Martin & Salomon, 2003). It therefore follows that the managerial time and efforts required to transfer such knowledge between network partners are likely to be high. Since the investment of managerial time and effort in ‘‘within network’’ knowledge transfer peaks during the creation of network relations, greater technological innovation is expected to increase the costs of network creation and hence further hamper firm performance. On the other hand, greater technological innovation is also expected be a source of higher rents in general, but especially if it can be leveraged by complementary technological knowledge, production resources or marketing capabilities of networked partners (Dyer & Singh, 1998; Kale et al., 2002). Hence, greater technological innovation in also expected to increase the long-term benefits of network relations and improve firm performance. Taken together we hypothesize that: Hypothesis 3. Greater technological innovation intensifies the negative performance implications of short-term network engagement and the positive performance implications of long-term network engagement.

DATA, MEASURES, AND METHODS The Sample Our hypotheses were tested on a sample of 147 Israel-based High Technology firms. High technology firms are a suitable setting for the current research as the literature on networks often relates to such firms (Lavie & Miller, 2008; Kumar & Nti, 1998; Stuart, 2000). Such firms are further likely to deploy technological resources that may be both a source of rents and costs and further moderate the linkage between network engagement and performance.

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The sample was derived from the full list of Israel-based high technology (Hi-Tech) firms constructed by the Dolev and Abramovitz Ltd consulting firm for the years 2005–2006. The Dolev and Abramovitz dataset included about 400 Israeli knowledge intensive firms that have reached the stage where they sell their products and represents the vast majority of the Israeli Hi-Tech sector. The Dolev and Abramovitz dataset is well recognized as a comprehensive resource for this sector in Israel. Relevant data for the study were collected from primary and secondary sources. Secondary data were mainly data available in the Dolev and Abramovitz dataset. Dolev and Abramovitz Ltd is a private company collecting annual information on the Israeli Hi-Tech industry. The data is being updated using phone surveys of the firms’ management. Dolev and Abramovitz Ltd publishes a yearly book which describes the entire Hi-Tech industry as well as periodical reports. Additional data that were unavailable in the Dolev and Abramovitz dataset were collected through interviews based on structured questionnaires with 2–3 senior management members of each surveyed firm. The questionnaire underwent multiple pretests. Overall, 165 interviews with randomly selected firms took place during the period January 2005–July 2006. Out of these 165 firms, we screened out 18 firms whose interviewees supplied incomplete data. This resulted in a sample of 147 firms. Basic T-test comparisons between the 147 participating firms and the 253 nonparticipating firms did not show evidence of any nonresponse bias in terms of the averages of: firm sales, number of employees, age of firm, firm valuation, and industrial classification. A single experienced research assistant (PhD student with vast professional experience in the Hi-Tec industry) conducted in-depth focused interviews in order to ensure uniformity in the way the responses were interpreted. The use of a dedicated researcher minimized the potential for interpretation errors. The possible concern of researcher bias was partly addressed by close interaction and frequent discussions between the interviewing research assistant and one of the authors. The usage of structured questionnaires was intended to elicit the views of the interviewee untainted by the interviewer’s perceptions. The questionnaires covered a wide range of ‘‘hard data’’ including: foreign markets sales distribution, the development of a new products and technologies and the distribution of human resources across value chain activities. These data were repeatedly reported for specific periods of time. Each such period reflected the time between subsequent investment rounds as indicated in the Dolev and Abramovitz dataset. This procedure enables us

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to directly tie between the data items reported and each firm’s value in each period. More specifically, for the period up to each round of investment (reflecting the firm’s new valuation), the interviewees were requested to report whether their firm has participated in ‘‘within the industry’’ or ‘‘outside their industry’’ network relations in R&D, production or marketing activities.1 All reported data were then cross checked against secondary sources available for the firms (web sites of the firms, stock exchange data, and financial newspaper archives) for verification and completion of missing data. Overall, this procedure resulted in 497 firmperiod observations for the 147 analyzed firms, where the average length of a period was reported to be around 1.8 years (with a minimum of one year and a maximum of 5 years and a standard error of 0.31 years). The relatively low standard error of period length allows us to build on periods when assessing network participation over time, as detailed below. The maximal number of investment rounds was seven.

Measures The variables required for the current study and their measures are detailed in appendix, Table A.1. Dependent Variable Firm value is our measure for firm performance. Common accounting-based profitability measurements (e.g., Return on Sales, Return on Assets and Return on Investment) are robust performance measures in large established corporations (Goerzen & Beamish, 2005), but they may not be appropriate measures for relatively small and young firms (such as the firms in our sample, see Table 1 for descriptive statistics of the sample). This is because such firms direct many of their resources to new product development (Hart, 1995; Lee, Lee, & Pennings, 2001) and have their evaluations derived from capitalization of future cash flows. Thus, firm value rather than firm profitability is chosen to be the dependent variable. For all firms, we had up to seven valuations enabling us to identify 497 firm-value observations. The value of firms was determined according to respective investments that were made in each firm and their resulting ownership percentages (‘‘after the money’’ valuation). Investments were either made by private investors, venture capital funds, corporate venture capital, acquisitions, or through public offerings. Since firm values were heavily skewed, we performed a logarithmic transformation in order to

0.33 (0.19) 0.39 (0.16) 0.57 (0.28) 0.03 (0.08) 130.57 (369.0) 10.12 (16.21) 3.81 (4.93) 5.67 (5.02) 0.19 0.00 0.32 0.21

0.27 0.29 0.18 0.18

0–114 0–17 3–35

0.02–0.99 2–3800

0.12 0.18

0.26

0–1

1

Industry network_ long termi

0.32

0.31

1

Firm valuei

0–1

0.01–2.00

Average (Std. Min–Max Deviation)

0.14

0.05

0.03

0.23

0.15

1

Industry network_ short termi

0.17

0.08

0.32 0.23 0.39 0.12

0.19

0.09

1

Empi Patenti

0.22 1

1

Salesi

Descriptive Statistics and Pearson Correlations (N=497).

Notes:  statistically significant at 0.1%;  statistically significant at 1%;  statistically significant at 5%.

Network experience (years) Age

Patenti

Empi

Salesi (in billion $US)

network_ short termi

Firm valuei (in Billion $US) network_ long termi

Variable

Table 1.

0.21

1 1

Network Age experience

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reduce skewness values. Our dependent variable is Lan_firm valuei and reflects each firm’s value at the end of period i (i ¼ 1 – 7, period 1 ¼ inception year to first investment year, period 2 ¼ first investment year to second investment year and so forth). Independent Variables For each period (between subsequent investments) the interviewees were asked to report whether their firm has participated in network relations in either Research and Development (R&D), production or marketing activities within or outside their industry. Network relations were defined as participation in licensing, outsourcing, alliances, or joint ventures in each value chain activity (R&D, production, or marketing). For each value chain activity and in each period the interviewees had to indicate whether their firm took part in within the industry networks (1=yes, 0=no) and/or outside the industry networks (1=yes, 0=no) and also indicate the exact mode of relation. From these reports we built a binary measure for the engagement in the creation of network relations in a given period. This measure was then used to build our independent variables. The measure network_short termi received a value of 1 for firms that reported participation in either R&D, production or marketing ‘‘within the industry’’ and/or ‘‘outside the industry’’ networks in a given period i, but not in the preceding period (i1), and 0 otherwise. In period 1 network_short termi received a value of 1 for all firms with network relations in at least one of the value chain activities. This measure enables us to test Hypothesis 1. The measure network_long termi received a value of 1 for firms that reported participation in either R&D, production or marketing ‘‘within the industry’’ and/or ‘‘outside the industry’’ networks in a given period i and in the preceding period (i1), and 0 otherwise. Naturally, in period 1 network_long termi received a value of 0 for all firms. This measure enables us to test Hypothesis 2. The number of patents (Patenti) that the firm applied for in each period (and granted at some later stage) was used as a proxy for firms’ technological innovation. The number of patents aims to reflect Hi-Tech firms’ technological innovation output. Both organizational economics scholars (e.g., Grilliches, 1990) and organizational capabilities scholars (e.g., Ahuja & Katila, 2001) highlight the superiority of the number of patent citations to the sole number of patent as an innovation output measures, as the former reflect the patent’s value. Yet, since the number of patent citations is commonly unknown at the point of investment we chose number of patents as our proxy for innovation output.2 We used the interaction

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between Patenti and network_short termi and the interaction between Patenti and network_long termi to test Hypothesis 3. Control Variables We controlled for the possible effects of several variables on firm value. First we controlled for the main effect of Patenti and Lan_network exp. The number of patents aims is expected to be positively correlated with firm performance (Buckley & Casson, 1976; Martin & Salomon, 2003). We have used the number of years of engaging in network relations up to each period (according to the length of periods in which each firm had reported its engagement in network relations) as a proxy for prior network experience. Lan_network exp reflects the logarithmic transformation of this measure and was required in order to reduce its skewness value. The main effect of prior network experience is expected to be positively correlated with performance (Anand & Khanna, 2000; Gulati et al., 2009). We further controlled for the natural algorithm (Lan) of firm sales (in $Billion US) at the end of each period (Lan_Salesi). Firm sales are expected to be positively correlated with firm value.3 A logarithmic transformation was used in order to reduce skewness values. Next, we controlled for the period of operation (in the context of firm value), as firms are likely to increase their value between subsequent valuation rounds. For this, we have created a dummy variable for each period. Another control variable was the mid year in each valuation period (Year) in order to control for exogenous effects of specific time periods. Year controls were also introduced as dummy variables. Finally, we controlled for firms age (Age) which should have a similar effect as firm period and for industrial affiliation. The latter variable, representing 8 Hi Tech sectors, was meant to control for inter-industry variance in firm values. Descriptive statistics and correlations of the major variables, presented in Table 2, show that the firms in our sample are fairly young and small to medium-sized in terms number of employees (average of 130 employees per firm) and sales (averaging $US 30 Million). It is also evident from Table 1 that the firms are valued relatively high compared to their sales volume with firm value averaging $US 330 Million. About 57% of the firms in the sample were involved in network relations at some point where 39% of them were involved in network relations for at least 2 subsequent periods (i.e., between three investments rounds). Network relations in production were most commonly used (76% of the firms reported on short term network relations), then in marketing activities (55% of the firms reported on short term network relations) and finally in R&D activities (20% of the firms

+ + + 497 21.30 0.15

0.51 (0.24) 0.32 (0.27) 1.25 (0.55) 1.32 (0.55)

5.63 (0.35)

5.10 (0.51)

0.12 (1.13) + + + 497 31.42 0.24

0.25 (1.14) 0.57 (0.25) 0.48 (0.24) 1.05 (0.45) 1.25 (0.21)

(2)

(1)

+ + + 497 36.93 0.30

0.42 (0.26) 0.27 (0.25) 0.56 (0.25) 0.55 (0.24) 0.12 (0.94)

5.54 (0.87) 0.43 (1.05)

(3)

(4) 6.12 (0.59) 0.34 (0.92) 0.67 (0.82) 0.48 (0.21) 0.24 (0.20) 0.55 (0.21) 0.51 (0.22) 0.18 (0.86) 0.18 (0.64) + + + 497 39.81 0.33

Pooled Ordinary Least Squares

+ + + 497 29.72 0.27

0.23 (0.13) 0.55 (0.19) 1.05 (0.19) 1.19 (0.13)

5.20 (0.77)

(5)

0.25 (0.63) + + + 497 32.45 0.26

0.88 (0.77) 0.39 (0.11) 0.57 (0.15) 1.18 (0.21) 1.14 (0.22)

4.83 (0.21)

(6)

+ + + 497 39.78 0.34

0.37 (0.29) 0.56 (0.27) 1.51 (0.14) 1.26 (0.12) 0.72 (0.40)

5.60 (0.38) 0.46 (0.73)

(7)

Within firm fixed effects

Long-Term and Short Term Network Relations Effect on Firm Value.

5.29 (0.34) 0.49 (0.86) 1.31 (0.74) 0.49 (0.25) 0.54 (0.51) 1.22 (0.45) 1.19 (0.19) 0.17 (0.41) 0.34 (0.71) + + + 497 46.35 0.42

(8)

Notes:  statistically significant at 0.1%,  statistically significant at 1%,  statistically significant at 5%, Standard Errors in parentheses (in the pooled OLS regressions, corrected for firm cluster)

Periodi Year Industry No. of Observations F-value R2

Patenti X network_ long termi

Patenti X network_ short termi

Lan_network exp

Age

Patenti

Lan_Salesi

network_ long termi

network_ short termi

Constant

Table 2.

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reported on short term network relations). Technological innovation output is about 10.1 patents per firm on average, reflecting the importance of patents for the analyzed firms. Finally, the distribution of firms across HiTech sectors is as follows: Capital Equipment (23%), Medical Devices (21%), Telecommunications (17%), Enterprise Software (11%), Storage and Data Centers (6%), Home Networking and Homeland Security (5%), Multimedia and Broadcasting (4%) as well as Other sectors including: Cellular, Chip Design, Internet, Biotech and Electronics (13%). Major correlations are observed between the alternative size measures (number of employees and sales) and between these measures and firm age. Short-term networks are negatively correlated with firm value while long term network relations are positively correlated with it. In addition, sales, patents, network experience, age, and number of employees all positively correlate with firm value. This implies that the larger, older, more network experienced and more technology intensive the firms in our sample, the greater their firm value. It is also evident that number of employees and firm age are negatively correlated with both short and long term network relations.

Methods We used two types of panel data models to analyze our sample: pooled Ordinary Least Squares (OLS) regressions and fixed within-effect regressions. Pooled OLS regression models were used to test between observed effects (i.e., inter-firm variance in the effect of short and long term network relations on firm value). The pooled OLS regression is estimated using the cluster method, which corrects for deviation in standard errors. The cluster method assumes that there is a correlation between observations of specific groups (firms, in our case). Incorporation into a cluster implies that the observations are independent across groups (firms), but not necessarily within groups. It calculates the variance in standard error for each firm separately and hence corrects for the possible deviation in standard error terms. It is noteworthy that clustering affects the estimated standard errors and variance-covariance matrix of the estimators (VCE), but not the estimated coefficients. The fixed effect models served to explain within-firm variation in the effect of network relations on firm value. Fixed effects models do not allow time-invariant control variables (such as industry and firm year of establishment) since these are perfectly collinear with the firm dummies.

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Hence, to include these variables in the fixed effects model we used the unit effect vector decomposition technique developed by Plu¨mper and Troeger (2004). In this approach the estimated firm-specific intercepts are regressed on the time-invariant variables and the residual from this regression is used as a predictor in a pooled OLS regression along with the time-varying and time-invariant variables. This effectively decomposes the firm-specific fixed effect into two orthogonal components: one which is explained by the timeinvariant variables – (for instance, network experience, age, year, and industry in our case) and a residual component of firm effects not explained by these variables (and hence caused by other, unobserved variables). While it produces the same R-square, the technique is more efficient than the fixed effects model, especially if the time-varying independent variables are ‘‘almost time-invariant’’ and if the sample is relatively small. It has also been shown in Monte Carlo simulations to outperform the pooled OLS, random effects, and Hausman–Taylor instrumental variables approaches in terms of consistency and unbiasedness (Plu¨mper & Troeger, 2004). The specification of our regressions is as follows: Lan_firm valuei= f(network_long termi, network_short termiLan_salesi, Patenti, Age, Lan_network exp, PatentiXnetwork_short termi, PatentiXnetwork_long termi, Periodi, Year, Industry)

RESULTS The results of our panel data model regressions are presented in Table 2. Models (1)–(4) refer to the pooled OLS regression while models (5)–(8) refer to the fixed effects regressions. In each set of regressions we first test the effect of the control variables on firm value (models 1 and 5, respectively), then we test the effect of long term network relations given the control variables (models 2 and 6), the effect of short term network relations (models 3 and 7) and finally both long and short term network effects (models 4 and 8). This is done in order to verify the robustness and consistency of our results. The coefficients of the pooled OLS models in Table 2 (models 3 and 4) indicate that network_short termi is consistently negatively correlated to Lan_firm valuei, this result is repeated for the fixed effects models (models 7 and 8) thus supporting Hypothesis 1. These models further support the hypothesis that technological innovation intensifies the negative effects of short term network participation on performance (see the negative

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coefficient of their interaction) and the hypothesis that network experience mitigates these negative effects (see the positive coefficient of the interaction of Lan_network exp and network_short termi). In the latter case the size of the interaction coefficients indicates that prior network experience is not enough to outweigh all the negative performance implications of short term network engagement. The coefficients of network_long termi are consistently positively correlated to Lan_firm valuei in models 2, 4, 6, and 8 hereby supporting Hypothesis 2. These models further support the hypothesis that technological innovation intensifies the positive effects of long term network participation on performance (as the coefficient of the interaction of patenti and network_long termi is positive). As for the control variables, Table 2 indicates that Lan_Salesi, Patenti, Lan_network exp and Age are all consistently positively related to Lan_firm valuei, thus supporting the expectation that larger, older, more network experienced and more technologically innovative firms are likely to outperform other firms in terms of value. As indicated in Table 2 these results hold when controlling for firm period, year and industry effects. R-square values reach up to 0.42 and all F-values are statistically significant at po0.1%. All average Variance Inflation Factors (VIF) were considerably lower than the critical value of 10 (Neter, Wasserman, & Kutner, 1990), thus ruling out potential multicollinearity suspicions.

Robustness Tests To further test the robustness of our results, we conducted several additional analyses. First, we tested the same set of models with an alternative performance measure: sales per employee (Emp_salesi). Results (available upon request) show that the long-term and short-term impact of network relations on this alternative performance measure remain the same (significant and positive for network_long termi and significant and negative for network_short termi). Likewise, the interactions of network_long termi and network_short termi with patenti and Lan_network exp have not changed. We have also replaced our technological innovation measure with two alternative measures: R&D investments in a given period (as reported by the firms) and the number of patent citations for patents applied in each period (and granted at some point) as reported by the US Patent and Trademark Office (USPTO). The inclusion of these alternative innovation

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output measures did not change any of the previous results. Replacing Lan_salesi with Lan_empi, as an alternative size measure, has also not changed results. Other robustness checks included the examination of short and long-term participation in network relations, separately for R&D, production and marketing activities. Here, results (available upon request) show that while network participation in all type of value chain activities has resulted with negative effects of short term network relations on firm value, positive longterm effects were only recorded for firms participating in R&D and/or marketing networks. These results highlight the importance of the latter two value chain activities for firm performance where the use of network relations in these value chain activities seems to contribute to firm value more than production-related networks.

Limitations and Future Research Avenues Several limitations of this study should be borne in mind. First, all the firms in the dataset originate from a single country (Israel). Hence, countryspecific characteristics such as the cultural distance from foreign networks and the costs implied by such distance (Lavie & Miller, 2008) may affect our results. Cultural distance, coupled with the relatively small size of the Israeli capital market, might also have an effect on the levels of firm value due to market imperfections and/or asymmetric information biases between Israeli firms and foreign investors. Country-specific characteristics, such as business culture or managers’ background, may also affect our findings. In addition, the sectoral distribution of the Israeli Hi Tech sector (and hence of our sample) is biased toward specific areas such as: capital equipment, medical devices, telecommunications, and information technology. These sectors do not necessarily represent the same costs and benefits of network relationships found in other sectors. Moreover, the fact that our sample consists of fairly young and relatively small-sized firms may imply that both the costs and benefits of network relations might be greater for this type of firms compared to more mature and established firms that may be able to use their business experience and large size to reduce network-specific resource commitment costs on the one hand, and be less affected from their network relations in terms of performance, on the other. Thus, future studies relating to larger and more mature firms originating in multiple countries and industries are important in order to enhance the external validity of this study’s results.

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This study builds on a binary measure of firm’s participation in network relations. Future studies should incorporate alternative measures of network participation, reflecting the extent to which firms are engaged in network relations such as: quality of interpersonal ties, the number of ties, their duration, and their diversity. Such measures may serve as better proxies for both the costs and benefits of engaging in network relations, as detailed in the conceptual framework. Likewise, future studies could test additional performance measures which are either market based or accounting based (Goerzen & Beamish, 2003, 2005) in order to test the robustness of the observed effects on different performance measures. Finally, in order to obtain a more precise view of the costs and benefits effects of engaging in network relations, the ideas presented in the current chapter can be tested using a case study method (e.g., Liebeskind, Oliver, Zucker, & Brewer, 1996), that allows a more fine grained level of analysis. Such an analysis could further illuminate the short term and long term performance effects of engaging in specific network relations.

DISCUSSION This study integrates insights originating in TC theory with the organizational capabilities approach to highlight an important and mostly overlooked cost facet of engaging in network relations – the costs of network-specific administrative resource commitment. It does so within a particular context – that of high technology firms, for which the rents and costs of technological knowledge are expected to be especially pronounced as well as significantly affect the observed relationships between network engagement and performance. By referring to the costs of generating network relations this study puts forward the view that network relations bear costs and risks that, in the short run, outweigh their benefits for the firm (Adler & Know, 2002; Hansen et al., 2001). While a large body of research is focused on the benefit side of network relations, the literature on their related costs and risks is much sparser (Adler & Know, 2002; Hansen et al., 2001; Kim, Oh, & Swaminathan, 2006). Moreover, extant discussion on the ‘‘cost side’’ of networks mainly stresses: overembeddedness in networks (Uzzi, 1997) that reduces the flow of new ideas and thus may result in parochialism and inertia (Kim et al., 2006; Powell & Smith-Doerr, 1994); over-internationalization of networks

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(Lavie & Miller, 2008) that makes firms subject to ‘‘cultural distance’’ related costs and over-diversification of networks where the costs of managing increasingly diverse and complex alliance network can overwhelm their benefits (Goerzen & Beamish, 2005). In this chapter we highlight a different cost facet: the ‘‘sunk costs’’ that are coupled with the specific and irreversible investments in the creation and development of network relations. Once established, network positions tend to be highly specific and cannot be easily transferred to other networks. This is because network partners are often likely to have specific sets of resources and routines that are embedded in specific relationship settings (Dyer, 1996; Dyer & Singh, 1998; Lavie, 2006). While a given network position may yield considerable benefits, the migration of such benefits to other networks is unlikely without a lengthy and costly process of building additional network relations. Whereas in some cases specific network relationships can also be used in other networks, most commonly re-generation of network relations is likely to infer sizable costs that are mostly sunk. The implications of the irrevocability of investment in network-specific relations are that firms should be selective in the choice of networks in which they engage. Not only might entering a network for short-term purposes bear greater costs than long term network engagement but it may also prove to be as costly as (or even costlier) than internal resource investment. Just like internal resource investment bears the risks of high switching cost and losses due to unexpected changes (Anderson & Gatignon, 1986; Hill et al., 1990; Williamson, 1985), administrative resource commitment to given networks entails high switching costs and potential losses when network relations unexpectedly terminate. Hence firms must make a trade-off between network benefits versus the managerial time and effort required to create network relations. This implies that each engagement in network relations, either in entering a foreign country, sourcing new technology or diversifying into different product markets, should be considered versus engagement in alternative networks as well as versus the investment of internal resources to such markets before such engagement is realized. This is so since in the short run the costs of network engagement exceed its benefits and since any unexpected termination of network relations leaves firms with the sunk costs invested in creating and developing specific network relations but without their associated benefits. On the other hand, negative short-term performance as a result of engaging in specific networks should not prevent firms from further engaging in these networks, as long as they have reasons

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to anticipate that the long-term benefits from such engagement will be greater than their costs. Furthermore, the negative effects of short term network participation and the positive effects of long term network participation are not expected to be uniform. We show that greater technological innovation intensives both effects, implying that firms that are more technology based are likely to face greater risks of prematurely ending network relations. On the other hand, if such firms are able to bear these negative short term performance implications and if their networks do not terminate prematurely they are likely to gain more from long term network relations than other firms. Our contribution to the network literature relates to the distinction between the costs associated with network-specific ‘‘administrative resource commitment’’ and the benefits derived from network relations. The conceptual roots of this type of resource commitment lie in the notions of resource specificity and its resulting costs, as highlighted by Buckley and Casson (1976), Klein et al. (1978), Teece (1986) and Williamson (1985). Administrative resource commitment to networks refers to the magnitude of investment in developing network relations and the irreversibility of such investments. Thus, it emphasizes the costs of developing such relations and the costs of transferring them to other networks. Thus this chapter contributes to the few network studies (e.g. Kim et al., 2006; Levinthal & Fichman, 1988) that are concerned with the impediments to changing network relationships. We build on the organizational economics literature to extend the above literature by arguing that impediments to change stem from the specificity of given network relations and the administrative resource commitments made to such networks. Our proposed framework can be expanded to distinguish between the resource commitments implied by firms becoming more relationally embedded in their networks compared to the resource commitments implied by firms becoming more structurally embedded in their networks. Firms differ in their levels of relational and structural embeddedness (Gulati, 1999; Nahapiet & Ghoshal, 1998; Rowley et al., 2000). Different goals and organizational contexts require different network relations and structures (Uzzi, 1997). It may be the case that in order to achieve either greater relational embeddedness and/or greater structural embeddedness, as per a given firm’s strategic goals, firms may be required to exhibit different levels of network specific related administrative resource commitments. It therefore follows that firms should not only evaluate their desired levels of

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relational and structural embeddedness, but also the administrative resource commitment required to achieve greater relational or structural embeddedness when deciding how to position themselves in their current and future networks. If, for instance, similar strategic goals (e.g., avoiding opportunistic behavior of partners) can be achieved either via a certain level of relational embeddedness (e.g., trust) or via a certain level of structural embeddedness (shared behavioral standards and collective sanctions), the cost implications of making the required investments for achieving each type of embeddedness are crucial. Our cost-benefit approach further implies that a distinction should be made between the ‘‘inputs’’ (cost generating) and the ‘‘outputs’’ (benefit generating) elements of network relations. This implies that it is not certain that investments in developing network relations will eventually yield the desired network benefits, especially when networks unexpectedly terminate, because different levels of inputs and outputs in the short and long term lead to differential performance outcomes. Yet, many studies confuse the two and use one as a proxy for the other. For instance, Levin and Cross (2004) have shown that investments in building strong ties do not always lead to trust. Clearly, similar levels of investments in the creation of network relations may lead to different outcomes (Kumar & Nti, 1998). This may be the result of exogenous factors (e.g., rivals’ actions), network-specific factors (e.g., cultural distance between partners), or firm-specific factors (e.g., differential absorptive capacity). We suggest that the investment in creating network relations should be theoretically and empirically distinguished from the outcomes they create in order to extend our comprehension of the network phenomenon, the process of network development and change, and the impact of network participation on firm performance. Thus, the integration of organizational capability and organizational economics insights has both practical and theoretical implications for the creation, continuation, and change of network relations. The identification of such implications is only made possible through this integration of the two analytical traditions.

NOTES 1. Separately for each such value chain activity. 2. We did use patent citations as an alternative proxy for innovation output in the robustness tests. 3. We also used the Lan of number of employees at the end of each period (Lan_Empi) as an alternative control reflecting firm size in the robustness tests.

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APPENDIX Table A.1. Variable name

Description of Variables and Measures.

Variable description

Measure

Source

Lan_Firm valuei

Lan of firm value (in Billion $ US) in period i, as determined by value of investment and percentage of ownership

Number

Dolev and Abramovitz dataset. Investments could be made by: private investors, venture capital funds, corporate venture capital, acquisitions or public offerings.

network_ long termi

Participation in networks in a given period i and also in the preceding period i1. Participation in networks in a given period i, but not in the preceding period. Lan of Sales (in Billion $US) at the end of period i Lan of number of employees at the end of period i Number of patents applied at period i (granted patents only) The periods between prior to current valuation Lan of network experience (in years) since a given firm’s inception Mid-year of period i

1 – yes 0 – no

Firms’ own reports cross checked against archival sources

1 – yes 0 – no

Firms’ own reports cross checked against archival sources

Number

Dolev and Abramovitz dataset.

Number

Dolev and Abramovitz dataset.

Number

US Patent and Trademark Office (USPTO)

i =1–7. 3 to 7 periods per firm

Dolev and Abramovitz dataset

Number

Firms’ own reports cross checked against archival sources

network_ short termi

Lan_Salesi

Lan_Empi

Patenti

Periodi

Lan_network exp

Year

Age Industry

Age of the firm at the end of period i Industry classification of firms within the Hi-Tech sector

Number

Based on date of valuation as reported in Dolev and Abramovitz dataset Dolev and Abramovitz dataset Dolev and Abramovitz dataset

CHAPTER 8 REGIONAL NETWORKS, ALLIANCE PORTFOLIO CONFIGURATION, AND INNOVATION PERFORMANCE Suleika Bort, Marie Oehme and Florian Zock ABSTRACT To maintain and enhance innovation performance, many firms nowadays look for resources from external sources such as strategic alliances and regional network embeddedness. While considering the important interdependencies among different alliances, research has established an alliance portfolio perspective. From an alliance portfolio perspective, firms can consciously configure the dimensions of their alliance portfolios such as partner characteristics, relational properties, or structural properties. However, within the context of alliance portfolio configuration, the role of regional networks has been largely overlooked. As most high-tech firms are regionally clustered, this is an important research gap. In addressing this gap, this study explores the link between regional network density, alliance portfolio configuration, and its contribution to firm innovation performance. We examine how regional network density and alliance partner diversity influences firm level innovation output.

Understanding the Relationship between Networks and Technology, Creativity and Innovation Technology, Innovation, Entrepreneurship and Competitive Strategy, Volume 13, 229–256 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1479-067X/doi:10.1108/S1479-067X(2013)0000013011

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We also investigate the moderating effect of overall network partner status and partner diversity on the link between regional network density and innovation performance. Our empirical evidence is derived from a longitudinal quantitative study of 1,233 German biotechnology firms. We find that regional network density and alliance partner diversity has an inverted U-shape effect on firm level innovation performance. However, overall network status as well as alliance partner diversity negatively moderates the link between regional network density and innovation output. Thus, our study contributes to a better understanding of the link between regional networks, alliance portfolio configuration, and firm level innovation performance. Keywords: Regional networks; innovation performance; inter-firm collaboration; alliance portfolio; biotechnology

INTRODUCTION This chapter aims to contribute to the literature dealing with regional networks, alliance portfolio configuration, and innovation performance. To maintain and increase innovation performance, firms are in the need to look for resource access outside their organizations through strategic alliances (Ahuja, 2000; Phelps, 2010; Srivastava & Gnyawali, 2011) and regional cluster networks (Gnyawali & Srivastava, 2013; Pounder & St. John, 1996; Tallman, Jenkins, Henry, & Pinch, 2004; Whittington, Owen-Smith, & Powell, 2009). In the literature of economic geography there is increasing support for the assumption that physical co-location and regional network embeddedness plays an important role to explain the success of firms. This is in particular the case for firms in high-tech industries. Prior research has shown that firms that are located within clusters have a higher new product innovation output (Deeds, DeCarolis, & Coombs, 1997), rates of growth (Canina, Enz, & Harrison, 2005) or survival (Sorenson & Audia, 2000; Stuart & Sorenson, 2003b) than non-clustered firms. In this context, it has further been argued that the key innovation advantage of regionally clustered firms can be traced back to superior knowledge creation and learning within geographical regions which can be explained through knowledge spillovers (Aharonson, Baum, & Feldman, 2007; Gilbert, McDougall, & Audretsch, 2008; Jaffe, Trajtenberg, & Henderson, 1993; Ter Wal & Boschma, 2011). According to Gilbert et al. (2008, p. 405)

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knowledge spillovers refer to the ‘‘direct or indirect transfer of knowledge from one party to another.’’ The knowledge spillovers a firm receives can increase firm level innovation output because it enables a firm to see the directions other firms are going and it empowers firms to take new opportunities. For example, Gilbert et al. (2008) found based on a sample of 127 IPO technology-based ventures in the United States that (technological) knowledge spillovers positively and significantly influence new venture product innovation. However, pure co-location cannot guarantee innovation performance. For example, Gilbert et al. (2008) also found that the knowledge spillovers received could not explain the higher innovation output of the locally clustered firms. In fact these authors found that as soon as they control for industry clustering, the significant spillover disappears. The authors conclude that there might be other important factors that explain the better performance of locally clustered firms. In an attempt to explain the innovation output of high-tech firms that are physically co-located, recent studies highlight the importance of linking networks and clusters (Gnyawali & Srivastava, 2013; Whittington et al., 2009). These authors come to the conclusion that the effects of networks can only be fully understood if the physical co-location of firms is also taken into account due to the ‘‘qualitative differences between local and global networks and their modes of interaction’’ (Whittington et al., 2009, p. 9). Yet, in the literature dealing with alliance portfolio configuration this physical dimension of regional network embeddedness has not been taken into account. An alliance portfolio refers to a firm’s ego-network of current and prior alliances that span along a variety of dimensions (i.e., partner types, alliance content, and scope) (Lavie & Miller, 2008; Wassmer, 2010). In fact, prior research has argued that firms can consciously configure the dimensions of their alliance portfolios such as partner characteristics (e.g., Lavie, 2007; Lavie & Miller, 2008), relational properties (e.g., Rowley Behrens, & Krackhardt, 2000; Tiwana, 2008), and structural properties (e.g., Gulati, 1999; Baum, Calabrese, & Silverman, 2000). Thus, firms can actively influence, shape, and manage the structure and composition of their alliance portfolio (Lavie, 2006; Ozcan & Eisenhardt, 2009) in terms of partner characteristics and structural properties. Thus, taking on a portfolio perspective ‘‘eschews the reductionism that occurs when an analyzed pair of firms is abstracted out of their embedded context’’ (Sarkar, Aulakh, & Madhok, 2009, p. 588). As such, the alliance portfolio accounts for the original theoretical rationale of the knowledge-based view that has been flawed by the single alliance perspective: knowledge can come from everywhere, and will come from everywhere at the same time (Grant, 1996).

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Hence, by studying alliance portfolios, the analysis shifts to an intermediate level, as the focus is on a focal firm’s simultaneous collection of alliances that is again embedded in a global network (Lavie, 2007). Yet, in this context, the important role of regional networks has been underestimated so far – a gap which the present study aims to fill.

THEORY AND CONCEPTUAL FRAMEWORK As set out by Grant (1996) knowledge is the most important asset in today’s business environment. As emerging high-tech industries oftentimes consist of SMEs, these firms – from a knowledge-based point of view – oftentimes do not have the ability and resources to solely rely on an exclusively internal accumulation of the knowledge and information required to successfully innovate (Rothaermel, 2001; Rothaermel & Deeds, 2004). In general, a firm’s knowledge base can be conceptualized by knowledge stocks and flows (Dierickx & Cool, 1989). Knowledge stocks are internal knowledge assets. Knowledge flows are external knowledge streams that may be assimilated and developed into stocks of knowledge (DeCarolis & Deeds, 1999). Conceptually, organizational knowledge is tightly linked to organizational learning, as firms generate knowledge through organizational learning (Levitt & March, 1988). Thus, the only way to enhance innovation performance is by constantly augmenting the organizational knowledge base (Iansiti & Clark, 1994; Spender, 1996; March, 1991). Prior studies have highlighted the integration of external knowledge flows and internal knowledge stocks as a means to generate innovation (DeCarolis & Deeds, 1999; Galunic & Rodan, 1998; Kogut & Zander, 1992). Recent research has affirmed the essential role of accessing external knowledge to enhance innovation (Grant & Baden-Fuller, 2004; Phene, Fladmoe-Lindquist, & Marsh, 2006; Rosenkopf & Nerkar, 2001). Accessing external knowledge flows is especially relevant for new ventures to generate innovation, as their internal knowledge space is limited. In this context prior literature has emphasized the importance of regional network embeddedness and new ventures’ alliances for enhancing innovation (Baum et al., 2000; Deeds and Hill, 1996; Saxenian, 1994; Shan, Walker, & Kogut, 1994; Stuart & Sorenson, 2003a). Especially geographic agglomerations of similar and supporting firms have been identified as beneficial for firm innovation. Next to providing access to required resources to initially start a new venture or a qualified labor force

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(e.g., Audretsch & Feldman, 1996; Saxenian, 1990; Stuart & Sorenson, 2003a), the innovative potential of new ventures can be facilitated whenever information, innovations, and knowledge developed by individual firms spill over to and diffuse between geographical proximate actors (e.g., Powell, Koput, & Smith-Doerr, 1996; Whittington et al., 2009). These knowledge spillovers are further encouraged with an increasing density of local interactions. As put forth by Gulati (1995) as well as Hagedoorn and Duysters (2002) dense networks allow for the development of trust between network participants and furthermore reduce the potential for opportunistic behavior. Thus density in regional networks additionally assists a firm in evaluating the reliability of received information and knowledge (Gilsing, Nooteboomb, Vanhaverbekec, Duystersd, & van den Oorda, 2008). Next to being embedded in regional networks firms can engage in numerous collaborative agreements spanning these geographical boundaries (Whittington et al., 2009). However, rather than being engaged in single interorganizational collaborative agreements firms in these days use to be involved in several yet different alliances at a time. Since learning and thus knowledge creation takes place in social interactions (Cook & Brown, 1999; Galunic & Rodan, 1998; Weick & Roberts, 1993) we conclude that new ventures can leverage knowledge flows stemming from their alliance portfolio configuration for strengthening their innovation performance. On the other hand, new ventures can develop distinct knowledge management capabilities (Lei, Hitt, & Bettis, 1996) that are effective in the specific context of alliance portfolios and may assist them to create innovative outcomes (Heimeriks, Klijn, & Reuer, 2009; Sarkar et al., 2009). Thus, collectively combined, alliance portfolio configuration and management allows new ventures to access selected knowledge flows and enhance innovation. Finally, the examination of structural network positions provides valuable insights into the potential access firms have to knowledge (Gulati & Gargiulo, 1999; Podolny & Stuart, 1995). As such, the occupation of central positions within the global industry network is associated with leveraged access to information and knowledge circulating in the global industry network (Gnyawali & Madhavan, 2001), lower failure rates (Uzzi, 1996), the rate of initial public offerings (Stuart, Hoang, & Hybels, 1999) and the innovative performance of firms (Whittington et al., 2009). Additionally, network positions are strongly related to a firm’s status (Bonacich, 1987; Podolny, 1993) as a ‘‘socially constructed, intersubjectively agreed upon and accepted ordering or ranking of individuals [y] in a social system’’ (Washington & Zajac, 2005, p. 284). Thus, status indicates an actor’s relative standing in a social system in terms of prestige, influence, bargaining

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power, and subsequently rent appropriation abilities in interorganizational relationships (Lin, Yang, & Arya, 2009; Podolny, 1993; Thye, 2000; Washington & Zajac, 2005). That is to say, that a firm’s (innovation) performance is influenced by its partners’ status level (Baum & Oliver, 1991; Podolny, 1994). Hence, firms can benefit from engaging in strategic alliances to improve their own status and thus attain ‘‘affiliative status’’ by taking on the prestige of more established partners (Hitt, Dacin, Levitas, Arregle, & Borza, 2000). However, although network embeddedness, alliance portfolio configuration, and network position have gained importance in the management literature (Hoffmann, 2007; Jiang, Tao, & Santoro, 2010; Lavie, 2007; Powell et al., 1996; Vassolo, Anand, & Folta, 2004; Wassmer & Dussauge, 2012), research simultaneously considering the impact and interaction of different levels of interconnectedness regarding new venture performance is only recently emerging (e.g., Gilsing et al., 2008; Whittington et al., 2009). Thus, in order to better understand how different sources of external knowledge can be explored and how the usage of these sources interact, we study regional networks, alliance portfolios, and network position and their interaction as wellsprings for external knowledge flows that new ventures can leverage for strengthening their innovation performance.

HYPOTHESES DEVELOPMENT Regional Network Density and Innovation Performance Prior research has shown that firms that are located within clusters have a higher new product innovation output (Deeds et al., 1997), rates of growth (Canina et al., 2005), or survival (Sorenson & Audia, 2000; Stuart & Sorenson, 2003a) than non-clustered firms. Prior research often attributes this superior performance effect to the knowledge spillovers firms in clusters can tap into (Gilbert et al., 2008, p. 405). According to Gilbert et al. (2008, p. 405) knowledge spillovers refer to the ‘‘direct or indirect transfer of knowledge from one party to another.’’ The knowledge spillovers a firm receives can increase firm level innovation output because it enables a firm to see the directions other firms are going and it empowers firms to take new opportunities. In fact, there is ample empirical evidence that firm level innovation output can be linked to the presence of the knowledge that firms in close proximity provide (Almeida, 1996; Gilbert et al., 2008; Jaffe et al., 1993). While firms that are not located in clusters receive knowledge

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spillovers through other means (Appleyard, 1996), this transfer is more time consuming. Thus, firms that are not located within a cluster might receive important knowledge spillovers later than clustered firms. While referring to the transfer of knowledge, prior research also often explains the superior innovation performance of regional clustered firms to the presence of regional networks. In fact, the size of the local network, measured either by the number of ties or the number of nodes, or as a proportion measured by density of ties, can be expected to provide an organization a larger reservoir of valuable knowledge to tap into. The denser the network, the more redundancy and repeated interaction leads not only to the development of trust and shared norms (Kilduff & Brass, 2010, p. 327) which facilitates knowledge transfer, but also to a quicker knowledge access. As a result, companies are better able to assess their partners’ capabilities and resources (Eisingerich, Bell, & Tracey, 2010). In fact, knowledge exchange in regional networks might be more effective due to more frequent interpersonal contacts (Lawson & Lorenz, 1999). For example, Saxenian (1990) found support for his assumption that the success of Silicon Valley can be traced back to the strong regional network ties that enabled firms to develop a shared identity, and in turn a forum to discuss common problems and provide solutions (Saxenian, 1990). A dense network structure with strong ties is important for the transfer of tacit, non-codified knowledge which in turn has a positive influence on absorptive capacity (Andersson, Forsgren, & Holm, 2001, p. 1016). In fact, Gilsing et al. (2008) similarly argue that density supports ‘‘the build-up of shared absorptive capacity’’ (Gilsing et al., 2008, p. 1727). The larger the network, the larger the potential knowledge flow, and the higher the probability that innovation in the form of patents occur. We therefore state: Hypothesis 1. The relationship between regional network density and new venture innovation performance is curvilinear. The slope is positive at low and moderate levels of regional network density and is negative at high levels of regional network density indicating an inverted U-shaped relationship.

Partner Diversity and Innovation Performance The recent literature suggests that the effectiveness of learning depends on the diversity of knowledge accessed (e.g., Lavie & Miller, 2008; Zahra, Ucbasaran, & Newey, 2009). In this spirit, the configuration of a firm’s

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alliance portfolio determines quality, quantity, and diversity of information the focal firm can access (Hoffmann, 2007). Thus, alliance portfolio configuration in terms of partner choice can become a liability by channeling and restricting the scope of accessible knowledge (Gulati, 1999). Accordingly, allying with a rather diverse set of alliance partners is supposed to positively impact on firm performance. Collaborating with a diverse set of partner firms prevents new ventures from inertia when it comes to the identification and processing of new knowledge that is required for a successful adaptation and expansion of business (Maurer & Ebers, 2006). That is, portfolios characterized by partner diversity impact positively on new venture performance since they provide access to more diverse and stimulating information and knowledge (Baum et al., 2000) and thus may limit a firm’s tendency toward local search that limits and hampers opportunities to derive innovations from the recombination of existing knowledge bases. Similarly, Zahra et al. (2009) suggest that there is a positive relationship between the number of different countries a firm operates in, and its innovative performance due to the enriched and less redundant knowledge bases the firms is exposed to. However, most research considering knowledge access and diffusion through alliances and networks implicitly follows Cohen and Levinthal’s (1990) prominent notion of ‘‘absorptive capacity’’ suggesting that the ease of knowledge assimilation and utilization strongly depends on the similarity of alliance partners’ knowledge bases. That is, collaborating with similar partners increases the potential for learning and innovation. In this vein, Lane and Lubatkin (1998) for example demonstrate that the interorganizational learning potential in dyadic alliances between pharmaceutical and biotechnology firms increases with growing overlaps in the firm’s knowledge bases. Similarly, Lindstrand, Mele´n, and Nordman (2011) recognize that many biotech firms which find their origins in university spin-offs tend to preferentially ally with science-related actors such as universities due to an assumed broader basis for mutual understanding. However, although increasing dissimilarities in partners’ knowledge bases negatively affect absorptive capacity they are believed to positively impact the potential for innovation. Gilsing et al. (2008) for example show that increasing technological distance in terms of dissimilarities between a focal firm’s and its alliance partners’ technology profiles initially have a positive impact on the number of explorative patents filed by the focal firm. Similarly, Lavie and Miller (2008) show that – while paving the way to access a diverse set of internationally distributed knowledge – increasing alliance portfolio internationalization has a sigmoid impact on a firm’s

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financial performance assuming evolving learning effects but simultaneously increasing liabilities of cross national and cultural complexities. Hence, there is evidence that the benefits derived from alliance portfolio configuration in terms of partner choice strongly depend on the degree of the resulting partner diversity. Since we believe new ventures to be unable to handle and assimilate flows of increasingly diverse knowledge flows, we argue that growing partner diversity predominantly complicates knowledge access and learning possibilities, ultimately reducing the derivable innovation potential for new ventures. Thus, rather moderate levels of partner diversity may offer opportunities for new ventures to strengthen their innovation performance resulting from access to complementing knowledge flows. Hence, we assume an inverted U-shaped relationship between partner diversity and new venture performance. This leads us to suggest: Hypothesis 2. The relationship between partner type diversity and new venture innovation performance is curvilinear. The slope is positive at low and moderate levels of partner type diversity and is negative at high levels of partner international diversity indicating an inverted U-shaped relationship.

Status, Network Centrality, and Innovation Performance Additionally, there is evidence that firms can benefit from engaging in alliances and subsequently from network embeddedness even more, if they are able to obtain certain structural positions within the overall industry network (Gulati, 1999). Assuming that information and knowledge diffuse within a firm’s network, those firms that occupy central positions in terms of being connected to numerous and diverse actors in the network are supposed to have superior and more diverse access to nonredundant knowledge and information flows (Burt, 1992). Gnyawali and Madhavan (2001) for example suggest that firm’s that hold central positions within an interfirm network benefit from a more timely access to novel information and knowledge, a superior ability to initiate additional complementing alliances and – equally important – a higher bargaining power what subsequently improves their bargaining position toward less central actors. This superior access to information is even increased if the firm is in turn connected to other extensively connected actors (Bonacich, 1987). When designing their alliance portfolios new ventures can either affiliate with

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extensively connected partners or less embedded alliance partners, that is, with high-status as well as low-status partners. According to the principle of ‘‘structural homophily’’ (Gulati & Gargiulo, 1999; Podolny, 1994), it is obvious that new ventures, which are usually characterized by low-status due to their liabilities of newness and smallness, can easily partner with other low-status partners. In contrast, high-status firms may not be willing to partner with low-status new ventures, but rather tends to favor firms of similar status as alliance portfolio partners, since partnering with low-status new ventures may weaken their own performance (Podolny, 1994; Washington & Zajac, 2005). As a consequence, low-status firms, such as new ventures tend to remain isolated on the periphery of the alliance portfolio, being tied to low-status firms (Gulati & Gargiulo, 1999). However, although according to the before mentioned idea of ‘‘structural homophily,’’ high-status firms have little incentives to partner with peripheral, low-status firms such as new ventures exceptions prove the rule. This especially applies, when new ventures dispose of or control resources or assets that a high-status firm might need, such as new technologies (Scott & Stuart, 2002). In particular, biotechnology new ventures are valuable upstream partners for large and experienced pharmaceutical and chemical firms (Baum et al., 2000). While these established pharmaceutical and chemical firms have the routines and competencies to manage a new drug to the regulatory process, new ventures do have highly specialized knowledge that incumbents often are dependent on for innovative outcomes (Rothaermel & Deeds, 2004). In a similar vein, Stuart, Ozdemir, and Ding (2007) perceive new biotechnology ventures as knowledge brokers between the science-oriented research communities and commercially oriented pharmaceutical firms. Hence, there is reason to suggest that new ventures are also able to build partnerships with high-status firms and thus benefit from ‘‘affiliative’’ centrality. Partnering with high-status partners allows access to knowledge pools that cannot be easily gained over the market (Lin et al., 2009). In particular, by adding high-status partners to their alliance portfolio, new ventures can tap into knowledge embedded in the larger network as well as into specialized human capital. On the one hand, alliances with high-status partners assist a new venture to access other valuable knowledgeable resources within the broader network of those high-status partners (Stuart, 2000) and thus leverage knowledge flows from the larger global network, in which the portfolio is embedded (Gulati, 1999). Furthermore, as alliances tend to be formed more likely between firms occupying central positions in the network structure (Chung, Singh, & Lee, 2000; Gulati & Gargiulo, 1999;

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Podolny, 1994), new ventures with high-status portfolio partners gain timely and easy access to emerging network resources that improves their innovation performance (Powell et al., 1996). On the other hand, those highstatus portfolio partners can easily access specialized human capital. This is due to the fact that highly qualified employees, such as specialized scientists, prefer to work for high-status firms (Frank, 1985). Hence, we propose that new ventures will benefit from a central network position in terms of increasing numbers of direct alliances and additionally from allying with well-connected alliance partners. Thus we propose a positive relationship between network centrality considering partner network status and a firm’s innovation performance: Hypothesis 3. The relationship between network centrality considering partner network status and new venture innovation performance is positive.

Moderating Effects of Alliance Portfolio Configuration on Regional Network Density As set out earlier especially new ventures are heavily reliant on their local geographic environment. Geographical clustering and resulting informal network relationships and interactions provide the firm with crucial access to resources to initially start a new venture, a qualified labor force, complementary firms and equally important geographically bound ‘‘knowledge spillovers’’ (e.g., Audretsch & Feldman, 1996; Saxenian, 1990; Stuart & Sorenson, 2003a). However, there is reason to believe that different types of enablers of innovative performance unfolding from interorganizational alliances and network embeddedness interact with each other and at some point may substitute for each other. Shaver and Flyer (2000) for example point out that firms benefit differently from regional agglomerations. They suggest that in contrast to competitively weaker actors especially stronger positioned firms have little to gain from agglomeration economies but rather have to prevent competitively valuable knowledge from spilling over into the local community (Shaver & Flyer, 2000). Furthermore, Tallman and Phene (2007) suggest that proximity may not always have an effect on innovation especially in cases were direct knowledge sources, for example, alliances are at hand. Similarly, drawing on the attention based view of the firm (Ocasio, 1997), Fernhaber and Li (2013) suggest and find evidence for decreasing attention paid to locally circulating information with increasing

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numbers of formal alliances. That is, at some point especially smaller firms and their management will have to focus their activities, since as Ocasio (1997) points out, attentional capabilities of human beings and thus firms are limited. Subsequently, new venture managers can only consider certain selected sources of knowledge and information due to a limited cognitive capacity. Since direct formal alliance partners are actively chosen by the respective firm (Lavie, 2006; Ozcan & Eisenhardt, 2009), we argue that especially the influence of regional network embeddedness, and thus the effect of informal geographically bound interactions, on firm innovation will decrease with increasing numbers of formal collaborative agreements. That is, the more formal alliance agreements a firm enters the less attention it will direct toward its geographical neighbors and the less it will focus on and assimilate the ‘‘local buzz.’’ In sum, this leads to our final two hypotheses suggesting a decreasing impact of regional network density on innovation performance with increasing alliance portfolio partner type diversity and a firm’s global network centrality. Hypothesis 4a. The higher a firm’s alliance portfolio partner type diversity the less positive the relation between regional network density and new venture innovation performance. Hypothesis 4b. The higher a firm’s network centrality in terms of partner network status the less positive the relation between regional network density and new venture innovation performance.

METHODOLOGY Research Setting: The German Biotechnology Industry The setting used for the purpose of this study is composed of the entire population of German biotechnology firms (in total 1,233 firms) founded in 1996 or thereafter. We examine this setting until 2011. Although the majority of firms were founded after 1996 (roughly 97%), our sample includes also some firms from the pharmaceutical and chemical industries who have changed their business model and transformed into biotechnology firms. Firms operating in biotechnology rely on knowledge extensive capabilities transforming scientific know-how in products (Hagedoorn,

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2002; Powell et al., 1996). For such a business model to be successful, global partnering is indispensable in order to source specialized knowledge from external institutions, such as multinational pharmaceutical firms. Due to institutional constraints firms in German biotech are increasingly sourcing knowledge globally. As a result, firms are engaged in multiple simultaneous alliances in R&D, marketing, or sales (Rothaermel & Deeds, 2004). In fact, biotechnology has been identified as the industry with the greatest partnering affinity among several industries characterized by high alliance activity (Hagedoorn, 2002). As such, this specific research setting is very valuable to examine the role of regional networks, alliance portfolio configuration, and network centrality for firm innovation performance.

Data We used several primary sources to compile our data. The first data source is the daily (de-) registration records of the German Commercial Register (‘‘Handelsregister’’). We used the German Commercial Register in order to determine the dates of market entry and market exit. In addition, we use the ‘‘Yearbooks of the German Biotechnology Industry’’ published yearly by the German Biocom AG. The ‘‘Yearbooks of the German Biotechnology Industry’’ are a collection of firm-specific information gathered from an annual survey of all organizations in the field of biotechnology. The addresses from this source were used to identify the geographic location of the firms, the number of employees, and their core business area. The third source is archival data coded from the monthly TRANSCRIPT newsmagazine that reports on the German biotech industry. Our last source is the individual daily press releases published by the respective firms in our sample. Based on these two latter sources we coded different firm events ranging from research and development alliances, marketing and distribution alliances to licensing agreements and other collaborative agreements in order to construct alliance portfolios and interorganizational networks. We used the time of the announcement of a collaborative event as the starting point of the respective event. We furthermore coded all reported termination events of collaborative events. However, in some cases termination dates are unknown. In order to overcome this issue we manually terminate collaborative agreements five years upon inception unless we find evidence for the persistence of the respective alliance. Finally, our patent data stem from records offered by the European Patent Office in Brussels.

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A previous version of this data base has been published earlier (Al-Laham & Souitaris, 2008). These data were used to construct an event history for each company. Event histories are data structures that include information on the number, timing, and sequence of the events that are being examined. Our variables constructed from the event histories are measured to the day. For example, our alliance variables (e.g., different types of alliances) are accurate to the day that the agreement is signed. Each firm’s history began at the time of its incorporation or qualification to do business and ended at the time of an event or at the end of the month, whichever came first. The organization’s second spell began on the following day and ended at the time of an event or the end of the month. This pattern continued until the firm exited (through failure or acquisition) or until the end of the observation period where spells were coded as ‘‘right censored.’’ This procedure that has already been described in Al-Laham and Souitaris (2008) allowed time-varying covariates to be updated throughout the firm’s history at monthly intervals. In those cases where only the month and year of an event could be determined, the day was set at the midpoint of the month to minimize errors in timing.

MEASURES Dependent Variable We measure innovation performance of a focal firm in terms of patent applications. Patents are formalized, codified, and explicit manifestations of innovative ideas, products, or processes and embody a firm’s technological and innovative knowledge (Al-Laham, Amburgey, & Baden-Fuller, 2010). Moreover, patent applications are the result of a highly uncertain R&D process (Kamien & Schwartz, 1982). If a firm disposes of a history of patenting, it has a foundation of (protected) technical knowledge that can enhance the rate of further innovation (Al-Laham et al., 2010; Dierickx & Cool, 1989; Hagedoorn, Link, & Vonortas, 2000). Thus, patents can be considered as an indicator of a firm’s innovation performance (DeCarolis & Deeds, 1999, Powell et al., 1996). Following methods used in prior research, we assigned a patent to a biotechnology firm on the date of application rather than the date of granting, because in general the application date is a more accurate representation of the date of innovation (Ahuja, 2000). Our dependent variable is measured on a daily basis. Thus, our dependent variable is the patent application rate l(t). This rate is defined as

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 qðt; t þ DtÞ lðtÞ ¼ lim Dt ! 0 Dt In this formula q refers to the discrete probability of an organization applying for a patent between t and t+Dt, accounting for the history of this process until time t (Al-Laham, Amburgey, & Bates, 2008, p. 352). This rate measures the probability that a biotech firm applies for a new patent and the rate increases, as the time between the events (patent applications) decreases.

Independent Variables Regional network density was measured as the sum of the alliance ties in the regional network in which a firm is located. The UCINET program was used to calculate this variable. This variable is updated on a quarterly basis. We accounted for all information on initiation and termination of the alliances that form the basis of the regional network. For the alliances where we could not identify the real terminate date or if we could not find information on an extension, we treated them as terminated after 5 years. To define whether or not an organization belongs to a regional network, we had to set geographic boundaries. The German postal system uses a fivedigit postal code. We focused on the two-digit level because it represented a compromise between a smaller geographic region such as the street level, and a larger region such as the city district or the state (Bundesland). A firm’s regional network is thus identified by the two-digit postal code. The location information to construct the regional networks was taken from the postal addresses published yearly in the BIOCOM AG yearbooks. Following this process, for example, the Rhein-Neckar regional network (including cities such as Heidelberg or Mannheim) was set to the two-digit postal codes 67, 68, and 69. Within these six regional networks, we identified twenty subsidiaries that are active in the field of biotechnology research and embedded in the regional network. Alliance partner diversity was measured constructing a Herfindahl index according to the formula: H¼

n X

ðs2i Þ

i¼1

In this formula si indicates the proportion of firms in the portfolio of organizational type i, and n is the number of different organizational types. The Herfindahl index ranges from 0 to 1, where small values indicate

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a heterogeneous, accordingly diverse portfolio of alliance partners and large values indicate dominant organizational types of alliance partners. Thus, the index describes the diversity of organizational types in the alliance portfolio (considering portfolio size). Network centrality was calculated using UCINET (Borgatti, Everett, & Freeman, 2002) on a quarterly base and measured using Bonaich’s (1987) power centrality that defines a firm’s centrality as its summed alliance connections weighted by the centralities of the respective alliance partners. Thus, the measure can be interpreted as an advanced measure of degree centrality (Freeman, 1979) that additionally considers alliance partners network connectivity. The measure is defined as: ct ða; bÞ ¼

1 X

abk Rkþ1 1 t

k¼0

In this expression R is the matrix of linkages, a is a scaling coefficient used to normalize the measure and b is the ‘‘attenuation’’ factor determining the strength of the effect of alliance partner connectivity on the centrality of the focal firm and 1 is a column vector of one. We set b to 0.5, thus suggesting positive effects of strongly connected alliance partners on the focal firm’s centrality while negative values refer to a connection to less central organizations. R denotes the matric of relations. High power centrality of a focal new venture implies that the firm occupies a central position within the global network in terms of being extensively connected to again wellconnected alliance partners.

Control Variables There are several other factors that are of course able to impact innovative performance. Thus, we included a set of control variables that are expected to affect the innovation performance of new ventures but have not been included in any of our hypotheses. First, we control for the yearly quarter of observation in order to account for time dependence and time-changing environmental conditions. Second, we controlled for firm age measured as the number of years since the founding of the new venture. In general, older firms are expected to face stronger inertial pressures as opposed to younger firm that do more frequently engage in explorative activities (Gilsing et al., 2008). However, on the other hand, younger firms are assumed to suffer from resource restrictions that might hamper their propensity to engage in

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complex and costly patent application processes (Gassmann & Keupp, 2007). Since larger firms have higher numbers of employees and are expected to have a wider-reaching network of industry contacts that allows them to draw upon a broader base of knowledge and personnel networks (Eisenhardt & Schoonhoven, 1996; Stuart, 1998) we controlled for firm size measured as the number of employees within each year of observation. Regarding the type of biotech firm we differentiated the firms in our sample referring to their focused application of biotechnology. That is we controlled for firms that predominantly focus on medical applications (‘‘red biotechnology’’/ type 1), agricultural applications (‘‘green biotechnology’’/ type 2), and industrial or environmental applications (‘‘white or gray biotechnology’’/ type 3), and firms that cannot be clearly categorized into a single category due to a broader application field of biotechnological methods, processes, and products. We differentiated these firms referring to their communicated main business area. Thus, we control for differences in firm specialization and business models that may impact a firm’s propensity to innovate and more specifically to patent.

Event History Model In reference to Amburgey (1986), we modeled the patent application rate l(t) as a stochastic point process. The rate was specified as an exponential function of the independent variables. This function is defined as lðtÞ ¼ expðbX t Þ. We also tested alternative functions, like a Weibull function, and we obtained the same results. All parameters were estimated using maximum likelihood with STATA 12 program. We clustered our estimation procedure by firm to decrease the impact of unobserved firm-specific effects (White, 1982). To evaluate the significance level of our variables in our model we used the t rations. We used likelihood ratio statistics to evaluate the overall goodness of fit of the different models (Al-Laham et al., 2008, p. 353). We use three models to evaluate our hypotheses. Model 1 is the baseline model that provides parameter estimates for the control variables only. Model 2 adds parameter estimates for the variables constituting regional network density and alliance portfolio configuration in terms of partner diversity and network status. Our final Model 3 introduces the pairwise interaction terms between regional network density and alliance portfolio diversity as well as network centrality. We interpret our findings based on our final and full Model 3.

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RESULTS Table 1 reports descriptive statistics including means, standard deviations, minimum and maximum values for all variables used in our analysis and the correlation coefficients for our variables. Most variables have a relatively low correlation coefficient, but most of them are significantly correlated with one another. This is due to the relatively large number of observation spells use for the correlations. Table 2 provides the results of our event history analysis to explain innovation performance operationalized as patent application event. The likelihood ratio tests that compares the different model with the baseline model by a random process is shown in the last row of each model. We evaluate our hypotheses on the full model (Model 3). In all our models positive values of the parameters reflect the probability that as the covariate increases, the rate of patent application also increases. Negative values of the covariates show that the rate decreases with an increase in the value of the covariates. The size of the covariate reflects the extent of the effect from covariate on dependent variable. Within an exponential model, the effect of a covariate can be interpreted as the percentage change in the rate based on the assumption that only one covariate alters its estimation (Al-Laham et al., 2008, p. 353; Blossfeld, Golsch, & Rohwer, 2007, p. 99). For example, in our results in Table 2 (Model 3) the covariate of age is 0.0229 which means that each additional year increases the probability that a new patent will be launched by about (exp(0.0229)1) 100%=2.3%. However, overall, the effects of the variables are related multiplicatively and are not independent of each other. For example, a simultaneous change in an increase in network density by 5 ties and in age by one year increases the patent application rate by about (exp(0.0278)^5 exp(0.0229) ^11) 100%=17.5%. Model 3 shows that the linear effect of regional network density is highly significant and positive (b=0.0278,  po0.001), while the squared term is highly significant and negative (b=0.0001,  po0.001) indicating an inverted U-shaped relationship, as suggested in Hypothesis 1. Thus, the positive effect of regional network density on the patent application rate diminishes with an increase in regional network density. While describing our results, it is important to note that our time scale (e.g., for alliance formation or patent application) is the calendar day. This leads to some small coefficient numbers and this should not be interpreted as an indicator of the magnitude of the effect but more a result of this time setting. Hypothesis 2, claiming an inverted U-shaped relation between alliance portfolio diversity and innovation performance is also corroborated by our

Quarter 92.968 Age of firm 12.750 No. of employees 38.835 Biotech type 0.788 Regional 3.887 network density Partner diversity 2.013 Partner status 0.433 56.36718 Regional network density  partner diversity 261.7505 Regional network density  partner status

Mean 123 37 3,041 3 232

Max 1 0.0721 0.0613 0.0216 0.0917

1

1 0.2094 0.4389 0.0193

2

1 0.0248

4

1

5

6

7

1

8

9

0.2463 0.503 1

0.377 0.0496 0.2134 1 0.0244 0.0057 0.0671 0.002 1 0.0517 0.018 0.5337 0.4257 0.0731

1 0.0662 0.0244

3

68579.82 0.0409 0.0111 0.0214 0.0128 0.4455 0.1744

0 155.889 0.1266 0.0996 694.693 3524.297 0.0363 0.0006 0 31529.33 0.0549 0.0301

60 0 1 0 0

Min

5001.476 157608.9

8.993 155.247 739.43

17.439 5.751 149.447 0.696 25.297

Std. Dev

Sample Descriptive Statistics and Correlation Matrix.

Note: All correlations were significant at po0.05 (based on 73,636 spells).

9

6 7 8

1 2 3 4 5

Variable

Table 1.

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Table 2.

Maximum Likelihood Estimates of Covariate Effects on Innovation Performance. Model 1

Model 2

Model 3

0.0456 (0.0006) 0.0339 (0.002) 0.0015 (0.0000)

0.0382 (0.0006) 0.0232 (0.0023) 0.0004 (0.0000)

0.0380 (0.0006) 0.0229 (0.0023) 0.0004 (0.0000)

0.4591 (0.0301) 0.3692 (0.0559) 0.4479 (0.1062)

0.2549 (0.0313) 0.4304 (0.0562) 0.3250 (0.1064)

0.2544 (0.0313) 0.4311 (0.0562) 0.3186 (0.1065)

(Regional network density)2

0.0256 (0.0014) 0.0001

0.0278 (0.0014) 0.0001

Partner diversity

(0.0000) 0.1386

(0.0000) 0.1382

(0.0033) 0.0013 (0.0001) 0.0002 (0.0000)

Control variables Quarter Age of firm No. of employees Biotech type Biotech type (red) Biotech type (green) Biotech type (gray) Main variables Regional network density

6.9100 (0.0669) 8186.13 6

6.1625 (0.0694) 11587.69 11

(0.0033) 0.0013 (0.0001) 0.0003 (0.0000) 0.0001 (0.0000) 0.0000 (0.0000) 6.1422 (0.0695) 11636.6 13

po0.001 73,636 1,233 6,416

po0.001 73,636 1,233 6,416

po0.001 73,636 1,233 6,416

(Partner diversity)2 Partner status (overall network) Regional network density  partner diversity Regional network density  partner status Constant Chi2 Degree of freedom Probability value Number of observation spells Number of firms Number of events (patents applications)

Note: Coefficient/(standard error);  po0.05;  po0.01;  po0.001.

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data. The linear effect of alliance portfolio diversity is positive as expected and highly significant (b=0.1382,  po0.001), while the squared term accounting for decreasing benefits of alliance portfolio diversity is negative and significant (b=0.0013,  po0.001). According to Hypothesis 3 we expect that innovative performance is a linear function of network centrality. Our results lend support to our reasoning. The sign for partner status is positive and significant (b=0.0003,  po0.001) Hypotheses 4a and 4b assumed a negative moderating effect of alliance portfolio diversity and partner status on the relationship between regional network density and new venture innovation performance. Based on the empirical evidence of our data, we can again confirm our hypotheses. The interaction term of alliance portfolio diversity and regional network density is negative and significant (b=0.0001,  po0.001), thus supporting Hypothesis 4a. Similar findings can be reported for the moderating effect of network status on the impact of regional network density on innovation performance. Again, the interaction term is negative and significant (b=0.0000,  po0.001). With regard to the control variables, our results show that the control variables included in our model are similar to those in the baseline model. Quarter, age, and number of employees have a positive significant effect on innovation performance. With regard to the different biotech types, we find that, compared to the largest category (various), being a red or green biotech firm increases innovation performance, while being a gray biotech firm which basically deals with industrial or environmental applications, decreases firm level innovation performance in terms of patenting.

DISCUSSION AND CONCLUSION The main contribution of this study lies in the investigation of uncovering innovation performance while linking regional network embeddedness, alliance portfolio configuration, and innovation performance. Although network embeddedness, alliance portfolio configuration, and network position have gained importance in the management literature (Hoffmann, 2007; Jiang, Tao, & Santoro, 2010; Lavie, 2007; Powell et al., 1996; Vassolo, Anand, & Folta, 2004; Wassmer & Dussauge, 2012), research simultaneously considering the impact and interaction of different levels of interconnectedness regarding new venture performance is only recently emerging (e.g., Gilsing et al., 2008; Whittington et al., 2009). As such, research oftentimes implicitly assumes that firms can readily assimilate incoming external

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knowledge from a diverse set of sources. However, there are also restrictions with regard to accessing and assimilating knowledge for innovation. In fact, our study shows that there is support for the conventional arguments that firms benefit from being co-located and in particular from a dense regional network. According to Whittington et al. (2009, pp. 92–93) there a three closely related factors that can explain why proximity has a positive impact on innovation performance. First, in a dense regional network, the flow of local scientific knowledge leads to an increase in innovation performance (see also Audretsch & Stephan, 1996). Second, through impersonal social networks these scientists are closely connected which also increases knowledge spillovers. Finally, labor mobility within dense regional clusters is another important aspect that facilitates knowledge exchange and thus leads to a higher innovation performance (Casper, 2007; Fleming, King, & Juda, 2007). However, as soon as we control for more formal ties, we find that the positive effect of regional network density disappears. In this sense, our study highlights important contingencies between regional network embeddedness and overall network engagement. Being regionally close can be a source of advantage, but these effects depend on the alliance portfolio configuration of a firm. Behavioral theory, a theoretical antecedent of the knowledge-based view, assumes that individuals are limited in their cognitive capacity (Cyert & March, 1963; March & Simon, 1958). This means that individuals have difficulties in assimilating, accumulating, and applying diverse knowledge (Simon, 1955). This is due to constraints with regard to neurophysiology and language (Williamson, 1975). The first refers to the fact that individuals are limited to a certain amount and scope of knowledge that they can receive, store, and process without errors. The second points to the phenomenon that individuals are unable to articulate all of the knowledge they dispose of (Williamson, 1975). As a result, individuals specialize on certain knowledge tasks which are integrated during the production process (Grant, 1996). Analogously, as individuals constitute firms (Chandler & Hanks, 1994), firms have a limited cognitive capacity and specialize on knowledge tasks within the industry value chain. We assume that due to their liabilities of newness and smallness new ventures suffer from a more limited cognitive capacity as opposed to more mature firms (Carroll & Hannan, 2000; Stinchcombe, 1965; Wiklund, Baker, & Shepherd, 2010). Hence, they are more limited in their ability to assimilate and apply extensive and diverse knowledge bases than mature firms. In particular, with regard to alliance portfolio partner type diversity, our study highlights that a greater variety of partners increases firm-level

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innovation output only in the beginning. After a certain point, the effect turns negative which illustrates that firms that have a higher partner type diversity have a lower innovation performance. These findings are in line with previous research that suggested that firms have difficulties to assimilate the knowledge inflow of relatively heterogeneous partner types and to transform this inflow into innovation success. With regard to alliance partner network status, our study also confirms prior research while showing a positive impact. Overall, our research implicates that firms need to have a closer look on the interaction between physical co-location of firms and alliance portfolio configuration.

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ABOUT THE EDITORS Barak S. Aharonson is an assistant professor of strategy, innovation, and entrepreneurship at Tel Aviv University. He joined the strategy faculty of Recanati Business School at Tel-Aviv University in 2011. Before joining TelAviv University, he served on the management faculty at Stern School of Business at NYU and at Olin Business School at Washington University in St. Louis. He received his Ph.D. from Rotman School of Management at the University of Toronto. The main focus of his research is on patterns of competition and cooperation among firms, and their influence on a firm’s behavior. His projects examine a firm’s competitive versus cooperative behavior and knowledge diffusion in networks, geographic agglomerations, and technological space. Uriel Stettner is an assistant professor of strategy, innovation, and entrepreneurship at Tel Aviv University. He obtained his Ph.D. from Tel Aviv University and completed his postdoctoral research at the Technion – Israel Institute of Technology. His research interests include the performance implications of organizational boundary choices, strategic innovation, technological innovation and management, as well as organizational knowledge creation and appropriation. He has published his research in high quality outlets such as the Academy of Management Annals. In addition to his extensive research activities, he is a frequent reviewer for many scholarly journals including Organization Science, Strategic Entrepreneurship Journal, Journal of Management Studies, Journal of Business Venturing, and Organization Studies. He has had extensive experience in several startup firms operating in the software and semiconductor industries and held a variety of managerial and technology focused positions in both Israel and the United States. Terry L. Amburgry is a professor of strategic management at the Rotman School of Management. His research is focused on the dynamics of interorganizational networks and organizational evolution with a particular focus on biotechnology. Terry is coconvenor of the Standing Working Group on organizational network research in The European Group for

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Organizational Studies. His research has been published in a variety of outlets including Administrative Science Quarterly, Academy of Management Journal, Strategic Management Journal, and Strategic Organization and Advances in Strategic Management. Shmuel Ellis is a professor of organizational behavior and management, the faculty of management, Tel Aviv University. His research interests include genealogical evolution of industries, evolution of social networks in new industries, entrepreneurship, organizational learning with particular emphasis on drawing lessons from successes versus failure, legitimacy, and cultural management. He has been published books and in journals such as Journal of Applied Psychology, Human Relations, Organization Science, Public Opinion Quarterly, Leadership Quarterly, IEEE Transactions on Engineering Management, and Human Resource Management, among others. Israel Drori is a professor of management, School of Business, College of Management Academic Studies and visiting professor, the faculty of management, Tel Aviv University and the Ross School of Business, University of Michigan. Professor Drori received his Ph.D. from UCLA. His research interests include genealogical evolution of industries, transnational and high-tech entrepreneurship, and organizational ethnography with particular emphasis on culture, trust, identity, legitimacy, cross cultural management, and organization of work. He has been published seven books and in journals such as American Sociological Review, Organization Science, Organizational Studies, Public Administration Review, and Human Resource Management, among others.

ABOUT THE AUTHORS Leonid Bakman, Ph.D., is the founder and executive director of the Israel Science Technology and Innovation Policy Institute; entrepreneur and business developer of Seed-Stage High-Tech Ventures; consultant in STI strategic development and policy design to the government of Israel. Suleika Bort is an assistant professor at the chair of strategic and international management at the University of Mannheim, Germany. Her current research interests include the emergence and evolution of networks and institutions, the diffusion of management knowledge and innovations, and organizational learning. She has published articles in journals such as Organization Studies, the British Journal of Management, and Schmalenbach Business Review. Gino Cattani is currently an associate professor of strategy and organizations at the Stern School of Business, New York University. He received an MA in management science and applied economics from the Wharton School at the University of Pennsylvania in 2001 and a Ph.D. in management from Wharton in August 2004. His research focuses the social-structural determinants of creativity and innovation, firm heterogeneity, and micro-determinants of industry dynamics. His research has been published in the Administrative Science Quarterly, Industrial and Corporate Change, and Organization Science. He is a member of the editorial board of Strategic Management Journal and Strategic Organization. David L. Deeds is the Schulze professor of entrepreneurship at The University of St. Thomas and director of The Morrison Center. Prior to coming to St. Thomas, he was a professor at The School of Management at The University of Texas at Dallas and academic director of the Institute for Innovation and Entrepreneurship. He has held faculty positions at The Weatherhead School of Management at Case Western Reserve University and The Fox School of Business at Temple University. During his career, he has published numerous articles in management and entrepreneurship journals including Journal of Business Venturing, Strategic Management Journal, Journal of Management, Research Policy and Entrepreneurship 259

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Theory & Practice, among others. He was awarded the NASDAQ fellowship in capital formation and was a named research fellow at Stockholm School of Entrepreneurship. In 2007, he was awarded the Haniel fellowship in entrepreneurship at Humboldt University in Berlin, Germany. His current research interests include new venture growth and adaptation, technological discontinuities, and technology commercialization. Prior to becoming an academic, he was the cofounder and president of LightSpeed Corporation. Irem Demirkan is an assistant professor of entrepreneurship at Suffolk University, Sawyer Business School in Boston, MA. Before joining Sawyer Business School, she held a faculty position in international business and strategic management at Northeastern University, College of Business Administration. In 2007, She received her Ph.D. from The School of Management at The University of Texas at Dallas in Richardson, TX. She has published articles in prestigious management journals including Management Science and Journal of Management. Her current research interests include interorganizational relationships, entrepreneurship, strategy, innovation, and exploration, and exploitation framework. Noam Frank is in the final steps of acquiring his master’s degree from the Hebrew University of Jerusalem. Currently he has finished writing his thesis on the effects of organizational prestige on interorganizational collaboration, in the Israeli high-tech industry. He is a research assistant to Professor Amalya L. Oliver and is the research coordinator at the Israel Institute for advanced studies. Niron Hashai is the head of the strategy and entrepreneurship area at the Hebrew University School of Business Administration in Israel. He obtained his B.Sc. in computer sciences from the Technion and his MBA and Ph.D. from Tel Aviv University. His research interests include theory of the multinational corporation, technological innovation and internationalization, growth patterns of small high technology firms, and the relationships between internationalization, product diversification, and performance. His research was published in top international business and innovation journals. He also serves on the boards of the Journal of International Business Studies and the Journal of International Management. He has also taught at New York University and the University of Bradford among other institutions. He is the cofounder and coorganizer of the Israel Strategy Conference (ISC) and was the Dunning Research Fellow for 2011–2012.

About the Authors

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Nandini Lahiri is an assistant professor at the Fox School of Business at Temple University. Her research focuses on R&D, knowledge diffusion, and firm boundaries. She examines these issues across a variety of sectors. She received her Ph.D. from the Ross School of Business at the University of Michigan in Ann Arbor, MI. Prior to Temple University, she was on the faculty at the Kenan-Flagler Business School of the University of North Carolina at Chapel Hill. Atul Nerkar is the Jeffrey A. Allred distinguished scholar and professor of strategy and entrepreneurship at the University of North Carolina at Chapel Hill’s Kenan-Flagler Business School. His research and teaching interests are in the area of strategy with specific emphasis on technology, innovation, and entrepreneurship. He examines these issues in a variety of sectors that include manufacturing electronics, chemicals, biotechnology, and pharmaceuticals. He received his Ph.D. from the Wharton School of the University of Pennsylvania. Prior to moving to UNC Chapel Hill, he was on the faculty of the Graduate School of Business at Columbia University. Marie Oehme is a Ph.D. student in strategic and international management at the University of Mannheim, Germany and has been working as a research associate at the University of Mannheim, Germany. She holds a diploma in business administration from the University of Cologne. Her research interests include interorganizational networks and new venture internationalization. Amalya L. Oliver is a George S. Wise chair in sociology at the Hebrew University of Jerusalem. She received her Ph.D. from UCLA. Her research interest span networks and interorganization collaboration, university– industry technology transfer, organizational learning in consortia, and scientific entrepreneurship. Currently she is conducting a large scale study on scientific misconduct. Daniele Rotolo is a research fellow at the SPRU–Science and Technology Policy Research (University of Sussex). He is a management engineer (magna cum laude) by training and received his Ph.D. (European doctorate) in innovation management and product development from Scuola Interpolitecnica di Dottorato (2011). He was a visiting researcher at University College London (UCL) in 2009–2010 and Stern Business School (New York University) in late 2010. He is currently a newsletter editor of the Technology and Innovation Management (TIM) division of the Academy

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of Management. His research interests lie in the network macro- and microdynamics featuring in the process of emergence of novel science and technology domains. Daniel Tzabbar is an assistant professor of strategy and Entrepreneurship, Drexel University. He received his Ph.D. from the Rotman School of Management, University of Toronto. His research focuses on creating and testing organizational and strategic theories related to the flow of inter and intra-organizational knowledge and the facilitation of learning and technological change. Alex Vestal is an assistant professor of technology management, College of Business, Oregon State University. He received his Ph.D. in management from the University of Central Florida in 2011. Focused on technology and innovation, his research interests include interfirm networks and learning, firm partnerships with industry and academia in high-tech clusters, and identity construction in geographic clusters. Florian Zock is an assistant to the President of the managing board (CEO) of TRUMPF headquartered close to Stuttgart, Germany. Before he joined TRUMPF, he was a Ph.D. student at the chair of strategic and international management at the University of Mannheim, Germany. He completed his Ph.D. in 2012. His dissertation dealt with firms’ strategic restructuring – in particular alliance portfolio configuration, business model design, and dynamic capabilities – as means to enhance their performance.