Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age [1 ed.] 9780857242006, 9780857241993

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SPANNING BOUNDARIES AND DISCIPLINES: UNIVERSITY TECHNOLOGY COMMERCIALIZATION IN THE IDEA AGE

ADVANCES IN THE STUDY OF ENTREPRENEURSHIP, INNOVATION AND ECONOMIC GROWTH Series Editor: Gary D. Libecap Previous Volumes: Volume 12: Volume 13: Volume 14: Volume 15: Volume 16: Volume 17:

Volume 18:

Entrepreneurship and Economic Growth in the American Economy, Gary D. Libecap Entrepreneurial Inputs and Outcomes, Gary D. Libecap Issues in Entrepreneurship, Gary D. Libecap Intellectual Property and Entrepreneurship, Gary D. Libecap University Entrepreneurship and Technology Transfer, Gary D. Libecap The Cyclic Nature of Innovation: Connecting Hard Sciences with Soft Values, Guus Berkhout, Patrick van der Duin, Dap Hartmann and Roland Ortt

Technological Innovation: Generating Economic Results, Gary D. Libecap and Marie Thursby Volume 19: Measuring the Social Value of Innovation: A Link in the University Technology Transfer and Entrepreneurship Equation, Gary D. Libecap Volume 20: Frontiers in Eco-Entrepreneurship Research, Gary D. Libecap

ADVANCES IN THE STUDY OF ENTREPRENEURSHIP, INNOVATION AND ECONOMIC GROWTH VOLUME 21

SPANNING BOUNDARIES AND DISCIPLINES: UNIVERSITY TECHNOLOGY COMMERCIALIZATION IN THE IDEA AGE EDITED BY

GARY D. LIBECAP University of California, Santa Barbara, CA, USA

MARIE THURSBY Georgia Institute of Technology, Atlanta, GA, USA

SHERRY HOSKINSON The University of Arizona, Tucson, AZ, USA

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

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2010 Copyright r 2010 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. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-85724-199-3 ISSN: 1048-4736 (Series)

Emerald Group Publishing Limited, Howard House, Environmental Management System has been certified by ISOQAR to ISO 14001:2004 standards Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print

CONTENTS LIST OF CONTRIBUTORS

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INTRODUCTION

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DIFFERENT YOKES FOR DIFFERENT FOLKS: INDIVIDUAL PREFERENCES, INSTITUTIONAL LOGICS, AND THE COMMERCIALIZATION OF ACADEMIC RESEARCH Riccardo Fini and Nicola Lacetera THE POLITICS OF NEGLECT: PATH SELECTION AND DEVELOPMENT IN NANOTECHNOLOGY INNOVATION Michael Lounsbury, Tyler Wry and P. Devereaux Jennings

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SCIENTISTS BEHAVING BADLY? CONFLICTS IN MULTIDISCIPLINARY COMMERCIALIZATION PROJECT TEAMS Angus I. Kingon, Ted Baker and Roger Debo

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THE EVOLUTION OF TEAM PROCESSES IN COMMERCIALIZING HIGH-TECH PRODUCTS Leslie H. Vincent

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THE ORGANIZATIONAL WORKSHOP: A CONCEPTUAL EXPLORATION OF THE BOUNDARY SPANNING ROLE OF UNIVERSITY ENTREPRENEURSHIP AND INNOVATION CENTERS Matthew M. Mars and Sherry Hoskinson v

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DIFFERENT STROKES FOR DIFFERENT FOLKS: UNIVERSITY PROGRAMS THAT ENABLE DIVERSE CAREER CHOICES OF YOUNG SCIENTISTS Rajshree Agarwal and Steven Sonka

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SCIENCE AND TECHNOLOGY ENTREPRENEURSHIP FOR GREATER SOCIETAL BENEFIT: IDEAS FOR CURRICULAR INNOVATION Lee Fleming, Woodward Yang and John Golden

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NAVIGATING THE ISSUES OF MULTIDISCIPLINARY STUDENT TEAMS SERVING UNIVERSITY SPIN-OFFS Sean M. O’Connor

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LIST OF CONTRIBUTORS Rajshree Agarwal

John Georges Professor of Technology Management and Strategy, Director of Innovation and Technology Management Initiatives, University of Illinois, Champaign, IL, USA

Ted Baker

Faculty Lead for Technology Entrepreneurship and Commercialization (TEC) Program, Associate Professor, Department of Management, Innovation and Entrepreneurship, North Carolina State University, Raleigh, NC, USA

Roger Debo

Director of Technology Entrepreneurship and Commercialization (TEC) Program, Department of Management, Innovation and Entrepreneurship, North Carolina State University, Raleigh, NC, USA

Riccardo Fini

Marie Curie Research Fellow, Imperial College Business School, London, UK, and Researcher, Free University of Bozen, Bozen-Bolzano, Italy

Lee Fleming

Albert J. Weatherhead III Professor of Business Administration, Harvard Business School, Boston, MA, USA

John Golden

2008 Visiting Scholar to Harvard Business School, Assistant Professor, University of Texas Law School, Austin, TX, USA

Sherry Hoskinson

Director of McGuire Center for Entrepreneurship, Co-director of Business/ Law Exchange, The University of Arizona, Tucson, AZ, USA vii

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P. Devereaux Jennings Associate Editor of Journal of Business Venturing, Francis Winspear Professor of Business, University of Alberta, Edmonton, Alta, Canada Angus I. Kingon

Director of Commerce, Organization and Entrepreneurship (COE) Program, Barrett Hazeline University Professor of Entrepreneurship and Organizational Studies, Professor of Engineering, Brown University, Providence, RI, USA

Nicola Lacetera

Assistant Professor of Strategy, Rotman School, University of Toronto, ON, Canada

Michael Lounsbury

Principal Investigator for National Institute for Nanotechnology, Director of Technology Commercialization Centre, Alex Hamilton Professor of Business, Strategic Management and Organization, University of Alberta, Edmonton, Alta, Canada

Matthew M. Mars

Lecturer and Research Liaison, McGuire Center for Entrepreneurship, Eller College of Management, University of Arizona, Tucson, AZ, USA

Sean M. O’Connor

Faculty Director, Entrepreneurial Law Clinic, Professor of Law, University of Washington Law School, Seattle, WA, USA

Steven Sonka

Professor, Interim Vice Chancellor, Public Engagement, University of Illinois, Champaign, IL, USA

Marie Thursby

Hal and John Smith Chair of Entrepreneurship, College of Management, Georgia Institute of Technology, Atlanta, GA, USA

Leslie H. Vincent

Assistant Professor of Marketing, Gatton College of Business and Economics, University of Kentucky, Lexington, KY, USA

List of Contributors

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Tyler Wry

SSHRC Doctoral Fellow, National Institute for Nanotechnology, and School of Business, University of Alberta, Edmonton, Alta, Canada

Woodward Yang

Gordon McKay Professor of Electrical Engineering and Computer Science, HBS University Fellow, Harvard Business School, Boston, MA, USA

INTRODUCTION Successful technology commercialization requires the integration of multiple perspectives and collaboration of experts from very different backgrounds. More often than not, key individuals in the process reside in different organizational units – each with their own mission, agenda, and culture. In large corporations, successful commercialization ultimately depends on coordination of marketing, legal, and research and development (R&D) personnel distributed across the firm. And, as innovation systems become more open, large and small companies alike increasingly collaborate with nonprofit institutions, either for technological expertise or as a source of inventions themselves (Chesbrough, 2003; Thursby, Thursby, & Fuller, 2009). This volume addresses challenges that often arise when individuals from technical, business, and legal environments must converge on the goal of commercialization. Specifically, studies on issues central to the commercialization of university technologies are presented from the perspectives of organizational behavior, marketing, economics, and sociology. The first four papers discuss recent research on cross-cultural aspects of commercialization in the university environment, while the last four focus on educational responses to the issues that arise. The papers were presented in a faculty development workshop on graduate education for professionals in innovationrelated careers, ‘‘Technology Commercialization: Crossing Cultures and Disciplines’’, which was held February, 2010, in the College of Management of the Georgia Institute of Technology, with generous support from the Ewing Marion Kauffman Foundation. Volume authors explore sometimes common, other times unique, and often overlapping problems and together initiate new lines of inquiry that correlate to challenges being explored in fellow author institutions—thus cultivating momentum for enhanced solution discovery. In Chapter 1, ‘‘Different Yokes for Different Folks: Individual Preferences, Institutional Logics, and the Commercialization of Academic Research,’’ Riccardo Fini and Nicola Lacetera explain a number of anomalies in academic entrepreneurship in terms of economic models that incorporate the preferences of academic scientists toward research. As compared with scientists who choose to work in industry, academics care not only about the monetary rewards of their work but also about the extent to which it xi

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contributes to their reputation and recognition among their peers. For such scientists, the opportunity cost of spending time developing discoveries for commercial markets reflects the extent to which they are diverted from other research projects. Thus, academic entrepreneurs will only engage starting companies for inventions with a relatively high expected value. This explains the anomaly that academic startups tend to be more successful than others, and, in fact, academic entrepreneurs may commercialize inventions sooner than their counterparts in industry. This difference in academic and industrial objectives also helps to explain the types of projects that companies delegate for sponsored research projects. In essence, since academics tend to take on projects only if they have considerable decision power over the direction of research, firms can take advantage of this as a way of committing to carry out projects with higher scientific value. In Chapter 2, ‘‘The Politics of Neglect: Path Selection and Development in Nanotechnology Innovation,’’ Michael Lounsbury, Tyler Wry, and P. Devereaux Jennings take a sociological approach to explain the emergence of technological paths by examining the role of star scientists in catalyzing the work of various actors in the development of carbon nanotubes. The authors hypothesize that technology paths often emerge, not because of the inherent efficiency of the underlying technology, but rather what they call benign political neglect of alternative paths as a result of herding behavior toward particular technology paths. They then demonstrate the use of patent data, and in particular, cross-citation of patents in different categories, as a tool to describe the development of knowledge trajectories. Early on, patent activity may be disperse, but as early contributors to a category demonstrate opportunities for the development of the technology that are not obvious, they influence others so that a particular path gains momentum. Patent categories that build on patents in the original category(ies) form a cluster of activity, and technologies within these categories exhibit a centrality that is positively related to patent creation. Eventually, these categories become saturated, so that their patent density is curvilinear in patent creation. As applied to nanotechnology, there was prolific inorganic nanotube patenting and cross-citation in a couple of classes (204 and 423), whereas patents in organic and polymer classes developed no such relational network on which to build. The authors attribute much of this pattern to the influence of Richard Smalley, who discovered Carbon 60, earning him a Nobel Prize in 1996, and who also helped structure the National Nanotechnology Initiative. Chapter 3, ‘‘Scientists Behaving Badly? Conflicts in Multidisciplinary Commercialization Project Teams’’ by Angus I. Kingon, Ted Baker, and Roger Debo addresses the behavioral problems and conflicts observed in

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multidisciplinary university commercialization teams. The chapter examines 59 commercialization projects undertaken by students as part of a multidisciplinary program on technology commercialization a major Research I university in the United States. The program, described in detail by Barr, Baker, Markham, and Kingon (2009), embeds real commercial projects from the university system in a two-semester sequence on Technology Entrepreneurship and Commercialization (TEC). The authors apply well-established ideas from the organizational behavior literature to understand patterns of selective perception and issue prioritization within the teams. Both business and STEM graduate students participated in teams advised by TEC faculty and coordinating with scientists contributing the technologies. Thus, team members came from multiple thought worlds and, as with many university commercialization projects, faced the need to adopt new cognitive processes in order for their projects to progress. In the process, conflicts often emerged as a result of cognitive differences interacting with motivational differences among team members. Interestingly, the authors find that most of the conflicts that arose involved the scientists collaborating with the teams, with the most unmanageable conflicts occurring with senior scientists. The chapter draws tentative conclusions regarding improved management practices aimed at managing such conflicts and improving university commercialization initiatives. Chapter 4, ‘‘The Evolution of Team Processes in Commercializing HighTech Products’’ by Leslie H. Vincent, provides a dynamic perspective on the impact of team processes, such as conflict, identification, and cohesion, on the effectiveness of functionally diverse teams. The underlying research is based on surveys of twenty teams during their participation in the Technological Innovation: Generating Economic Results (TI:GERs) program (Libecap & Thursby, 2008; Thursby et al., 2009). In this program, teams were comprised of business and law students along with PhD students in science or engineering. The research reveals that social processes, in particular cohesion and trust, were key drivers of objective team performance. High- and low-performing teams differed significantly with respect to task-focused interaction over time, as well as functional conflict. Although high- and low-functioning teams started with similar task-focused interaction as they strove to understand the technology, over time only the high-performing teams continued to communicate and discuss tasks. Furthermore, high-performing teams increased in their trust and task cohesion over time as compared with low-performing teams. In lowperforming teams, feedback associated with poor performance tended to undermine trust, ultimately leading to the breakdown of processes.

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In Chapter 5, ‘‘The Organizational Workshop: A Conceptual Exploration of the Boundary Spanning Role of University Entrepreneurship and Innovation Centers,’’ Matthew M. Mars and Sherry Hoskinson explore the organizational boundary-spanning role of university entrepreneurship and innovation centers in facilitating and mediating the interorganizational transactions that most often underpin academic entrepreneurship. Located outside of department boundaries, and typically populated by individuals with knowledge of different logics of stakeholders in academic entrepreneurship as well as experience in forming and managing entrepreneurial partnerships, such centers are uniquely placed to act as intermediaries in the process. Drawing on examples from the McGuire Center for Entrepreneurship at the University of Arizona, they discuss the role of centers in (1) managing the various agendas and expectations of stakeholders within and outside of the academy, (2) providing clarity of purpose to the entrepreneurial endeavor, (3) clarifying ownership rights throughout the entrepreneurial process, and (4) maximizing the potential of individuals to contribute to venture success. Examples include the creation of instruments for use in partnerships, such as memorandums of understanding and protocols for the evaluation of the social and ecological value propositions of new ventures, as well as offering a university-approved minor in entrepreneurship for PhD students in nonmanagement fields. In Chapter 6, ‘‘Different Strokes for Different Folks: University Programs that Enable Diverse Career Choices of Young Scientists,’’ Rajshree Agarwal and Steven Sonka draw on data from Scientists and Engineers Statistical Data System (SESTAT) from 1996 to 2006 to argue that many future scientists are underserved by traditional advanced degree programs, since they face future career choices that require skills beyond basic science tools. In particular, only 26 percent of scientists choose the traditional academic path, focusing on basic research, and almost two-thirds of these scientists combine basic with applied research. Over the period examined, many scientists switch from basic research to entirely applied research, often to industrial positions, including those in entrepreneurial ventures. These demographics support the notion that rather than a ‘‘one size fits all’’ approach to graduate education, academic institutions should strive to provide a portfolio of opportunities for students. The chapter discusses the benefits and costs of four program types, including certificates in entrepreneurship and management for nonbusiness majors, professional master’s programs, and experiential opportunities. Examples of such programs currently offered by the University of Illinois are presented.

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In Chapter 7, ‘‘Science and Technology Entrepreneurship for Greater Societal Benefit: Ideas for Curricular Innovation,’’ Lee Fleming, Woodward Yang, and John Golden discuss the goals and design of a new masters degree for scientists being considered at Harvard University, which responds to the nontraditional demand for scientists discussed in Chapter 6. The proposed program is a co-terminal masters’ degree in Entrepreneurial Science and Technology aimed at science and engineering undergraduates who might aspire to (1) individual or technology management positions in established organizations, (2) entrepreneurship in the public, private, or nonprofit sectors, or (3) graduate work in engineering or science or professional degrees, including business, medicine, law, or policy. The goal would be to give students concise but complete skill-sets in entrepreneurship and teamwork, and effective career networks across diverse professions. The authors propose that this can be done within an intense one-year curriculum, such that students would remain technically current (and possibly develop the application of their technical research during the degree). The chapter also discusses alternate and existing models for entrepreneurship education as compared to the proposed master’s degree. Chapter 8, ‘‘Navigating the Issues of Multidisciplinary Student Teams Serving University Spin-Offs,’’ by Sean O’Conner, focuses on the need for programs to train professionals such as lawyers and consultants, who provide services to entrepreneurial firms. It provides an overview of the Entrepreneurial Law Clinic at the University of Washington, which provides a multidisciplinary teaching, research, and service platform that assists university spin-offs while developing the next generation innovation ecosystem. The chapter revisits an important theme addressed from other perspectives in Chapters 3 and 5: that is, identifying who the client is in the university setting – the university that owns the technology or the faculty researcher whose technology is behind the spin-off. How the clinic mediates among the different visions for how to commercialize the technology via the spin-off is also addressed, as well as how to ensure that all the different students involved are being properly supervised and that all project members are keeping appropriate confidentiality toward the technology and business plans. The chapter shows that the conflicts and confusion that naturally arise for team projects often provide the best teaching moments for student team members, supervisors, and faculty alike. Taken together these chapters provide a rich array of approaches to understand the multiple goals, perspectives, types of expertise needed for successful commercialization of technological inventions. Although the

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approaches differ, they all affirm the fact that conflicts among the primary stakeholders are inevitable. Importantly, they provide much needed insights on ways to manage these conflicts, as well as examples of novel educational programs designed to produce new cohorts of professionals with multidisciplinary perspectives that might mitigate future conflicts.

REFERENCES Barr, S. H., Baker, T., Markham, S. K., & Kingon, A. I. (2009). Bridging the valley of death: Lessons learned from fourteen years of commercialization of technology education. Academy of Management Learning and Education, 8(3), 370–388. Chesbrough, H. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School. Libecap, G., & Thursby, M. (Eds). (2008). Technological innovation: Generating economic results, advances in the study of entrepreneurship, innovation, and economic growth. In: Advances in the study of entrepreneurship, innovation, and economic growth (Vol. 18). Oxford: JAI Press. Thursby, M., Thursby, J., & Fuller, A. (2009). An integrated approach to educating professionals for careers in innovation. Academy of Management Learning and Education, 8(3), 389–405.

Marie Thursby Editor

DIFFERENT YOKES FOR DIFFERENT FOLKS: INDIVIDUAL PREFERENCES, INSTITUTIONAL LOGICS, AND THE COMMERCIALIZATION OF ACADEMIC RESEARCH Riccardo Fini and Nicola Lacetera ABSTRACT In this chapter, we review the literature that analyzes how the peculiar missions, rules, and incentive systems in the scientific community affect the process and outcomes of the commercialization of academic research. We will focus on how the peculiar institutional logics of academia determine the decision of academics to commercialize their research, and how these logics affect the outsourcing of research from firms to academic laboratories, as well as the attempts of firms to reproduce academic incentive systems within their research labs by allowing their researchers to publish and offering them financial rewards based on their standing in the scientific community. Finally, we report on research that has analyzed how the rules of the scientific community might lead to the production, transfer, and commercialization of false knowledge. Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 1–25 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021004

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1. INTRODUCTION Over the past 30 years, a rich body of research has focused on knowledge commercialization activities by academic researchers. In addition to the traditional mandates of teaching and research, academic entrepreneurship has started to be considered a third mission in which academic institutions engage (Etzkowitz, 2004; Shane, 2004). The underlying rationale for this is to be rooted in the impact that university-based knowledge has on hightechnology sectors and, more generally, on the knowledge economy. Because academic knowledge may be hard to transfer, it becomes desirable to involve academic organizations and scientists directly in commercially oriented activities (Agrawal, 2006; Etzkowitz, 2004; Gibbons, Limoges, Nowotny, Schwartzman, & Scott, 1994; Stokes, 1997; Zucker & Darby, 1995). As a result, policymakers in both the United States and Europe have implemented legislation aimed at stimulating the involvement of universities in the commercialization of research (Geuna, Salter, & Steinmueller, 2003). Understanding how the process of commercialization of academic research operates and assessing its impact become relevant exercises for both public policy analysis and managerial considerations. Academic knowledge is transferred to industry and to the marketplace through many different channels including new business creation, university– industry collaborations, and the patenting and licensing of inventions (Rothaermel, Agung, & Jiang, 2007). During the past decade, the level of academic entrepreneurship has increased dramatically. In the United States, the number of new U.S. patent applications from academic institutions has risen from fewer than 3,000 in 1996 to more than 10,000 in 2006. The number of start-up companies that have been formed to commercialize university research has grown from fewer than 200 in 1996 to almost 500 in 2006, and the number of licenses and options executed by academic institutions has increased from slightly more than 2,000 in 1996 to slightly more than 4,000 in 2006. Finally, gross license income received by academic institutions has increased from less than $400 million in 1996 to more than $1.2 billion in 2006 (Association of University Technology Managers (AUTM), 2006). Although we have a clear picture of the degree of, and trends in, the involvement of academic organizations and researchers in commercially oriented activities, less is known about whether knowledge commercialization activities by universities and academic researchers can offer something that other actors (e.g., firms) cannot replicate. If the commercialization of research takes place in universities in the same way that it does in firms, there would be no reason to involve academic organizations in commercially oriented activities.

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A number of studies in Economics and Sociology have characterized academia – and, more generally, the scientific community – as institutions with their own set of missions, rules, and incentive systems. In particular, openness, peer recognition and evaluation, freedom of inquiry, and incentives based on a ‘‘winner-takes-all’’ journal publication system are the main rules that define the ‘‘institutional logic’’ (Thornton & Ocasio, 1999) of the scientific community. As a result, the individuals who are attracted to academic versus business organizations may vary according to their affinity for these different types of logic (Fini, 2010; Ronstadt, 1990). In this chapter, we review the literature that provides the theoretical underpinnings and empirical evidence for the peculiarities of ‘‘academic entrepreneurship.’’ In Section 2, we focus on business creation and ‘‘direct’’ commercialization by academics. We ask how the decision to undertake commercially relevant research and to commercialize its outcome differs between the academic and business environments. In Section 3, we review the literature and report examples of two other firms in which academic and industrial research and commercialization connect, and, again, focus on how the different types of institutional logic determine these relationships and their outcomes. First, we address the increasing corporate trend of outsourcing research projects to academic research teams. Second, we analyze the costs and benefits for firms that attempt to reproduce academic incentive systems within their research labs by allowing their researchers to publish and offering them financial rewards based on their standing in the scientific community, a practice that has become increasingly common, especially in research-intensive industries. In Section 4, we focus on a topic that is not typically discussed in the context of the commercialization of research: scientific fraud. Not all published scientific knowledge (and, by extension, not all of the knowledge that has been commercialized) is produced truthfully and honestly, and the potential social and economic consequences can be substantial. Therefore, we set out to analyze this phenomenon and to offer insights into how to detect and prevent it. Finally, Section 5 offers concluding remarks and speculates on directions for future research.

2. WHAT DISTINGUISHES ACADEMIC ENTREPRENEURS, AND WHY DOES ACADEMIC ENTREPRENEURSHIP MATTER? Although scholarly interest toward the commercialization of academic inventions is limited to the past 30 years, the history of the involvement of

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academics in research with high commercial potential is much longer, offering numerous examples of how the different types of logic that characterize academic and industrial environments have affected the way in which commercially relevant research has been performed, as well as its outcomes. The following three examples offer insights into how these different types of logic operate and their consequences. The discovery and subsequent commercial exploitation of the transistor in the late 1940s are attributed to Bell Labs, the research unit of AT&T, and, in particular, to the three scientists (Bardeen, Brattain, and Shockley) who led the project and shared the Nobel Prize in 1956. In the same years, parallel to the research performed at Bell Labs, a team at Purdue University led by Karl Lark-Horovitz (an authority in solid-state physics) was conducting very similar research. Just like their colleagues at Bell Labs, the academic scientists at Purdue University were also aware of the potential economic and social impacts of their research as well as the possibility of profiting from it.1 However, the two teams chose very different paths of research. On the one hand, scientists at Purdue University focused on single-disciplinary research paths with high pure scientific value and potential for publication but no immediate applicability. In particular, the scientists focused on the unique conducting properties of certain materials, such as the element germanium. Research was mostly driven by the willingness to contribute to the advancement of the field, rather than seeking to hit the market with a blockbuster technology or device. On the other hand, research at Bell Labs, although it had scientific content, was more multidisciplinary, and there was more intense communication between scientists with different backgrounds. It was also kept secret and could be diffused only several months after patent applications; for a period, Bardeen, Brattain, and Shockley even shared the same room (something that was deemed inconceivable in an academic setting). There also were clear priorities about the direction of the research, which had been decided and imposed by the top R&D management team. This gave the sense of a common, practical goal to be achieved (see, for example, Shockley, 1956; Nelson, 1962; Braun & Macdonald, 1978; Hoddeson, 1980; Bray, 1982, 1997). Contrary to an alleged uniqueness and diversity of the research at Bell Labs, if compared to other industrial settings and its supposed similarity to a university environment as claimed by many observers, a careful reading of the available accounts and a comparison to what was simultaneously (and independently) happening in a ‘real’ academic laboratory reveal that the organization and the rules of Bell Labs were not so different from what one would have expected from

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profit-seeking, economically focused agents, despite being significantly different from most of the contemporary academic settings. It is this ‘‘normality’’ in their research organization and objectives, rather than any kind of diversity and uniqueness, that explains the great success of Bell Labs and the anticipation of the discovery of the transistor by as much as a decade (according to Riordan & Hoddeson, 1997). Thirty years later, in the case of another major scientific advancement, the synthesis of human insulin, history seemed to repeat itself. Again, very similar patterns of research were performed in both industrial and academic environments, and, again, different approaches were chosen by different teams. It is important to mention that biological research with pharmacological relevance is typically multidisciplinary. Knowledge flowing from other disciplines, such as chemistry and physiology, will facilitate the translation of scientific knowledge into drugs. Alternatively, scientists may choose to explore biological properties through their single-disciplinary lenses. The research team at Genentech that engaged in insulin research in the 1970s comprised scientists from different backgrounds. It also adopted an approach to the synthesis of human insulin that was meant to reach the result faster but did not include major scientific novelties. In particular, they opted for less restrictive regulatory controls (i.e., synthetic DNA) with lower scientific content and novelty but shorter expected time to completion. Conversely, researchers at both Harvard and the University of California at San Francisco saw in the research on human insulin a potential for major scientific content, and they focused their activities on these novelties rather than on finding a fast method to reach the final result. In contrast to the private researchers, the academic teams originally focused on other living beings (e.g., rats) to be studied before humans, and chose a longer, more time-consuming path (e.g., cDNA cloning) with greater scientific content and expected novelty. Genentech eventually was able to patent, license, and profit from the discovery of the synthetic insulin (see Hall, 1987; Stern, 1995; McKelvey, 1996). Finally, the research efforts to map the human genome in the late 1990s again follow a similar pattern. Celera Genomics, using the ‘‘shotgun’’ sequencing advocated by Gene Meyers, completed a draft of the human genetic code in 2000. This procedure, which used random fragments rather than progressing through the entire DNA strand piece-by-piece, was faster than others. Employing this procedure, the company was able to close the time gap with the National Institutes of Health (NIH)-led consortium of academic and public sector researchers, achieving the simultaneous publication of the results by the two teams (Davies, 2001).

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Despite the similar quality of the researchers, the similar area of research, and the temporal simultaneity of their engagements, the academic and the industrial labs, in all of these cases, have followed different paths. Several characteristics of the academic paths are similar across the three cases – and so are the behaviors of the industrial labs – despite the differences in scientific disciplines and historical periods. The evidence from these historical cases appears consistent with recent large-sample evidence that compares the conduct and performance of companies that involve academic scientists and companies that do not – or even research that compares the attitudes and behaviors of single entrepreneurs with and without academic backgrounds. With regard to the attitudes and behaviors of individual entrepreneurs, Fini (2010) uses a survey instrument to compare two sets of entrepreneurs who have established their firms in high-technology industries. One set of entrepreneurs (the academic entrepreneurs) were affiliated with an academic institution whereas the others were not; the two sets were carefully matched on a number of observable characteristics. Academic entrepreneurs, when compared to non-academic entrepreneurs, were found to have specific idiosyncrasies. In particular, academic entrepreneurs had better educational profiles, were more active in patenting activities, and had established fewer companies compared to non-academic entrepreneurs. In terms of motivation to start a company, academic entrepreneurs were less interested than non-academic entrepreneurs in the additional independence and freedom of ‘‘being your own boss;’’ rather, their main objective was to commercialize the outcome of their own research. Finally, although the two sets of entrepreneurs did not show significant differences in their technical skills and confidence in their entrepreneurial abilities, academic entrepreneurs were significantly more risk averse than were non-academic entrepreneurs. The presence of systematic differences in the characteristics of the individuals who commercialize research from academic and non-academic backgrounds is paralleled by differences in the organization and performance of companies that opt to engage or not engage academics in their research activities. Argyres and Liebeskind (1998) document that several attempts by universities to spin out companies have been received with diffidence by private investors because the institutional and organizational arrangements were not deemed to be economically promising. A study by George, Zahra, and Wood (2002) finds that academic spin-offs tend to be more innovative but do not necessarily achieve greater financial performance than private start-ups. Lerner (2004) reports that academic organizations have

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encountered difficulties when they directly sponsored industrial research activities (see also Bok, 2003). Although Rothaermel and Thursby (2005) find that incubator firms with an active involvement of academicians have lower rates of failure, they also conclude that these firms take longer to be ‘‘promoted’’ (i.e., to exit from the incubator and become independent companies). Audretsch, Lehmann, and Warning (2005) examines the impact of a firm’s choice of location for the purpose of accessing knowledge spillovers from universities. The results suggest that the impact of university output on a new firm’s location is sensitive to both the type of knowledge and mechanism used to access that knowledge. Finally, Colombo and Piva (2010) argue that academic high-tech start-ups exhibit peculiar ‘‘genetic characteristics’’ that have an enduring imprint on firm development. In particular, they show some differences in the composition of the founding teams, the strategies that firms adopt to enlarge their initial competence endowment, and the growth patterns. All of this evidence is consistent with the idea that, in order to understand the entrepreneurial process undertaken by academics as well as its differences from the ‘‘standard’’ entrepreneurial process, we need to consider the different incentives, rules, and identities to which academics respond. Lacetera (2009a) proposes an economic model to consider these aspects. The model analyzes the behavior of academic entrepreneurs through a study of two key decisions: whether to undertake a commercial opportunity and the timing of commercialization. To identify the peculiarities of academic entrepreneurship, the outcomes obtained by an academic entrepreneur are compared to those of a non-academic (or industrial) entrepreneur who faces the same choices. The basic model has two stages; in each stage, a scientist (academic or industrial) chooses whether to perform additional research activities before moving to commercialization or to commercialize immediately. Additional research activities delay commercialization but reduce commercialization costs; thus, they have an investment value. The scientist can choose among different types of research that are more or less effective in reducing commercialization costs or that are more or less ‘‘applicable.’’ For example, according to a number of studies, pre-commercial research is more applicable if it is multidisciplinary (Rosenberg, 1994; Stern, 1995; Brewer, 1999; Llerena & Meyer-Krahmer, 2003; Rinia, van Leeuwen, van Vuren, & van Raan, 2001; Carayol & Thi, 2003; Boardman & Bozeman, 2004; Page, 2007). Both academic and non-academic entrepreneurs receive benefits from successful commercialization, but academic scientists, unlike industrial researchers, also derive direct benefit from the performance of research with

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no direct economic value (e.g., in the form of publications and peer recognition). The benefit, in turn, may depend on the type of research that is performed if some types of research are more consistent with the way the reward and recognition system works in the academic scientific community. For example, multidisciplinary research, while more instrumental in increasing the benefit of commercialization, may not be consistent with how the peer review system works because the academic reward and organizational systems are based on disciplines and departments. Academic entrepreneurs are, therefore, characterized as having multiple missions: they derive direct utility from the completion of a project and the monetary returns from its commercialization (just like industrial actors) as well as from the research activities that precede commercialization. Research has both an investment value and an immediate consumption value for academics who are interested in commercialization. This dual value of research, the presence of different types of pre-commercial research, and the differences in objectives and incentives in different institutional environments (universities and firms) are the key features of the model in explaining heterogeneity in the behaviors. A first result of the model offers an interpretation for the various cases in which academic researchers have been slower than industrial researcher in reaching commercializable outcomes or have not commercialized at all and have just ‘‘shelved’’ their discoveries. For a vast range of values of the model parameters, academic scientists are more reluctant to commercialize research because they find it too costly to abandon the research activities that generate the highest peer recognition in the scientific community. In addition, academic researchers will tend to forsake commercial projects with positive but low commercial value and will pursue the purely scientific alternative. By contrast, company scientists tend to be more willing to undertake these marginal projects with economic and potentially social value. Therefore, a self-selection mechanism is present; the observed success of academic entrepreneurs may, therefore, derive from the fact that, on average, university researchers move to commercialization only if the prospects are very good. This self-selection mechanism might explain evidence that suggests that the involvement of academics in the commercialization of their research often leads to positive outcomes (see, e.g., Zucker & Darby, 1995; Cockburn & Henderson, 1998; Shane, 2004; Rothaermel & Thursby, 2005; Toole & Czarnitzki, 2005; Agrawal, 2006). However, it also warns that a main driver of these findings might just be a selection effect rather than, for example, a differential capability effect.

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A somewhat counterintuitive outcome is also possible in the model: a university-affiliated scientist may opt to commercialize an invention sooner than scientists who are not university-affiliated. This would be the case if the ‘‘industrial scientist’’ found it profitable to engage in additional ‘‘applicable’’ research whereas the ‘‘university scientist’’ would be unable to justify the cost of continuing this line of research and/or if the directrecognition benefit of performing this additional research would not be significant. This result offers a potential explanation for the findings of Jensen and Thursby (2001) and Lowe (2002) that academic researchers tend to start their companies or license their findings very early (i.e., when some additional research may still need to be performed). Finally, the result that firms have incentives to do research is also consistent with the evidence of outstanding research performed in industrial labs through history. Interestingly, an exclusive orientation to economic profits might lead a company to fully appreciate the investment value of research whereas the concurrent existence of multiple motives may inhibit an academic scientist’s investment in research. These results, taken together, tell us that we might observe both success stories as well as missed opportunities of academic entrepreneurs. From a managerial standpoint, moreover, these results imply that attracting talented academic scientists may be very costly given the additional opportunity costs that would need to be covered. Scientific and commercial incentives, if juxtaposed in their ‘‘pure’’ form, may collide instead of reinforcing each other. From a university policy perspective, if the aim of promoting academic entrepreneurship is to increase both the scientific and the commercial value of research, then, in some cases, academicians are not the appropriate agents of such policy. Reforms of reward criteria for academic scientists and the promotion of multidisciplinary research, for example, may help to avoid premature commercialization and reach a balance between science and industry. David (2005) proposes ‘‘bridging institutions’’ with rules different from both the industrial and the academic environments. Bozeman (2002) shows that, in University Science and Technology (S&T) Centers, which are often funded by both public and private entities, scientists are rewarded according to somewhat different rules than those that prevail in purely academic laboratories. For example, peer review is not the only metric, and multidisciplinary work is encouraged. The development of these hybrid organizations, partially autonomous even if not totally separated from the academic environment, might represent a viable strategy for the promotion of science-based entrepreneurship. The benefits of these organizational and institutional changes, however, need to

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be weighed against a few potential costs. For example, it may be difficult for an academic organization to sustain different rules and incentive systems within its boundaries.

3. UNIVERSITY–INDUSTRY RELATIONS, WITHIN AND OUTSIDE FIRM BOUNDARIES The involvement of academic researchers and organizations in the commercialization of research activities can occur in a number of different ways; the direct undertaking of a business venture – as described in the previous section – is perhaps an extreme example. Most often, the engagement of academics in commercial activities occurs in conjunction with for-profit companies through the direct involvement of both parties in research activities. In the performance of research activities, university–industry relations can take several forms: research contracts on single projects, agreements for the funding of several projects, and the creation of university-based research centers funded by one or more firms. In particular, especially in research-intensive industries, firms tend to outsource research projects, collaborating with universities in carrying out more general-purpose research (Geiger, 2004; Mowery & Teece, 1996; Motohashi, 2004; National Science Foundation (NSF), 2002; Santoro & Chakrabarti, 2002). Also, some companies allow their scientists to spend some time in academic institutions or in research foundations where they can interact with their peers toward the development of research that may be of potential interest to the company. University–industry relations appear to be more frequent in those sectors in which basic research is understood to be closer to commercial applications and in such disciplines that are in the early phases of their evolution (Hall, Link, & Scott, 2000; Geiger, 2004). These areas include the life sciences, especially since the emergence of biotechnology, as well as some branches of engineering and information technology and, recently, nanoscience. Overall, the past 30 years have seen a substantial increase in formal relations between companies and universities. Between the early 1980s and the late 1990s, for example, the percentage of university research that has been funded by industry has increased from 3.5% to about 8.0% (NSF, 2002).2 Such an increase is even more compelling if considered in absolute terms. Around 1.4% of all industry-funded R&D is currently performed by

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academic organizations. This share has almost doubled in the past two decades. In fact, since the late 1970s, many managers, academic scholars, and policymakers have seen the development of stronger and more formal ties between firms and universities as a key asset for preserving America’s industrial competitiveness and innovativeness. At a time when academic research was thought to be both too distant from industry needs and too difficult to transfer and apply, industry–university formal relations started being perceived as a way to better produce, transmit, and diffuse knowledge. To facilitate these relations, policymakers have intervened with several provisions, such as the 1986 Federal Technology Transfer Act. In recent years, following this logic, several Western European countries have begun to adopt similar legislative provisions (Geuna et al., 2003). Some of these formal industry–university relations involve a substantial commitment of time and financial resources by the parties. For example, the chemical company Monsanto entered a 20-year relationship with the Washington University in St. Louis in the early 1980s. The financial resources expended by the company were between $2 million and $5 million annually. Still in 1980, the Massachusetts Institute of Technology (MIT) signed an $8 million, 10-year research agreement with Exxon for research on combustion engineering (Kenney, 1986). MIT has also entered into two other major alliances with the biotech company Amgen (with a financial commitment from Amgen for about $35 million over 9 years) and with DuPont in 2000. In 1998, the agri-pharmaceutical company Novartis signed a $25 million, 5-year, nontargeted research deal with the Department of Microbial and Plant Biology at the University of California, Berkeley for the development of several projects (Lawler, 2003; Press & Washburn, 2000). In 2000, MIT also formed an alliance with DuPont for research related to biotechnology and biomaterials. The alliance has been renewed recently, and the areas of research have been expanded to include nanotechnology and alternative energy. DuPont’s total investment in the alliance is about $60 million. The outsourcing of research activities to academic scientists presents somewhat of a puzzle. At least in principle, companies can ‘‘reproduce’’ the research capabilities of university labs within their walls, and many historical cases (including those described in the previous section) show that the scientific quality of R&D at companies is not inferior to that conducted in academic contexts. Moreover, by collaborating with universities, and by outsourcing research more generally, companies effectively delegate decisions about the direction of research to independent organizations. In particular, delegating power to an academic partner implies giving

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decision rights to actors whose priorities might not align with those of the company. What if a project has high scientific potential and can lead to high scientific recognition but turns out to have low commercial value? Moreover, how would the parties agree on the best course of action? The approach to the study of academic research as emerging from a peculiar set of institutional characteristics on the one hand, and studies on the Economics of Organization focused on the costs and benefits of performing activities within an organization rather than by outsourcing on the other, offers us an interpretation of the costs and benefits for firms to collaborate with universities. On the one hand, the norms of academia and, more generally, of the scientific community guarantee that scientists are, to a large extent, shielded from strict considerations about economic value in the choice of their topics of research and in the direction of their research (Merton, 1973; Ben-David, 1977; Mokyr, 1990; David, 2004; Argyres & Liebeskind, 1998). Together with openness, this greater freedom of inquiry is a key characteristic of the academic environment. On the other hand, a pivotal difference between developing a project in-house and outsourcing it to (or collaborating with) an independent partner is that, in the latter case, the firm will typically surrender some degree of authority over the project. The firm may not have the unilateral right to terminate the project without cause; alternately, this right may be granted but only with restrictions, and any change of direction may need to be agreed upon among the independent partners. For example, if the economic prospects from a given project turn out to be unsatisfactory for the firm during the performance of the research, it would be difficult for the firm to terminate the project or to modify its direction if the authority over the research is shared and there is disagreement. Moreover, at best, the firm may be able to terminate the funding of the project; but if the independent partner has other sources of financing, the focal firm cannot prevent the project from being continued. In contrast, by bringing a project in-house, a firm gains greater discretion through higher formal authority. It would be easier for the firm to terminate or modify the project toward more economically promising directions. Lacetera (2009b) proposes a model that addresses these tradeoffs. He moves from the idea that if scientific workers (regardless of their institutional affiliation) care about bringing a scientifically relevant project to completion (so as to receive peer recognition for their findings), and if the scientific value is not strictly correlated with economic value, then a firm may find it profitable to ‘‘tie its hands’’ and delegate some decisionmaking power to an organization that, by its own institutional nature, is committed to the pursuit of scientific value. Moreover, a scientist may be

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more motivated to supply productive effort for a project if she is more confident that the project will not change direction or will not be terminated before completion. Such enhanced motivation is valuable for the firm as long as it also increases the probability of a positive economic return from a given project. Delegation of decision power to an academic organization may serve as a commitment device for the firm. Keeping tighter reins on a project within the boundaries of the firm may come at the cost of a softened behavioral response from the scientists. Following this line of reasoning, we would expect companies to involve academic partners for research projects in which the potential impact of scientists’ efforts is relatively more important than the ability to promptly modify the direction of research to adapt to economic conditions. We would also expect companies to prefer a hierarchical relationship when it is more important to maintain greater discretion and flexibility and when the scientists’ personal and professional investments in the project are less important to its overall success. Evidence from a number of cases, as well as from large-sample studies, seems to lend support to this view. As aforementioned, in 1998, Novartis signed a $25 million, 5-year nontargeted research deal with the Department of Microbial and Plant Biology at University of California, Berkeley for the development of several projects (Press & Washburn, 2000; Lawler, 2003). The parties formed a committee with the power to allocate funds to the research projects that the academic researchers proposed. Of the five seats on the committee, Novartis was granted only two. This choice would have been interpreted as a signal that the company could not impose its prerogatives over the group’s decisions about which projects to promote. Because the type of research that was the object of the original agreement covered broad applications that were both scientifically relevant and economically promising, it can be argued that the company cared more about providing the strongest possible incentives to the scientists than being able to promptly terminate or redirect a project. However, the growing popular as well as legislative opposition to genetically modified foods arguably reduced the breadth of application of the research funded by Novartis. These environmental changes might also have reduced the expected returns from the original research projects, which, in turn, increased Novartis’s need to have greater authority over projects, thus making a deal with an independent academic partner less sustainable. The deal, in fact, was not renewed in 2003 (Lawler, 2003; Institute for Food and Agricultural Standards (IFAS), 2004). Another case of interest is represented by the $35 million deal between MIT and Amgen (Lawler, 2003). However, after some major changes in Amgen’s leadership – changes

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that re-oriented the firm away from a major focus on R&D – the research relations between the two parties were drastically downsized in favor of more attention toward marketing-oriented business models (Lawler, 2003). These changes can be expressed, again, as a decrease in the alignment between scientific and economic value, given the new focus of the firm in generating value through marketing more than through research. In contrast, MIT and DuPont renewed their alliance from 2000 for an additional 5 years, adding $25 million to the original $35 million committed in 2000. Interestingly, the agreement has been extended to cover other research areas beyond the original focus on biotechnology and biomaterials. These new areas include nanotechnology, which is thought to have a vast range of applications (see Brown, 2005) and is in a very early stage. Preliminary evidence from a sample of research contracts between biotech companies and academic organizations (detailed in Lacetera, 2009b) indicates that, when the research project is potentially applicable to a higher number of diseases, companies delegate more decision power; however, companies retain more power for longer projects. Mansfield and Lee (1996) find that prestigious universities receive relatively less funding from firms than less prestigious universities. The authors conjecture that firms may find it more costly to fund prestigious universities because the contractual conditions that they would impose would be even more restrictive for a firm. These costs notwithstanding, firms appear to value the higher abilities of scientists in top universities for projects that are less narrow and specific – projects that are more fundamental nature. Broader projects are, indeed, those in which a firm would be more willing to sacrifice some authority in order to enhance the scientists’ efforts, which would likely be higher in broader and more fundamental projects because of the greater potential for peer recognition. The difficulties for firms to interact with major research universities are also implicit in the findings of Masten (2006) who shows that research-oriented universities have an internal authority structure very different from those of companies. Finally, Veugelers and Cassiman (2005) find that collaborations between companies and universities are more frequent when risk is not an important obstacle to innovation. Consistent with the provided arguments and aforementioned examples, we might conclude that university–industry collaborations would be particularly profitable when the parties recognize and respect each other’s goals. From a policy standpoint, this approach is consistent with ‘‘middle ground’’ positions related to the desirability of stronger formal ties between companies and academic organizations. These positions argue that

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universities should attempt to pursue research directions related to issues of actual, concrete relevance. However, they also stress that academic organizations should stick to their original missions of research and education, and should not transform into business organizations (Beckers, 1984; Rosenberg & Nelson, 1994; Howitt, 2003; Nelson, 2004). Therefore, institutional diversity should be preserved. If companies cannot fully replicate, within their boundaries, an academic organization by credibly delegating decision power over projects to their scientists, there may still be value in trying to replicate some aspects of the way in which the scientific community operates. A number of influential papers have shown that companies might benefit from letting their scientists publish their findings in peer-reviewed journals and even rewarding them financially for these publications (Cockburn, Henderson, & Stern, 1999; Henderson & Cockburn, 1994). Two main arguments are brought to explain these results. First, providing strong incentives to perform research with high scientific value (i.e., publishable) makes it optimal for a firm to also strengthen incentives for scientists to engage in more applied, commercially oriented research (Cockburn et al., 1999). Second, if scientists have a ‘‘taste for science,’’ and care about being recognized by their peers in the scientific community, then they might accept lower salary offers, thus reducing direct labor costs for ‘‘science-friendly’’ firms (Stern, 2004; Aghion, Dewatripont, & Stein, 2008). Lacetera and Zirulia (2010) qualify these statements and findings through a model that considers the incentive provision to scientists in an environment in which companies compete in the product market and the outcomes of research are not perfectly appropriable by a firm, especially if a firm allows its scientists to publicly disclose their findings and rewards them on this basis. A first major result is that incentives for basic and applied research are complementary only if either the level of product market competition or the degree to which scientific knowledge spills to other firms is low. If firms compete fiercely, then allowing scientists to freely disclose scientific information may not be profitable if it is difficult to protect the intellectual property that is associated with those findings because competitors could derive an advantage. In a context in which firms do not compete directly (e.g., they are highly differentiated), then high knowledge spillovers do not have negative consequences on the originating firm, and companies can fully exploit the power of complementarity. The same effect occurs when companies compete fiercely but scientific knowledge is easier to protect. An implication of this finding that is relevant for empirical research is that the structure of the product market and the IP regimes need to be

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controlled for when assessing the determinants of scientists’ pays and incentive structures. A direct managerial implication is that, when designing incentive systems and trying to integrate different sets of incentives, companies also have to consider the type of competition they face in their final market as well as the peculiar nature of scientific knowledge as a public good, which can be difficult to appropriate. The second result from Lacetera and Zirulia (2010) is that, when company researchers have a high taste for science, companies find it more profitable to further stimulate efforts in basic, scientifically valuable research through monetary incentives. In turn, this leads a firm to also increase the power of the incentives for applied research. Therefore, firms prefer to reinforce the non-monetary, ‘‘academic’’ incentives through the wage schedule. Scientists who are more eager to maintain their links to the scientific community even when employed by a firm – and who are allowed to do so – are not necessarily ‘‘cheap.’’ Instead, these are the scientists who should be given more powerful incentives for the performance of both basic and applied research.

4. COMMERCIALIZING FALSE SCIENCE? UNDERSTANDING ACADEMIC FRAUD AND ITS IMPACT A basic tenet behind any current analysis of knowledge transfer and commercialization from academia to industry and the market is that the knowledge in question is ‘‘true’’ and that the scientific results have been obtained honestly. This is not an innocuous assumption. Scientific fraud occurs and might have major economic and social consequences. In the early 2000s, the work on organic transistors by Bell Labs physicist Jan Hendrik Scho¨n (who published in the most prestigious peer-reviewed journals) was considered to be potentially revolutionizing for the entire silicon chip industry. Most of these findings were later found to be almost entirely fabricated (Gross Levi, 2002a, 2002b; Bell Labs, 2002; BBC, 2004; Ossicini, 2007). Starting in the 1980s, Eric Poehlman published influential works on the benefits of hormone therapy to prevent obesity in women. This research, again, had a great impact on the pharmaceutical industry, on the medical profession, and, of course, on the lives of many women. And, again, almost all results of this 20-year effort were discarded when Poehlman was found to have fabricated most of them and had excluded evidence on the

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risks of hormone therapy (Chang, 2004; Office of Research Integrity, 2005; CBS, 2005; Kintisch, 2006).3 A case that reached the popular news and increased public awareness about the existence and consequences of scientific fraud is represented by the Korean biologist Woo-Suk Hwang. He rose to fame in 2004 thanks to a series of breakthroughs in the field of stem-cell research. In a number of articles in top journals, he claimed that he had created human embryonic stem cells through cloning – a discovery that made him the most esteemed stem-cell scientist in the world. The scientific and health-related consequences of his findings were predicted to be enormous as was their economic potential. The re-examination of his findings by other researchers, however, revealed that Hwang’s results were fraudulent. For example, most of the cell lines were faked, and pictures of allegedly different cells were really pictures of the same cell (Kolata, 2005; Fifield & Cookson, 2006; Reuters, 2006). A number of empirical studies of scientific misconduct reveal that the phenomenon is not limited to a few ‘‘exemplary’’ cases but is much more widespread. Freeland Judson (2004) and Pozzi and David (2007) document a steady flow of new cases opened and allegations confirmed at the U.S. Office of Research Integrity (ORI) over the past decade. Swazey, Anderson, and Louis (1993) report that about 10% of the scientists who responded to their surveys have witnessed episodes of scientific misconduct. Martinson, Anderson, and de Vries (2005) find that, whereas only few scientists admit having explicitly fabricated data, 10–15% of scientists admit to having performed such behaviors as omitting data that did not conform to their ex ante theories without any justifiable basis for their choice.4 How could companies, policymakers, and society prevent the production of false knowledge or detect it promptly? What characteristics of the scientific community lead to fraud, and what are the mechanisms to prevent it? An analysis of the institutional characteristics of academia and the scientific community in general offers insight into these questions. Lacetera and Zirulia (forthcoming) elaborate on a game-theoretic model of the research and publication process that includes the possibility of fraud. The model is based on two key aspects of the research and publication process. First, a scientist has an informational advantage over an external observer about the research that she is performing and the success or failure of her experiment; she is also able to control what part of her research is made public and transferred to others. Second, the agents called to verify the results of a scientist’s research are the scientist’s peers – as reviewers, readers, or journal editors. This is a key aspect of the scientific community as a community of peers. And, as members of the same community, the

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verifiers might or might not have strong incentives to perform thorough checks. For example, a peer who evaluates the article of a colleague might derive a benefit simply because he appreciates an advance of science. Also, he may benefit – directly or indirectly – from a certain result being published; this result might contribute to legitimating the field of research the reader is also working on and may be complementary to her work. A peer reader, however, might also derive disutility from the publications of a colleague because they might be in competition over the priority in a certain field or finding; in this way, the success of a colleague may reduce the room for contributions by another. Although in this latter case a peer reader might have strong incentives to perform thorough checks of an article and possibly to report suspicions of fraud, in the former case, the incentives would be much lower. The model shows, first, that the types of research that are more likely to be fraudulent and the types of scientists who are more likely to commit fraud differ from the type of research and scientists who are discovered to be fraudulent. In particular, the probability of detecting misconduct is higher for radical research, although frauds are more common in incremental research; similarly, it is more likely that fraud will be discovered in the work of a scientist with a less notable reputation than in the work of an acknowledged leader, even if the probability of publishing a fraudulent paper is higher for the star. Radical findings or relative inexperience in the field seem to attract higher scrutiny from peers, thus discouraging dishonest behavior in the first place. Second, some policies aimed at reducing undetected fraud, such as a reduction in the costs of replicating other scientists’ research and softening competition among researchers, can backfire, increasing undetected misbehavior. For example, softening the ‘‘publish or perish’’ paradigm might reduce competition among scientists so that scientists might have a lower incentive to cheat; however it would also reduce also the incentives to verify fraudulent results, leading to ambiguous outcomes. Also, a more active role of editors in policing misconduct (modeled as an additional layer of verifications before a paper is published) does not always serve as a deterrent because it eliminates the incentives of readers or peer evaluators to check themselves. These results imply that there may be a good deal of fraud of which the scientific community is not aware, and that most of these frauds are of a different nature than the ones that are in fact discovered. We may therefore have only a limited and distorted sense of the amount and type of scientific misconduct, if we rely on reports and anecdotes of scientists who were, indeed, caught cheating. In addition, policies deemed to unequivocally

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discourage frauds, such as facilitating replication and data sharing, softening the pressure to publish, and involving journals’ editorial boards into checking for frauds, do not necessarily elicit the expected virtuous behaviors. When interacting with academic organization for the transfer of knowledge to commercial applications, or when drafting policies to facilitate this knowledge transfer and its commercial applications, both companies and policymakers would benefit from understanding the institutional and individual dynamics that might lead to scientific fraud, and might also invest in trying to prevent and detect it with additional instruments.

5. CONCLUSIONS Scholars and policymakers consider the engagement by academics in knowledge transfer activities as a natural step in the development of the modern university, in addition to the more traditional mandates of education and research. A key difference between academic organizations and business organizations, even when engaged in comparable research commercialization, is given by the different rules and incentives in the two environments, and, potentially, by the individual differences in the agents involved. A growing body of theoretical and empirical research characterizes these differences and explores their consequences on the rate and success of knowledge transfer and research commercialization. In this chapter, we systematically reviewed the literature focused on differences between academic and private entrepreneurship. According to this literature, both organizational and individual behaviors are influenced, shaped and biased by a set of rules, incentives, and missions that are coherent with the logic of their alma mater institutions. We have focused on a few themes that, in particular, allow understanding the role of academia and its institutional features in the process of knowledge creation and transfer to industry and the marketplace. The focus on individual and organizational determinants of the commercialization behavior by academics (e.g., through the establishment of a new business), can be useful to shed some lights, for example, on the heterogeneity of firms’ life cycles, performance and growth, and on the success of different technologies in reaching the market. The analysis of such aspects in the organization of R&D activities by firms, such as the decision to outsource projects to academic groups or to replicate the incentive systems typical of the scientific community in their labs, might allow us to assess best practices

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in managing research teams, either in academic or private labs. Finally, a better understanding of the causes and consequences of scientific fraud might help prevent the transfer of fake knowledge and the negative social and economic effect that it generates. Further research is warranted to provide further light on these phenomena. Additional empirical research would allow testing of some of the theoretical predictions and address some of the concerns that available models raise. For example, empirical research on the differential performance of ventures that actively involve academic researchers should account for the higher selectivity of academics when deciding to engage in commercialization, as opposed to non-academics, because of the higher opportunity costs of giving up other activities. Also, empirical studies of the impact of ‘‘academic’’ incentives offered by firms to their scientists should control for the competitive conditions in the product market, as well as for the strength of intellectual property protection of a given technology. Finally, further empirical research is needed to help detect the production of false science, and also to assess its effects. Finally, most of the available research on the different institutional logics of academia presupposes, more or less explicitly, that the behavior of academics and non-academics are rational responses to the set of rules and incentives of the particular organization to which they are affiliated to. Individual heterogeneity in the ‘‘taste’’ for the different rules is allowed to explain how individuals sort into different institutional realms (Roach & Sauerman, forthcoming). However, more complex psychological mechanisms might be at play to explain the behavior of individuals, either independent of the institutional rules they are subject to, or in interaction with them. Some studies have already explored different attitudes toward risk and enterprising behaviors (Fini, 2010). Additional research is desirable to explore whether other cognitive and behavioral differences, such as time discounting, preferences for fairness and equity, self image, and (over)confidence play a role in determining differential behaviors.

NOTES 1. It is important to mention that, in the 1940s, universities could file for patents; moreover, Purdue University owned patents before entering semiconductor research. 2. Cohen, Florida, and Goe (1994) argue that, if we consider that the government sometimes provides part of the funds for industry-sponsored projects, then industry currently participates in a higher share of academic projects (about one-fifth, in dollar terms).

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3. Poehlman was the first academic scientist to be given prison time for falsifying data in grant submissions. 4. These studies also attempt to quantify the degree of plagiarism in science. Although this is, too, a serious violation of the academic norms, its business and societal consequences appear less serious. See also Arce, Enders, and Hoover (2008), and Enders and Hoover (2004, 2006). A further, somewhat related line of research focuses on the determinants of knowledge-sharing among scientists. See in particular Haeussler (2009) and Haeussler, Jiang, Thursby, and Thurby (2009).

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THE POLITICS OF NEGLECT: PATH SELECTION AND DEVELOPMENT IN NANOTECHNOLOGY INNOVATION Michael Lounsbury, Tyler Wry and P. Devereaux Jennings ABSTRACT In this chapter, we examine the development of a technology path in the nanotube (NT) field – one of the most well-developed areas of nanotechnology. Although early developments suggested that there were equally viable pathways related to the development of carbon nanotubes (CNTs) and others made with organic molecules and polymers, carbonbased technologies became valorized. We show how the carbon science path developed and try to unpack how it happened. We argue that it was not due to the inherent efficiency or applications of CNTs, but to sociopolitical dynamics. Even though much intellectual property research focuses on patent-level analysis, we underscore the importance of patent categories as key cognitive elements that organize the different knowledge domains within the world of NT patenting. We show that interlinkages between patent categories are crucial to the formation and development of a particular technology path. In unpacking the selection of the carbon Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 27–58 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021005

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science path, we highlight the key role played by a cadre of star scientists and the political neglect of alternative pathways as the field herded toward the CNT path.

INTRODUCTION The idea of ‘‘path’’ has become an increasingly important focal point for a wide variety of scholars who seek to theorize how the patterning of historical events and related processes fundamentally imprint and leave legacies embedded in artifacts, practices, and modes of thought, interpretation, and interaction (e.g., Mahoney, 2000; Pierson, 2000; Schneiberg, 2007). The contemporary allure of this concept has stemmed from the development of the notion of ‘‘path dependence’’ to argue against more simplistic ‘‘efficiency’’ arguments rooted in neoclassical economics (e.g., David, 1986; Arthur, 1989). A prototypical example is that of the Q-W-E-R-T-Y keyboard that provides an inefficient way to type, but that continues to exist because the technology became locked-in due to its widespread diffusion (see David, 1986). This conceptual development opened the door to a wider consideration of the processes by which paths get constructed (Bijker, Hughes, & Pinch, 1987; Garud & Karnoe, 2001; MacKenzie, 1992; Rip, Misa, & Schot, 1995). For instance, evolutionary economic approaches have focused on how certain technological paths get locked-in as a result of increasing returns rooted in learning and the generation of positive externalities (e.g., Nelson & Winter, 1982; Arthur, 1989). Sociological and management research has alternatively focused on the concrete social mechanisms by which technological paths emerge, often amidst contestation (Law & Callon, 1988; Garud & Karnoe, 2001). For instance, Lounsbury, Ventresca, and Hirsch (2003) showed how collective mobilization in support of recycling technologies reversed the apparently locked-in technology of waste-to-energy incineration in the U.S. solid waste management field by altering field-level cultural categories. However, we still have little systematic understanding about the emergence of technological paths. Dosi, Orsenigo, and Labini (2005) claim that research on path creation emphasizes highly contingent processes that are not amenable to generalization. For this reason, evolutionary economists have turned away from the study of path emergence in favor of a focus on how paths, once established, become locked-in as a result of

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technological momentum that makes them difficult to alter or redirect. To develop more general insights about technological path creation, we cultivate a structural approach that shows that the emergence of patterned linkages among categories of technological knowledge and practice has a constitutive effect on subsequent innovation trajectories (Henderson & Cockburn, 1996; Henderson, Jaffe, & Trajtenberg, 1998; Podolny & Stuart, 1995; Wagner & Leydesdorff, 2005). In addition to providing a generative mechanism to explain path emergence, our approach eschews strong assumptions about technological lock-in (e.g., Pierson, 2000) and shows how a technological path, once selected, can enable considerable creative activity, and multiplex lines of development. We further argue that an adequate explanation of path emergence must account for how a path gets chosen over other alternatives. Drawing on political approaches to technological change (e.g., Frickel & Moore, 2006; Kleinman, 1995), we suggest that path selection involves the politics of neglect (Frieden & Kaplan, 1975). This does not necessarily involve malicious politics, but a more subversive neglect of alternative paths that is political in the sense that potentially fruitful directions for innovation become inhibited as the collective pathos of a field becomes attached to one path over others. We examine path emergence and selection in the context of nanotube (NT) technology. Empirically, we utilize patent categories as an analytical focal point to assess how various strands of knowledge and practice are drawn onto the main path of development while other alternatives languish. NT research is a relatively well-defined area of nanotechnology with a wide range of commercial applications (Meyyappan, 2005). The first NT patent was issued in 1992, and 880 had been issued through 2004, when we ended our data collection. We pay particular attention to how patents are categorized by the U.S. patent office (USPTO). Each USPTO patent is assigned into one of over 400 technology classes (categories) that reflect its primary attributes and distinguish it from other types of inventions. This process involves multiple expert examiners who follow an assiduously laid out method for determining appropriate categorization, as well as a series of checks by senior staff designed to ensure that the process plays out consistently across patents. Drawing on network methods, we explore how evolving relationships among patent categories created the substantive infrastructure for a technological path linked to the synthesis and applications of carbon nanotubes (CNTs) (see Podolny & Stuart, 1995; Powell, Koput, & Smith-Doerr, 1996; Wartburg, Teichert, & Rost, 2005 for cognate approaches at the individual

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patent level). More specifically, we show that the emergence of thick patterned linkages among categories is a key mechanism in path creation because it creates a structure where certain categories cluster together in meaningful ways – driving subsequent innovation – while others are relegated to the margins of the field. Examining NT patenting from its inception, we further probe the underlying dynamic that resulted in the field cultivating a collective pathos linked to carbon-based NTs while neglecting a potentially viable path linked to the synthesis and applications of organic and polymer NTs. We argue that this was not due to the inherent efficiency or applications of CNTs vis-a`-vis these alternatives, but rather to sociopolitical dynamics. In particular, we show how a group of prominent scientists, and Rick Smalley in particular, worked to catalyze collective action among various dispersed organizations, which resulted in an ever expanding array of actors and innovations being drawn together on the CNT path. In contradistinction, similar dynamics were absent around categories related to organic/polymer NTs; as a result, this nascent path withered before significant development could take place. In the next section, we develop a number of hypotheses about path emergence. Integrating extant insights with our more structural approach, our aim is to develop a positive, general argument about path development at the technoscientific frontier where open innovation reigns. We then describe our data and methods and discuss the results of our analysis. In addition to demonstrating how interlinkages among patent categories provide a foundation for path construction and herding behavior in an innovation field, we probe the dynamics through which the CNT path became valorized – driving collective action and attention – over other alternatives.

THEORY AND HYPOTHESES In developing a more general perspective on path selection and development, we aim to highlight the importance of patent classes (categories) (Wagner & Leydesdorff, 2005; Wartburg et al., 2005) and how their operation in the wider structure of knowledge shapes the future direction of patenting. As with other types of categories, patent classes are important cognitive nodes within a field: they group similar items together and distinguish them from others (see Bowker & Star, 1999). Through this act of grouping, categories provide shorthand for quickly organizing and processing vast amounts of information and, in the process, shape actors’

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perceptions, interpretations, and attention (e.g., Lounsbury & Rao, 2004; Ocasio, 1997; Weick, 1995). This type of shared cognition among field members importantly aids in the coordination of diverse and geographically dispersed actors (see Chesbrough, 2003). Accordingly, we argue that it is important to attend to the influence of cognitive elements like categories when studying fields of technoscientific advance where development relies on distributed action. Our approach integrates insights from extant literature on path dependence with studies of categories and categorization – in particular, those that highlight the relational composition of categories (e.g., Breiger, 1974; Mohr, 1998). More specifically, we consider the ways in which the comparative properties of different categories can shift a field toward some areas of development over others. In addition, we argue that the emergence of patterned linkages among categories may be a key mechanism of path creation that joins disparate areas of knowledge together in ways that focus attention and action among field members. Extant insights from the path dependence literature suggest that organizations and fields lock-in on specific areas of advance because development is additive. Thus, high levels of activity linked to a specific technology may produce momentum that is difficult to reverse or break away from (see Garud & Karnoe, 2001; Pierson, 2000). In the context of patent categories, this type of path dependence may occur when innovation concentrates in specific categories. Learning theory suggests that, as the number of patents increases in a category, so should the likelihood of new ones being formed to exploit these opportunities. But at some point there will be a negative effect due to crowding and competition (March, Schulz, & Zhou, 2000; Szulanski, 1996). For instance, learning rules research has shown that in the case of universities, a large and increasing density of administrative rules in a category decreases the likelihood of new rule births (Schulz, 1998) and that in the case of water law the number of legal rules in a domain has an inverted U-effect on rule creation and revision (Jennings, Schulz, Patient, Gravel, & Yuan, 2005; Schulz, Jennings, Patient, Yuan, & Gravel, 2006). This is akin to arguments made by organizational demographers that stress how the dynamics of organizational foundings in a population will be curvilinearly related to the organizational density of that population (Carroll & Hannan, 2000). In the case of patent categories, early entrants (actors) may perceive opportunities or possibilities for a technology/knowledge domain that are not obvious or inherent to that technology. Subsequently, once a technological path gains momentum and the general perception of technological

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opportunity spreads, more actors will likely enter the arena (Dosi, et al., 2005; Henderson & Clark, 1990). However, as a technological path becomes saturated with researchers and technologists, the perceived opportunities may dissipate and future activity may become depressed. Thus: H1. The density of patents in a category will be curvilinearly related to patent creation in a category. Another potential explanation for path emergence is the development of positive externalities that accrue to path-dependent innovation but not divergent activities. This basic argument is common in studies of patenting (e.g., Wagner & Leydesdorff, 2005) and is mirrored in the broader learning and knowledge creation literatures (e.g., Gavetti & Levinthal, 2000; March et al., 2000; Szulanski, 1996). Several researchers studying patents have emphasized how the attractiveness and development of certain technologies can be understood by assessing the relative importance of a patent vis-a`-vis others within a broader system of technological development (e.g., Henderson et al., 1998). A patent is considered to be important when it is highly cited by other patents because this provides its holder with financial rewards and prestige (Henderson et al., 1998; Podolny & Stuart, 1995). This concept can be extended with little revision to the category level. In systems of technological advance, some patent categories may emerge as more important than others, with their constituent patents accruing citations at a higher rate than those in other categories. We expect that the potential rewards for participating in important patent categories will push actors toward further development in these highly valued areas. Thus: H2. The importance of patents in a category will be positively related to patent creation in a category. Although we expect that the properties of individual categories will be important catalysts in the development of particular paths of technological development, we believe that a meaningful understanding of this process requires attention to the relationships that can develop among categories. In this way, our approach builds on studies that suggest that technoscientific fields can develop virtual forms of organization where shared cognition about fruitful lines of advance is shaped by patterns of co-citation (referencing) among academic papers and individual patents (e.g., Wartburg et al., 2005). Such co-citation analyses reveal a broad social topography that has been shown to shape the rate and direction of future innovations

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(e.g., Podolny & Stuart, 1995). In fact, Sorenson and Fleming (2004) have demonstrated that co-citation of scientific work outweighed both proximity and specialization of nodes as factors in the distribution and development of future scientific articles and patents in several industries (see also Henderson & Cockburn, 1996; Henderson et al., 1998; Wagner & Leydesdorff, 2005). Although co-citation research does not consider the role of knowledge categories, we argue that in order for a unit of knowledge (e.g., a patent) to be understood by wider audiences, it must be assigned to a category that represents a specific area of the knowledge structure. To date, however, most research in this vein has focused on the microstructuring of firms and individuals through their categorization of new knowledge. For instance, research on individual-level cognition and technological development has shown how belief structures among researchers shape decisions that are made about the fruitfulness of different paths for exploration, design, and engineering. Garud and Rappa (1994) demonstrated in the case of cochlear implants how evaluative routines reinforced researchers’ beliefs and delimited the possibilities for development. Similarly, Tripsas and Gavetti (2000) highlighted how managerial cognition shaped firm responses to technological change in the digital imaging industry. Integrating insights from the literatures on co-citation and cognition in science and technology, we explore how the linkages among categories can structure innovation processes at a more macro, field level (Ghaziani & Ventresca, 2005; Lounsbury & Rao, 2004). All knowledge systems contain implicit grouping, hierarchies, and comparisons (Espeland & Stevens, 1998). According to institutional and network theorists, this hierarchy is best conceptualized not as a strict arrangement, but a rough set of relationships that should be mapped flexibly (Friedland & Alford, 1991; Powell et al., 1996; Uzzi, 1999). This research suggests that in the context of patent categories, a great deal of cross-citation will reveal categories that are more central to the development of the field of knowledge (Antonelli, 1995; David, 1975; DiMaggio & Powell, 1983; Wasserman & Faust, 1997). This hierarchy may be extended and deepened over time, resulting in the emergence of an organized core-periphery structure (Fligstein, 2001; Shils, 1988). In it, patent categories that build on each other will form cliques of related technological development that may generate more patents in an area. Conversely, we expect that categories that are not linked into this knowledge structure (or are weakly linked to it) will be marginalized and fail to stimulate further development (also see Podolny & Stuart, 1995).

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Accordingly, we think that centrality in the knowledge structure will be related to patent creation. Thus: H3. The centrality of a patent category will be positively related to patent creation in a category, particularly as the industry evolves. Finally, to the extent that patterned linkages among patent categories knit discrepant lines of development into a coherent path of development, we think this may enhance the utility of explanations linked to categorylevel attributes. With regard to density, the temporal development of the knowledge structure in tandem with the accretion of knowledge in particular areas should enhance the path-dependent nature of technological innovation. Not only will the most central categories tend to be fulcrums for knowledge and intellectual property development, but they will be particularly robust sites for patent creation if the category has exhibits a good deal of prior patenting activity (i.e., density). This means that patent creation will be especially fostered in categories that are both central nodes in the patent citation network and have a high density of extant patents. This basic argument can be extended with little revision to the interaction between category importance and centrality. Thus: H4. The patent category density  centrality interaction will be positively related to patent creation in a category. H5. The patent category density  importance interaction will be positively related to patent creation in a category.

DATA AND METHOD Empirically, we explore one key area of nanotechnology: NTs. Although the broad field of ‘‘nanotechnology’’ is somewhat ambiguously defined, NT technology comprises a relatively well-bounded subfield (Meyyappan, 2005). The focus on developing intellectual property in the area began in the early 1990s after Sumio Iijima illustrated CNT synthesis in an article published in Science (Iijima, 1991). CNTs consist of graphitic layers seamlessly wrapped in a cylindrical shape and capped with pentagonal rings. Although only a few nanometers in diameter, they are extremely strong and have unique properties related to electrical and thermal conductivity, as well as optoelectronic transmission (Meyyappan, 2005). Potential technological applications include new kinds of diodes, transistors, probes, sensors,

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actuators, field emission arrays, and flat panel displays. In addition, NTs can be created from organic and polymer compounds. Although these are not as strong as CNTs, they have potential uses in computing and chip development because of their utility for creating nanowires and logic-gates. Moreover, their solubility points to considerable potential for applications in biotechnology and drug delivery. Our data consist of all NT patents issued by the USPTO until the end of 2004. For each, we recorded its title, issue year, abstract, inventors, assignees, kind of assignee (e.g., corporation, university, government, etc.), country of assignee, class of patent, examiner, and number of journal references. We also created a related database of all patent citations made by an assigned patent. To identify all NT patents, we searched the USPTO database for the terms ‘‘nanotube,’’ ‘‘carbon and nanotube,’’ and related terms such as ‘‘buckeyball,’’ ‘‘fullerene,’’ and ‘‘C60’’ in the title, abstract, and claims section of patents. This yielded 880 patents, the first of which was issued in 1992. This was cross-checked with data from NanoBank. Since the number of patents in 1992 was trivial (n ¼ 3) and our independent variables were lagged by a year, our analyses run from 1994 to 2004. Fig. 1, which plots the number of NT patents granted by year, shows that the number of patents created was fairly steady up until around 2000 when patenting accelerated considerably. This change may be due to the start of the nanotech race. In 2000, President Clinton authorized the National Nanotechnology Initiative, which was seeded with $500 million in 2001 to support nanotech ventures. Since this time, the U.S. government has 250 200 150 100 50 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Fig. 1.

Number of CNT Patents Created, 1994–2004.

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MICHAEL LOUNSBURY ET AL.

continued to increase the pot and has invested approximately $1 billion in 2005. Western Europe and Japan also invest hundreds of millions of dollars every year, and many countries throughout the world have important initiatives in this area. Although the dramatic rise of CNT patenting is interesting in its own right, here we focus in a more detailed way on the direction of this growth as a function of patent classes (i.e., categories) in which it was occurring. Early work on patents focused on the count of individual patents in an industry or area of technology as indicators of innovation and technology development (Henderson et al., 1998). More recent efforts have examined the underlying classes (Wartburg et al., 2005) or families of patents (Harhoff, Scherer, & Vopel, 2003). The classes and families are based on the individual patents, but group like patents and weight ties across patent groups, often through co-citation or other patent impact measures. Similarly, we view patents as being embedded in the overall patent classification system. This system is an extraordinarily detailed way of segregating patents by kind of application or function of a patent, creating a specialized hierarchy of knowledge (Blau, 1977; Jennings et al., 2005; March et al., 2000). Each patent class corresponds to a knowledge category in the structure. To assess the role of patent categories in shaping the distribution and volume of patenting, our data structure is organized by patent category years (i.e., patent class information per year). Our data contain a total of 4,510 category/year observations.

Dependent Variable Our dependent variable is the number of new patents assigned in a calendar year within a patent category (three-digit class). As a count variable, it is truncated at the upper end (in this case by 19) and bounded at the lower end by zero, with an average of about 2 or 3 patents per category/year. Counts in a patent class have been used as a covariate in the patent literature and theorized as important outcomes (Harhoff et al., 2003).

Independent Variables The density of patents within a category is measured as the cumulative number of patents from all years in a category up through the year before the new patent category count. The density squared of patents within a

37

The Politics of Neglect

category is simply the square of this number and is included to account for diminishing patent rates as crowding and competition set in. The importance of a patent category is based on Henderson et al.’s (1998, p. 123)measure, which summarizes the direct and indirect citations of a focal patent from the prior art section of all patent filings into a total count measure of citations. We summed the citation count of all patents in a class up through the prior year and divided by the maximum number found across all classes to scale the variable between 0 and 1. The impact of position in the knowledge structure is captured using closeness centrality within the knowledge structure in the prior year (Borgatti, Everett, & Freeman, 1999). To calculate this, we began by constructing a two-mode matrix of focal patent classes by cited patent classes with cells in the matrix containing the number of citations from patents in a given three-digit patent class going to patents in a receiving three-digit class. This enabled us to draw on network analytic techniques to analyze what network analysts refer to as joint involvement or affiliation data (e.g., Breiger 1974). Next, unlike many studies using affiliation data, which conventionally include data on director interlocks or cross-citations (Palmer, Jennings, & Zhou, 1993), we examine the extent to which categories are similarity-based or share similar citation patterns. Finally, we used UCINET to calculate and sum the deviations of closeness centrality scores for a given vertex (patent class) from maximum closeness centrality (the central point), and then divides that figure by the maximum closeness centrality score (Borgatti et al., 1999). Theoretically, the minimum point is 0, and deviations are summed (negative) differences from it. Closeness centrality provides the most appropriate measure of spatial distance when not analyzing direct ties between actors as is the case with affiliation matrices (see Wasserman & Faust, 1997). Our approach to the analysis of centrality and the overall structure of patent categories is consistent with other approaches to patents that are based on co-citation (Henderson et al., 1998; Wagner & Leydesdorff, 2005).

Control Variables We include several controls in our models. Evidence suggests that actors tend to imitate the behavior of the prominent members of their field (DiMaggio & Powell, 1983). Within technoscientific fields such as NT technology, large corporations and star scientists have been shown to be key actors in this regard (Darby & Zucker, 2003; Latour, 1988;

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MICHAEL LOUNSBURY ET AL.

Powell et al., 1996; Zucker, Darby, & Brewer, 1998). Accordingly, we control for the percentage of patents in each category associated with star scientists (those with þ1,000 citations to their scholarly articles and the percentage associated with large corporations (sales þ$500 million at t1). We also control for the generality of a patent category, which is the average number of patent categories (three-digit classes) that are considered relevant by USPTO officials to focal patents within a given class (see Henderson et al., 1998, for a related approach). In each patent class per year, we summed the total number classes other than the main class (distinguished by bolding) referred to by each patent in a focal patent class and divided by the total number of patents in that class. This measure taps into the breadth of relevance to different types of knowledge (categories) among patents within a category. Calendar year is included to combat temporal variation. This is a series of dummy variables, with 2004 as the excluded baseline category. New category tracks when a patent category enters our analysis for the first time. The creation or use of new categories signals new market opportunities and is typically associated with the rise of new entrants that aim to create and take advantage of new resource spaces (Aldrich, 1999). This is calculated using the year of issue in the USPTO data (USPTO, 2005) and is coded as ‘‘1’’ when the category first appeared in our data set and ‘‘0’’ otherwise. The average days to issue from application in a category was included to control for possible slow down and acceleration differences among patents as the knowledge structure and overall field changed. Finally, we controlled for geographic location in two ways. We controlled for country of patent assignee using the percentage of patents in a category from the United States as well as the percentage from Japan – the two dominant countries in the CNT field. While the United States is a leader in nanotech development, Japan was among the first countries to endorse a large-scale nanotechnology research program in the mid-1990s. As Fig. 2 shows, the two are by far the dominant players in NT patenting. The international effect of patent creation and co-citation has been noted by several researchers studying patents (Wagner & Leydesdorff, 2005). The regional cluster effect in patenting is captured by coding the percentage of new patents within a category created in California, Massachusetts, and Texas. This global variable captures regional share, a simple way of measuring both the linear effects of concentration and controlling for concentration in other regions. Other research (Heidenreich, 2005; Miriani, 2004, Saxenian, 1996) has also used measures that capture the degree of regional concentration of intellectual property or innovation. Although a

39

The Politics of Neglect 100 80 60 40 20 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 U.S.

Japan

Korea

Rest of World

Fig. 2. Percentage Distribution of CNT Patent Assignees by Country, 1994–2004.

more proximate region-focused set of variables may provide alternative operationalizations, our measure casts a broader (and more conservative) net to capture clustering effects that may exist. All independent and control variables were lagged by one year and updated annually.

Method of Analysis We conceptualized NT patent foundings as an arrival process where the dependent variable is a nonnegative count variable. The parameter of interest in this process is the arrival rate, defined as the instantaneous probability of arriving at state (yþ1) at time (tþDt), as given in the following: ly ðtÞ ¼ lim

Dt!0

Pr½Yðt þ DtÞ  YðtÞ ¼ 1jYðtÞ ¼ y Dt

(1)

where Y(t) is the cumulative number of entries up to time t. The baseline model formulation assumes that ly(t) ¼ l and that the conditional probability of Yt arrivals in any time interval is governed by the probability law: PrðY t ¼ yt xt Þ ¼

elðxt Þ lðxt Þyt Y t!

(2)

where the expected number of entries in each period E(Yt) ¼ lt equals the variance. This is the procedure for a normal Poisson regression where lt is

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MICHAEL LOUNSBURY ET AL.

the deterministic function of the covariates. But in cases of overdispersion, which is when the conditional variance of the entry process exceeds the conditional mean (as it did with our data), a stochastic component is needed in the entry rate to address this problem. To address this problem, negative binomial regressions are conventionally used (see Carroll & Hannan, 2000; Cameron & Trivedi, 1986; but see Henderson & Cockburn, 1996). Patent foundings were analyzed using the nbreg command in Stata 9, which generates estimates using maximum likelihood techniques.

RESULTS Table 1, which provides the means, standard deviations, and correlations for the variables used in our analyses, shows that there are no correlational problems. Table 2 reports results from our negative binomial analyses of patent creation rates per category year. Model 1 provides a baseline model with just controls. Models 2 through 4 incrementally add hypothesized variables. Model 6 is the full model that includes all control and hypothesized variables. All models with hypothesized variables show significant improvement in fit over the baseline model. Among control variables, the percentage of patents assigned to star scientists and large corporations within a category do not significant affect patent creation in any model. This does not mean that organizations are unimportant; just that they do not play a central role in focusing innovative activity within the field. We also observe that category generality does not have a positive effect on patent creation. In fact the generality variable is significantly negative in model 1. This finding may be partially understood in the context of sociocognitive theories that emphasize that coherent, taken-for-granted category schemes facilitate the automatic processing of the social world by actors (Rosch & Lloyd, 1978). In contradistinction, an overly general category has limited utility as a sensemaking mechanism (Weick, 1995) and will tend to be abandoned as a focal point for organizing activity (Bowker & Star, 1999). Moving on, new patent category creation and the percentage of U.S. assigned patents are positive and significant in the first model, but lose significance in subsequent models. Hence, neither the opening of new knowledge categories nor a particular country’s involvement in a category has much explanatory power with regard to patent creation. Fig. 2 plots the percentage of patents issued by country and offers some insight into the lack of a country effect.

.151 763.94 .38 .15 .14 .60 .18 7.38 168.79 .08 2.20 13.57

1. New patent category 2. Average issue days 3. % United States 4. % Japanese 5. % MA, CA, and TX 6. % large corporations 7. % Star scientists 8. Density 9. Density2 10. Importance 11. Generality 12. Centrality

Significance at p ¼ 0.05.

Means

Variables

Table 1.

.359 250.98 .42 .29 .24 .43 .27 10.70 646.99 .21 1.08 14.12

Standard Deviation .04 –

2 .19 .04 –

3

.03 .14 .49 –

4

.02 .11 .12 .07 –

5

.01 .04 .13 .10 .13 –

6

.02 .10 .18 .08 .18 .45 –

7 .24 .17 .22 .14 .10 .02 .04 –

8

.03 .13 .16 .07 .07 .02 .03 .88 –

9

Means, Standard Deviations, Correlations of Variables.

.48 .04 .12 .08 .05 .05 .10 .49 .34 –

10

.37 .08 .05 .05 .11 .06 .04 .07 .06 .01 –

11

.14 .02 .13 .05 .01 .08 .17 .20 .14 .19 .02 –

12

The Politics of Neglect 41

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MICHAEL LOUNSBURY ET AL.

Table 2.

Negative Binomial Analysis of Patent Creation, 1994–2004.

Variables Constant 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 % Large corporations % Star scientists Generality New patent category Average issue days  100 % United States % Japanese % MA, CA, and TX Density Density2  100 Importance

Model 1 .748 (.233) .280 (.213) .551 (.248) .390 (.222) .823 (.218) .641 (.221) .733 (.219) .598 (.200) .476 (.172) .336 (.147) .175 (.136) .078 (.131) .025 (.212) .088 (.047) .806 (.107) .017 (.020) .231 (.137) .179 (.186) .182 (.197) .081 (.006) .051 (.007)

Model 2 .380 (.215) .161 (.190) .030 (.223) .078 (.198) .288 (.199) .254 (.198) .386 (.196) .331 (.179) .212 (.151) .066 (.129) .037 (.115) .121 (.118) .057 (.199) .067 (.043) .139 (.104) .013 (.018) .088 (.124) .108 (.168) .137 (.185) .076 (.006) .050 (.007) .405 (.199)

Model 3 .660 (.224) .026 (.192) .194 (.224) .006 (.197) .442 (.198) .283 (.195) .495 (.193) .251 (.178) .211 (.149) .041 (.126) .185 (.124) .120 (.119) .143 (.200) .068 (.044) .139 (.101) .015 (.018) .117 (.124) .121 (.168) .113 (.190) .066 (.006) .041 (.007) .435 (.195)

Model 4 .573 (.229) .023 (.194) .150 (.225) .001 (.196) .413 (.199) .272 (.195) .492 (.193) .246 (.178) .208 (.149) .032 (.126) .279 (.134) .118 (.119) .129 (.199) .066 (.043) .135 (.101) .012 (.018) .096 (.125) .143 (.168) .101 (.188) .071 (.007) .045 (.007) .417 (.194)

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The Politics of Neglect

Table 2. (Continued ) Variables

Model 1

Model 2

Model 3

Model 4

.016 (.004)

Centrality

.012 (.004) .061 (.033) .105 (.049)

Density  centrality  100 Importance  centrality Log likelihood D Likelihood w2 ratio Pseudo R2

634.35 24.14 .12

552.60 187.64 .23

543.88 205.08 .25

542.18 208.48 .25

Notes: Standard errors in parentheses; one-tailed tests for hypothesized variables. po.10; po.05; po.01.

As one might expect, that figure shows that U.S. actors have always been assigned the vast majority of U.S. CNT patents. However, the percentage share of U.S. assignees declines over time as Japan’s activity grows to around 20 percent by the mid-1990s and continues at around that pace until the present. Also, new countries such as Korea begin taking out patents in the late 1990s with various other countries also developing intellectual property in the field. Hence, while the United States and Japan still maintain the largest share of patents in the NT field, over time, there is a steady internationalization of patent assignees. By the end of 2004, patents had been assigned to actors in 26 countries. Table 2 further shows that geographic clustering does not affect the count of patents per in a category. This is partially explained by the growing international spread of assignees already noted, but a similar dispersion is also evident if one just focuses on the United States. Fig. 3 provides a citylevel map of dispersion of all patent assignees within the United States from 1994 to 2004. Cities in MA, TX, and CA are important areas of NT development, but two things are clear. First, regional agglomeration has expanded. Patenting activity is focused not only in Northern California, Boston, and Austin-Houston but also up and down the Northeast corridor, around Chicago, North Carolina, Tennessee, Northern Florida, and Arizona. Second, the number of cities with multiple patents outside these regions is quite large. By 2004 NT patents had been issued to in 181 U.S. cities across 36 states. In H1 and H2, we argued that the density and importance of patents in a category should focus innovation by leading a field to lock in on areas with

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MICHAEL LOUNSBURY ET AL.

Fig. 3.

Cumulative CNT Patents Issued by Assignee City, 1992–2004.

strong momentum and rewards for participation. As Table 2 shows, patent density has a strong and significant effect in each of the models where it is included. As patents begin to be created in a category, it appears that a certain degree of momentum is generated and an increasing number of actors begin to focus on developing intellectual property in that area of the knowledge structure. However, after a certain point when there is a high number of patents in a category, crowding effects take over and subsequent rates of patenting decrease. This effect may reflect both a legitimacy (Powell & DiMaggio, 1991) and competition result (Carroll & Hannan, 2000) for the increasing patent foundings, with moderate to high levels of density indicating the acceptance of the patent class as an area for development, as well as a lack of intellectual and resource saturation in the category; but legitimacy may go down later as the foundings decrease, which may be partly due to lack of new resources but also exhaustion of that path of development. What seems more interesting is that this effect continues to occur in each of the panel years we examined. Given the relatively limited knowledge domains that exist, it suggests that little learning from past levels of density has occurred. Indeed, when we examine the effects of density and density squared in the 1992–2000 era versus the 2001–2004 era, the effects are strong in both periods. All in all, the findings provide particularly robust support for the curvilinear relationship between density and path dependence, which we predicted in H1.

The Politics of Neglect

45

Model 2 shows that category importance is positive and significant, in line with extant theory and our prediction in H2. However, this effect turns negative and significant when the patent density term is added in model 3. Why? Density and importance are correlated at a moderate (0.49) level, suggesting some competition between them to explain variance. An examination of the distribution of both confirms this. Importance is highly skewed and sparse, with few patents receiving many cites and many patents receiving few; whereas density is much more evenly distributed. Thus, in the NTs field, there is little support for H2 that more cited patent (i.e., important) categories are also more likely to be those where considerable development takes place. In H3, we argued that the structure of knowledge as shaped by the centralization of the category system would influence future patent foundings. The results for model 4 support this claim: category centrality has a significant, positive effect on the issuance of patents in categories net of factors. As such, we find considerable support for H3. As further support for this point, model 4 shows that the interactions of centrality with density and importance are positive and significant. Although it is perhaps not surprising that rates of patent creation will be especially high when a category has both a good deal of prior activity and is also central in the overall knowledge structure, it is important to stress that these effects supercede other explanatory factors related to regional clustering, importance and generality which are predominant in the patenting and entrepreneurship literatures. Finally, we find it particularly striking that the hypothesized effect of importance on patenting only becomes apparent in its interaction with centrality. Thus, although the literature on path dependence argues that increasing returns can lead a field or organization to lock-in on certain lines of advance, our results suggest that this has little independent effect. Rather, importance appears to be a complementary mechanism in a broader and more nuanced process of path emergence.

Path Selection and the Politics of Neglect To further explore the role of category centrality as a mechanism of path selection and emergence, we created multidimensional scale (MDS) plots to assess the evolution of the category structure over time. MDS provides a visual representation of the Euclidean distances between categories: categories that share similar patterns of prior art citation are plotted near each other, whereas dissimilar categories are far apart. In addition we also

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MICHAEL LOUNSBURY ET AL.

examined the clusters within the MDS plots using hierarchical cluster analysis and compared these results to those from a hierarchical cluster analysis (centroid method) of the centrality scores used in model 3. The main issues were whether clusters were evident in the hierarchically order category system and whether the overall system exhibited a core–periphery structure. For illustrative purposes, Fig. 4 shows the MDSs of patent categories in four well-spaced panels: 1993, 1997, 2000, and 2004. These plots suggest that, over time, there is dramatic growth in the number of patent categories used. In addition, while the knowledge structure is initially somewhat fragmented, it slowly becomes more centralized as clustering develops among core patent categories. Looking at NT patenting between 1992 and 1994, it is clear that the knowledge structure is fragmented. Consistent with this assessment, cluster analysis shows little evidence of categories joining together to form a meaningful path of development. By 1998, however, visual inspection shows that the category structure is becoming more ordered with groups of categories clustering together. Hierarchical cluster analysis also shows two loose clusters beginning to form. In the lower left of the MDS plot there is evidence of a main cluster of categories linked to the synthesis of inorganic CNTs (classes 204 and 423) and their rudimentary applications in bulk materials (class 252), conductors (class 257), coatings (class 427), and compositions (class 428). In the upper right, the early formation of another cluster appears that is linked to the creation of organic and polymer NTs (classes 523–570) and their application in areas such as fuel additives (class 40) and plastic coatings (class 106). Further packing of these clusters is evident in 2000, but by 2004 the nascent path tied to organic and polymer NTs has dissipated completely; the category structure is dominated by two clusters related to CNT synthesis and applications. At the lower left is a cluster of categories related to CNT synthesis and an ever expanding array of applications. Above this is a closely related, but distinct, cluster based on the uses of CNTs in flat panel displays (classes 313, 315, 439, 445) and computing (classes 365, 369, 385, 438) – a cluster where numerous corporations engaged in block patenting strategies by patenting across multiple categories (see Henderson & Cockburn, 1996). Although this ‘‘patent thicket’’ facilitated increased relational connection between patent categories comprising the secondary cluster of CNT applications, a focus on corporations does not help us understand which patent categories are central since their activity is dispersed across categories.

Fig. 4.

Nonmetric MDS Plots of the NT Patent Categories Used by Year 1994–2004.

2000

1993

2004

1997

The Politics of Neglect 47

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MICHAEL LOUNSBURY ET AL.

However, the same is not true of star scientists. In unreported models, we found that the existence of a high percentage of patents in a category assigned to star scientists was a robust predictor of centrality. Hence, although the existence of a stock of university patents in a category does not directly predict patent foundings, the activities of their most prominent members indirectly contributes as a key predictor of centrality. This suggests that, even in a corporate dominated field of intellectual property development, knowledge generated from universities might provide a crucial underlying foundation for technology development. Although much research has scrutinized the role of universities in technology commercialization (e.g., Agrawal & Henderson, 2002; Owen-Smith & Powell, 2003), the role of universities in shaping the primacy of different knowledge domains requires further investigation. Indeed, more detailed analysis of our data suggests that star scientists were not only drivers of centrality, but that a cadre of prominent inorganic scientists were key in catalyzing the development of the CNT technology path. As Fig. 4 shows, there were initially two development areas of development in the NTs field that seemed to provide the seeds for path development: inorganic (CNT) and organic/polymer approaches to NT synthesis. Yet, as the knowledge structure took shape, significant path development only occurred with inorganic, CNT technology. This was evident in key indicators: patenting in inorganic classes rose significantly and thick patterned links between categories emerged to enable robust path development. We found the opposite for the nascent path of organic/ polymer approaches: patenting levels fell, key actors began to exit, and few consistent linkages amongst categories emerged. Fig. 5 shows the comparative patent activity in these areas from 1992 to 2004. From 1992 to 1995, classes 204 and 423 (chemistry/inorganic chemistry), 523–528 (polymer compounds), and 532–570 (organic compounds) accounted for 44 patents. The other 26 classes where NT patents were issued in these years each averaged less than half of one patent per year. Extant literature provides a number of possible explanations for why some areas of technological advance flourish while others flounder. Rational actor models argue that individuals evaluate the relative merits of different technologies and select the one with the highest quality, greatest utility, or highest potential future value. As a result, technically superior areas of innovation are thought to dominate technically inferior ones. This view is prevalent in economically oriented explanations of innovation and can also be seen in work, which argues that the early diffusion of an innovation rests on technical considerations. Although it is very difficult to observe the

The Politics of Neglect

Fig. 5.

49

Patent Activity in Inorganic, Organic, and Polymer Classes: 1992–2004

quality or utility of an invention directly, previous studies have used the number of citations that a patent receives as a proxy measure (Podolny & Stuart, 1995). On the basis of this, the rise of inorganic NT patenting might be explained if more important patents were being issued in the area. However, we find little support for this argument. In the first four years of path emergence, patents in inorganic and organic/polymer categories were cited about the same number of times: on average, 1.06 versus 0.82 cites/year. Another potential explanation for the selection of the inorganic path is density-dependent lock-in (Arthur, 1989; Nelson & Winter, 1982). Although our results show that category density significantly predicts subsequent patent activity, a slightly different picture emerges when comparing the emergence of nascent paths. Extending our category level arguments, we would predict that the NT field would lock-in on technical areas that generated the most activity in the early stages of field emergence. However, we find little support for this argument. From 1992 to 1995, there were 19 patents issued in inorganic classes compared to 25 in organic/polymer classes. Another line of argument suggests that the status of actors associated with an area of innovation enables its rise to prominence in a field (Podolny & Stuart, 1995). The general observation is that the activities of higher status actors receive greater attention from relevant audiences than the activities of lower status actors. Merton (1968) termed this phenomenon the ‘‘Matthew Effect’’ and showed that a scientists’ status affects the

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perceived quality of his or her work. Thus, technical innovations are more likely to be viewed as important if they come from high status actors. According to Podolny and Stuart (1995, p. 1233), ‘‘if actors working in a technological area expect that a technology will be superior, they will devote more resources to (it) y consequently, the technologies sponsored by highstatus actors are more likely to be rapidly developed.’’ Different approaches have been taken to assess status in technological fields. One equates status with the importance of an actor’s inventions to previous areas of technical advance (e.g., Podolny & Stuart, 1995). In this context, the number of highly cited patents issued to an actor is a status measure. Using this metric, we would actually expect areas of organic and polymer science to generate more technological activity than inorganic science. In the first five years of NT patenting, DuPont, Shell, and Exxon accounted for almost 75 percent of the patents issued in organic and polymer classes. Each firm had an extensive patent portfolio and a number of highly cited patents. Conversely, early activity in the inorganic classes centered on scientists working in universities and research institutes. Although many were highly regarded for their academic work, none had an extensive patent record prior to entering the field of NT technology. Another approach to measuring status in technological fields looks at actors’ academic standing as measured by citations to their scholarly articles. For example, Zucker and colleagues have shown that the initial advances in biotechnology developed around the efforts of star scientists in a handful of research universities (Zucker et al., 1998). In the NT field, however, inorganic and organic/polymer categories included similar proportions of patents issued to star scientists (defined as the top quintile of patenting scientists measured by citations to their scholarly articles). As such, inventor status does not appear to be a key differentiating factor explaining why inorganic NT technology flourished while organic and polymer technologies floundered. Table 3 details the early similarities among these nascent paths for NT technology development. Although initial star scientist activity was dispersed, qualitative investigation indicates that certain star scientists played a key role in facilitating the selection of inorganic chemistry related classes over possible alternatives in the development of a CNT technology path. In particular, Rick Smalley from Rice University was a highly visible and important advocate of the inorganic chemistry direction. He and his team published a key article in Nature detailing their discovery and synthesis of buckminster fullerenes, or Carbon 60 (C60). C60 was a new carbon allotrope and the first fundamental advance in carbon science since the discovery of two diamond derivatives

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The Politics of Neglect

Table 3.

Patent Activity in Inorganic and Organic/Polymer Classes: 1992–1995.

Patent Classes

Inorganic 204, 423 Organic/polymer 523–570 a

Patents % Patents % Patents to Large to Star Firmsa Scientists Average 19 25

16 72

53 40

1.13 0.82

Times Cited per Year Standard Deviation

Minimum Maximum

1.06 0.83

0 0

4 3

Corporations with average revenues above $500 million/year in the period.

(chait and carbon VI) in the early 1970s. Accordingly, the discovery generated a great deal of research interest and, reflecting its importance to scientific advance, was rewarded with the 1996 Nobel Prize in Chemistry. Smalley also became a key scientific adviser to the Clinton administration and helped to structure the National Nanotechnology Initiative (passed in 2000) in a way that facilitated the robust development of inorganic chemistry approaches to CNTs. Star scientists also actively acted as cultural brokers (Hargadon & Sutton, 1997) to knit their innovations together into a coherent and meaningful path around CNT technology. Analysis of the ‘‘prior art’’ sections of early patents taken out by star scientists yields a consistent and extensive pattern of linkages among patent categories and the development of a clearly identifiable path. As we have noted, early inorganic patents clustered in classes 204 and 423. As the field developed, heavy patenting activity and cross-citation among patents in these classes emerged, creating a meaningful core inorganic identity in NT technology that bridged categories of practice, which shared an interest in different approaches to the creation of carbon nanostructures. Indeed, classes 204 and 423 were among the most active areas of NT patenting across the years of our analysis and began to include patents from progressively diverse sets of actors over time. Moreover, the pattern of prior art citations in later patents also mirrored those made by the early star scientists, reinforcing this identity core. Conversely, early patents in organic/polymer classes failed to develop a dense relational network that could provide meaningful foundations for path development in these areas. There were some highly cited patents in these areas. For example, Exxon patent #5292813 ‘‘fullerene-grafted polymers and processes of making’’ was among the most highly cited of all NT patents in the early 1990s. However, unlike inorganic science, the

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majority of these citations came from patents outside of the NT field. In fact, only about half of the organic and polymer patents from 1992 to 1998 cited any prior art, and those that did only made sporadic links to each other. Thus, a consistent pattern of links among patent categories failed to emerge in these areas with the result that classes failed to cluster together in a meaningful path of technology development within the field. Table 4 compares patterns of prior art citations found in inorganic, organic, and polymer patent classes from 1992 to 1998 (when patenting in inorganic classes accelerated, activity in organic/polymer classes fell sharply and the nascent path began to dissipate). For parsimony of presentation, we only report data where there are at least two linkages between classes. Our argument is not that CNT applications were necessarily superior to other types of NTs based on organic and polymer molecules, but that collective action catalyzed by superstar scientists such as Rick Smalley who favored the inorganic chemistry path was the key. Pioneering innovators in polymer and organic chemistry were unable to assemble the right group of star scientists to signal the importance of their alternative developments. Some of these were (and still could be) potentially important, including novel drug delivery and treatments using organic NTs. However, once the inorganic chemistry path was set in motion and attracted corporate interest, alternative paths quickly withered. It is a kind of politics of neglect because, at a technoscientific frontier such as nanotechnology, there is a scarce supply of scientists whose attention is important for path development.

DISCUSSION In this chapter, we demonstrated that CNT path creation is importantly shaped by patent categories and how they become linked in the overall knowledge structure of the CNT field. In particular, we showed that the density of patents in a category provided crucial momentum and a solid foundation for subsequent patent creation, but at high levels of density, crowding effects kicked in depressing patent growth. In addition, patent creation was higher in categories that were central in the overall patent citation network, based on how prior art from different categories was drawn on to motivate and substantiate new patent claims. Moreover, patent creation was especially driven by categories that had both a high density of patents and were central nodes in the overall knowledge structure of the field. These mechanisms provided more explanatory power than factors such as patent category importance (in citations) and generality (breadth of

The Politics of Neglect

Table 4.

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‘‘Prior Art’’ Citations; Inorganic and Organic/Polymer Classes: 1992–1998.

category relevance). Although category importance was positive and significant in one of the models, it turned negative and significant when the density variable was added. This suggests that at the category level, the accumulation of activity within a category (density) provides a more robust signal of importance than citations across categories. The generality finding may be interpreted to suggest that categories that are too broad become ambiguous (Weick, 1995) and therefore lack the proper conditions for intellectual property development. This is consistent with much sociocognitive work on categorization (e.g., Bowker & Star, 1999). We also showed how a core–periphery structure began to emerge over time. Initially, knowledge domains were quite fragmented with little crossinteraction. As the field developed, more knowledge domains began to be utilized and cross-citations grew dramatically, suggesting that developments in different knowledge domains were increasingly used as inputs for each other and the field became more unified. However, with this unification came the development of hierarchy. Some knowledge domains (i.e., patent categories) became more central nodes for technology development and experienced a higher degree of intellectual property creation. Other patent categories were more peripheral feeders into the center. Our analyses indicated that while corporations have dominated the development of CNT intellectual property throughout the time period studied, it was patent categories with a high stock of university-based patents that were more central. Future work is needed to explain the mechanisms by which categories become central or shift from the periphery to the core of a knowledge structure. In addition, future research is needed on the earliest stages of path development to understand how particular lines of development get selected

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over others, and how a hierarchical structure of knowledge develops. Extant economic arguments stress either the randomness or efficiency of path selection. In contradistinction, we have emphasized the political nature of path selection. The politics we have highlighted are not necessarily overt, but have to do with the selective attention and interests of key actors that have the ability to direct scientific and technology development in certain directions. As this ensues, viable and societally important alternatives can often become neglected. In our case, the use of NTs for medical devices and treatment became eschewed. This is political because, when science and technology have broader societal implications, commons aspects of technoscientific development become crucial and require some sort of oversight, especially when commercial interests come into play. In the case of CNTs, large commercial corporations directed science toward narrow short-term goals, and there were few independent funders supporting more plural path developments. The issues we raise dovetail with the growing disquiet regarding the openness of academic science and technology, and the ability of the general public to access its insights to solve pressing societal problems. For example, research on scientific development has shown that scientific areas that more heavily rely on patenting experience more limited innovation, less information sharing among scientists, and less productive university– industry relationships. In addition, a commercial focus can perversely inhibit directions that might have high social value. Rhoten and Powell (2007) remark that: Traditionally, university settings explored arenas that industry did not pursue. But in the absence of market incentives, it is not obvious where knowledge generation for the public interest and social good may emerge in areas such as vaccines or low-cost technologies. In some circumstances, new models of public and proprietary science have fostered the development of first-to-the-world medicines and affordable communications technologies, but in other realms, such as renewable energy, widely available breakthroughs have not been forthcoming.

The research we present is not definitive, but meant to provoke thought. We need much more research on how scientific inquiry and technoscientific development gets winnowed or directed, and with what consequences. Does the valorization of particular domains of knowledge lead to the more impoverished development of other domains of knowledge that might be otherwise equally attractive? What are the roles of funders in path construction – do they follow the herd or do they exhibit truly independent judgment? Similarly, does the emergent knowledge structure have feedback effects upon what science is done and what technology is developed?

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In other words, is early path development self-reinforcing? By taking up such questions, social scientists could contribute immensely to our understanding of science and technology and help shape policies directed to protect our science commons.

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SCIENTISTS BEHAVING BADLY? CONFLICTS IN MULTIDISCIPLINARY COMMERCIALIZATION PROJECT TEAMS Angus I. Kingon, Ted Baker and Roger Debo ABSTRACT This chapter addresses the behavioral problems and conflicts observed in multidisciplinary university commercialization teams. We examined 59 commercialization projects at one U.S. university, supplemented by a similar number of projects at other universities in the United States and Europe. We applied well-established ideas about distinctive ‘‘thought worlds,’’ including both cognitive and motivational factors to understand patterns of selective perception and issue prioritization. The resulting analysis allows us to draw tentative conclusions regarding improved management practices aimed at managing the conflicts and improving university commercialization initiatives. We discuss the generalizability of the results.

Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 59–86 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021006

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INTRODUCTION The commercialization of university technology is an important topic of both practical and scholarly interest. In the United States, universities are responsible for about 56% of basic science research, 12% of applied research, and 13% of research and development – measured by funding (Boroush, 2010). Many problems and challenges to technology commercialization have been identified and studied, with levels of analysis ranging from macrolevel studies of ‘‘national innovation systems’’ (Bamiro, 2007; Fagerberg & Srholec, 2008; Hart, 2009), to mid-level studies of institutional gaps and such problems as the so-called ‘‘Valley of Death’’ (Barr, Baker, Markham, & Kingon, 2009; Branscomb & Auerswald, 2001) between emerging technology and commercialization processes and resources, to microlevel studies focused on the multidisciplinary nature of the commercialization task and the challenges this creates (e.g., Dougherty, 1992; Lovelace, Shapiro & Weingart, 2001). The importance of universities as a source of scientific research means that special attention has been paid to the issues of commercialization of university science and technology (e.g., Siegel & Wright, 2007; Thursby & Thursby, 2007; Siegel & Wessner, 2007; Siegel, Veugelers, & Wright, 2007; O’Shea, Allen, Chevalier, & Roche, 2005). This chapter builds on the body of research on the challenges to managing university technology commercialization in order to provide some guidance that we believe is useful for predicting and dealing with some of the behavioral problems that frequently appear during these multidisciplinary projects. It develops a framework useful for understanding and managing issues that arise in the formation and work of multidisciplinary commercialization teams. We draw on simple and longstanding insights about distinctive patterns of perception and prioritization of issues within organizations and extend these insights to the multidisciplinary efforts at technology commercialization that often take place in loosely structured temporary project teams within universities. We then illustrate the framework by using it to describe and analyze a series of issues and episodes of ostensibly ‘‘good’’ and ‘‘bad’’ behavior that we have observed during the course of more than a hundred technology commercialization projects over more than a decade.

SELECTIVE PERCEPTION AND PRIORITIZATION OF ISSUES We ground our framework on some of the most longstanding and fundamental insights in the organizational behavior literature. The primary

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features of selective perception as they apply to functional and disciplinary distinctions within organizations have long been known. People perceive what their background, education, and current goals and activities have prepared them to perceive. When situations are simple and clear, people from a variety of perspectives may see things in very similar ways. The more complex and ambiguous the situation is, however, the greater the extent to which different people will see different things (Bruner, 1957). In organizational research, this implies that executives and other employees in different functional areas will perceive distinctive sets of issues as confronting the organization (Simon, 1947). Moreover, even when people are aware of issues across the different functional areas of an organization, they tend to prioritize and respond to those issues in their own departments and functions as the most important for the organization as a whole. In other words, ‘‘Behavior is very much a function of position’’ (Simon, 1995). We are particularly interested in understanding technology commercialization projects. Research in technology intensive product innovation1 in large firms shows that cross-functional collaboration – especially, but not only, between collaborators focused on market or business issues and collaborators focused on technological issues – is often essential to commercialization success, but that such collaboration fails regularly (Cooper & Kleinschmidt, 1986; Souder, 1987). Diversity creates disagreement and reduces adherence to budgets, timelines and other behavior constraints, but these effects can be managed, especially when members feel free to express doubts and when members communicate in a collaborative manner (Lovelace et al., 2001). Doherty’s field work investigating 18 product development efforts – with varying degrees of success – across five firms showed that members of different departments inhabit distinctive ‘‘thought worlds’’ (Douglas, 1987) that shape how members identify and make sense of issues. These thought worlds can then contribute to ‘‘interpretive barriers’’ that make it difficult for members of different departments to see and collaborate on prioritizing and responding to the same issues (Dougherty, 1992, p. 179). Managing such conflicts, interpretative barriers and communication requirements creates demands that may call not only for skilled leadership but both structural and cultural changes (Lovelace et al., 2001; Dougherty, 1992). In this chapter, we focus on a specific sort of commercialization project: attempts to commercialize university technologies through the development of products or services2 offered to market through new ventures. In particular, we examine projects initiated as part of structured programs that rely on interactions between people from very distinctive parts of the universities. We focus on one set of interactions: those between research

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scientists and graduate students from various departments including management, engineering, and science disciplines. Our primary observations come from several different instantiations, across several universities in four countries, of a commercialization process that includes a sequence of graduate courses enrolling students from both Management and STEM disciplines. Our setting has several features that make prior insights about the difficulties of managing selective perception and prioritization particularly useful. First, because many of the projects we observe represent attempts to commercialize early stage and emerging technologies, sometimes with ambiguous sets of potential features and functions and even unknown likelihoods of working at all. Our setting is probably more ambiguous on the technology side than many product development efforts found in commercial organizations. This suggests that differential thought worlds and selective perception are likely to play a substantial role in shaping the processes we observe. Second, although the university is a complex organization, it is in many ways a weakly hierarchical and weakly integrated organization. Although all of the major participants in the processes we observe – students, people from tech transfer offices, and science and engineering research faculty – are members of the university, unlike the case of cross-functional teams in business organizations, there is at no level of the university an individual or office that can assert hierarchical control over all of these participants.3 Further, the nature of authority in a university, whether hierarchical or technical, is often unclear or contested. There is typically no project ‘‘leader’’ able to impose by fiat decisions to mitigate the results of disagreement and ineffective communication (Lovelace et al., 2001). Thus, project dynamics are likely to be more organic and negotiated than what we would expect to find in more integrated and effectively hierarchical organizations. There is also little by way of university wide common culture that can be modified instrumentally to improve collaboration over the short term (Dougherty, 1992). Instead, the primary interventions available to help shape project success are training and process adjustments, as we will discuss below. Despite the unique attributes of our context, however, we believe that our framework may be of some general use in understanding and managing some of the microlevel processes of university technology commercialization more generally, and for any commercialization processes that involve high levels of ambiguity and the inability to bring multidisciplinary teams under simple hierarchical authority. More narrowly, we believe that

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our framework and observations may have utility for anyone trying to manage technology commercialization projects within a university, whether the processes are part of the university curriculum and course offerings or not.

THE FRAMEWORK The basics of our framework are very simple. We assume that key members of multifunctional teams inhabit different thought worlds based on their positions in the university’s role structure and their professional preparation for these roles. We also assume that distinctive differences among different thought worlds lead to differences in selective perception and prioritization of issues, which in turn shape patterns of behavior. The basic role structure of the university provides the context for development of the thought worlds, and therefore provides a structural explanation useful for understanding behavioral issues that arise during commercialization. To the extent that the university role structure is nonmalleable over the short term, this implies that improvement of project team performance requires nonstructural solutions to structural issues. The degree of project ambiguity, driven by technology, product, and market uncertainties, is expected to shape the extent to which differences in thought worlds play an important role in shaping individual behavior patterns. For example, science faculty members typically inhabit different thought worlds than graduate students. In addition, graduate students in STEM disciplines may – perhaps to a more limited extent – be influenced by different thought worlds than MBA students. We expect differences in the extent of the influence of different thought worlds based on the length and degree of professional immersion in a position. Thus, for example, someone who has run a tech transfer operation for a decade is likely to be more influenced by that thought world than is someone new to the job, and masters students in electrical engineering will be less influenced than senior faculty in the discipline. In addition – explaining how position shapes differences in how individuals perceive and prioritize issues – it is possible to identify and distinguish independent effects of internalized cognitive versus external motivational factors (Dearborn & Simon, 1958). We draw on this distinction to help in understanding, anticipating, and managing behavioral issues in multidisciplinary commercialization projects.

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METHODOLOGY The research is based on a study of 59 commercialization projects involving more than 250 project team members during the course of the past 13 years. These multidisciplinary projects were undertaken by graduate student teams as part of a two-course graduate Technology Commercialization sequence at a major Research I state university (hereafter termed the ‘‘State University’’). The course sequence combines the educational objective of teaching and embedding technology commercialization and technology entrepreneurship skills with the commercialization objective of creating real business proposals and subsequently new technology businesses. The program has been described in detail elsewhere (Kingon, Thomas, Markham, Aiman-Smith, & Debo, 2002; Barr et al., 2009). Although information is available for a longer time period, we focused on the 59 projects for the period 1997–2009 where complete documentation of each of the teams and their reports are available, along with the written exchanges of information for the period 2005–2009. The three authors were instructors of the course, and simultaneously managers of the commercialization projects. We had CVs containing (inter alia) information about individual backgrounds, education, and work experience of all team members. Project teams typically consisted of four to seven people, and each team contained both MBA and STEM students (MS and PhD). Occasionally a team included a student with a non-STEM, non-MBA background (such as Technical Writing, International Studies, Psychology, and History). During the period 1997–2005, team members with similar technical backgrounds (such as computer science or life science or materials science or electrical engineering) were grouped into the same team (but always with MBAs in the team). From 2005 on, a different strategy was adopted in which the teams where constructed to deliberately maximize the technical diversity in each team. At the beginning of each annual course sequence, each team constructed a common document describing their team objectives and constraints. Additionally, the authors held a general discussion with the class (i.e. the commercialization project team members) about their individual motivations and objectives. Even more importantly, at least two of the three authors interacted with each of the 59 teams, and at least one author held discussions with each team or members of each team on a weekly basis for the 9-month duration of each commercialization project. One of the authors gained additional experience and insights by being a team member on one project team. The technologies for the commercialization projects were sourced primarily from the State University, as well as universities from the same

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state university system and neighboring universities. Additional technologies were sourced from other regional universities, research institutions, and both large and small companies. Most of these were ‘‘early stage’’ technologies, emerging out of the discovery science. This increased the ambiguity of the projects, with application directions and product concepts not defined at the point the commercialization project was initiated. The other group of stakeholders influencing the commercialization projects was the scientists from whom the technologies originated. Interactions occurred directly between the commercialization team and the scientists. However, it is important to note that the interactions were reported and documented by the teams, and non-normal interactions were reported to the authors (in our role as the course instructors). Furthermore, where there was an indication of significant conflict, a meeting was typically held to discuss or resolve issues, with one or more of the authors present in the meeting. Thus we were alerted to, and sensitized to, conflict between the teams and the extra-team stakeholders. Intra-team conflicts and dynamics became apparent during the regular discussions with the teams. The three authors together utilized the above information and theoretical framework discussed earlier to construct descriptions of typical thought worlds and motivations for the actors and stakeholders in the commercialization program. Clearly, the description of each thought world is incomplete – we describe only those aspects of the thought worlds that were related to the commercialization activities. These thought worlds and motivations were utilized to develop an understanding of the observed patterns of behavior. The data and insights gained from the above 59 projects were supplemented by data and interaction on university commercialization projects taking place in parallel at 13 additional universities in the United States, the United Kingdom, and Portugal. The projects, when combined with the State University projects, numbered well over 100, and were linked by a common core process (we term this a commercialization ‘‘process’’ in subsequent text) for teaching and commercialization (Barr et al., 2009). However, differences in actors, structure, and culture between programs at the different universities in different countries allowed us to make comparisons and generate additional insights into patterns of behavior, sources of conflict, and managerial implications. This chapter draws on examples across all of the university settings. The investigation reported in this chapter was not focused on individual project success. We want to emphasize that the sorts of conflicts we analyze in this chapter can usually be avoided or managed. Despite the conflicts

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reported in the chapter, the U.S.-based projects following the process we analyze attracted about $300 million in equity investment and supported the creation of approximately 1,000 jobs to date (Barr et al., 2009).

PATTERNS OF CONFLICT Table 1 briefly outlines several distinctive thought worlds and variations for the actors in the commercialization projects. The table describes the distinctive thought worlds of MBA students, MS students in STEM disciplines and PhD students in STEM disciplines. First for the students and then for the research scientists, the table describes distinctive cognitive elements followed by motivational elements of the thought worlds we observed. Here, we provide a short description of a typical project to illustrate the activities of the players, and the way in which projects typically played out4: Life Science project: A faculty member was working on a sophisticated but routine chemical analysis facility, allowing breakthrough improvements in the characterization of disease precursors in specific human tissue samples, thereby resulting in a related breakthrough in the modeling of disease probabilities and progression and also in detection. The university had protected the ideas through patent application, and the faculty member was strongly interested in commercialization. The university technology transfer office on the other hand was convinced that there was no commercial value in the ideas. During the course of the 9-month project the students developed an attractive business opportunity for which the technology provided a strong advantage, and they continued to work with the research scientists and a faculty member from the business school. A full business proposal was developed over the course of the next year, and a new venture was launched, with the university licensing the IP to the startup, mainly in exchange for royalties on future revenues. The faculty member left the university to participate in the venture full time, while the students graduated and went back to their full time jobs or took positions in industry. The business grew, and currently has annual revenues of approximately $50 million, and the university has realized a nice royalty stream.

Our framework allowed us to identify four patterns of inter-role conflict across the projects we observed, along with one pattern of individual level struggle and learning. In the sections that follow, we describe each of these patterns in terms of our framework, and provide illustrative examples. Then, we use the framework to describe and suggest some approaches to fruitful management of the conflicts, also identifying a few that seem particularly difficult to manage.

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Table 1.

Thought Worlds.

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Table 1.

(Continued).

It is important to note that the patterns we describe are ‘‘ideal types’’ in the Weberian (Weber, 1978) sense – constructs that are useful for identifying and distinguishing between empirical cases, but not typically found in pure form. That is to say, although we describe types and cases in which the predominant factors were either cognitive or motivational, the majority of cases involved some admixture and indeed some interaction of these factors. It is also important to note that in the vast majority of cases we observed: (1) conflicts were managed by routine program processes (as described below); (2) conflicts which did emerge were managed with little apparent damage to outcomes; and (3) the lesson learned by participants from the conflicts they experienced were likely worth the costs and frustrations.

‘‘I Want to Run this Company’’ – Overly Self-Serving Motivation The first pattern is very simple. During the course of the projects we observed, the potential financial value of entrepreneurial and commercialization activities often does not become apparent until months of joint and combined effort from all of the participants has identified or created the opportunity. In general, this joint work creates a sense of shared opportunity, mutual respect, and a common belief that everyone who is interested in participating in further development and exploitation of the opportunity has earned their place. However, on occasion, one or two participants will come to the (oftentimes accurate) conclusion that although all team members’ work was necessary to explore the opportunity, some or all of the participants are expendable for the process of exploitation. These one or two participants may then begin to behave in a manner intended to

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exclude other participants to claim a larger share of the potential financial and other rewards for themselves. We observed two forms of this conflict, which we label the ‘‘greedy scientist’’ and the ‘‘greedy MBA.’’ Greedy Scientist We consider this the single most abusive and morally questionable source of conflict we observed. On several occasions, scientists who had explicitly agreed to work as members of commercialization project teams and to support the licensing of their technology to the team in the event that an opportunity became apparent, simply ‘‘stole’’ the project from the rest of the team once the opportunity had been developed. Semiconductor project: The technology was robust and innovative, the scientist was well-known, and she made it clear that she wished to commercialize the technology, and had in fact founded a small company to facilitate the process. The company already had two employees, and some funding for development. There was an agreement that the commercialization team would undertake business development, with the understanding that they would ‘‘earn a right to participate’’ in a commercial outcome if they made relevant and useful contributions. The team made good progress, including the identification of unique products based upon the technology, research into customer needs, and the development of a business model and a compelling business case. Toward the end of the 9-month project, however, it became clear that the scientist and her business partner were not going to allow the commercialization team to participate further, despite prior promises and the students’ clear and significant contributions. She appropriated the work that the students had undertaken, and continued the commercialization process without the students.

Greedy MBA In other cases students, sometimes teaming with a collaborator from the local business community, began taking steps planned to ‘‘cut out’’ other participants from the process and rewards of opportunity exploitation. The most common observation was of one or more MBA students moving the project in a new direction, without involving or informing other students. Life science project: It was seven-months into the 9-month project. The commercialization project team, working with a technology disclosed to a nearby university, had created an impressive product concept, an efficient and cost-effective device to be used for home medical care. By this point in the project, the proof of concept had been performed, bottom-up market research allowed a strong value proposition and business model to be developed, and the business plan was evolving and had captured the attention of the dominant competitors in the market. It had become clear that the project represented a real, high-growth startup opportunity and that a venture should be formed around it quickly. Then, mayhem erupted within the team, driven by the excessive selfinterests of team members. One particular team member, an MBA student, sought to

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Less frequently, students decided that they also did not need the further support of the scientist and sought ways to work around the scientists’ intellectual property claims, even if the scientist had actively participated and supported the commercialization process and was interested in continuing to be engaged.

‘‘I Do Science’’ – The Clash of Ostensibly Pure and ‘‘Impure’’ Motives Every project we observed involved one or more scientists who, as a prerequisite for participation, had committed to participate and to devote time and resources to support the projects. Most commonly, the claims on scientists involved the need to bring students up to speed on some details of the technology, assessment of what further applied work would be required to create commercially valuable elements, and cooperation on seeking funding and organizing activities to engage in further development. Discussions with each scientist at project inception established that, despite whatever commitment they had to continue doing pure ‘‘discovery science,’’ they were also motivated to do the work to see their discoveries become embedded in useful and potentially profitable goods and services. Unfortunately, the scientists sometimes appeared to lack the degree of self-understanding, and particularly the understanding of the depth of their own commitment to discovery science, to make their commitments to the commercialization project work meaningful. As other project members began to develop commercial opportunities and to make claims on these scientists’ time and resources – generally, claims for less than three hours per week – some scientists would begin to realize that anything that took away from their focus on discovery did not really fit into their primary goals or motivations. Despite their earlier (apparently self-believed) claims of interest in supporting commercialization activities, these scientists’ motivations were at strong variance with the motivations of the other participants committed to commercialization. In these cases, the primary source of variation in the degree of damage done to projects was driven by the scientists’ timing in realizing their lack of motivation to do commercial work and their timing in communicating this realization to other participants. Fortunately, when scientists exhibited

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reluctance to help out early in the projects, other participants were able to extract themselves and move on to alternative opportunities. Other times, scientists’ reluctance seemed to build cumulatively, such that they were eager participants early on, but grew tired of the work three to six months into the projects. Less frequently, scientists would appear eagerly engaged through the entire process of market and technology research and opportunity development, and would not disengage until the projects approached the point where it was time for other participants to ‘‘pull the trigger’’ on beginning to exploit the opportunities the projects had discovered or created. Only in these very late cases did we ever observe clear communication from the scientists to other participants that they would not be involved. The rest of the time, other participants had to infer the scientists’ growing reluctance from their behavior. Cancer detection project: The project progressed smoothly through the two-semester sequence. The faculty member was off campus, and the students had limited access to him, as he was a busy clinical researcher. However, the intellectual property protected an extremely innovative concept for detecting an important form of cancer. Proof of concept had been demonstrated, and it appeared that the approach was a viable solution to an extremely pressing need. It was apparent to everyone involved that this was an opportunity that should be pursued. The team members were eager to continue, but it was necessary to interact with the scientist to ensure continued technical development, to raise funding, and to formalize the team and the way forward. But the scientist simply became unavailable, and it became apparent that his primary research was simply far too high a priority relative to the commercialization project. Eventually, the commercialization team members gave up any expectation of increased interaction, and they went their separate ways. The opportunity was lost.

‘‘I Know and You Don’t’’ – COLLIDING THOUGHT WORLDS As noted elsewhere (Barr et al., 2009), the cross-disciplinary nature of the commercialization projects we observed tended to create deep awareness across both MBA and STEM students of the limitations of their own knowledge. Most scientists, especially if they had little direct experience with commercialization, also understood the projects as learning opportunities, and they displayed an openness and willingness to engage that enhanced the learning for all participants. However, we also observed several other patterns, each a variation on a theme of arrogance about one’s own level of knowledge or even intelligence relative to other participants.

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Incorrect and Unsupported Beliefs and Claims about Technological Capabilities The projects we observed typically involved early stage technologies, sometimes earlier even than proof of concept, generally before the existence of a robust prototype. At some point in most projects, therefore, participants have to act on the supposition that a scientist’s technology can do what the scientist claims it can do. The single biggest cause of failure we observed across projects was the late discovery that the scientific optimism (which, of course, is very different from the cognitive stance we expect to see among scientists in their scholarly work) had caused an overstatement of what the technology could do. Lack of Awareness of What You Don’t Know Most good scientists appear to be aware of other teams at other universities or in industry working in the same problem space as their own labs. They also, as a matter of course, keep up to date with new publications in their primary areas of research. We were therefore somewhat surprised at how often scientists appear to be completely unaware of competing commercialization projects, and also unaware of potentially competing science that provides competing technological capabilities to those that the focal scientist is trying to create. In most cases we found that the university scientists were not keeping up with the patent literature in their own or in these relevant competing fields. In many of the projects, the commercialization teams brought patents to the attention of the scientist, in some cases even when the scientist had filed disclosures himself. We were even more surprised, however, at the high levels of confidence some scientists exhibited in the face of their ignorance about competing labs and commercialization activities. Project participants unaccustomed into dealing with this false confidence were at risk of failing to do primary competitive research, based on scientists’ claims that they already knew what was going on. Overconfidence about Entrepreneurial Capabilities and Outcomes It was relatively common to observe excessively high levels of confidence on the part of the scientist regarding both his/her entrepreneurial decisionmaking capabilities, and the likely success of the intended venture if he were to lead the venture creation. This entrepreneurial overconfidence is well documented in the literature (Busenitz & Barney, 1997; Baron, 2000), but has not been discussed as a source of conflict. However, in the case of our projects, the overconfidence of some scientists led to conflict, primarily because it also resulted in the scientist downplaying the role and value of the student team members.

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Software project: The scientist was clearly successful, and well-respected in the university, and in his field. He had developed advanced data parsing algorithms, and had recognized that the method could be applied to a class of problems in the area of consumer products. He was extremely keen to create a commercial venture from his technology, and had hired students to undertake key software development. However, he had not specifically identified the product or products that would be produced, nor identified the markets, and there was also a great deal of variance as to the potential position in the value chain, and what the business model could be. As a result, a small commercialization team set out to help resolve these key business questions. However, the relationship between team and scientist was never smooth, and it became apparent that the scientist required absolute control over all the aspects of commercialization, and saw himself not as a member of the team that would found a venture, but as the sole owner and decision-maker. Furthermore, while he accepted some input from the commercialization team, it was clear that he considered himself far more expert on matters of commercialization than they, despite clear lack of experience. He continued to develop the venture, but the commercialization team members completed the business proposal and moved on to other activities.

I’m Smart and You are Not Finally, most scientists and particularly some of the most successful scientists we observed were remarkably humble and respectful of other project participants. A very small number, however, exhibited an astounding degree of intellectual arrogance that on occasion, intimidated or at least put off other participants. This attitude appears to be much stronger than simple entrepreneurial overconfidence (Busenitz & Barney, 1997; Baron, 2000). Our speculation is that this arrogance was somehow related to a belief that the superiority of science over other forms of knowledge somehow was attached to the scientist as a person, or that it was related to a sense that, compared to established scientists, students are somehow intellectually ‘‘unworthy.’’ The display of this arrogance ranged from a refusal to explain their science on the basis that ‘‘you won’t be able to understand it anyway,’’ to taking exception to any question or discussion that in any way appeared to challenge the scientist’s prior beliefs or claims.

Master and Servant – An Unsavory Stew of Cognition and Motivation As we noted earlier, very few of the conflicts we observed were rooted in only the cognitive or only the motivational elements of individuals’ thought worlds. The typology and examples above are based on cases in which one cause appeared predominant. More often, however, conflicts were generated from some mix of cognitive and motivational components and even from

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the interaction of the two. We focus here on just two particularly informative cases. You are Unworthy and I Find this Project Attractive The following is an example of a case where there did not appear to be one single dominant factor driving the behavior of the scientist which resulted in conflict. Electronic device project: The scientist is well known, with an impressive publication and patent portfolio. The commercialization team is mature and motivated. The proof of concept has been demonstrated. The understanding is that there is a real potential for a new venture, and across repeated discussions the scientist has given explicit assurances he would partner with the rest of the commercialization team to create the venture. However, while expressing this commitment, he has simultaneously been negotiating with the TTO for the exclusive rights to the IP. The commercialization team was taken by surprise when an announcement was made of the creation of a new venture (with support from the TTO) and with the complete exclusion of the commercialization team members. By way of explanation, the scientist made the comment ‘‘they are just students, they are getting an education, and they work for us’’ and ‘‘we own whatever they produce y’’ The interaction had a deep impact on the commercialization team members, and they harbor strong negative feelings towards both the TTO staff member and the scientist.

In this case there were clear signs that there was likely to be conflict. The attitude towards the student commercialization team surfaced early, with a display that might be described stylistically as ‘‘the students are minions and have little to teach me, and ‘business’ is so easy that I can learn what I need to know with very little effort.’’ The motivational aspect of greed began to emerge, and became apparent with the announcement of the sole ownership of the new venture, suggesting strongly that both cognitive and motivational factors were in play. In this case the cognitive or motivational factors were sufficiently strong that either alone was likely to result in conflict. Of greater interest was the situation in which selfish motivation and cognitive overconfidence occurred at seemingly low or moderate levels, but together were enough to lead to destructive outcomes. Life Science Project. The scientist is enthusiastic about commercialization of research. He talks about and thinks about applications for his own research, which appears to have attractive potential for innovative new products. He talks about the need for collaboration. He appears to be the ideal scientist for participation in a commercialization process. The commercialization team spends the nine-month period developing product concepts, developing a business model, commercialization strategy and business plan. On the surface, the interactions with the scientist appear to be going fairly well, but

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there are some strains developing under the surface. On deeper analysis of the interactions, it appears that an underlying overconfidence of the scientist is playing out in the interactions with the commercialization team, with the sense slowly emerging that the scientist holds the view that the student’s task is to deliver information to him, for him to utilize to make key decisions. Additionally, to protect his financial interests, the scientist has already formed a venture entity, and it becomes increasingly unlikely that any of the students will be able to participate.

In contrast with earlier cases that we have described, all of this plays out in a friendly and cordial way, in the context of apparently open and honest interactions. It was very difficult to point to overt signs of ‘‘bad’’ behavior. Thus, this form of what we consider destructive behavior did not require an extremely greedy or an overtly arrogant scientist – just someone who was a little of both.

‘‘Wow, I See the World Differently Now!’’ – The Transformation of Thought Worlds In the case of the MBAs, we would understand their learning as primarily linear, with strong components of both motivational changes and selfefficacy (Barr et al., 2009). During the course of their projects, MBAs added perspectives and skills relevant to technology entrepreneurship, expanded their language and skills, and interacted with a diverse network of people, both inside and outside their teams. On the other hand, we understand the impact of the experience on many of the STEM students as ‘‘transformative.’’ They expressed and reflected a dramatic change in their perceptions and perspectives of their science/technology, and they dramatically broadened their decision-making processes. STEM students appeared to develop a much better understanding of science and technology in the context of business and, more broadly, society. For example, this was strongly reflected in the dramatically different way in which they articulated ‘‘their’’ technologies and technology capabilities through the course of the project, and their improved ability to articulate the economic and social value and utility of their science and technology to people outside their domain of expertise. On a few occasions the same ‘‘transformation’’ could be observed in the faculty scientists with whom we were interacting, as we discuss later. We interpret the reason for differences between business and STEM graduate students as follows: All students faced a high degree of project ambiguity, particularly at the start of the projects. But while the business students’ trained thought worlds had somewhat prepared them for

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irreducible uncertainty, ambiguity and risk, the STEM students were accustomed to dealing with challenges that were – in principle, at least – answerable. The science and engineering thought worlds encompass a faith in the ability of science, given enough time and enough resources, to reduce uncertainty, ambiguity, and risk. Thus, the challenge of facing essentially unanswerable questions about how the future would develop, how early stage technologies would pan out, how markets and competitors would behave and whether an opportunity could be constructed often struck the STEM students as more foreign and more frustrating than how it struck the MBA students. As a result, the STEM students were forced to adopt what were – for them, to a much greater extent than for the business students – radically new perspectives, frameworks, and decision-making processes in order to progress. The methods that we were using facilitated the adoption of the new cognitive processes. In many cases these new cognitive processes accommodated both their prior thought world, and the entrepreneurial circumstances in which they were operating. We observed that a few (a small minority) of the scientists or engineers did not make the cognitive transformations, and continued to struggle to ‘‘make sense’’ of the activities throughout the projects.

DISCUSSION AND IMPLICATIONS FOR MANAGING UNIVERSITY COMMERCIALIZATION TEAMS The thought worlds framework appears to have utility in providing a simplifying and clarifying lens through which to view the interactions between the players involved in the commercialization of university technology. In particular, dividing the thought worlds into cognitive and motivational components provides further insights, especially with regard to how relatively low levels of greed and arrogance could interact to undermine even the most promising opportunities. In considering the MBA and STEM students within the teams, we have noted that we observed rather low levels of conflict or dysfunction related to these members of the teams, and especially in comparison with the levels of conflict created on occasion by some of the research scientists who were the sources of technologies. This was despite the high levels of ambiguity faced by the business/management and more especially the STEM students at the outset of the projects. As mentioned in the previous section, we ascribe this to two factors: (1) the relative malleability of their thought

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worlds, given that most were young in their careers, and they had selected themselves into an educational program and (2) the fact that we had provided process guidance to address the differences in thought worlds and to address the ambiguities. It is worth making a couple of observations based on a comparison between the U.S. and the Portuguese programs. First, for a period of time we observed a stark ‘‘division of labor’’ within the teams at one location in Portugal, with the scientists undertaking the technology tasks, and the MBAs providing ‘‘market research.’’ Such a division of labor meant that nobody’s way of looking at the world was challenged very severely, thus lessening the educational experiences, but also resulting in some conflict as any shortcoming was automatically the fault of the ‘‘other’’ group within the team. This situation was largely remedied by taking care within the process to set the expectations and rationale at the outset, and to ensure that tasks were shared. Second, for a period of three years, the MBA students at a Portugal location associated with the commercialization projects were from a Portugal MBA program that did not emphasize or systematically teach entrepreneurship. The thought worlds of the community of MBA students from this institution strongly promoted, and even guarded, the normative preference that students should pursue careers in major corporations or in consulting firms; the MBA students’ thought worlds appeared to be somewhat less malleable as a result. Although outcomes were still satisfactory, and there was not overt conflict, which we ascribe to the strength of the guiding process, it was clear that the program was having less impact on these MBA students than the students who were more committed in learning new venture creation skills. We turn now to discuss the scientists. It seems clear from our investigation that it was the interactions that involved the scientists (the technology sources) that were the most complex, difficult to manage, and most likely to result in conflicts. Using the thought world framework we could discern patterns of behavior that corresponded to a clash of the thought worlds of the scientists with those of the rest of the commercialization team members. Cognitive differences resulted in a series of the conflicts, and others could be ascribed to a clash of motives, as discussed in the previous section. We need to emphasize that these scientists were not located within the project teams, but instead met with the teams outside the class times. The frequency of meetings between team members and scientist varied from project to project. Additionally, the scientists were not exposed to the details of the process followed by the team, although the teams were expected to provide regular feedback to them.

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It is useful to make a further comparison with the parallel program in Portugal, where in each case the scientist’s research group had direct membership on the team, and participated in the entire process within the course structure. University research groups applied to participate in the program and went through a selection process. These research groups proposed the individuals who would participate in the course, normally proposing two team members. These participants included a mix of senior faculty members and junior researchers (such as post-doctoral research associates), or even senior PhD students. We observed fewer scientist-related conflicts in Portugal than we did in the case of the U.S. program. Where conflict or difficulty did occur, it primarily involved the senior scientists. Although there were clearly other factors that could be at play, our observations are consistent with the notion that the thought worlds of the junior scientists are less strongly embedded, and that well-structured process can overcome the potential sources of conflict arising from the differences in thought worlds and the complexities faced by junior scientists than is the case for senior scientists. Let us return now to the U.S. programs where the scientists were not tightly coupled into the teams. The team members interacted with them on a regular basis, but the scientists were not themselves following the structured process. The comparison with the Portugal programs (described earlier) provides fairly strong evidence that strengthening the integration of the scientists with the team, or providing some support process that includes the scientists, would mitigate much of the conflict. We observed one case within the 59 State University projects in which the scientist worked very closely with the team, even attending many of the lectures and team sessions. The scientist was a mid-career scientist who wanted to understand the commercialization process. In this one case there was no conflict, but more importantly there was a strong indication that the thought world of the scientist was shaped by the experience. He reported that ‘‘I will never be able to look at my science in the same way again,’’ suggesting that the perspectives gained affected the choices he would subsequently make regarding his experimental research. These pieces of information point once again to the value of developing process management approaches to overcoming difficult to change structural issues, and thereby improving interactions with the scientists in technology commercialization projects. However, the investigation also made it very clear that when the cognitive elements of the thought world are very strongly entrenched (almost all such cases involve successful senior scientists) or there are strongly self-serving motives, then none of the managerial interventions appear to be effective.

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The pattern of behavior of these scientists with respect to commercialization seems very difficult to change. The combination of the two factors (entrenched self-regarding cognition and self-serving motivations) appears to be particularly harmful and challenging to overcome once a project has begun, even when both are present only in relatively mild form. A final note on the issues and struggles faced by team members: we have already mentioned the prevalence of cognitive biases among entrepreneurs, including over-confidence (Busenitz & Barney, 1997). Although we certainly observed these among team members, particularly early in the sequence, the process was designed to minimize the impact of these biases through a strong emphasis on requiring team members to base their predictions on factual evidence, mostly acquired through extensive ‘bottom-up’ primary market research. Furthermore, the process required an accounting of risks associated with unconfirmed hypotheses or assumptions. Although not eliminating the tendencies, it appeared to provide an effective way of reigning in the proclivity to make overconfident predictions without grounding evidence. This chapter has not focused on the Technology Transfer Offices (TTOs), but their role is important in the overall effectiveness of the technology commercialization process of the university. The TT officers, at a minimum, interact with the scientists and technology commercialization programs as part of their intellectual property management duties. During the course of the program that we examined at the State University, we could see an evolution of the TT organization, corresponding to a change in their thought worlds. We have seen the same evolution occurring in other universities, although the pattern may be at an earlier stage, particularly in Europe. A protectionist culture evolved after the enactment of the BayhDole and Gramm-Rudman legislation in the early 1980s. This culture is conservative, not very risk-taking, and dominated by rules and routines. This resulted in conflicts with scientists if the patent protection process appeared too slow and cumbersome, and if it restricted their ability to work with industry. In many universities, particularly in the United States, this evolved into an expansionist culture, where the TT organizations began to understand the income opportunities associated with licensing, and began to focus more on commercialization, undertake business development, support new venture creation, and even manage venture funding. Problems of conflict occurred when the TTOs believed that this expanded function was their exclusive domain within the university. With time and (often hardearned) experience this evolved into the pragmatist culture, where the TTOs recognized the advantages of partnering and playing a supporting rather

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than dictatorial role in technology commercialization. At the same time the TTO objectives have evolved from one of a cost center, to one in which the TT organization plays a more strategic role in maximizing the commercial and social impact of the university. With the pragmatic approach, conflicts between scientists and the TT organizations have typically reduced. Thus, the thought worlds and the structures of the TTOs provide a nuanced university context in which the technology commercialization interactions play out. Changes to the TTO can bring about improvements. Although these may be slow in coming, at least two of the universities we have observed have undertaken programs to quicken and shape such change.

Major Learning Outcomes from the Project The investigation has yielded the following lessons: 1. Most of the conflict has involved the scientists who were collaborating with the commercialization teams, but not tightly coupled into the teams. (Sometimes scientists do behave badly!) 2. In most cases, conflicts could be managed, and processes and direct interventions were effective. 3. Where it became apparent that conflicts were not manageable, they usually involved senior scientists with deeply embedded thought worlds. (These leopards could not change their spots!) 4. It emerged that, on occasions, a mix of a little arrogance and a little greed, subtle enough to not be readily apparent, could lead to conflict and disruption of the commercialization project. 5. There was relatively little conflict within the teams, and we argued that this was due primarily to the extensive supporting process designed to set common goals and expectations, and to provide guidance in the case of the inevitable ambiguities. It is equally clear that ‘‘structural solutions’’ would not be sufficient to manage the conflicts described in this investigation.

Specific Managerial Recommendations: The investigative framework leads to several specific recommendations on which we now discuss.

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1. Vetting of scientists. One implication of our work is that if the thought worlds of certain scientists are too deeply entrenched to manage the resultant conflicts, then our recommendation is that it is probably expedient to exclude these scientists from a program where the commercialization is undertaken by relatively junior persons, such as the graduate Business and STEM students. This calls for a vetting process. First, the vetting process should be designed to probe for those scientists who are so strongly entrenched in the cognitive processes associated with discovery science that they will not ascribe sufficient importance to commercialization, and the associated time or effort. The identification of this group may not be straightforward, for the reason given earlier that many of these scientists will state an interest in commercialization, but the lack of assigned priority may only emerge later. Secondly, the vetting process should exclude scientists who are interested in commercialization but have a very strong motivation to control the commercialization process and its benefits (usually implying a scientist-founded startup). Thirdly, the vetting process should identify the ‘ideal scientists’. Our work suggests that this is a discovery scientist who is producing high-quality research results (science quality correlates positively with commercialization success: O’Shea et al., 2005), is interested in commercialization, and wants his/her research to have commercial and social impact, but acknowledges his/her lack of expertise in the commercialization arena. Based on our data, we believe that it would be possible to generate a set of probing questions that would help to support the vetting process. We are currently engaged in trying to develop such an instrument. Fourthly, the vetting process could steer the scientists into the most appropriate commercialization route, as discussed in (2), below. 2. Provide alternative commercialization routes: ‘‘One size does not fit all.’’ Universities are typically motivated to improve their overall commercialization performance as well as keep their faculty happy. As a result, it would not be expedient to exclude the scientists aforementioned without providing alternative routes to satisfy their commercialization ambitions. In fact, recognizing that business development is the key bottleneck to enhanced commercialization of university technology, multiple complementary commercialization routes involving different business development resources are strongly needed. Examples of complementary routes include:  The graduate commercialization teams that are the focus of this study, and who undertake a complete business development case to drive the commercialization process. Owing to the more junior level of the team,

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this route should be reserved for projects in which the source of technology does not deviate too strongly from the model of the ‘‘ideal’’ scientist described above.  Business development staff in the TTO undertaking business development in conjunction with the individual science faculty member.  Provision of information, resources, and faculty development workshops that support those faculty that wish to undertake the business development associated with commercialization on their own.  A ‘‘matchmaking’’ service that matches the individual faculty member to an outside entrepreneur who undertakes the major work of business development. Where the vetting process suggests that self-serving motivation is strongly embedded and/or the scientist’s belief in his/her entrepreneurial capabilities are strongly embedded, it would be logical to direct the scientist toward undertaking the business development of commercialization on their own, making use of support services provided by the university. It is also tempting to think that the match-making route also may be effective for cases where the scientist is matched with an experienced and highly regarded entrepreneur. We speculate that mutual cognitive and motivational accommodation and respect may make the thought world of the scientist more malleable. Of course, it is clear that there is significant risk in this approach. The number of these well-qualified serial entrepreneurs who are available at the appropriate time is very small, and the university may not wish to accept the considerable risk of conflict and loss of a valuable partner. And the risk is real: we can point to one example where the University went to considerable lengths to accommodate a ‘‘star scientist’’ with entrepreneurial aspirations. He was partnered with a senior, experienced entrepreneur, who attempted to work with the scientist, but eventually was forced to walk away from the partnership. This experience is related to others described by Pollock, Fund, and Baker (2009). 3. Provide supportive processes: Our analyses strongly suggest that the provision of carefully designed processes to support the activities of the commercialization teams, particularly where the team includes students, is effective in avoiding or reducing conflicts, and should be considered in some form where universities are undertaking commercialization activities.

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4. Pay attention to the relationship between the scientists and the commercialization teams. Our data also suggests that it may be valuable to better integrate the scientists with the commercialization teams, or at least provide some improved processes to manage the expectations of the team and the scientists. The following represents a range of possibilities:  Include junior scientists within the commercialization team. In Portugal the science group elects several members onto the commercialization team, including senior scientists and post-doctoral researchers.  Provide some limited process that brings the scientists and commercialization team together at particular prescheduled points in time or according to pre-specified milestones to jointly address questions raised in the commercialization process. The underlying objectives would be to increase the vesting of the scientist in the commercialization project and increase the scientist’s understanding of the commercialization process, thereby attempting to bridge thought worlds, or better yet, induce mutual accommodation and learning.  At a minimum, greater effort should be made to ensure that the motivations of the scientists and the team members are aligned. To that end, for example, the State University has implemented a ‘Letter of Intent’ where commercialization team and scientist meet to discuss their motivations and expectations, and sign a common document that articulates these.  Recognizing that a significant source of conflict was scientists’ sometimes inaccurate depictions of their technologies’ capabilities (see ‘colliding thought worlds’, above), the State University has recently implemented some supporting processes that cause the commercialization team to iterate back and directly address issues of whether the technology ‘‘actually works’’ in a manner that supports the product advantages imagined in the commercialization project. These processes force the commercialization teams to ask some ‘‘hard’’ questions, to seek data relevant to the research scientists’ claims and in some cases to seek outside scientific opinions.

CONCLUSIONS This chapter focused on commercialization projects at the State University, where the primary ‘‘workers’’ on the commercialization team were MBA and STEM graduate students. We were also able to draw useful

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comparisons to universities in the United States and abroad. The ‘‘thought world’’ framework, with its cognitive and motivational components, appears to provide a useful investigative lens through which to view much the interactions between actors in commercialization processes, and may therefore be broadly applicable to commercialization processes, even those outside the universities. However, the ability to draw inferences by comparison with related programs at other universities, in the United States and abroad, implies that the above results and specific recommendations should be applicable more broadly to university commercialization activities.

NOTES 1. For the purposes of this chapter such product innovation is ideally the same as technology commercialization; they differ mainly in terms of patterns of failure. A ‘‘technology commercialization’’ focus may be less likely to integrate market issues and a ‘‘product innovation’’ focus may be less likely to integrate technological issues. 2. We have used the term ‘‘products’’ to refer to both products and services. In most projects, technology commercialization results in both product and service embodiments, although the strategic assessment of commercialization usually results in an emphasis of one over the other. 3. Of course, in large organizations, the lowest-level position with true hierarchical authority over a highly multidisciplinary team, sourced from many different departments, may be at a very high level indeed, compared to the main participants in the project. This would suggest parallels between our university setting and those in large commercial organizations. 4. The examples in this chapter have been stylized and inconsequential details altered to assure anonymity; in addition, we refrain from identifying any specific example with any specific university.

ACKNOWLEDGMENTS We acknowledge the contributions of Dr. Joao Claro, of EGP Porto, and of Dr. Pedro Vilarinho of COTEC, Portugal. This research was supported by a grant from the U.S. Department of Education’s Fund for the Improvement of Postsecondary Education (FIPSE).

REFERENCES Bamiro, O. A. (2007). A framework for the strategic design of science and technology policy for African development. African Development Review – Revue Africaine De Developpement, 19(1), 217–255.

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THE EVOLUTION OF TEAM PROCESSES IN COMMERCIALIZING HIGH-TECH PRODUCTS Leslie H. Vincent ABSTRACT This chapter examines the role of team processes in predicting overall effectiveness for multidisciplinary teams charged with commercializing new technologies. Theory suggests that both social- and task-related processes are essential in order for diverse teams to achieve their full potential. Furthermore, these team processes evolve over time, creating even more complexity related to technology commercialization. A panel of teams is surveyed over time to capture this dynamism and the role of key social and task processes. Results suggest that social team processes, such as cohesion and identification, predict affective performance (i.e., team satisfaction and commitment). Objective team performance is primarily a function of task cohesion and trust. Furthermore, affective performance serves as a mediator between social team processes and objective performance for these high-tech teams. Post-hoc analyses examine the differences in the development of both task and social processes for high- and low-performing teams. High-performing teams have higher levels of task-focused interaction, functional conflict and task Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 87–118 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021007

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cohesion early on in the commercialization process as compared with low-performing teams. Effective teams establish key social processes early on, which provides the foundation for team success.

INTRODUCTION The use of multidisciplinary teams has grown in importance as organizations tackle complex and uncertain issues (Van der Vegt & Bunderson, 2005). The primary advantage associated with such diverse teams is that multiple perspectives and areas of expertise are brought together to provide the necessary knowledge required to encompass and represent all aspects of the problem at hand. This is particularly important for highly complex tasks such as technology commercialization where expertise in the technology, the business environment, and the ability to protect the technology are critical to the successful commercialization of the technology. Successful technology commercialization requires the integration and collaboration of multiple perspectives. Although multidisciplinary teams have the potential for enhanced performance, the path to success is not as straightforward as it may seem. Simply putting together a team of diverse individuals does not necessarily translate into superior outcomes. Rather, it is through interaction and communication among experts from different areas that encourage the sharing and cross-fertilization of diverse perspectives that ultimately leads to better solutions and decisions regarding the task. Although there has been a tremendous amount of research that has examined the link between team composition diversity and performance (e.g., Harrison, Price, & Bell, 1998; Cronin & Weingart, 2007), investigations into the role of team processes as a mediator of this relationship are just beginning to emerge. There has been a call for more research examining team processes that enable diverse teams to achieve innovative outcomes and ultimately greater performance (Van der Vegt & Bunderson, 2005). In addition to examining the role of team processes as a mediator in the diversity-performance relationship, there is also a need to understand how these processes evolve over time. Most research examining team processes such as conflict, identification, and cohesion have examined the process– performance relationship at one point in time. Yet, teams themselves are characterized as being complex systems that evolve and change over time (McGrath, Arrow, & Berdahl, 2000). Not only do team processes have

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implications for performance, but performance itself provides inputs to team functioning as a team stays together over time and receives feedback based on team successes or failures (Ilgen, Hollenbeck, Johnson, & Jundt, 2005). Therefore, the focus of teams research is shifting to explore the reasons behind why some teams perform at higher levels than others. Thus, dynamic investigations of team performance are essential given that performance is a function of team composition and processes that evolve over time. The objective of this research, then, is to add to the literature investigating the role of team process variables in explaining key performance outcomes of multidisciplinary teams. Furthermore, these team processes are examined over time to understand the dynamic nature of teams, especially those engaged in a highly uncertain and complex task such as technology commercialization. The chapter is organized as follows. I begin with a discussion of key team processes that are likely to influence team outcomes such as commitment, satisfaction, and task performance. I follow this with an overview of the sample employed in the study as well as the research methodology. Results are then presented followed by key insights garnered from this research.

CONCEPTUAL BACKGROUND AND FRAMEWORK Most of the research examining team effectiveness has used the InputProcess-Output (I-P-O) framework as a theoretical foundation to explain team effectiveness (Steiner, 1972; McGrath, 1984; Hackman, 1987). The I-P-O model suggests that team inputs lead to team processes, which then impact the ultimate performance the team achieves. In other words, there is an indirect relationship between team inputs and performance through team processes. Team inputs can include factors such as team composition, structure, ability, and motivation. Team processes provide a mechanism by which these inputs are transformed as the team works toward a common goal. Key team processes examined in the past have included variables such as trust, cohesion, identification, communication, and conflict among many others. Finally, output refers to the overall effectiveness of the team and is comprised of both affective and objective outcomes. Effectiveness can include team performance, team learning, adaptation as well as team satisfaction and commitment. Although the use of the I-P-O framework is wide spread within the teams literature, it is not without limitations. One such limitation stems from the underlying assumption that teams are a function of inputs and processes at

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one point in time. Thus, the I-P-O framework does not necessarily capture the complexity of team functioning when examining the effectiveness of teams over time. This is especially problematic when examining crossfunctional teams that are much more dynamic and complex than the framework can account for. Teams focused on technology commercialization face various types of uncertainty, including technological, market, and environmental uncertainty, that make the task more complex and unpredictable (Wall, Cordery, & Clegg, 2002; Burns & Stalker, 1961; Griffin, Neal, & Parker, 2007). Owing to the unpredictability associated with the task, it is difficult to formalize task requirements and team member roles from the onset (Ilgen & Hollenbeck, 1991). Therefore, roles and responsibilities, as well as task requirements, emerge over time and in response to changing conditions related to the technology, environment, and consumer demands (Katz & Kahn, 1978). To address the limitation associated with the static nature of the I-P-O framework, researchers have expanded the original model to include a feedback loop from performance to input to capture the evolution of teams as they work together (Ilgen et al., 2005). In this expanded framework, evolution of teams occurs over three distinct phases: forming, functioning, and finishing. Team development begins with formation where team members with diverse backgrounds and skills that come together to achieve a common objective or purpose. As the team works together over time, the team enters into the functioning phase where they manage both relational and task issues. The final phase of team development is finishing, where the team has completed the task and is disbanded. For the purpose of this research, we use the expanded I-P-O framework to understand the role of team processes in predicting team effectiveness for functionally diverse teams. The proposed model is shown in Fig. 1. Given that by definition all our teams are multidisciplinary in nature, the input side of the framework is not explicitly modeled. The focus is then on key team processes (those relating to the task and those that are social in nature) that will enhance or detract for the overall effectiveness of these teams as they work together over time to plan a strategy for commercializing a high tech product. The specific variables are discussed below.

TEAM OUTCOMES Team effectiveness is often considered as a multidimensional construct where effectiveness can be thought of in terms of both affective and objective team

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Team Processes

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Team Outcomes

Task

Affective

• Task-focused Interaction

• Satisfaction

• Conflict

• Commitment

• Team Citizenship Behavior Social • Cohesiveness

Objective

• Identification

• Team Performance

• Trust

Fig. 1.

Proposed Theoretical Model of Team Performance.

outcomes (Cohen & Bailey, 1997). Affective outcomes are important outcomes to examine when investigating effective team processes, as they provide insight into how team members feel about the team itself. This study focuses on satisfaction with the team and team commitment to assess the affective outcomes associated with the team. Furthermore, both satisfaction and commitment have been linked to objective performance. Performance generally refers to a team’s ability to effectively complete a task (Mullen & Cooper, 1994; Chang & Bordia, 2001). For this study, team performance is defined as the extent to which a team is able to develop a comprehensive commercialization strategy for their technology. This is a global measure of performance at the team level. In this case, performance does not refer to the market performance of the technology because the strategies have not been implemented, and it is not possible to access market performance. These technologies are at such an early stage that even being able to formulate a commercialization strategy is a significant performance milestone. .

TEAM PROCESSES I focus on two broad categories of team processes that are important to team functioning in multidisciplinary teams (Perry-Smith & Vincent, 2009). The first group of team processes are those that team members establish to manage the completion of the task. These factors relating to the task are

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critical to the team’s ability to complete the work required. These processes include communication relating to the task, functional conflict, and team citizenship behaviors. The second category of team processes considered vital to effective team functioning are social in nature. Teams must develop an identification to the team beyond their professional identities, group cohesion, and trust in order for communication to occur among team members so that the true performance potential of multidisciplinary teams can be achieved. Each of these key processes is discussed below.

Task Team Processes Task-Focused Interaction Task-focused interaction refers specifically to the interaction that occurs within the team as it relates directly to the team’s task (Hackman, 1987). Task-focused interaction improves information transfer among team members and the effective transfer of knowledge (Bharadwaj & Menon, 2004). Therefore, task-focused interaction provides an assessment of whether a group is efficiently and effectively engaged in the task, thereby ultimately increasing the performance potential of the team (Stewart & Barrick, 2000). Teams engaged in task-focused communication focus on generating ideas and planning the activities of the team to accomplish the task. From this, teams share ideas and discuss the different alternatives to accomplish the task, as well as reaching consensus as to the appropriate path to complete the work. Once a game plan has been established, the focus of task-focused interaction becomes that of execution (McGrath, 1984). Therefore, the role of this interaction is important throughout the team’s entire work on the project and evolves over time and is dependent on the nature of the task itself. In the case of multidisciplinary teams focused on technology commercialization where the task is characterized as being complicated and dynamic, teams are required to spend more of their efforts on conceptual tasks (generating ideas and plans, and choosing between alternatives) that occur early on in the team process. Tasks relating to execution are not as taxing on team resources relative to those that require the team to bring together diverse viewpoints to come up with a novel solution (Stewart & Barrick, 2000). Teams must develop an appropriate way to communicate regarding the task to enhance performance and reach key milestones and the importance of these interaction processes change over time (e.g., Barry & Stewart 1997; Campion, Medsker, & Higgs, 1993).

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Functional Conflict Multidisciplinary teams are especially susceptible to the occurrence of conflict within the team. Conflict is defined as the process resulting from the tension between team members due to either real or perceived differences (DeDreu & Weingart, 2003). Multidisciplinary teams are, by their very nature, diverse, and while this diversity is beneficial, in that many different areas of expertise and knowledge are brought together to work on a highly complex problem, it is this diversity that opens the door to conflicting viewpoints and process breakdowns. Individuals from different backgrounds have different perceptions of the task and the context from which the task has evolved (Amason, 1996), resulting in different approaches to how to complete the task and complete it well. While, in general, conflict is thought to be a negative for a team, there are times when conflict can be beneficial. Functional conflict is constructive in nature and refers to the challenging of ideas, beliefs, and assumptions held by other team members relating to the task and achieving shared objectives (Amason, 1996; Baron, 1991; Cosier, 1978; Tjosvold, 1997). This is particularly beneficial for teams engaged in complex tasks that require innovative solutions such as technology commercialization. Functional conflict forces members within the team to consider diverse perspectives and integrate these differing viewpoints into workable solutions across multiple areas of expertise (Eisenhardt & Bourgeois, 1998; Menon, Bharadwaj, & Howell, 1996). Adding to the complexity of these diverse viewpoints is that multidisciplinary teams often face greater challenges as to how to proceed ahead with the task than groups with similar backgrounds (Jehn, Northcraft, & Neale, 1999). Therefore, conflict can arise about not only the knowledge required to complete the task but also the approach the team should use to complete the work. The link between functional conflict and team decision-making effectiveness has received considerable attention in the literature. Teams that have functional conflict relating to the task make better decisions, in that they are forced to consider multiple alternatives and greater elaboration and thought regarding different approaches to completing the task (Menon et al., 1996; Schulz-Hardt, Jochims, & Frey, 2002; Hollenbeck, Colquit, Ilgen, LePine, & Hedlund, 1998; Hollenbeck et al., 1995). The beneficial impact of functional conflict is even more so in multidisciplinary teams where diverse individuals were put together to integrate diverse perspectives, such that a higher quality outcome is achieved (Jehn et al., 1999; Schwenk, 1990). Functional conflict promotes creative solutions and team learning (Amabile, 1988; Andrews & Smith, 1996; Slater & Narver, 1995). In addition to better performance

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outcomes, functional conflict also enhances team consensus and commitment for teams that are facing an unstructured and uncertain task (Amason, 1996; Jehn, 1995). Team members engaged in debating their perspectives with others will feel more involved in the decision process, resulting in higher levels of commitment and satisfaction. Team Citizenship Behaviors The final routine that is posited to impact team outcomes is that of team citizenship behaviors. Team citizenship behavior is a reflection of the extent to which team members are willing to go the extra mile to help others on the team as they complete the task. A high level of team citizenship behaviors is important in that it encourages team members to focus on the activities necessary for the team to be successful first and provides a means of offsetting dysfunctional behaviors that can undermine team performance (Bharadwaj & Menon, 2004). The focus of the team is no longer on one particular discipline or area of expertise, but rather the integration of these perspectives into a common goal for team success. Team citizenship behaviors are an integral part of integrating diverse viewpoints within the team as well as assisting in the open communication between team members that is necessary to develop a comprehensive solution to complex problems (Adams, Day, & Dougherty, 1998; Madhavan & Grover, 1998). Furthermore, team citizenship behaviors are linked to increased team satisfaction and commitment.

Social Team Processes Group Cohesion Group cohesion refers to the strength of ties among team members (Hogg, 1992). Social cohesion among team members is generally viewed as a desirable quality of high-performing teams and leads to increased levels of consensus among team members (Sethi, Smith, & Park, 2001). Despite the benefits associated with cohesion, the development of cohesion can be a challenge for multidisciplinary teams (Harrison et al., 1998). The development of cohesion occurs within multidisciplinary teams over time as they engage in repeated interactions to complete the task as team members gather more information regarding other members and base their opinions based on facts as opposed to stereotypes or superficial categorizations as in the beginning of the team process. Therefore, cohesion is a dynamic process that teams engage in that promote team functioning.

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There are different types of group cohesion that facilitate increased performance in teams. The first of these is cohesion relating to the task. Task cohesion refers to the affective process by which team members are united in trying to accomplish a common objective. It includes the open communication about team members’ roles and responsibilities and results in open communication lines that are necessary to bring together diverse perspectives. The second category of group cohesion is social in nature and is focused on the relationships of individuals within the team. It is important to distinguish among the different types of group cohesion because they impact key team outcomes in different ways. Task cohesion has been linked to superior team performance, while social group cohesion has a greater influence on affective performance outcomes such as team satisfaction and commitment (Change & Bordia, 2001). Although there has been considerable research regarding cohesion and performance, there has been limited empirical research that examines the evolution of these different types of cohesion over time. This research investigates the importance of both task and social cohesion in predicting team performance, satisfaction, and commitment over time in multidisciplinary teams. Identification A key challenge facing multidisciplinary teams is overcoming preconceived biases and stereotypes regarding the background and expertise of other team members. Thus, it is critical for team members to build a stronger identification with their team (i.e., superordinate identity) than that of their professional or functional area in order for effective outcomes to occur (Sethi, 2000). The process of building a team identity requires team members to feel personally invested in the team. Furthermore, superordinate identity leads individuals within the team to consider other team members that possess expertise from other areas as being more similar, in that they all belong to the same team and are pursuing the same objective (Van der Vegt & Bunderson, 2005). Perceiving other team members as being more similar promotes communication, resulting in an appreciation of the different perspectives these team members bring to the table when approaching the problem (Ashforth & Mael, 1989). Past research has demonstrated that teams with higher levels of identification are better able to integrate diverse ideas and come up with more innovative solutions (Sethi et al., 2001). Teams with high superordinate identity focus on the task first, and the characteristics and qualities associated with their professional area second (Mackie & Goethals, 1987). This shift in perspective is important in that it promotes knowledge transfer among team members as well as encourages

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debate and discussion among team members about the task, resulting in superior performance and team functioning (Deshpande & Zaltman, 1982; Maltz & Kohli, 1996). Trust Trust refers to the attributions made by individuals on the team regarding the intentions and objectives of others on the team (Smith & Barclay, 1997). Trust impacts the ability of the team to make decisions related to the task and teams with high levels of trust make better decisions than those with lower levels of trust (Korsgaard, Schweiger, & Sapienza, 1995). Trust facilitates open lines of communication and willingness to share information with other team members and a requirement for cooperation among team members (Mayer, Davis, & Schoorman, 1995). Increased cooperation lead to better decision making and ultimately superior performance outcomes for multidisciplinary teams. Not only is trust an important prerequisite to team performance, but trust also facilitates team satisfaction and commitment (Gladstein, 1984). Furthermore, trust promotes a culture where opportunism is minimized resulting in higher levels of satisfaction among team members (Smith & Barclay, 1997). Research has also demonstrated that trust reduces the level of dysfunctional conflict within teams and results in higher levels of commitment to the team and its common purpose (Morgan & Hunt, 1994).

METHODOLOGY Sample The sample for this study consists of student teams participating in the Technological Innovation: Generating Economic Results (TI:GER) Program at Georgia Tech and Emory University. These teams are comprised of Science and Engineering PhD students from Georgia Tech, MBA students from Georgia Tech, and JD students from Emory University. The teams participate in the TI:GER program over the course of two years with the primary objective of developing a commercialization strategy for each Science and Engineering PhD student’s research. This research surveys 20 teams (80 students) during their participation in the program and examines the impact of team processes on the effectiveness of the team as they work toward their goal of developing a plan for commercializing their technology. Each student was asked to complete multiple surveys throughout their

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Table 1. Time

April 2004 September 2004 October 2004 December 2004 February 2005 April 2005

Overview of Data Collection.

Cohort Surveyed

TI:GER TI:GER TI:GER TI:GER TI:GER TI:GER TI:GER

Teams Teams Teams Teams Teams Teams Teams

(c/o (c/o (c/o (c/o (c/o (c/o (c/o

Number of Teams Responded

Number of Surveys Collected

13

30

9 7 9 7 7

20 22 28 24 20

2004) 2005) 2005) 2006) 2005) 2006) 2006)

participation in the program. In addition, objective outcome measures for team performance were collected from outside industry experts and team supervisors. Data were collected over six different time periods from April 2004 through April 2005. During this time, three different cohorts of TI:GER teams were surveyed. Therefore the data collection includes four teams during their last semester of participation in the program, nine teams throughout both years of participation, and seven teams during their first year in the program. Table 1 provides a description of the data collection efforts.

Measures Existing measures present in the literature were adapted for this study. Table 2 provides a summary of the definitions used in this research for the independent and dependent variables, as well as the control variables included in the model. The individual items for each measure can be found in Appendix.

Team Process Variables To ensure the reliability of the measures used in this study, the items were subjected to a confirmatory factor analysis (CFA) and yielded acceptable fit for the measurement model. Cronbach’s alpha was calculated for each individual scale and found to be greater than 0.70, as suggested by Nunnally (1978). In addition, the composite reliability was also calculated and found to be of an acceptable level (Hair, Anderson, Tatham, & Black, 1998).

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Table 2.

Definition of Team Process, Performance, and Control Variables.

Variable Task-focused interaction Functional conflict

Team citizenship behavior Group cohesion

Identification

Trust

Commitment Satisfaction Performance Potency Learning orientation

Performance orientation – avoid Performance orientation – prove Role clarity Task interdependence Outcome interdependence

Definition Refers to activities within the team including work and information flow with the objective of completing the task at hand (Bharadwaj & Menon, 2004) Conflict among team members relating to the task that the team is working on as opposed to conflict focused on interpersonal relationships (Amason, 1996) Refers to the degree to which team members are willing to help other members of the team beyond what is expected in order to enhance team performance (Van der Vegt, Van de Vliert, & Oosterhof, 2003) Refers to the strength of interpersonal ties among team members (Hogg, 1992; Zaccaro & McCoy, 1998; Sethi et al., 2001); Cohesion can refer to both the task and the social relationship held within a team (Chang & Bordia, 2001) The extent to which team members relate, or identify, with the team as opposed to their respective functional areas or professions as well as perceive a personal stake in the success of the team (Mackie & Goethals, 1987; Sethi, 2000; Tajfel, 1982) Refers to the willingness of a team member to be open to the actions of another team member in completing the task even if they are unable to observe or manage the activities (Mayer et al., 1995) Refers to the strength of identification or participation to a team (Bishop, Scott, & Burroughs, 2000) Refers to an overall assessment of a team member’s happiness with the team and its output (Hackman & Oldham, 1980) Extent to which a team is able to meet established objectives (Hoegl, Weinkauf, & Gemuenden, 2004) Refers to the collective belief held within the team that they can be effective (Shea & Guzzo, 1987; Kirkman & Rosen, 1999) Refers to the team’s desire to continually acquire and master new skills and views tasks as opportunities to improve competence (Kohli, Shervani, & Challagalla, 1998; Dweck & Laggett, 1988) Refers to the motivation to perform well on tasks to avoid looking inferior in terms of team capabilities to external parties (VandeWalle, 1997) Refers to the motivation of a team to perform well to demonstrate ability to others to be judged as being capable (VandeWalle, 1997) Extent to which the team members roles and responsibilities are defined (Kiesler, 1978) Degree to which team members must interact to complete the task (Hackman, 1969; Wageman, 1995; Gilson & Shalley, 2004) Degree to which the significant outcomes an individual receives depend on the performance of the team as a whole (Wageman, 1995)

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The average variance extracted was calculated to ensure that the measures exceeded the commonly accepted threshold of 0.50 (Hair et al., 1998). Finally, because the research uses the team as the unit of analysis, it is necessary to aggregate the data from that of the individual to the team. To justify the appropriateness of aggregation, I calculated several commonly used statistics justifying aggregation (rwg, ICC(1), and ICC(2)). The average median rwg for the independent variables ranged from 0.75 to 0.96, which is well over the 0.70 cutoff criterion suggested by James, Demaree, and Wolf (1984). The average ICC(1) and ICC(2) for the variables also indicated sufficient agreement and reliability of individuals on a team with respect to these measures to justify aggregation to the team level. Team Performance The items for team performance collected from external evaluations were analyzed with principle-components factor analysis with varimax rotation. The items loaded on one factor. Once the items were purified, the measures were analyzed through CFA. One item from the team performance scale was deleted due to poor fit.1 Once again, because the data are designed to assess the team level, it is necessary to aggregate the data from that of the individual to the team. The average median rwg for team performance is 0.81 among external evaluations, which is well over the 0.70 cutoff criterion suggested by James et al. (1984). The average ICC(1) and ICC(2) for the team performance measure is 0.24 and 0.39, respectively. These indices indicate sufficient agreement and reliability of individuals to justify aggregation to the team level. Control Variables Past research has demonstrated that other team level variables can impact team performance over and above the influence of team processes. Therefore, the following variables are included in the analysis to ensure that there are not alternate explanations of the results: potency, learning orientation, performance orientation, role clarity, task interdependence, and outcome interdependence. These controls can be classified as other input variables in the I-P-O model and were included to provide greater confidence in the results obtained. The definitions for each of these controls are listed in Table 2 and the items used to measure them in the survey can be found in Appendix.

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Data Analysis The descriptive statistics and intercorrelations for the independent variables are presented in Table 3. Before analyzing the data, the variance inflation factor (VIF) for the variables was assessed to ensure that multicollinearity was not an issue in the current dataset. The VIF statistic was easily under the cutoff of 10 as proposed by Hair et al. (1998), suggesting that collinearity is not a problem. The panel data collected from the TI:GER teams throughout their participation in the program was analyzed using fixed and random effects regression. Standard regression may yield biased results in this case because they require assumptions that do not hold when repeated measurements of individuals are taken. Therefore, analysis of panel data requires that special attention be given to the covariance structure of the data due to the sequential nature of data collection. This sequential nature arises because data collected close in time can have higher correlations with each other than those collected with further intervals in between. Standard ordinary least squares (OLS) cannot be used to estimate a random effects model because of biased estimated standard errors. Therefore short for generalized least squares (GLS) will be used to estimate the models. To overcome this interdependence, a fixed effects model is estimated to examine the impact of the team process variables on team commitment, satisfaction, and performance. A fixed effects model is appropriate to use because it is able to model a group-specific constant term in the regression model and that this group specific constant (ai) does not vary over time (Greene, 2003). The fixed effect model assumes that this constant, ai, is correlated with the set of independent variables being estimated. Additionally, a random effects model can be used to analyze panel data. In this case, the random effects model assumes that the group-specific constant is uncorrelated with the independent variables and is a random element. To test which assumption holds, both a fixed effects model and random effects model are estimated and then the Hausman specification test is used to ascertain whether the measured factors are orthogonal to the measured covariates. To estimate the models, I use a generalized least squares procedure for an unbalanced panel (Wooldridge, 2001). I run both the fixed effects model for the individual level data (with a fixed effect for the individual and team) and supplement this analysis with a random effects model such that the Hausman specification test can be run. A significant Hausman w2 indicates that the fixed effects model provides commitment more consistent estimates than the random effects model.

X2

X3

X4

X5

X6

X7

X8

X9

Descriptive Statistics and Correlations. X10

po0.05.

Mean Standard deviation

4.86 1.13

5.44 0.78

4.99 1.25

5.35 0.93

3.74 1.15

4.60 1.03

4.50 5.48 1.20 0.86

5.24 0.79

X12

X13 X14

4.33 5.04 4.47 1.06 1.02 0.89

1.00 0.16 1.00 0.30 0.05 1.00 0.09 0.43 0.14 1.00

X11

4.10 4.70 0.86 0.96

Task-focused interaction 1.00 Functional conflict 0.70 1.00 Team citizenship behavior 0.74 0.81 1.00 Group cohesion – task 0.15 0.10 0.08 1.00 Group cohesion – social 0.08 0.00 0.04 0.49 1.00 0.10 0.57 0.59 1.00 Identification 0.16 0.05 Trust 0.10 0.07 0.14 0.51 0.54 0.65 1.00 Potency 0.11 0.10 0.10 0.65 0.42 0.44 0.45 1.00 0.62 0.42 0.43 0.46 0.81 1.00 Learning orientation 0.11 0.18 0.14 0.01 1.00 Performance orientation – avoid 0.03 0.06 0.01 0.12 0.23 0.32 0.15 0.02 0.12 0.00 0.40 0.14 0.25 Performance orientation – prove 0.09 0.04 0.13 0.17 0.12 0.15 0.01 0.18 0.43 0.17 0.04 0.03 0.40 Role clarity 0.17 0.11 .26 0.22 0.01 0.12 0.23 0.40 0.37 0.23 Task interdependence 0.06 0.16 .23 0.21 0.23 0.41 0.28 0.27 0.20 0.33 Outcome interdependence 0.11 0.17

X1

Table 3.

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The following equations are estimated to formally test the hypotheses: Satisfactionijt ¼ b0 þ

t X 14 X

bk X ijk þ aij þ ijt

t¼1 k¼1

Commitmentijt ¼ b0 þ

t X 14 X

bk X ijk þ aij þ ijt

t¼1 k¼1

Performanceijt ¼ b0 þ

t X 16 X

bk X ijk þ aij þ ijt

t¼1 k¼1

where: Xij1 ¼ task-focused interaction for individual i in team j Xij2 ¼ functional conflict for individual i in team j Xij3 ¼ team citizenship behavior for individual i in team j Xij4 ¼ group cohesion – task for individual i in team j Xij5 ¼ group cohesion – social for individual i in team j Xij6 ¼ identification for individual i in team j Xij7 ¼ trust for individual i in team j Xij8 ¼ team potency for individual i in team j Xij9 ¼ team learning orientation for individual i in team j Xij10 ¼ team performance orientation – avoid for individual i in team j Xij11 ¼ team performance orientation – prove for individual i in team j Xij12 ¼ role clarity for individual i in team j Xij13 ¼ task interdependence for individual i in team j Xij14 ¼ outcome interdependence for individual i in team j Xij15 ¼ satisfaction for individual i in team j Xij16 ¼ commitment for individual i in team j.

RESULTS The results from the analysis are presented in Table 4. The specific results are discussed below beginning with main effect results relating to the impact of team processes on performance accounting for the feedback of team performance in timet1 into the role of team processes at time. From here, results are presented comparing highly effective teams with those that have lower performance.

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Table 4.

Panel Fixed Effects Regression Results. Satisfaction

Commitment

Performance

0.37

2.88

3.05

Team process variables Task-focused interaction Functional conflict Team citizenship behavior Group cohesion – task Group cohesion – social Identification Trust

0.08 0.16 0.17 0.06 0.17 0.34 0.02

0.05 0.01 0.15 0.05 0.19 0.40 0.23

0.08 0.12 0.15 0.36 0.03 0.2 0.57

Control variables Potency Learning orientation Performance orientation – avoid Performance orientation – prove Role clarity Task interdependence Outcome interdependence

0.53 0.23 0.41 0.06 0.07 0.02 0.11

0.55 0.44 0.18 0.03 0.19 0.10 0.10

0.69 0.11 0.12 0.1 0.28 0.17 0.11

– –

– –

0.613 0.291 22.43 0.7 161 20

0.610 0.274 22.03 0.72 161 20

Constant

Satisfaction Commitment Sigma-u Sigma-e F statistic R2 n k

0.24 0.93 1.137 0.441 2.85 0.14 116 20

n ¼ individual sample size and k ¼ team sample size.

po0.05, po0.01.

THE IMPACT OF TEAM PROCESSES ON PERFORMANCE OUTCOMES Results support the impact of key team process variables on team performance. With respect to satisfaction, results suggest that it is the social processes within the team that enhance team member satisfaction. Social group cohesion and identification were both significant predictors of team satisfaction (b ¼ 0.17, po0.01 and b ¼ 0.34, po0.01, respectively). Team citizenship behavior was also a significant driver of satisfaction (b ¼ 0.17, po0.05). Similar results emerged for team commitment. Again, team

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processes focused on building social capital within the team are drivers of team commitment. Group cohesion and identification are significant predictors of commitment (b ¼ 0.19, po0.01 and b ¼ 0.40, po0.01, respectively). Trust is also significantly related to commitment (b ¼ 0.23, po0.01). The only task-related process that is significant with respect to team commitment is team citizenship behavior (b ¼ 0.15, po0.05). Therefore, satisfaction and commitment are both enhanced in teams where team members identify with the team and feel strong interpersonal ties among the group. Furthermore, team members’ willingness to help others above and beyond what is required of the task also enhances team affective outcomes. Team trust is also an important process in building team commitment. Results did not find support for the role of task team processes as key drivers of overall team performance. The only social process variables that do impact team performance were group cohesion relating to the task (b ¼ 0.36 po0.05) and trust (b ¼ 0.57, po0.01). Team commitment was also a significant predictor of team performance (b ¼ 0.93, po0.01). Results suggest that in multidisciplinary teams comprised of diverse individuals, it is trust that is a key process in enhancing performance. Additionally, team members need to feel united in trying to reach a common goal in order for superior performance to occur. As a follow-up, I also tested if team commitment served as a mediator between other team process variables and team performance using the Sobel test. Results suggest that the influence of other key team processes do impact performance through team commitment. Team commitment mediates the relationship between team citizenships behaviors (Sobel test statistic ¼ 1.29, po0.10), social group cohesion (Sobel test statistic ¼ 1.59, po0.05), identification (Sobel test statistic ¼ 1.61, po0.05), and trust (Sobel test statistic ¼ 1.54, po0.10). Social team processes are significant drivers of team performance both directly and indirectly through team commitment. The only task-related team process that influences objective performance is that of team citizenship, although this relationship is indirect.

EVOLUTION OF TEAM PROCESSES FOR HIGH- AND LOW-PERFORMING TEAMS To examine the difference between high- and low-performing teams, several post hoc analyses were conducted. First, the highest performing teams and lowest performing teams were identified in the dataset. For these teams I examined the means reported by team members among the key team

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process variables in the study. From this analysis, several key differences among highly successful and not so successful teams emerged. In high-performing teams, the role of task processes differs significantly than those of low-performing teams. High-performing teams engage in a significantly higher amount of task-focused interaction than low-performing teams, and the longer the teams are in place the more this interaction goes up. The exact opposite finding is true for lower performing teams. Although these teams begin with a similar level of task-focused interaction as high-performing teams, the amount of this communication decreases over time. Functional conflict also differs across high- and low-performing multidisciplinary teams (Fig. 2). High-performing teams have higher levels of functional conflict than low-performing teams suggesting that teams that engage in creative debate regarding the task end up with superior solutions and ultimate performance related to the task. Social team processes also evolved differently across high- and lowperforming teams. One interesting finding relates to group cohesion. In highperforming teams, group cohesion related to the task is significantly higher than social group cohesion. The opposite finding is true for lower performing teams suggesting that teams that bond over the task at hand result in superior performance outcomes. Furthermore, high-performing teams have relatively stable levels of cohesion over time, while lower performing teams start out at lower levels of cohesion early on, and while this does increase over time, it does not lead to superior performance outcomes for the team (Figs. 3 and 4).

Fig. 2.

Comparison of Functional Conflict in High- and Low-Performing Teams over Time.

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Fig. 3.

Comparison of Group Cohesion – Task in High- and Low-Performing Teams over Time.

Fig. 4.

Comparison of Group Cohesion – Social in High- and Low-Performing Teams over Time.

Team identification did not differ across high- and low-performing teams. Therefore, while identification is an important team process, it does not explain differences in objective performance (although it does impact performance indirectly).

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Finally, trust within top and bottom performing teams was examined. Although high-performing teams and low-performing teams both started out with relatively higher levels of trust, the key differentiator is that highperforming teams had increasing levels of trust over time while trust in lower performing teams decreases as the teams worked together on the task. The result supports and supplements our earlier finding that trust is a key process within multidisciplinary teams focused on complex tasks. Therefore, the next section focuses on the evolution of trust within the team setting.

THE ROLE OF TRUST AND GROUP COHESION IN PREDICTING PERFORMANCE Trust and group cohesion relating to the task emerged as the key processes responsible in explaining superior performance outcomes in diverse teams. When examining the data collected from the teams, high-performing teams had higher levels of trust and cohesion early on in the process of commercializing the technology. Therefore, to understand the impact of establishing a cohesive team with trust early on, I compare teams where this occurred to those that did not have higher levels of cohesion and trust early on. Results from these analyses are presented in Tables 5 and 6. Results support the notion that teams that establish task cohesion and trust early on report higher levels of team effectiveness across all three of our outcome measures. Task cohesion and trust early on in team development result in higher levels of performance, satisfaction, and team commitment than in teams reporting lower levels. Teams that establish higher levels of cohesion and trust early on also had higher levels of social group cohesion and identification; however, there were no differences among the task process variables examined. Therefore, the establishment of task cohesion and trust early on in the process were not related to differing levels of taskfocused interaction, functional conflict, or citizenship behaviors.

CONCLUSION This research examines the role of task and social processes within a multidisciplinary team focused on the highly complex and uncertain process of technology commercialization. This is a dynamic investigation of the role of these key processes on both affective and objective team performance.

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Table 5.

LESLIE H. VINCENT

Impact of Task Group Cohesion Early in the Team Process. Cohesion High Early

Cohesion Low Early

t-test

Team process variables Task-focused interaction Functional conflict Team citizenship Cohesion – social Identification Trust

4.98 5.52 5.13 4.25 5.17 5.00

4.73 5.35 4.83 3.20 3.99 3.96

1.39 1.38 1.53 6.59 8.75 6.11

Outcomes Satisfaction Commitment Performance

5.41 5.34 4.36

4.08 3.89 4.19

8.91 9.52 2.07

Controls Potency Learning orientation Performance Orientation – avoid Performance orientation – prove Role clarity Task interdependence Outcome interdependence

5.95 5.63 4.02 4.91 4.62 5.18 4.62

4.97 4.82 4.20 4.49 4.02 4.90 4.30

8.99 7.56 1.32 2.92 3.76 1.82 8.91

Overall results find that social processes are key drivers in predicting affective outcomes. This makes sense given that social processes are focused on creating a culture within the team where members feel accepted and comfortable sharing their ideas without fear of judgment. Therefore, team satisfaction and commitment should be higher in teams where open communication on diverse viewpoints is both encouraged and accepted. Objective team performance was primarily driven by task cohesion and trust, although other processes impacted objective performance indirectly through team commitment. Surprisingly, results did not support the role of task processes on team effectiveness. A series of interviews conducted with the members of these teams may shed some insight as to why these did not emerge as key drivers. Most of the technologies examined in this study were at a very early stage in development. Therefore, all the teams, regardless of the performance outcomes, were focused on ramping up their own knowledge related to the technology before they could identify knowledge within their own areas of expertise to provide additional insight into the task of commercialization.

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Table 6. Impact of Team Trust Early in the Team Process. Trust High Early

Trust Low Early

t-test

Team process variables Task-focused interaction Functional conflict Team citizenship Cohesion – task Cohesion – social Identification

4.84 5.39 5.00 5.73 4.16 5.09

4.89 5.49 4.96 4.78 3.14 3.89

0.28 0.81 0.15 7.43 6.18 8.81

Outcomes Satisfaction Commitment Performance

5.32 5.23 4.39

3.96 3.78 4.19

8.96 9.29 2.28

Controls Potency Learning orientation Performance orientation – avoid Performance orientation – prove Role clarity Task interdependence Outcome interdependence

5.06 5.90 5.60 3.96 4.52 5.17 4.68

4.86 4.71 4.31 4.47 4.06 4.87 4.16

9.41 8.53 2.57 2.60 2.78 1.85w 3.79

w

po0.10; po0.05.

The following are quotes from team members that illustrate the struggle the teams had with learning what the technology was and how to apply it. The big decision was to narrow down what we were doing. It was difficult to nail down what we were going to focus on regarding the technology. We did not have any problems in the process or functioning of the team but rather had to decide about the task and where to focus the technology. (PhD Student, Team 3) This technology is completely new to us so it has been difficult. We have been relying heavily on [the inventor] along the way. (MBA Student, Team 7) Understanding the technology has been a major issue and the majority of the burden has fallen on [the inventor]. (JD Student, Team 12)

From these quotes you can see that the interactions within the teams were often dominated by the technology expert and therefore the true benefits associated with using multidisciplinary teams (i.e., the bringing together of diverse perspectives and cross-fertilization of ideas) did not manifest itself to the full extent early on in the process. This could be one potential

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explanation as to why the role of task processes did not emerge as a significant driver of team effectiveness. This research also sheds some insight as to why some teams are more effective than others, addressing a key limitation currently in the literature (Ilgen et al., 2005). By examining high- and low-performing teams in isolation, this research uncovers some differences in team processes over time that impact overall effectiveness. Although overall results do not find support for the role of task processes in predicting team performance, post hoc investigations do point to the fact that high- and low-performing teams do differ with respect to task processes. High-performing teams and low-performing teams each start out with a similar level of task-focused interaction. However, as these teams evolve over time, high-performing teams engage in even more task-focused interaction while the levels of low-performing teams decreases. This supports the qualitative explanation above regarding the teams’ learning curve associated with the technology. High- and low-performing teams are both engaged in task-focused communication early on when they are struggling to get a solid grasp on what the technology is. However, highperforming teams continue to discuss and communicate about the task even after they have mastered the technology, whereas results suggest that, once team members have a good handle on the technology, the interaction related to the task in low performing teams decreases. High- and low-performing teams also differ with respect to functional conflict. Recall that functional conflict can be beneficial to multidisciplinary teams in that it promotes debate on multiple perspectives, such that a better conclusion is reached by the team. This notion is supported with the data in that high performing teams have greater levels of functional conflict, resulting in increased processing of diverse ideas, which ultimately leads to a better performance outcome. Finally, high-performing teams have greater levels of task cohesion than low-performing teams. There is little variability in this task cohesion over time as the team works toward their common goal. High-performing teams also have higher levels of trust that increase as the teams work together over time, whereas low-performing teams have decreasing levels of trust over time, which can hinder their performance potential. As high-performing teams received feedback on their performance, this only boosts their confidence and trust in other team members. However, feedback related to lower performance undermines the trust established within these diverse teams and ultimately leads to even lower levels of performance and greater process breakdowns. This research yields significant insights for the management of multidisciplinary teams. It is critical for team members to feel accepted and

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comfortable within the team (i.e., social processes) in order for the full potential of the team to be realized. Furthermore, establishing cohesion and trust early on in the process leads to higher levels of effectiveness. Teams are dynamic systems that respond to feedback over time. Therefore it is critical to set them up for success early on so that positive feedback only reinforces key team processes that will have lasting impact on the success of the team.

NOTE 1. The team performance item deleted was ‘‘This team’s strategy will require a great deal of modification before it can be used to commercialize this technology.’’

ACKNOWLEDGMENT I gratefully acknowledge financial support for this research from NSF IGERT-0221600.

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APPENDIX. MEASURES Task-focused interaction (Bharadwaj & Menon, 2004; Denison, Hart, & Kahn, 1996)  In our team meetings, we often get sidetracked discussing peripheral issues.a  After an issue is raised, we quickly decide what to do about it.  Team meetings are well organized and productive.  The team was focused throughout the project to get it done. Functional conflict (Bharadwaj & Menon, 2004; Menon et al., 1996; Barclay, 1991)      

There is consultative interaction and useful give-and-take. Disagreements between team members impaired discussions of issues.a There was constructive challenge of ideas, beliefs, and assumptions. Members were comfortable about raising dissenting viewpoints. Different opinions or views focused on issues rather than on individuals. Even people who disagree respected each other’s viewpoints.

Team citizenship behaviors (Bharadwaj & Menon, 2004; Posdakoff & MacKenzie, 1994) Some team members do not pull their fair share of the workload.a  The team members care about the team and work to make it one of the best.  Team members give the team’s work the highest priority. 

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The team members ask, ‘‘what can I do for the team,’’ rather than, ‘‘what can the team do for me.’’  The team members are willing to help others above and beyond the call of duty. 

Group cohesion – task (Chang & Bordia, 2001) Team members: Are united in trying to reach its goal for performance. Take responsibility for any mistake.  Try to help if members have problems relating to the task.  Communicate freely about each other’s responsibility.  

Group cohesion – social (Chang & Bordia, 2001) Team members: Would rather go out on their own than as a team.a  Rarely socialize together.a  Like to spend time together outside of work hours.  Stick together outside of the team project. 

Identification (Sethi, 2000) Team members:      

Feel strong ties to the team. Behave like a unified team. Are committed to common project objectives. Behave like they were driven by their respective agendas.a Value their membership to the team. Feel that they have a personal stake in the success of the team.

Trust (Jarvenpaa & Leidner, 1999; Mayer et al., 1995) Team members: Would not let one team member have influence over issues that are important to the project.a  Would be comfortable giving other team members a task, which is critical to the project, even if they could not be monitored.  Would be comfortable giving other team members complete responsibility for the completion of this project.  Wish they had a good way of overseeing the work of other team members on the project.a 

Team commitment (Bishop et al., 2000) Team members: 

Are all committed to this team.

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Find that their values and the team’s values are very similar. Really care about the fate of this team.  Inspire the very best from each other in the way of performance.  Believe that this is the best of all possible teams for which to belong.  

Team satisfaction (Van der Vegt, Emans, & Van de Vliert, 2000) Team members: Feel proud to belong to this team. Are glad they belong to this team and not another team.  Are willing to exert extra effort to help this team succeed.  

Performance (created for this study) Overall, the strategy developed by this team is likely to be successful. This team has developed a comprehensive plan for commercializing their technology.  The team is well positioned to capitalize upon protectable intellectual property rights.  

Potency (Guzzo, Yost, Campbell, & Shea,1993; Kirkman & Rosen, 1999) Team members:        

Have confidence in themselves. Believe the team can be extremely good at producing high-quality work. Expect to be known as a high-performing team. Feel they can solve any problem that comes up. Believe they can be very productive. Can get a lot done when [team] works hard. Believe no job is too tough. Expect to have a lot of influence over the commercialization of the technology.

Learning orientation (Bunderson & Sutcliffe, 2003) Team members: Look for opportunities to develop new skills and knowledge. Are willing to take risks on new ideas to find out what works. Like challenging and difficult assignments that teach new things.  See learning and developing skills as very important.  Like to work on tasks that require a lot of skill and ability.   

Performance orientation – avoid (VandeWalle, 1997) Team members:  

Prefer to avoid situations where the team might perform poorly. Avoid taking on a new task if there is a chance they will appear rather incompetent to others.

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Believe that avoiding a show of low ability is more important than learning a new skill.  Are concerned about taking on a task if team performance would reveal that the team had low ability. 

Performance orientation – prove (VandeWalle, 1997) Team members: Are concerned with showing that it can perform better than its peers. Figure out what it takes to prove its ability to peers.  Enjoy it when others are aware of how well the team is doing.  Strive to prove its ability to others.  

Role clarity (Sawyer, 1992) Team members:     

Are uncertain of the expected results of the team project.a Are certain of their duties and responsibilities. Are uncertain of how their work relates to the overall objectives of the team.a Are certain of goals and objectives for their role. Are certain of the aspects of the work that will lead to a positive impact for the team.

Task interdependence (Sethi, 2000) Team members: Are required to jointly make important project-related decisions. Are dependent on the information and expertise of others to successfully complete the task.  Are dependent on the cooperation of other members to successfully do their jobs.  

Outcome interdependence (Sethi, 2000) Team members: Are responsible for their respective tasks and not for the overall project outcome.a  Evaluation depends on how well they perform on their respective responsibilities and not on the performance of the overall task.a  Are accountable to their respective tasks and not to the team.a  Rewards depend on how well they perform on their own tasks and not the performance of the overall project.a 

Note: All measures used a seven-point scale, where 7 ¼ strongly agree and 1 ¼ strongly disagree. a Indicates a reverse-scored item.

THE ORGANIZATIONAL WORKSHOP: A CONCEPTUAL EXPLORATION OF THE BOUNDARY SPANNING ROLE OF UNIVERSITY ENTREPRENEURSHIP AND INNOVATION CENTERS Matthew M. Mars and Sherry Hoskinson ABSTRACT In this chapter, we consider the tensions that arise at the intersection of various organizational units (i.e., academic departments, research centers, and administrative areas) and actors (i.e., professors, graduate students, investors, and secular entrepreneurs) that are commonly involved with academic entrepreneurship and the exploration of the entrepreneurial dimensions of science. Using the premises of organizational boundary spanning (e.g., Aldrich & Herker, 1977; Thompson, 1967; Tushman & Scanlan, 1981), we organize our discussion around the role of university entrepreneurship and innovation centers in facilitating and mediating the interorganizational transactions that most often underpin academic entrepreneurship. Specifically, we illustrate and discuss the role university entrepreneurship and innovation centers play Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 119–138 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021008

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in (1) managing the various agendas and expectations of stakeholders within and outside of the academy, (2) providing clarity of purpose to the entrepreneurial endeavor, (3) clarifying ownership rights throughout the entrepreneurial process, and 4) maximizing the potential of individuals to contribute to venture success.

INTRODUCTION Starting around 1980, higher education in America has increasingly become linked to the marketplace (Slaughter & Leslie, 1997; Slaughter & Rhoades, 2004). The impetuses for this shift include the passing of market-oriented policies such as the Bayl-Dohl Act of 1980,1 declines in annual increases of government funding that pushed colleges and universities to more aggressively seek privately funded resources, and the emergence of the knowledge-based economy. The resulting market permeation has created remarkable transformations within the postsecondary academy. In articulating the theory of academic capitalism, Slaughter and Rhoades indicated that market forces have driven the creation of new cross-disciplinary knowledge circuits, the formation of market-oriented interstitial organizations (e.g., technology transfer offices (TTOs)), an expansion of relationships between institutions and intermediating organizations (e.g., nongovernmental organizations), and the enhancement of managerial capacities within the academy. All four of the theoretical constructs of academic capitalism are reflected in the growth of university entrepreneurship and innovation centers. (Mars, 2006). In this chapter, we consider the organizational position of such centers as hubs of academic entrepreneurship, and more specifically the associated boundary-spanning role that facilitates and mediates relationships between otherwise mostly disconnected internal and external actors. Observations of early-stage entrepreneurial projects that involve technological and scientific innovations that have been explored and advanced through the McGuire Center for Entrepreneurship at The University of Arizona are used to illustrate the discussion points. The discussion points include the role university entrepreneurship and innovation centers play in (1) managing the various agendas and expectations of stakeholders within and outside of the academy, (2) providing clarity of purpose to the entrepreneurial endeavor, (3) clarifying ownership rights throughout the entrepreneurial process, and (4) maximizing the potential of individuals to contribute to venture success.

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The contemporary knowledge-based economy has pushed for greater collaboration between higher education institutions and industry. However, the many differences in the institutional and cultural perspectives of academia and industry present a myriad of challenges during market-based collaboration. More specifically, academic entrepreneurship is a sophisticated process that occurs across complex circuits that run both across colleges and universities and between the academy sector and the private sector. The organizational units and actors that populate each connection along these circuits have distinct values and agendas that can sometimes be irrelevant to or in conflict with the values and agendas of units and actors located elsewhere along the circuits. This chapter is focused on the role of university entrepreneurship and innovation centers in facilitating the collaborations between researchers, institutions, and market actors that are inherent to academic entrepreneurship. Furthermore, we are most concerned with academic entrepreneurship that involves high-tech and scientific innovations that are developed within university laboratories. The reason for this narrow view is the centrality of scientific and technological innovations to the current knowledge-based economy (Powell & Snellman, 2004). Accordingly, from this point forward, we use ‘‘academic entrepreneurship’’ to mean entrepreneurial activities that originate within the academy and involve high-tech and scientific innovations. The set of circuits associated with academic entrepreneurship most often includes scientific researchers, university units capable of crystallizing technological and scientific innovations into protected intellectual properties (IP) (e.g., TTOs), university units capable of assisting in market strategies for moving innovations from the academy to the marketplace and incubation (e.g., university entrepreneurship and innovation centers), and market actors who are capable of raising investment dollars and managing the commercialization of innovations. These various organizational actors and units commonly share few, and possibly no, professional values and can have very different agendas when it comes to the nature, purpose, and potential outcome of academic entrepreneurship. For instance, scientists may view newly developed innovations as opportunities to generate new resources to support research, whereas universities may see the innovations as opportunities to respond to calls to contribute to their local economies and the betterment of society, and investors and corporate interests likely view the innovations as potential sources of market access and financial profits. However, the variation in values and agendas is not an inherent indication of conflict. Instead, the variations draw attention to the need for a mediator to bridge the gaps between stakeholders and to facilitate a

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strategy that adequately satisfies all parties. Without such an intermediary, activities associated with academic entrepreneurship may become stalled or abandoned. This chapter explores the mediating role of university entrepreneurship and innovation centers, which involves spanning and bridging the organizational boundaries that surround distinct stakeholders and resolving conflicts that may occur as academic (scientific and altruistic) and market logics collide.

BOUNDARIES OF IDENTITY AND LOGICS This chapter explores the intersection of distinct organizational identities that intersect through the processes of academic entrepreneurship. The exploration is in part theoretically guided by the cognitive view of organizational boundaries and identities (Dutton & Dukerich, 1991; Porac, Thomas, Wilson, Paton, & Kanfer, 1995). Specifically, organizations are social structures that are defined by boundaries that dictate what and how work is done, and what external pressures are allowed to influence the nature of such work (Dutton, Dukerich, & Harquail, 1994). Santos and Eisenhardt (2005) contended that organizational boundaries largely shape the contextual identities of organizational members. Weick (1995) argued that organizational members perform collective ‘‘sensemaking’’ within the parameters of identity boundaries to bring continuity and consistency to otherwise ambiguous environments. Also, individuals create a close identification with organizations to minimize uncertainties associated with shifting environmental conditions (Hogg & Terry, 2000). Furthermore, organizational boundaries are not always rigid, but rather are often fluid and able to shift to adapt to changing environmental conditions (Gioia, Schultz, & Corley, 2000). Accordingly, it is expected that shifts in the identities of organizational members accompany the reshaping of organizational boundaries during periods of adaptation. In the context of academic entrepreneurship, organizational identities are likely to become altered as the distinct domains of the academy and the private marketplace collide. The formation of organizational boundaries and identities is inherent to the entrepreneurial process (Santos & Eisenhardt, 2006; Katz & Gartner, 1988). Also, entrepreneurial ventures are not homogenous organizations. Instead, entrepreneurial ventures are understood to be dynamic organizations that are composed of ‘‘multidimensional socioeconomic relationships’’ (Larson & Starr, 1993, p. 11). Specific to academic entrepreneurship, Nicolaou and Birley (2003) argued that the ways in which university-born

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enterprises are created and launched are notably influenced by the degree and nature of scientists’ endogenous and exogenous social networks. Thus, the organizational identities of emergent ventures that center on scientific and technological innovations developed within university laboratories are likely to have to account for a range of stakeholders that view entrepreneurial activities through a diverse range of cultural lenses. Considering member identification with an organizational identity is an important factor in the determination of venture success (Fiol, 2001), the creation of a flexible organizational boundary and multidimensional identity that is satisfying to the scientific team (e.g., professors and students), investors, and managerial partners is critical. Institutional logics have been shown to be important factors in determining the identities of organizations across a wide range of fields and professions (Bastedo, 2009; Glynn & Lounsbury, 2005; Mars, 2009; Mars & Lounsbury, 2009; Thornton, 2002). These logics help shape organizational identities and provide the rails on which specific organizational members patrol boundaries and monitor for opportunities for organizational adaptation and change. For instance, Bettis and Prahalad (1995) contended that dominant logics emerge within organizations to both retain organizational identities and adapt to environmental changes and pressures. Scholars such as Fiss and Zajac (2004), Lounsbury (2007), and Marquis and Lounsbury (2007) have indicated that institutional logics are often in competition with other logics and thereby create tension related to organizational behavior and change. Importantly, logics are not always constant and can change based on exogenous shocks (Clemens, 1999; Schneiberg & Clemens, 2006). For example, Glynn and Lounsbury showed in their study of critical reviews of the Atlanta Symphony Orchestra how a new dominant logic that is a blend of aesthetic and market logics emerged following a 1996 musician strike. However, dominant logics play a major part in purposefully preserving the established identities of organizations. For example, Marquis and Lounsbury showed how the U.S. small banking industry aggressively resisted the national banking movement, which was a response that was deeply rooted in community logic. The preservation function of logics can sometimes occur at the expense of organizational and field evolution. For instance, Mars showed how the dominant public good logic of higher education has limited the ability of higher education scholars to examine new forms of student social movements that are market-oriented (e.g., social entrepreneurship). In the context of academic entrepreneurship, the dominant logics of otherwise isolated organizations (e.g., universities and investment groups) meet throughout entrepreneurial processes.

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University entrepreneurship and innovation centers are vital to the disentanglement of competing logics that is necessary to make academic entrepreneurship possible.

BOUNDARY SPANNING ROLES The role of boundary spanning is well documented in the management and sociology literatures. In 1967, Thompson argued boundary spanning activities either establish linkage between otherwise disconnected organizations or preserve the separation between organizations to prevent or minimize environmental disturbances. Aldrich and Herker (1977) defined boundary roles as the links between organizations and the environments in which they both operate in and come in contact with. These researchers stated, ‘‘Information from external sources comes into an organization through boundary roles, and boundary roles link organizational structure to environmental elements whether by buffering, moderating, or influencing the environment’’ (Aldrich & Herker, 1977, p. 218). The action of boundary spanning is initiated and managed by individuals who are technically competent and widely networked within and beyond their own unit and organization (Tushman & Scanlan, 1981). In a study of alliance strategies involved with global joint ventures, Inkpen and Dinur (1998) demonstrated the importance of organizational boundary spanning to the acquisition of knowledge that is not otherwise available within isolated organizations. In the context of academic entrepreneurship, boundary spanning must occur when scientists, investors, and managers converge during the creation of new ventures. For instance, without boundary spanning, scientists would have more limited access to investment and research dollars, investor access to scientific and technological innovations would be constrained, and managers would be less able to gain scientific and technological input during periods of troubleshooting and product development.

ACADEMIC CULTURE AND THE DISCIPLINARY LANDSCAPE Colleges and universities are complex organizations that are composed of a wide range of academic subcultures, which are largely determined by disciplinary norms and values. Biglan (1973a, 1973b) provided a model for

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categorizing fields of study according to three dimensions. The three dimensions proposed by Biglan are paradigm (hard versus soft), application of knowledge (pure versus applied), and the relationship of disciplines to the life sciences. Biglan’s categories can be partially recognized in Stokes’ (1997) description of Pastuer’s Quadrant, which outlines the structural and functional differences between pure and applied science. According to Biglan’s model and Pastuer’s Quadrant, disciplines within the same category share common norms, values, and sets of practice, whereas those in different categories share few norms, values, and standard practices. Due to in part such differentiation, disciplines have been recognized as ‘‘academic tribes’’ whose norms and values are notably different from and are often in conflict with those that frame other fields of studies (Bailey, 1977; Becher, 1989, 1994). Academic tribes are understood to operate autonomously, with the exception being periods of crisis when tribes ban together to resist or force institutional change. Ylijoki (2000) argued that disciplinary cultures determine the moral order of fields, which dictate how professors and students approach academic and professional practices. Consistent with the premises of the tribal analogy, moral orders are varied and help make disciplinary fields distinct. On the basis of this line of inquiry, it is appropriate to conclude that disciplinary-based norms, values, and moral orders together form the organizational boundaries and the identities of fields of study. Higher education scholars have argued that the postsecondary academy is governed by two co-existing knowledge/learning regimes: the public good regime and the academic capitalist regime (Slaughter & Rhoades, 2004). Although these regimes can sometimes converge in subtle and narrow ways (Mars, 2008), these mostly distinct systems are often in opposition. On the one hand, the public good regime is longstanding and positions academics as intellectuals who are free to conduct scholarship and instruction without concern over political and economic incentives and consequences. Accordingly, knowledge is viewed as a public good and not constructed in the context of market application and value. On the other hand, the academic capitalist regime has emerged over the course of the past three decades and as a result of market permeation. Academic capitalism is characterized by institutional actors (e.g., professors, administrators, and students) engaging in market and market-like activities in pursuit of resources or personal profits. Accordingly, knowledge is considered IP and becomes a commodity that holds potential economic value. Academic entrepreneurship is a representation of academic capitalism (Mars, 2006). Despite the academic capitalist regime now being an institutionalized system across higher

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education, many (if not most) scientists and researchers continue to view their work in the context of the public good regime. Consequentially, professors who engage in entrepreneurial activities that involve their research are forced to reconsider their work in the context of the private marketplace. This recontextualization requires the reconciliation of cultural conflicts, which is often a difficult task. The cultural conflicts that occur within the academy as a result of academic entrepreneurship are compounded by clashes between university and market actors. Just as the organizational boundaries and member identities of disciplinary fields (and academia in general) influence how knowledge is constructed as mostly a public good, the organizational boundaries and member identities of the secular marketplace shape how mainstream entrepreneurs and investors conceive the value of universityborn knowledge and innovation. More specifically, the convergence of academic and market actors over the course of entrepreneurial processes connects notably different cultures and requires those on both sides of the collaboration to engage in boundary spanning tasks. On the one hand, academics are required to look beyond science as a public good and are challenged to view their disciplinary domain in the context of commercial applicability. On the other hand, market actors must develop and demonstrate an appreciation for the balance between the scientific purity and applicability of research and innovation. The market permeation that has driven the institutionalization of academic capitalism makes the proffered convergence more feasible and functional. Accordingly, we next explore the role of university entrepreneurship and innovation centers, which are representative units of academic capitalism (Mars, 2006), as intermediaries that help facilitate the boundary spanning associated with both sides of the academic entrepreneurship process.

CONCEPTUAL EXPLORATION The following conceptual exploration of the role of university entrepreneurship and innovation centers in facilitating organizational boundary spanning is presented within four closely related sections. The sections are framed according to the following points: (1) managing the various agendas and expectations of stakeholders within and outside of the academy, (2) providing clarity of purpose to the entrepreneurial endeavor, (3) clarifying ownership rights throughout the entrepreneurial process, and (4) maximizing the potential of individuals to contribute to venture success. These

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Market Logics

Academics (Professors, Scientists, Students)

Scientific Logics

University Entrepreneurship Centers

Scientific Logics

Fig. 1.

Entrepreneurs/Investors

Market Logics

Position of University Entrepreneurship Centers as Organizational Workshops.

points are highly common challenges that confront entrepreneurial collaborations that involve university and market actors. University entrepreneurship and innovation centers often function as the ‘‘organizational workshops’’ where academic and market actors are connected, collaborations are formed and managed, and the issues that arise as scientific and market logics converge are confronted (Fig. 1). In other words, university entrepreneurship and innovation centers are agents that promote and facilitate the organizational boundary spanning that is inherent to academic entrepreneurship. The exploration is guided by observations made in the University of Arizona’s McGuire Center for Entrepreneurship over the course of numerous entrepreneurial collaborations involving university scientists, students, and outside investors and entrepreneurs. Also, in certain cases, practical tools designed and implemented within the McGuire Center for the purpose of bridging academic and market actors and facilitating productive entrepreneurial collaborations are offered.

Agendas and Expectations As already discussed, the agendas and expectations of the actors involved in academic entrepreneurship are widely varied. The position of university researchers on the appropriateness of academic entrepreneurship is a key variable in the academic entrepreneurship equation (Renault, 2006).

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However, while academic entrepreneurship is becoming an increasingly institutionalized practice (Colyvas & Powell, 2007), the rationales of why individual professors and scientists engage in market-oriented activities remains unclear (Rothaermel, Agung, & Jiang, 2003). The dominant scientific logic suggests that professors and scientists place high value on discovery and the creation of new knowledge. There is also likely to be an underlying altruistic commitment to the betterment of society through scientific advancement. Accordingly, professors, researchers, and students2 are likely to enter into entrepreneurial arrangements with the goal of securing resources for future research and making innovations available for public use through market channels. The market logics that drive external actors to engage in academic entrepreneurship are clear. Universities are a primarly location where knowledge that leads to innovation and economic development is created (Conceicao & Heitor, 1999). This makes universities particularly valuable to mainstream entrepreneurs and investors who are seeking strategic opportunities within a knowledge economy that is heavily dependent on the rapid progression of scientific and technological innovation (Powell & Snellman, 2004). In contrast to the uncertain motives of academic entrepreneurs, mainstream entrepreneurs and investors pursue market opportunities that are linked to university innovations with the goal of being competitive within a technologically and scientifically driven knowledge economy. The different logics and associated goals and expectations of academic and mainstream entrepreneurs are not in inherent conflict. Just as activist and market logics can become blended through eco-entrepreneurship (Mars & Lounsbury, 2009), academic entrepreneurship can cause scientific and market logics to coalesce. However, logic blending requires organizational boundary spanning, which can be a slow and complicated process that may derail emergent entrepreneurial endeavors. One assumption that can be made is that those who seek the support of university entrepreneurship and innovation centers do so because the entrepreneurial process is beyond the scope of their narrow fields of expertise. This convergence of actors with limited knowledge beyond their expertise can promote a sense of uncertainty, confusion, and distrust, which in turn can limit or prevent effective communication, the free exchange of ideas, and an overall productive collaboration. In particular, initial conversations and negotiations may be stalled by a lack of familiarity among the actors, and ultimately, opportunities to create value may not be fully identified or captured. One key step in facilitating productive interactions between academic and mainstream entrepreneurs is clarifying the goals of all parties

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and developing clear expectations of all stakeholders at the outset of forming collaborations. In short, all sides of the transaction must be clear about their own goals and expectations, as well as those with whom they will be collaborating with. University entrepreneurship and innovation centers are organizational units that can be uniquely positioned to act as intermediaries between academic and mainstream entrepreneurs and investors. Administrative and academic professionals who are intimately aware of academic and scientific values and the inner workings of the academy populate these centers. These same professionals also tend to have notable experience in forming and managing entrepreneurial partnerships between university and market actors. Thus, university entrepreneurship and innovation centers provide an organizational platform on which academic and mainstream entrepreneurs can converge, where logics can begin to be blended, and goals and expectations can be established and clarified. The McGuire Center for Entrepreneurship has developed a structured protocol for assessing the social, ecological, and other non-commercial value propositions of emergent ventures. This protocol, which is an adapted form of Geyer and DuBuisson’s (2009) life cycle-based framework for environmental assessments of eco-entrepreneurship, is primarily a learning tool used by entrepreneurship students. However, the same tool allows for the careful assessment of the various types of value created through the entrepreneurial application of scientific and technological innovations, which sometimes provide compelling reason for pursuing applications that are otherwise beyond the initial purpose of a particular innovation.

Clarity of Purpose The goals and expectations of academic and mainstream entrepreneurs inform and are informed by the perceived purpose of entrepreneurial ventures. Accordingly, individual actors involved in an entrepreneurial collaboration may consider the purpose of the emergent venture very differently. On the one hand, academic entrepreneurs may view the creation of social and ecological value as a top priority of a particular venture. On the other hand, mainstream entrepreneurs and investors are most likely to consider the generation and accumulation of economic wealth the primary purpose of a new venture. Also, academics often pursue entrepreneurial opportunities to gain more resources to support their ongoing research agenda, whereas entrepreneurs and investors seek opportunities to grow the business in terms of both scope and scale. Thus, academic entrepreneurs

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may view entrepreneurial activities as short-term obligations that are born out of the necessities created by resource dependency. In other words, entrepreneurship can be a peripheral activity that is used to fund the core scientific activities of professors and, in some cases, students. (Of course, academic entrepreneurs may also be motivated by personal profit.) Mainstream entrepreneurs and investors, however, may view the purpose of a venture as a more long-term commitment that should be a top priority of all those involved. Academic entrepreneurs may not be willing or able to commit the time that the entrepreneurs and investors expect. In some cases, the preceding differences in the understanding of purpose may be too extreme and unable to be overcome. However, in other cases, initial discussions and negotiations between academic and mainstream entrepreneurs can clarify the various purposes of venture sand lead to a vision that is mutually satisfying to all sides of the equation. The creation of inclusive visions for the ventures is a process of negotiation and consensus building. Considering neither academic nor mainstream entrepreneurs and investors are neutral players, negotiations may become constrained or ineffective. University entrepreneurship and innovation centers can be viewed as a neutral entity that is capable of leading negotiations and helping to identify a vision that is satisfying to all parties. Recall that boundary spanners filter information in an effort to determine what they are willing and unwilling to introduce to their organizations and their own organizational identities. University entrepreneurship center staff should help reframe various positions and concepts in such ways that will allow academic entrepreneurs and mainstream entrepreneurs and investors to more liberally filter various perspectives and beliefs. This liberal filtering is beneficial to the process of coming to a vision of purpose that is conducive to entrepreneurial progress and success. In the case of the McGuire Center for Entrepreneurship, formal memorandums of understandings (MOUs) are drawn up at the onset of entrepreneurial collaborations. These memorandums primarily clarify points of ownership and terms of partnerships. However, the development and initiation of these agreements also promote earlier negotiations and subsequent agreements of the underlying goals and expectations of the emergent ventures. The execution of the MOUs is also vetted by law students and faculty who participate in a mock law firm model that is a joint program between the McGuire Center, Eller College of Management, and the James E. Rogers College of Law at the University of Arizona. Looking inward, professors and graduate students sometimes cocreate knowledge for the purpose of scientific discovery and commercialization within academic capitalist environments (Mars et al., 2008). This observation

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is especially relevant to the science and technology fields. Just as academic and mainstream entrepreneurs may view the purpose of a venture differently, so too may professors and students. Whereas professors may pursue entrepreneurial opportunities as attempts to fund their research, students are more likely to pursue entrepreneurial activities in the hopes of developing private sector careers. This proposition is supported by research that indicates students who earn advanced degrees in science and technology fields are increasingly opting for careers in private industry over those in academia (Stephan, 2001). However, the dominant model of graduate education that remains intact centers on students being treated as apprentices who are being prepared and socialized to continue and expand the work of their professors within an academic setting. Thus, professors may not be supportive of their students viewing entrepreneurship as a career path and thereby discourage them from fully pursuing entrepreneurial activities. In fact, some professors may prevent students from becoming involved in an entrepreneurial activity under the argument that such efforts are distracting. Graduate students are at a disadvantage in this scenario, considering the authority held by professors. University entrepreneurship and innovation centers may serve as a resource to students who are interested in and well equipped to independently span the boundaries of the academy through entrepreneurship. For instance, the McGuire Center has recently begun offering a university-approved minor in entrepreneurship for doctoral students in the nonmanagement fields. This curriculum provides science and technology students (as well those in any other nonmanagement discipline) with an opportunity to gain a background in entrepreneurship and develop an awareness of how their developing scientific or technological expertise ‘‘fits’’ with the entrepreneurial paradigm.

Intellectual Property Rights and Ownership When knowledge becomes a commodity, ownership must be determined and property rights need to be established. TTOs have become an institutionalized feature of research universities and are the organizational units that are responsible for protecting and managing university-held IP. The process of protecting of IP begins with professors (or graduate students) disclosing novel findings or devices. Once disclosure has occurred, the TTOs attempt to protect the IP by obtaining patents or copyrights. Once IP has been protected, academic entrepreneurs and mainstream entrepreneurs and investors must negotiate licensing agreements with TTOs, which are

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acting on the behalf of universities. This is an over-simplified description of the IP protection and management sequence. Importantly, this sequence is complicated, saturated with paperwork, time consuming, and remarkably lengthy. Ironically, this process, which was designed to promote academic entrepreneurship, may actually hinder the development of entrepreneurial ventures that are based on university-developed innovations. University entrepreneurship and innovation centers can act as liaisons between both TTOs and professors (and graduate students), and TTOs and mainstream entrepreneurs and investors. In particular, the center can help educate both sides of the transaction on the technology transfer process. This educational function helps the academic and mainstream entrepreneurs and investors filter information and determine how best to adapt to the bureaucratic structure of university IP protection and management. Formal tools for examining IP conditions and implications can help in this critical evaluation process. For example, the McGuire Center and Arizona Office of Technology Transfer has developed an IP decision tree that, through a series of detailed questions, leads evaluators through the various IP spheres and to a conclusion regarding the ownership status of particular innovations. Beyond simply protecting and managing IP, determinations of who has authority over the use of IP once it has been accessed are essential. How innovations are applied and at what cost are important considerations that must be made throughout the lifetime of a business. First, academic entrepreneurs may have unreasonable expectations of pricing. For example, some academic entrepreneurs may want to offer innovations at a price that is low enough to make products or services widely accessible, whereas others may desire a pricing model that is higher than what is warranted. Second, academic entrepreneurs may have strong beliefs regarding how the science and technology they developed is applied within the private marketplace. For instance, they may want to limit a particular device to medical applications and thereby avoiding defense applications. On the other side of the transaction, mainstream entrepreneurs and investors may be much more motivated to seek market opportunities that extend beyond original applications. Also, these primary market actors are also likely to follow reliable pricing models that are based on economics rather than instinct and passion. Tangible and intangible issues of ownership are likely to have a heavy influence on how decisions pertaining to the distribution and application of innovations are made.

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Contribution Maximization The capacity of university entrepreneurship and innovation centers to facilitate boundary spanning, and the convergence of scientific and market logic is critical to the development of productive entrepreneurial partnerships. As previously discussed, the opportunity to form entrepreneurial partnerships under the consultation of university entrepreneurship professionals provides a framework useful in resolving initial tensions that accompany boundary spanning and logic convergence. This ability to develop early resolutions and agreements makes room for each party of the partnership to identify the various forms of capital they stand to bring to the emergent venture. For instance, professors and their students will be freer to focus on how their scientific and technological expertise can be strategically leveraged, whereas mainstream entrepreneurs and investors can give full attention to raising the financial and human capital needed to fund the venture. Also, in certain cases, students who are studying entrepreneurship may be introduced to a venture and tasked with developing a business plan and formulating an entrepreneurial story that will clearly articulate and legitimize the business model to external audiences. Ironically, the unique contributions of the various stakeholders that bring critical capital to a venture are also the same sources of the tension and conflict that may arise in the formation stage of the partnership. In short, the capacity of university entrepreneurship and innovation centers to manage the early tensions that arise out of boundary spanning and logic convergence clears the way for more strategic focus on the contributions and assets (human, financial, intellectual, and social) each party brings to the emergent venture.

Boundary Spanning Within the Academy The four preceding sections offer a conceptual view of university entrepreneurship and innovation centers as ‘‘organizational workshops’’ that help facilitate boundary-spanning activities and promote effective collaboration between academic entrepreneurs and mainstream entrepreneurs and investors. However, academic entrepreneurship should not be understood only as a dichotomous transaction between those actors within the academy and those positioned in the external private sector. Instead, the diverse academic landscape of higher education that is composed of distinct disciplinary-based cultures should also be considered when attempting to understand the boundary spanning that occurs throughout

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the entrepreneurial process. In other words, academic entrepreneurship also involves boundary-spanning activities within universities and between academic departments and units. For instance, entrepreneurship that involves professors and students in the science and technology fields is likely to occur differently according to disciplinary cultures that are either pure or applied, and life or nonlife sciences.3 For example, a professor of biology is less likely to approach her research with direct concern over the potential applications of her findings compared to a professor of biotechnology. In this case, the biology professor would be required to confront and filter more information than the biotechnologist when engaging in entrepreneurial activities that center on the application of research. Also, academic entrepreneurship is often a cross-disciplinary process that connects otherwise isolated fields of study. For example, an entrepreneurial venture may bring together the biology professor and biotechnology professor, which would require each researcher to span the boundaries of their distinct (albeit closely related), disciplinary fields. Thus, academic entrepreneurship includes not only the spanning of university boundaries but also traversing disciplinary lines that mark the topography of the academy. University entrepreneurship and innovation centers are uniquely positioned as market-oriented interstitial units (Mars, 2006; Slaughter & Rhoades, 2004) that are capable of assisting in the reconciliation and blending of otherwise distinct (if not conflicting) logics that is inherently involved with boundary spanning and academic entrepreneurship.

CONCLUSION The market permeation that has led to the institutionalization of academic capitalism has spawned pervasive organizational shifts within colleges and universities (Slaughter & Rhoades, 2004). Whereas basic science remains a mainstay of scholarship, the ways in which new knowledge is both created and transferred from higher education institutions to society through market channels has evolved. Historically, knowledge has been transferred in the manner it has been created, which is a process centered on peer review and scholarly publication. However, the current knowledge-driven economy demands that knowledge be transferred in a manner consistent with the way it will be used. Accordingly, academic entrepreneurship and marketoriented collaborations between otherwise isolated academic and commercial actors have increased. Moreover, centers that adopt and embed this

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element as mission critical will be positioned to have significantly increased impact in the movement of innovation and advancement of research. In this chapter, we have explored the unique role of university entrepreneurship and innovation centers as organizational workshops that create an intermediating space for academic and market actors during the entrepreneurial process. This neutral space helps facilitate the formation of productive entrepreneurial partnerships between otherwise disconnected actors, which requires both boundary spanning and the convergence of distinct and sometimes conflicting field-specific logics. This exploration is limited to one university entrepreneurship center. Thus, the observations and propositions that are provided cannot be generalized in any empirical fashion. However, we do contend that the chapter provides a useful perspective for university entrepreneurship professionals who face the challenge of bridging organizational gaps that threaten the vitality of academic entrepreneurship. Also, this exploration has developed a foundation for future research that examines the role of intermediating units in promoting and facilitating boundary spanning and the convergence of institutional logics within contemporary organizational environments. Whereas organizational research has studied competing and blended logics (Bastedo, 2009; Glynn & Lounsbury, 2005; Lounsbury, 2007; Marquis & Lounsbury, 2007; Mars & Lounsbury, 2009; Thornton, 2002), little attention has been given to the role of third parties in the facilitation of logic convergence. For example, an empirical study of boundary spanning activities that transpire within university entrepreneurship and innovation centers would produce important insights into the mechanics behind the blending of academic and market logics within the academic capitalist regime and contribute new insights on how organizational blending occurs within cross-disciplinary contexts.

NOTES 1. The Bayh-Dole Act of 1980 permits universities to retain ownership of intellectual properties created in through the use of federal research dollars. Although the effectiveness of this piece of legislation as a stimulant to academic entrepreneurship is not clear (see Mowery, Nelson, Sampat, & Ziedonis, 2001), the act does encourage market permeation in the research institution sector of U.S. higher education. 2. Research by Mars, Slaughter, and Rhoades (2008) indicates that graduate students, especially those in the science and technology fields, are important

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contributors to the creation of knowledge and are increasingly exercising their individual market agency from within the contemporary capitalist academy. 3. Biglan’s (1973a, 1973b) third disciplinary category is soft versus hard. Science and technology fields are categorized universally as hard. Academic entrepreneurship involving fields considered to be ‘‘soft’’ (e.g., education and social sciences), which are not considered in this chapter, are likely to involve distinct approaches to entrepreneurial collaborations.

REFERENCES Aldrich, H., & Herker, D. (1977). Boundary spanning roles and organization structure. The Academy of Management Review, 2(2), 217–230. Bailey, F. G. (1977). Morality and expediency. Oxford: Blackwell. Bastedo, M. N. (2009). Convergent institutional logics in public higher education: State policymaking and governing board activism. The Review of Higher Education, 32(2), 234–309. Becher, T. (1989). Academic tribes and territories. Berkshire, UK: Open University Press. Becher, T. (1994). The significance of disciplinary differences. Studies in Higher Education, 19(2), 151–161. Bettis, R. A., & Prahalad, C. K. (1995). The dominant logic: Retrospective and extension. Strategic Management Journal, 16(1), 5–14. Biglan, A. (1973a). The characteristics of subject matter in different academic areas. Journal of Applied Psychology, 57, 195–203. Biglan, A. (1973b). Relationship between subject matter characteristics and the structure and output of university departments. Journal of Applied Psychology, 57, 204–213. Clemens, E. S. (1999). Continuity and coherence: Periodization and the problem of institutional change. In: F. Engelstad & R. Kalleberg (Eds), Social time and social change: Perspectives on sociology and history (pp. 62–83). Oslo: Scandinavian University Press. Colyvas, J. A., & Powell, W. W. (2007). From vulnerable to venerated: The institutionalization of academic entrepreneurship in the life sciences. Research in the Sociology of Organizations, 25, 219–259. Conceicao, P., & Heitor, M. V. (1999). University role: On the role of the university in the knowledge economy. Science and Public Policy, 26(1), 37–51. Dutton, J. E., & Dukerich, J. M. (1991). Keeping an eye on the mirror: Image and identity in organizational adaptation. Academy of Management Journal, 34(3), 517–554. Dutton, J. E., Dukerich, J. M., & Harquail, C. V. (1994). Organization images and member identification. Administrative Science Quarterly, 39(2), 239–263. Fiol, C. M. (2001). Revisiting an identity-based view of sustainable competitive advantage. Journal of Management, 27, 691–699. Fiss, P. C., & Zajac, E. J. (2004). The diffusion of ideas over contested terrain: The (non) adoption of a shareholder value orientation among German firms. Administrative Science Quarterly, 49(4), 501–534. Geyer, R., & DuBuisson, M. (2009). A life cycle-based framework for environmental assessments of eco-entrepreneurship. In: G. D. Libecap (Ed.), Frontiers in eco entrepreneurship research: Advances in the study of entrepreneurship, innovation and economic growth (Vol. 20, pp. 53–78). London: JAI/Elsevier Press.

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Gioia, D. A., Schultz, M., & Corley, K. G. (2000). Organizational identity, image, and adaptive instability. Academy of Management Review, 25(1), 63–81. Glynn, M. A., & Lounsbury, M. (2005). From the critics’ corner: Logic blending, discursive change and authenticity in a cultural production system. Journal of Management Studies, 42(5), 1031–1055. Hogg, M. A., & Terry, D. J. (2000). Social identity and self-categorization processes in organizational contexts. The Academy of Management, 25(1), 121–140. Inkpen, A. C., & Dinur, A. (1998). Knowledge management processes and international joint ventures. Organization Science, 9(4), 454–468. Katz, J., & Gartner, W. (1988). Properties of emerging organizations. Academy of Management Review, 13(3), 429–441. Larson, A., & Starr, J. A. (1993). A network model of organization formation. Entrepreneurship: Theory & Practice, 17(2), 5–15. Lounsbury, M. (2007). A tale of two cities: Competing logics and practices variation in the professionalizing of mutual funds. Academy of Management Journal, 50(2), 289–307. Marquis, C., & Lounsbury, M. (2007). Viva la resistance: Competing logics and the consolidation of U.S. community banking. Academy of Management Journal, 50(4), 799–820. Mars, M. M. (2006). The emerging domains of entrepreneurship education: Students, faculty, and the capitalist academy. Unpublished Ph.D. dissertation, The University of Arizona, Tucson, AZ. Mars, M. M. (2008). The socially-oriented student entrepreneur, November. Presented at the Annual Meeting of the Association of the Study of Higher Education, Jacksonville, FL. Mars, M. M. (2009). Conceptual boundaries and pathways: Exploring the institutional logics of higher education scholarship on college student social movements and activism. Education, Knowledge, & Economy, 3(2), 121–140. Mars, M. M., & Lounsbury, M. (2009). Raging against or with the private marketplace? Logic hybridity and eco-entrepreneurship. Journal of Management Inquiry, 18(4), 4–13. Mars, M. M., Slaughter, S., & Rhoades, G. (2008). The state-sponsored student entrepreneur. The Journal of Higher Education, 79(6), 638–670. Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2001). The growth of patenting and licensing by U.S. universities: An assessment of the effect of the BayhDole Act of 1980. Research Policy, 30(1), 99–119. Nicolaou, N., & Birley, S. (2003). Academic networks in a trichotomous categorization of university spinouts. Journal of Business Venturing, 18(3), 333–359. Porac, J. F., Thomas, H., Wilson, F., Paton, D., & Kanfer, A. (1995). Rivalry and the industry model of Scottish knitwear producers. Administrative Science Quarterly, 40, 203–227. Powell, W. W., & Snellman, K. (2004). The knowledge economy. Annual Review of Sociology, 30, 199–220. Renault, C. S. (2006). Academic capitalism and university incentives for faculty entrepreneurship. Journal of Technology Transfer, 31, 227–239. Rothaermel, F. T., Agung, S. D., & Jiang, L. (2003). University entrepreneurship: A taxonomy of the literature. Journal of Industrial and Corporate Change, 16(4), 691–791. Santos, F. M., & Eisenhardt, K. M. (2005). Organizational boundaries and theories of organization. Organization Science, 16(5), 491–508. Santos, F. M., & Eisenhardt, K. M. (2006). Constructing markets and organizing boundaries: Entrepreneurial action in nascent fields. Academy of Management Proceedings, J1.

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Schneiberg, M., & Clemens, E. S. (2006). The typical tools for the job: Research strategies in institutional analysis. Sociological Theory, 24(3), 195–227. Slaughter, S., & Leslie, L. (1997). Academic capitalism: Politics, policies, and the entrepreneurial university. Baltimore, MD: Johns Hopkins University Press. Slaughter, S., & Rhoades, G. (2004). Academic capitalism and the new economy: Markets, state, and higher education. Baltimore, MD: Johns Hopkins University Press. Stephan, P. E. (2001). Educational implications of university-industry technology transfer. The Journal of Technology Transfer, 26(3), 199–205. Stokes, D. E. (1997). Pasteur’s quadrant: Basic science and technological innovation. Washington, D.C.: Brookings Institution Press. Thompson, J. D. (1967). Organizations in action. New York: McGraw-Hill Book Co. Thornton, P. (2002). The rise of the corporation in a craft industry: Conflict and conformity in institutional logics. Academy of Management Journal, 45(1), 81–101. Tushman, M. L., & Scanlan, T. J. (1981). Boundary spanning individuals: Their role in information transfer and their antecedents. Academy of Management Journal, 24(2), 289–305. Weick, K. (1995). Sensemaking in organizations. London, UK: Sage. Ylijoki, O. H. (2000). Disciplinary cultures and the moral order of studying: A case-study of four Finnish university departments. Higher Education, 39(3), 339–362.

DIFFERENT STROKES FOR DIFFERENT FOLKS: UNIVERSITY PROGRAMS THAT ENABLE DIVERSE CAREER CHOICES OF YOUNG SCIENTISTS$ Rajshree Agarwal and Steven Sonka ABSTRACT In this chapter, the authors assert that traditional advanced degree programs underserve young scientists, and train them primarily for a career in academia pursuing basic research. Data drawn from the Scientists and Engineers Statistical Data System (SESTAT) from 1996 to 2006 show that only one fourth of all scientists are engaged in basic academic research. The majority of young scientists pursue alternative career paths in applied and in industrial research settings. Several such career options are highlighted, and the conclusion is drawn that graduate education should be broadened to provide students with complementary business and entrepreneurship knowledge, skills and attitudes required for success in each option. Four examples of innovative programs that address this need at the University of Illinois are discussed, $

Both authors contributed equally.

Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 139–164 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021009

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including a Certificate in Entrepreneurship and Management (CEM) for Life Scientists, a Certificate in Business Administration (CIB) for Nonbusiness Majors, the Illinois Professional Science Master’s (PSM), and various nondegree, experiential opportunities.

1. INTRODUCTION The dynamism of the U.S. economy has been fueled with continued advances in basic and applied science, conducted within both academic and industry boundaries. And science-driven innovation is commonly perceived as a key to future success, for firms and for nations. Such innovation requires large numbers of highly trained individuals with graduate level education (Masters; PhD) to provide the human capital necessary to fuel that innovation. Within the United States, this training occurs at institutions where academic scholars define and enforce the advanced degree programs in higher education. It is natural, therefore, that successful academic scholars focus on providing the knowledge and experiences that are most likely to offer success for future academic scholars. Furthermore, as external funding has become a larger source of research funding at academic institutions, the experiential component of the advanced degree has increasingly become focused on accomplishing the research project and publishing the associated results as a means to attract further funding for the professor’s program. The research thesis, therefore, is a primary means of evaluating success for the advanced degree candidate. The current approach thus focuses on the student as a potential academic scientist and the thesis/dissertation as the chief indicator of a student’s contribution to knowledge. This mainstream model has contributed greatly to the improved wellbeing of society. U.S. academic institutions account for much of the technological and economic development both in their proximate region and at large. However, individuals with advanced degrees may pursue a career that encompasses participation in the private and public sectors as well as in academia. Furthermore, the individual’s lifetime of intellectual contributions likely will not be limited to their thesis, or follow-on contributions to their disciplinary literature. As the demand for innovation from society accelerates and manifests itself in a host of career options for young scientists, there is also a need for a renewed assessment of whether we in

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academia equip our students with the knowledge, skills, and attitudes required for success. Doing so requires us to take stock of the following questions: What are the potential career paths available to students after an advanced (e.g., PhD) degree? What are the needs for these career paths, and how well are they satisfied by current academic programs? How may we devise programs that are cognizant of these unmet needs, and of constraints on time and money that can be devoted to these programs? The purpose of this chapter is to contribute to the dialog regarding needed enhancements to advanced educational programs in the Sciences.1 We provide a first attempt at answers to the questions raised earlier, drawing from extant wisdom in the received literature (Section 2) and statistical data on career trajectories of scientists (Section 3) to outline a potential portfolio of career paths available to students graduating with advanced science degrees (Section 4). Finally, in Section 5, we provide an overview of some program development initiatives at the University of Illinois and a comparison across the options as they address the unmet needs, subject to varying levels of relevant constraints.

2. INSTITUTIONAL AND POLICY BACKDROP The critical role of science in modern societies has been long recognized, as in ‘‘Science The Endless Frontier’’ (Bush, 1945). Perhaps the most influential thesis ever written on the subject in the United States, the report led subsequently not only to the historic creation of the National Science Foundation (NSF) and the National Institutes of Health (NIH) but also to a dramatic redefinition of the role of the academic institution vis-a`-vis the activities it undertakes. A basic premise of the report was that for-profit firms will not undertake socially optimal levels of investment in basic vis-a`vis applied science. Highlighting the unmet need for investment in basic science, the report turned toward American colleges and universities and argued for federal support of basic science within their domains to advance the frontiers of science while preserving the autonomy that is highly valued by scientists (Bush, 1945). Consistent with this notion, the norms and culture within American universities for most of the 20th century emphasized the quest for pure science (Merton, 1973; Smokler, 1983) and eschewed the application or commercialization of scientific knowledge for its profit motive (Etzkovitz, 1998). These cultural norms also had important consequences for the training provided to students pursuing higher education, particularly at the

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graduate – Master’s and PhD – levels. The ‘‘apprenticeship’’ model emphasized goals consistent with the pursuit of ‘‘basic’’ research, which were also consistent with the incentive structure that rewarded publications and advances in pure science and were supported with government-funded grant research that did not necessarily recognize applications of the scientific discoveries as goals of equal importance. As a result, a typical PhD student enrolled in an American institution of higher learning focused on basic knowledge creation, and the training offered by these programs emphasized the ‘‘know-why, know-how and know-what’’ most applicable to basic science undertaken in academic institutions alone. Careers in applied domains, or in industry, were considered to be secondary in importance, and options only if the student ‘‘could not make it’’ in academia. For example, Stephan (1996) notes ‘‘most students enter graduate school with the expectations of eventually working in the academic sector, and these preferences are reinforced when in school’’ (p. 1214). More recently, the original ‘‘science-push’’ linear model proposed by Bush (1945) – that basic research precedes applied research and should be conducted independently of it – has been modified to recognize the potential synergies between basic and applied science, as best highlighted by the influential study on Pasteur’s quadrant by Stokes (1997). In addition to the potential singular focus on either basic or applied science, Stokes (1997) drew attention to the confluence of the two. On the policy side, this view underpinned the passage of the Bayh-Dole Act in 1980, which allowed basic researchers and universities to patent publicly funded research results so as to increase the flow of social benefits (Sampat, 2006). Consistent with this shifting focus, the evolving norms of science in universities recognize the dual roles of basic and applied research not as an either-or (Dasgupta & David, 1994; Etzkovitz, 1998; Rosenberg, 1990) but as complementary factors that interact in a nonlinear manner, involving both ‘‘science-push’’ and ‘‘demand-pull’’ (Schilling, 2008). This trend is not only evident from the exponential growth in patenting by universities in the past 20 years (Sampat, 2006) but also from the increasing number of academic/industry partnerships and the promotion of private spin-off companies (Lowe & Ziedonis, 2006; Mowery, Nelson, Sampat, & Ziedonis, 2001; Murray & Stern, 2007; Thursby, Jensen, & Thursby, 2001; Walsh, Cohen, & Cho, 2007). Whereas the changes mentioned earlier have manifested themselves in what academic scholars focus on, graduate educational programs have only slowly began to adapt and change. In part, this is because the increasing linkages between basic and applied science and among universities and firms are not without its detractors. There is a fear that the commercialization of

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academic research inhibits the free exchange of scientific information and stifles scientific progress by violating the academic norm of communalism (Argyres & Liebskind, 1998; Heller & Eisenberg, 1998; Merton, 1973; Murray & Stern, 2007). There also is a fear that, given trade-offs between fundamental basic research and applied research with commercial value, universities will be lured away from their core mission of basic or fundamental investigation or begin producing research of lower ‘‘quality’’ (Etzkovitz, 1998; Goldfarb, 2008). As a result, the training of ‘‘labor’’ in the markets for scientists has exhibited significant inertia and largely ascribes to the traditional ‘‘apprenticeship’’ model described earlier. Economists and sociologists alike have been concerned with labor markets for science for decades (Arrow & Capron, 1959; Blank & Stigler, 1957; Dasgupta & Maskin, 1987; Diamond, 1986; Levin & Stephan, 1991; Stephan, 1996; Stern, 2004). In conducting a review of the literature, Stephan (1996) stated that, while the supply side of scientific labor markets have generally been well studied, demand-side considerations have been more difficult to specify, in part because of incomplete knowledge of the behavior of universities and governments. To the best of our knowledge, a systematic study of both supply- and demand-side considerations for scientific labor markets is lacking. On the ‘‘demand side’’ of these labor markets, even though basic and applied science are viewed as synergistic in institutional settings of both industry and academia, scientific knowledge and discoveries are purposefully used differently. Academic science has a winner-take-all nature in which priority is placed on being recognized as the first one to discover a scientific breakthrough, whereas industry science, even that which is basic, needs to be tied ultimately to profitability that stems from technological application. As Stephan (1996) notes, ‘‘firms can ill afford to fund research that has little promise of eventually relating to the company’s objectives’’ (p. 1211). In academia, applied research may well take a backseat to basic research, given cultural norms and incentive structures that create hierarchies between basic and applied research focus (Etzkovitz, 1998; Merton, 1973). Furthermore, because of differences in the financial models that help fund research activities, both the rates and the level of compensation schemes differ significantly across the two institutional settings. On the ‘‘supply side’’ of labor markets, research ability and preferences are often singled out as distinctive the characteristics that affect scientific employees’ occupational and activity choices and compensation structure (Dasgupta & David, 1994; Stern, 2004). People differ in their abilities to learn extant knowledge and develop new innovations, be it due to innate

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differences in intelligence or prior investments in knowledge. In the context of preferences, independent of ability, scientists may vary regarding their taste for the quest for basic research, and their desire to apply scientific principles for economic and technological development. Such preferences are a function of received norms and culture, which may be informed critically during the course of receiving scientific education. Furthermore, both the ability and the preferences of scientists are likely to change over the scientists’ life cycle (Levin & Stephan, 1991). A second important characteristic of scientific labor markets is ‘‘indivisibility,’’ because both sides represent a bundle of resources and capabilities. On the supply side, Stern (2004) discusses the strong positive correlation between ability and preferences observed among scientists. Although higher ability scientists command higher compensation, other things constant, they may also have a greater ‘‘taste for science,’’ resulting in their willingness to accept a lower wage as a compensating differential. Accordingly, equilibrium wages can be lower or higher for higher ability scientists. An important unanswered question from extant literature is whether the observed positive correlation between ability and ‘‘taste for science’’ in academic scientists is due to the sorting mechanism of scientists across academic or industry careers or because the two are causally connected. A second type of indivisibility can manifest itself longitudinally. Winnertake-all norms in academia (Merton, 1973) may lead some scientists to build their career strategically. To leverage success in scientific contests, scientists may sacrifice a part of monetary rewards at earlier stages of their life cycle to choose the most productive research environment, invest heavily in building human capital, and ‘‘cash out’’ in later stages (Dasgupta & David, 1994). This implication can also be derived from the traditional human capital model with a finite time horizon (Levin & Stephan, 1991). Thus, the earnings profile of such scientists can become steeper with respect to labor market experience. On the demand side, too, indivisibility in job characteristics results from the nonscience-related activities that are bundled with research, both in a university or in a firm setting. Faculty are required to invest time in teaching and service, for instance, and similarly, scientists in firms are required to invest in activities or scientific projects that may not be immediately aligned with their scientific interests. Such indivisibilities impact career choices given that they may represent different types of distractions for scientists, given their main objective of conducting research. To summarize the preceding discussion, the preceding issues highlight the need, as social science researchers, for increased attention toward

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developing ‘‘matching models’’ of markets for scientists and increasing our understanding of the ‘‘science of science and innovation.’’ In the interim though, the issues also require us, as institutions that provide education and training for scientists, to take stock of the employment options for young scientists, understand the potential career portfolios, and develop programs that cater to multiple options, not just one or two.

3. EMPIRICAL EVIDENCE OF ALTERNATIVE JOB OPTIONS At the heart of career decisions facing young scientists are questions related to what they do: basic or applied science; and where they do it: university or industry. We turn to an empirical examination of career options based on these two criteria, drawing from a unique and comprehensive database collected by the NSF. The Scientists and Engineers Statistical Data System (SESTAT) developed by the NSF is based on a survey of Baccalaureate, Master’s, and Doctoral graduates in science and engineering fields. The data files used in this study are the restricted use of SESTAT for the years 1995, 1997, 1999, 2003, and 2006, supplemented with the 2001 National Survey of Recent College Graduate (NSRCG) and the 2001 Survey of Doctorate Recipients (SDR). SESTAT is a comprehensive and integrated system of information about the education, employment, and demographic characteristics of scientists and engineers educated and trained in the United States. An attractive feature of SESTAT for our objectives is that it contains a large and broad scope of variables that enables us to systematically investigate characteristics of scientists on a reasonably disaggregated level. The data permit us to sort scientists into bins, as defined by their choice of where they work and what they do. The large number and representativeness of the SESTAT observations also permit us to draw generalizable conclusions. Table 1 provides the estimated population frequency of the scientists employed in the four cells represented by the intersection of basic versus applied science and academia versus industry. These estimates are based on weighted counts derived from SESTAT 2003 data for the survey of scientists and engineers (including Baccalaureate, Masters, and PhD degree holders).2 The four types of jobs are mutually exclusive. Several aspects of Table 1 are noteworthy. As can be seen from the estimated percentages of employment in the diagonal cells, 26 percent of scientists are employed in basic science at universities, whereas almost

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Table 1.

Estimated Scientists Population Frequency (Weighted Counts). Counts

Percentages

Basic Science

Applied Science

Basic Science

Applied Science

Academia

204,542

167,865

Total: 26.0 Column: 66.2 Row: 54.9

Total: 21.3 Column: 35.1 Row: 45.1

Industry

104,393

310,596

Total: 13.3 Column: 33.8 Row: 25.2

Total: 39.4 Column: 64.9 Row: 74.8

Total

787,396

Source: Authors’ estimation, using 2003 SESTAT data and sample weights in SESTAT.

40 percent are employed in applied science in an industry setting. In contrast to the assumption that research within universities is primarily of a basic nature, the estimates in Table 1 reveal that more than one-fifth of the total scientist population is estimated to conduct applied research in academia. And while the numbers show that basic research is less likely to be conducted in industry settings, a nontrivial 13 percent of the scientist population is focused on basic research in industry. Thus, contrary to the notion that there is a division of labor for basic and applied science across academia and industry settings, the current statistics reveal that both institutional settings provide opportunities for scientists to undertake the different types of research. Although the data show that the majority of the employment is within the diagonal cells, a significant percentage of scientists are nonetheless employed in the off-diagonal categories. The data are consistent with the premise behind the 1980 Bayh-Dole Act that applications of basic research for technology and economic development be a university mission. The data are also consistent with firms needing to engage in basic research (Hounshell & Smith, 1988; Rosenberg, 1990) for the development of absorptive capacity (Cohen & Levinthal, 1990). The statistics discussed earlier are based on a question that required respondents to identify their principal activity. In a different question, respondents could identify all activities that utilized more than 10 percent of their time. In Table 2, we use this information to examine the extent to which scientists classified their work as being both basic and applied.3 Sixty-five percent of university scientists reported doing both basic and applied research, as did 56 percent of industry scientists. Importantly,

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Table 2.

Estimated ‘‘Pasture’’ and ‘‘Edison’’ Type Scientists Population Frequency (Weighted Counts). Counts

Percentages

Both basic and applied science

Within institution

Total

241,480 233,810

64.8 56.3

30.7 29.7

Academia Industry

Source: Authors’ estimation, using 2003 SESTAT data and sample weights in SESTAT.

Table 3.

Transition Probability between Survey Periods. Next Period Academia

Current Period

Industry

Basic

Applied

Basic

Applied

Academia

Basic Applied

86.4 18.8

9.6 77.6

0.7 0.1

3.3 3.5

Industry

Basic Applied

4.4 0.7

1.5 0.9

38.8 5.8

55.3 92.7

Source: Authors’ estimation, using 1995 to 2006 restricted-use SDR data.

approximately 60 percent of the scientist population thus report on doing work that has both basic and applied components. Thus, the either-or distinction between basic and applied research seems even more blurred in both settings, in line with Pasteur’s quadrant (Stokes, 1997). Finally, we turn to the percent of people who transition between job categories. As longitudinal data are available from the SDR component of SESTAT, we note that the statistics reported in Table 3 contain only those scientists who have doctoral degrees. Not surprisingly, the diagonal terms in the transition matrix presented in Table 3 are high – most scientists continue to do what they used to do and work in the same institutional setting that they were previously employed in. However, the off-diagonal terms are once again nontrivial. Almost 10 percent of scientists who classified themselves as basic, academic researchers, move to applied research in academia, and yet another 3 percent move to applied research in industry. Less than 1 percent move to basic research in industry. Approximately 20 percent of applied researchers in academia transition into doing basic research in academia, and another 3 percent move to applied research in industry. The highest transition is

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experienced by scientists conducting basic research in industry, though it is to move toward more applied aspects of the work within industry (55 percent). The preceding descriptive statistics, taken together, have important implications for the programmatic training we provide scientists as they choose among diverse career paths. What is remarkable, though, is that only 26 percent of the total scientist population choose to pursue the traditional path of being an academic focusing on basic research. Even among these scientists, almost two-thirds report activities that combine basic with applied research, and almost 15 percent are likely to switch entirely out of basic academic research. This implies that the vast majority of the student population in higher education, particularly when pursuing graduate degrees, may be underserved by the dominant paradigm of the apprenticeship model, which implicitly assumes that basic academic research is the norm and optimal career choice for scientists. What are their alternative options? The broad brush dichotomy of the career options, based on basic versus applied research and industry versus academic settings, subsumes several career options to which we turn in the next section.

4. ALTERNATIVE CAREER PATHS FOR SCIENTISTS This section discusses five stereotypical descriptions of alternative career paths for individuals with advanced degrees in the sciences and engineering. They are intentionally offered as generalizations because they are meant for illustrative purposes. Also, we deliberately move away from the simplistic characterization of basic/applied research conducted in either academic or industry settings. There are two main reasons for our doing so. One, the various options are more representative of the diverse career paths than the more aggregate options described in the preceding section. Importantly, as we move toward defining programs that cater to the diverse career needs, it is imperative for us to identify the business, economics and legal knowledge, skills, and attitudes that complement the scientific domain knowledge for each career choice. As we explain momentarily, these complementary skills are important even for the scientist who pursues traditional academic and basic research, though the relative importance and use of these skills will vary across the career options. 4.1. Academic Scholar for Life We begin with this dominant stereotype, which is commonly considered as the current driving theme for graduate education. The notion is that our

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current systems prepare advanced degree students to be at the cutting edge of disciplinary knowledge when they get their degree. Furthermore, the academic culture sets the expectation that the most desirable professional career will extend that cutting edge by acquiring ever deeper levels of expertise along a narrow domain. Success within this framework occurs when the individual secures an academic position, publishes aggressively to achieve tenure, and continues to excel in conducting discipline-driven scientific research throughout their career. Not all individuals will be able to achieve sustained success throughout their careers. For them, devoting more time to teaching or even committing administration are the fall-back career options.

4.2. Corporate Leader This individual may initiate their career doing disciplinary-driven research in academia or industry. (If the start is in academia, a relatively early shift is made to industry.) Recognized as a potential corporate leader, the individual is targeted for management training, typically by securing the MBA degree. Historically, but with decreasing incidence today, the costs for securing the MBA are heavily subsidized by the employer. Success for this path is based on demonstrated performance as a corporate manager and executive, not as a scientist. The disciplinary knowledge associated with the science-based degree, along with the human skill development that occurred in getting the degree, often is advantageous, but it is not required. For both the Academic Scientist for Life and the Corporate Leader, the educational paths and experiences are well defined and are available. However, that is not the case for other career paths that are emerging as science-driven innovation becomes more prevalent.

4.3. Research Enterprise Manager This career path exists both in industry and in academia. In industry, this individual is expected to be responsible for a number of managerially related tasks within the organization’s research enterprise. Financial management and planning, collaboration with other units within the organization, and human relations are skills of critical importance. Even in the first few years within the organization, an individual’s performance is affected by their

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ability to exercise these skills – even if the individual’s key performance metrics relate to science-based productivity. Some individuals in this role will rise in the organization and receive managerial skill development support over time, possibly moving over to the Corporate Leader path at some point. Others will be frustrated as ‘‘they do their job’’ but ‘‘just aren’t appreciated.’’ These individuals may be likely to conjure up images of Dilbert, the popular cartoon character who struggles through an unsatisfying professional life that is confined to a standard cubicle. In academia, scientists increasingly are discovering that, to achieve research success in their discipline, they need skills that extend beyond those acquired in their graduate degree programs. In business school jargon, those skills are the attributes required to successfully create, sustain, and grow a profit center within the organization. (Of course, most research universities are not-for-profit organizations, so it might be more accurate to substitute the term ‘‘contribution margin’’ for profit.) Whatever the term employed, the increasingly common reality is that the leaders of our science enterprises need to have the skills to do more than ‘‘conduct their research.’’ They also need to generate the financial support necessary to fund the direct costs of their research efforts as well as contribute to the support of the larger university organization. Again financial management and planning, collaboration with other units within the organization, and human relations are skills of critical importance. In addition, the scientist (even if by default) is responsible for the strategic direction of their enterprise. 4.4. Commercialization Manager Commercialization of research results potentially could be included within the managerial expertise components of the prior category. However, there are sufficient distinctive features of this responsibility that it warrants a separate category. The most critical of these is the need to have an appreciation of intellectual property alternatives, market place potentials, and the associated market path implications. Market path alternatives extend from licensing, to joint ventures, to start-up ventures. In the academic setting, the faculty member may play a key role in deciding which alternative to pursue. 4.5. Entrepreneur The entrepreneur who establishes a science-driven entity in essence combines the responsibilities of the research enterprise manager with those

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of the commercialization manager to become a ‘‘corporate leader,’’ but without an existing organizational structure. While no individual is likely to be able to possess all the knowledge and skills necessary for a successful startup organization, the entrepreneur is responsible for assembling the team of people who can contribute those skills. We note of course that individuals can and do transition between the preceding stereotypical career options, as is already evidenced in Table 3. Importantly, individuals also take on dual roles; historically, these have been exemplified by Pasteur, who is both the ‘‘father’’ of modern microbiology and the provider of many life-saving (pasteurization of milk) and life-enhancing (beer fermentation) innovations (Stokes, 1997). More recently, technology transfer through university-firm agreements or through academic scientist entrepreneurship provides scientists, regardless of their primary domain of work, to engage in complementary activities that advance both basic and applied science (Lowe & Ziedonis, 2006; Mindruta, 2009; Mowery et al., 2001; Murray & Stern, 2007; Thursby et al., 2001; Walsh, et al., 2007).

5. A PORTFOLIO OF EDUCATIONAL PROGRAMS FOR ENTREPRENEURSHIP AND MANAGEMENT EDUCATION OF SCIENTISTS Given the diverse career options available to students in science and engineering, it behooves us to ask, as faculty in academia who are responsible for training the next generation of society’s thought leaders, an obvious question: Are our students succeeding in their careers because of us, or in spite of us? Put differently, are we being innovative and entrepreneurial in the program offerings that cater to their diverse career needs? We now turn to the various options, in addition to the traditional pursuit of full-time business degree programs (e.g., MBA), that take into account differential needs and the time and money constraints faced by different students. We use, for purposes of illustration, programs developed by faculty and administrators at the University of Illinois, in response to the need for an enhanced educational experience for graduate students in the sciences. In each case, there is an explicit recognition that the goal is to enhance the science-based core rather than to dilute it. In describing the programs, we identify the literacy and experiential elements of educational programs for nonbusiness students. We define

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‘‘business literacy,’’ as the knowledge of basic business-related concepts, so that students are familiar with the ‘‘jargon’’ used in industry as it relates to economics, finance, accounting, marketing, organizational behavior, entrepreneurship, and strategy concepts. Experiential learning relates to the development of skills and attitudes through the application of business concepts in simulated or real life business contexts. In an ideal scenario, devoid of opportunity costs and constraints, students would be able to maximize both the literacy and the experiential opportunities available to them. However, the limited time and financial resources of students implies that students need to make pragmatic choices. Thus, rather than prescribe a ‘‘one size fits all’’ option, the notion is that a portfolio of opportunities be available from which students can select options that best meet their needs. The discussion will focus on the following four types of programs, because the traditional MBA option is a well-understood professional degree program.    

CEM for Life Scientists, Certificate in Business (CIB) for Nonbusiness Majors, Illinois Professional Science Master’s (PSM), and Experiential Opportunities

The section will conclude with a comparison of the potential benefits and costs of the four program types.

5.1. Certificate in Entrepreneurship and Management for Life Scientists The Certificate in Entrepreneurship and Management (CEM) is a program for entrepreneurially minded MDs, DVMs, and PhDs in the life sciences and other disciplines who are interested in understanding the business, economic, and legal issues in biotechnology ventures. Candidates for the CEM should possess or be pursuing a graduate or professional degree in a life sciences field or working in industry. Key elements of entrepreneurship, such as creating a business plan and managing intellectual property, are emphasized. Topics of particular interest in the life sciences are included, such as managing the Food and Drug Administration (FDA) approval process and conducting clinical trials. The program combines both classroom and more experiential types of learning, through internships, networking opportunities, and guest speakers.

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The curriculum consists of the following six modules:      

Regulatory policy, intellectual property portfolio, and ethical considerations; Managerial accounting and financial management; Marketing and strategic decision-making environments; Entrepreneurial strategies and technology management; Leadership and management; and Integrative clinical trials

The modules are structured to provide in-depth coverage on a particular issue and may be taken as part of the certificate program or as stand-alone sessions. Detailed descriptions of the modules are included in Appendix A. In addition to the content-oriented modules, a business simulation experience is conducted as each student cohort proceeds through the program. Working in teams, the simulation provides a sense of the decisionmaking situations associated with business innovation. Furthermore, it provides the opportunity for students to integrate content from across the program’s modules. The program is composed of 90 contact hours. It is offered over two semesters and is designed to provide a basic understanding of the knowledge and skills necessary to meet the challenges of managing an academic or industrial laboratory group or business. In addition to the modules and simulation, students are encouraged to pursue internships and other experiential type activities after completing the formal component of the certificate. Assistance in obtaining internships is provided to interested students. Full tuition for the program is slightly less than $4,000. Because of generous support from industry and university sponsors, scholarships are available. With the scholarship support, student fees are reduced to slightly under $1,000. Fees for working professionals are approximately $2,000 with scholarship support.

5.2. Certificate in Business for Nonbusiness Majors The University of Illinois College of Business CIB is a 10-session program for graduate students who are interested in the basics of a business education but have limited time to pursue that goal. The CIB program educates graduate students seeking nonbusiness degrees in the strategic framework necessary for making informed business decisions, and the interrelationships among the functional areas of business. Each session is

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conducted in a three-hour lecture/discussion format. Graduate students from disciplines across the campus participate in this certificate program. A description of the certificate outline with session titles and topics is provided in Appendix B. The focus of this effort is to provide fundamental business literacy concepts in a manner that requires minimal financial and time commitments of the students. Student tuition for the entire set of 10 sessions is $600, but because of scholarships available through the College of Business, most students pay $350 for the program. 5.3. Illinois Professional Science Master’s The Illinois PSM is a nonthesis graduate program that offers an MS degree. The PSM is designed to allow the student to pursue advanced training in science or mathematics while simultaneously learning critical business skills. The program is designed to provide a solid background in a chosen area of science or mathematics along with business knowledge and skills vital for today’s industry and high-tech management through an integration of four key curriculum components:    

Science or mathematics courses; Business or ‘‘professional’’ courses; Industry seminar series; and Internship

Currently, three MS degree programs are available in the Illinois PSM, with additional programs being developed. The three currently available programs are as follows:  Agricultural production: Advanced interdisciplinary education in science and agribusiness are the core of the agricultural production program. Specializations in food animal production, crop production, and sustainable production systems are available.  Bioenergy: Bridging science and business to meet sustainable energy demand is the purpose of the bioenergy program. Graduates should be well prepared for positions of responsibility in bioenergy and related biosciences.  Food science and human nutrition: Flexibility in curriculum and career options are hallmarks of the Food Science and Human Nutrition program. Expertise in nutrition and food science, combined with business skills, provides many career options in the food, pharmaceutical, and ingredient sectors.

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The business component of each PSM degree is identical and includes eight required courses that provide business knowledge and skills particularly relevant to scientists. Two of these are taught by the School of Labor and Employment Relations:  Human Resource Management for Scientists and Engineers (two hours) and  Sociotechnical Systems (two hours). Six intensive one-hour courses are taught by the College of Business. Topics include:       

Global Strategy; Managing Technology and Innovation; Human Resource Management for Scientists and Engineers; Accounting for Managers; Financial Management; Marketing and New Product Development; and Entrepreneurship

The PSM degree is fashioned as a 16-month experience of three semesters and a summer session. Tuition and fees for the entire program are approximately $30,000 for Illinois residents and about $40,000 for nonresidents.

5.4. Experiential Opportunities As noted previously, the PSM degree includes an internship experience and participation in an industry seminar series within the required structure of the program. The CEM incorporates an integrative simulation activity and encourages students to complete an internship. The CIBA concentrates on providing literacy without an experiential element. Numerous other experiential learning opportunities are available to students on a large research campus such as the University of Illinois. Although typically available to all students, the culture of graduate education tends not to encourage graduate students to pursue such activities. Part of the gap relates to information, as graduate students tend not to be ‘‘in the loop’’ relative to communication efforts that are focused on undergraduate students. Furthermore, the time requirements of graduate studies and research/teaching responsibilities limit exposure to such opportunities.

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Despite these impediments, such efforts can be valuable to graduate students interested in innovation. A few examples of these opportunities are discussed below. Founded in 1996, Illinois Business Consulting is the premier student consulting organization in the College of Business. It has conducted approximately 750 projects for over 500 clients worldwide. The primary mission of Illinois Business Consulting is education, enabling students to apply their class room learning to real business problems. Illinois Business Consulting conducts 40–50 business consulting projects each year for established organizations of varying sizes (Fortune 500 to small businesses, both for-profit and not-for-profit) and allows students to build problemsolving, project management, and client management skills. Illinois Business Consulting recruits graduate and undergraduate students alike to execute the engagements and leverages expertise from across the campus to respond to client and project needs. The Lemelson-Illinois Student Prize, administered by the Technology Entrepreneur Center in the College of Engineering, is awarded on an annual basis to an undergraduate or graduate student who has created or improved a product or process, applied a technology in a new way, redesigned a system, or demonstrated remarkable inventiveness in other ways. (This award is offered in partnership with the Lemelson-MIT Program at the Massachusetts Institute of Technology.) Idea To Product (I2P) is a unique academic competition looking at ideas in their earliest stage. I2P is not a business plan competition; it requires only a one-page submission, done according to a strict format, outlining an idea for a product (or service) and its market. This approach makes it possible for people across campus to develop and present ideas, obtain feedback, and start the process of invention or product development and commercialization. I2P is an international competition based at the University of Texas at Austin. The Cozad New Venture Competition (CNVC) is designed to encourage students, researchers, and community members to create new sustainable businesses in the Champaign-Urbana area. The competition encourages the development of the entrepreneurial spirit through teamwork and competition. Teams are invited to create a business plan around a topic of their choice. Assistance is available in the form of mentors, workshops, and courses to guide teams through the phases of business plan creation. Three competitive divisions provide for a wide range of opportunities for different ventures. The Commercial Venture Division is focused on viable ventures with high commercial potential. The Social Venture Division encompasses profit or nonprofit ventures that have an emphasis on creating

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and sustaining social value. The Green Venture Division is for ventures with a focus on innovative products or processes that address ecological or conservation concerns. Nine teams are selected to present their business plan at a finals event. Teams in the final round compete for an estimated $60,000 in cash, plus valuable in-kind prizes. Finalists and winners have the opportunity to meet with venture capitalists, early stage investors, and successful entrepreneurs who will serve as judges for the finals round.

5.5. Moving to a Framework On a large, decentralized research campus, program initiatives tend to ‘‘pop up’’ as a thousand points of lights because of the entrepreneurial nature of faculty, staff, and students. Sometimes, however, it seems that the better metaphor might be dandelions in spring. Although an individual dandelion can be viewed as attractive, too many in the homeowner’s yard is not desirable. The four educational offerings described earlier, along with the MBA degree, provide a range of value generating options to students who participate in them. However, it also is useful to view the attributes of those offerings in the context of a framework recognizing that students can select one or multiple components to optimize their experience given their interests and research constraints. Key dimensions of the five program types are presented in Table 4. The time and financial commitment dimension of the five program types is relatively self-explanatory. Literacy in business, economics, and law generally is perceived as having value for advanced degree graduates who enter into corporate research and development roles. The literacy versus application debate is an interesting one relative to entrepreneurship. Some argue that acquiring literacy in the topics of business, economics, and law is not particularly helpful to students who strive to be entrepreneurs. Instead, it is argued that such budding entrepreneurs benefit most from case competitions or discussions with experienced entrepreneurs. Others feel strongly that having a fundamental understanding of the concepts of business, economics, and law provides a better foundation for the entrepreneur as they establish a new venture. Further proponents of the value of literacy component note that many science-based entrepreneurs do not directly establish new ventures after receiving their degree. Instead, a typical path is to spend a number of years in a corporate setting immediately after graduation.

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Comparison of Key Dimensions of Alternative Educational Offerings. Time and Financial Commitment

Certificate in Business Administration Certificate in Entrepreneurship and Management Professional Science Master’s Experiential Opportunities Master’s in Business Administration

Literacy in Business, Economics, and Law

Targeted Domain

Cohort Based

Integrative and Applied

Minimal

Yes

No

No

No

Moderate

Yes

Yes, to life sciences

Yes

Yes, internships encouraged

Extensive

Yes

Highly variable Extensive

No

Yes, to targeted Yes careers Yes Yes

Yes, internships required Possibly

Yes

No

Yes

Yes

The cohort-based and targeted domain dimensions of Table 4 relate to the notion that learning can be affected by the context within which information is presented. Often, it is perceived that learning new concepts within a specific domain (life sciences, bioenergy) will allow for more rapid assimilation of new material. Similarly, educational experiences can be enhanced by learning within a student cohort over time rather than showing up for a series of lectures as one person among many. Experiential activities can be highly variable in terms of individual student involvement and effectiveness. Many campus-based experiences are organized in a business context (project consultation or business plan development). Among a group of busy students, it is often most efficient for the individual students to specialize on the task they know best. In those cases, the nonbusiness major can end up in the role of a consultant providing technical expertise rather than being integrated into the process of learning the business concepts that might be of most value to the student. As noted previously, one potential benefit of displaying the program types along a number of dimensions is to provide the beginning of a framework that can be used to guide both interested students and further program development. For example, the graduate student who aspires to be a corporate leader should envision the need for a formal business degree, typically the MBA, as they plan their educational trajectory. Alternatively, a student may see a corporate career as a possibility but not in a managerial role. An offering, such as the CIB or the CEM, could provide a relatively

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low-resource entry into the terminology and concepts of business. The graduate student focused on an entrepreneurial career could link the certificate program with one or more experiential opportunities. Potentially, these efforts could be highly tailored to the research interests of the student.

6. CONCLUDING REMARKS Science-driven innovation is widely perceived as a key to future economic success of firms and of nations. Such innovation will require large numbers of highly trained individuals to provide the human capital necessary to fuel that innovation. However, the system that currently provides advanced degree education appears not to have adjusted to best prepare individuals for the dynamic career paths that they likely will face. Advanced degree graduates in the sciences likely will be at the forefront of the innovation required to remain competitive. This chapter offers a framework to hopefully allow us to begin to imagine means by which graduate education offerings can be enhanced to better enable their graduates to succeed in innovation.

NOTES 1. The term ‘‘sciences’’ is used broadly to include the wide range of disciplines for which advanced degrees are offered and which are relevant to economic innovation. 2. SESTAT provides its users with sample weights for the purpose of population counts estimation. The reported statistics are derived from survey questions that ask the respondents to group themselves exclusively as either basic or applied scientists based on most hours devoted to an activity. Specifically, Table 1 was constructed based on information about ‘‘the most hours activity during a typical week on the principal job’’ (variable name in SESTAT: WAPRI). 3. Table 2 was constructed based on information about ‘‘Activities occupied at least 10 percent of time during a typical week on the principal job’’ (variable name in SESTAT: WAAPRSH and WABRSH). The sample is the same as the one used for Table 1.

ACKNOWLEDGMENTS We thank the Ewing Marion Kauffman Foundation for grant funding used to support this research. We are grateful to Atsushi Ohyama for assistance on the empirical analysis, which is based on restricted access data from the

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National Science Foundation (NSF). The use of NSF data does not imply NSF endorsement of the research methods or conclusions contained in this chapter. The manuscript has benefited from comments received from Marie Thursby and the conference participants at the 2009 REER conference and the 2010 Kauffman Workshop on Technology Commercialization. All errors are ours.

REFERENCES Argyres, N., & Liebskind, J. (1998). Privatizing the intellectual commons: Universities and the commercialization of biotechnology research. Journal of Economic Behavior and Organization, 35, 427–454. Arrow, K. J., & Capron, W. M. (1959). Dynamic shortage and price rises: The engineer-scientist case. Quarterly Journal of Economics, 73(2), 292–308. Blank, D. M., & Stigler, G. J. (1957). The demand and supply of scientific personnel. New York: National Bureau of Economic Research. Bush, V. (1945). Science the endless frontier: A report to the president on a program for postwar scientific research. Washington, DC: U.S. Government Printing Office. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Quarterly Science, 35, 128–152. Dasgupta, P., & David, P. (1994). Toward a new economics of science and technology. Research Policy, 23(5), 487–521. Dasgupta, P., & Maskin, E. (1987). The simple economics of research portfolios. Economic Journal, 97, 581–595. Diamond, A. M. (1986). The life-cycle research productivity of mathematicians and scientists. Journal of Gerontology, 41(4), 520–525. Etzkovitz, H. (1998). The norms of entrepreneurial science: Cognitive effects of the new university-industry linkages. Research Policy, 27(8), 823–834. Goldfarb, B. (2008). The effect of government contracting on academic research: Does the source of funding affect scientific output? Research Policy, 37, 41–58. Heller, M., & Eisenberg, R. (1998). Can patents deter innovation? The anticommons in biomedical research. Science, 280, 698–701. Hounshell, D. A., & Smith, J. K. (1988). Science and corporate strategy. New York: Cambridge University Press. Levin, S. G., & Stephan, P. E. (1991). Research productivity over the life cycle: Evidence for academic scientists. American Economics Review, 81(1), 114–132. Lowe, R. A., & Ziedonis, A. A. (2006). Overoptimism and the performance of entrepreneurial firms. Management Science, 52(2), 173–186. Merton, R. (1973). The sociology of science: Theoretical and empirical investigation. Chicago, IL: University of Chicago Press. Mindruta, D. (2009). Markets for research: A matching approach to university-industry research collaboration. Ph.D. thesis, University of Illinois, Champaign, IL. Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. (2001). The growth of patenting and licensing by U.S. universities: An assessment of the effects of the Bayh-Dole Act of 1980. Research Policy, 30, 99–109.

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Murray, F., & Stern, S. (2007). Do formal intellectual property rights hinder the free flow of scientific knowledge?: An empirical test of the anti-commons hypothesis. Journal of Economic Behavior and Organization, 63(4), 648–687. Rosenberg, N. (1990). Why do firms do basic research (with their own money)? Research Policy, 19(2), 165–174. Sampat, B. N. (2006). Patenting and academic research in the twentieth century: The world before and after Bayh-Dole. Research Policy, 35(6), 772–789. Schilling, M. A. (2008). Strategic management of technological innovation (2nd ed). Boston: McGraw Hill/Irwin. Smokler, H. (1983). Institutional rationality: The complex norm of science. Synthese, 57(2), 129–138. Stephan, P. E. (1996). The economics of science. Journal of Economic Literature, 34(3), 1199–1235. Stern, S. (2004). Do scientists pay to be scientist? Management Science, 50(6), 835–853. Stokes, D. E. (1997). Pasteur’s quadrant. Washington, DC: Brooking Institution Press. Thursby, J. G., Jensen, R., & Thursby, M. (2001). Objectives, characteristics and outcomes of university licensing: A survey of major U.S. universities. Journal of Technology Transfer, 26(1–2), 59–71. Walsh, J. P., Cohen, W. M., & Cho, C. (2007). Where excludability matters: Material versus intellectual property in academic biomedical research. Research Policy, 36, 1184–1203.

APPENDIX A. MODULES WITHIN THE CEM FOR LIFE SCIENTISTS PROGRAM Module One: Regulatory Policy, Intellectual Property Portfolio, and Ethical Considerations This module deals with regulatory policy and its impact on organizations and with creating and protecting intellectual property. At the end of this module, students should be able to: Understand how regulatory policy is created through interaction of multiple stakeholders, Understand the influence of regulatory policy on organizational strategy and performance, Explore different mechanisms for creating and protecting intellectual property, Craft an industry appropriate intellectual property strategy, Recognize the importance of ethical considerations in business decisionmaking, and Understand the impact of ethical issues of business strategy and performance.

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Module Two: Managerial Accounting and Financial Management The module’s content focuses on corporate financial management and capital acquisition and the use of financial information for decision-making and control. At the end of this module, students should be able to: Learn to interpret financial statements; Learn to assess the potential profitability of capital expenditures; Utilize appropriate financial information for decision-making, organizational control, and accountability to stakeholders; Understand the factors surrounding new project investment as well as financing a continuing operation; and Understand risk-return relationships, capital budgeting, and conduct financial analysis and planning.

Module Three: Marketing and Strategic Decision-Making in Global Environments The module’s content incorporates strategic decision making and global strategy with marketing and market plan development. At the end of this module, students should be able to: Learn why an effective marketing plan is a key component of successful laboratory/business strategy; Develop an understanding of what constitutes a competitive advantage within the business environment; Understand factors that determine the scope of an organization, and when diversification results in synergies; and Understand how differences in institutional and competitive conditions across countries may impact competitive advantage when operating in a global environment. Module Four: Entrepreneurial Strategies and Technology Management The content of this module includes new venture development from idea inception to firm foundation, technology management, and growth strategies.

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At the end of this module, students should be able to: Understand the challenges encountered in starting a new venture, Create a basic business plan for a new venture, Determine the issues involved in growing a new venture, Understand the importance of the management of technology when transforming a scientific invention into a commercial innovation, and Create strategies for dealing with technological change and dynamic industry environments. Module Five: Leadership and Management The content of this module encompasses managing risks from various sources (technology, regulatory, market, finance, and execution) and on negotiation and conflict resolution processes. At the end of this module, students should be able to: Learn effective ways of organizing personnel and capabilities, Understand the differences between Management and Leadership, Learn how to extract the maximum value from your resource base over time, Develop an appreciation of the challenges in designing effective teams and creating a work environment where people can work in teams and groups to create a win-win situation, Learn skills for effective conflict resolution and negotiations, and Learn how to design and manage technology alliances and joint ventures. Module Six: Integrative Clinical Trials The focus of this module is on the role of integrative clinical trials in advancing life science discoveries to benefit society. At the end of this module, students should be able to: Understand the clinical trial pathway; Demonstrate the practical application of risk management for a biomedical/ scientific concept; Understand the relevance of the current regulatory environment for novel drugs, biologicals, and devices; Discuss the ‘‘ownership society’’ of science and scientific culture; and Cultivate a personal philosophy on the commercialization process in an entrepreneurial setting.

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APPENDIX B. CERTIFICATE IN BUSINESS COURSE OUTLINE Lecture Innovation, Organizational Behavior Leadership, and and Leadership Entrepreneurship

Essentials of Marketing and New Product Development Managing Innovation and Technology

Measurement, Reporting, and Control

Entrepreneurship and Growth Strategies of New Ventures Accounting Measurement, Reporting, DecisionMaking, and Control Financial Management

Ethical Behavior in Organizations Integration

Process Management

Organizational Design and Environment

Strategic Management

Topic Individual motivation within organizations Management style Conflict and negotiation Maximizing employee performance Customer analysis Market segmentation New product decisions Evolution of technology in organizations Bringing a product to market One-page business plan Growth strategies for young and small firms Basics of financial statements Analysis of financial accounting Basics of management accounting Decisions using management accounting Asset classes Valuation principles Corporate board structure Creating shareholder value Capital cost and structure Ethics exercise Personal versus professional ethics Whistleblowers Designing processes for manufacturing and delivering goods and services Studying processes to evaluate them Involving people working on processes in improving processes Understanding complex organizations Organizational culture and values Change and adaptation in organizations Fitting together functional strategies Competing in technology-intensive industries

SCIENCE AND TECHNOLOGY ENTREPRENEURSHIP FOR GREATER SOCIETAL BENEFIT: IDEAS FOR CURRICULAR INNOVATION Lee Fleming, Woodward Yang and John Golden ABSTRACT In this discussion, we sketch the motivation and design for a co-terminal master’s degree in Entrepreneurial Science and Technology. We aim the degree specifically at science and engineering undergraduates who would go on to (1) individual or technology management positions in established organizations, (2) entrepreneurship in the public, private, or nonprofit sectors, or (3) graduate work in engineering or science or professional degrees, including business, medicine, law, or policy. The goal would be to give students concise but complete skill-sets in entrepreneurship and teamwork, and effective career networks across diverse professions. It is our hope that this can be done within an intense one-year curriculum, such that students would remain technically current (and possibly develop the application of their technical research during the degree). We discuss alternate and existing models for entrepreneurship education and explain how our conception differs. Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 165–182 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021010

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INTRODUCTION We begin with the premise that science and engineering has had a net positive impact on society and that there exists great potential to increase that positive impact. There is certainly no shortage of problems that science and engineering might help solve, for example, energy and sustainable development, personal and global health, and security and personal freedom. Science and technology alone will not solve these challenges; as the 2010 Copenhagen talks illustrated, they will only be effective as part of a larger societal discussion and effort. Our goal, through innovation in science and engineering entrepreneurship education, is to make the scientist and engineer more effective in joining the discussions and leading the efforts. Ultimately, we hope to increase the impact of society’s investment in science and engineering, and foster greater economic, health, and environmental welfare. To accomplish this, we propose a series of innovations in science, engineering, and entrepreneurship education. Most of these ideas are not new, and many of their components currently exist in engineering and other schools around the world. We hope to make a contribution, however, with the integration of these ideas into a master’s degree in Entrepreneurial Science and Technology (hereafter referred to as ‘‘EST’’). Note that we define ‘‘entrepreneurship’’ very broadly, in the sense of innovation and problem solving outside of extant processes and institutions, and managing such efforts effectively. The program is meant to foster entrepreneurial initiative and team building in many different forms – whether in a for-profit business (large or small, incumbent or start-up), a nonprofit grassroots organization, a research lab, or an experimental government program. Furthermore, it is specifically aimed at students with a scientific or technical background, with the intent to make them more effective scientists and engineers. We begin defining science and technology–based entrepreneurship. We continue by sketching a simple model and briefly describing the target student population. We then motivate the need for EST, why current curricula do not fulfill this need, and discuss various extant models in this educational space. Finally, we specify our design objectives for the degree and sketch some initial directions. It is important to note that our thinking does not take place in a vacuum; instead, this chapter seeks to guide the development of a new program at Harvard University. However, we hope to foster a broader conversation, across universities, such that all can benefit. Many of the examples in this chapter rely on the authors’ direct experiences, and we apologize in advance for the personal bias – and ask that our

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colleagues at other institutions share their experiences, which we will eagerly incorporate in to our discussion.

A SIMPLE MODEL OF SCIENCE AND TECHNOLOGY ENTREPRENEURSHIP We define science-based and high-technology entrepreneurship as the application of science and technology in solving societal problems. It is important to stress that our goals include much more than commercial entrepreneurship, for example, founding nonprofit organizations, accomplishing innovation in an existing organization, or transferring science and engineering knowledge out of the lab more effectively, even if the entrepreneur stays in that lab. Besides the obvious path of starting a new firm, this definition would include the conventional and standard development of science and technology-based products, from established labs inside incumbent firms. It also includes developing a new medical device within an academic hospital, applying science research to implement more effective public health regimes, and devising a legal agreement which facilitates the transfer of knowledge and/or intellectual property. Although we realize that this is an overly broad use of the word entrepreneurship, we do not want to call the degree a Master’s in ‘‘Societal impact multiplier for pure science and engineering degrees’’ or call it certification that the scientist or engineer is more aware of how their work can benefit society. (We are very open to suggestions on names.) We start with a brief and very simplified model of science and hightechnology–based entrepreneurship. The model is heavily stylized, often cliche´d, and surely not comprehensive, but we present it as the background for our thinking about the EST degree. Novel science and engineering ideas are usually created by active researchers, whether in firms, universities, or garages. In order for those ideas to find fruitful applications, however, they need to become understood and considered by entrepreneurs. The original inventor can also be an entrepreneur, although this is increasingly rare, due to the explosion of technical and nontechnical knowledge in today’s world (Jones, 2008). Hence, the inventor needs to find and work closely with a multitalented entrepreneurial team. In sum, the generation of entrepreneurial opportunities occurs most fruitfully in a social context of active research and active consideration of the potential problems that research might solve. Assuming this social context that throws up entrepreneurial ideas, the next and typically concurrent challenge is to build teams of people who

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can turn ideas into reality (again, we believe in the necessity of effective and functional teams of multiple disciplines). This can occur formally or informally in multiple contexts, within schools, incumbent firms, or professional networks. Cobbling together the necessary resources is also a big challenge at this point in entrepreneurial efforts. Hence we propose a simple (though hopefully not simplistic) solution to increase the rate of science-based and high-technology entrepreneurship and to give science and engineering students (1) the concise set of skills that complement (and do not atrophy) their technical abilities, so that they can work within entrepreneurial teams, and (2) the professional and interdisciplinary networks with which to form those teams.

WHO SHOULD ENROLL IN A MASTER’S DEGREE IN ENTREPRENEURIAL SCIENCE AND TECHNOLOGY? We envision a target population for EST of very good to exceptional science and engineering undergraduates. These students would either immediately apply their education in solving problems (this would typically occur when they hire into existing organizations or start new organizations), or they would go on to advanced science or engineering or professional degrees. Many such students enter college with as much as a year of advanced standing, such that they complete the undergraduate requirements in three years. At Harvard, such students often complete a terminal master’s degree in their fourth year. Other schools, such as Stanford, offer a similar program (referred to as a co-terminal degree), for fourth and fifth year undergrads. We would see EST as a natural candidate for such a terminal master’s degree. Although we ultimately think some variant of this degree might be appropriate for doctoral students in science and engineering, we defer that discussion at this point. We do not envision EST as an ‘‘MBA light’’ or substitute for education in law, policy, or health. We do not envision a degree that will ‘‘re-tread’’ a mediocre engineer into another professional. Instead, we see the degree as providing an intense and concise introduction for elite students who (1) wish to remain technical but also want to understand and increase the impact of their research upon the world and/or (2) ultimately wish to enter nontechnical professions but contribute at the intersection of that profession with science and technology. This degree would seek to build bridges for both populations. For the first population, we would hope to give the student the understanding with which to collaborate with professionals from

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outside her discipline; for the second population, we would hope to give the student a deeper understanding of the relationship between science and technology and the larger world, before their professional training. Although we realize that an extra year prior to graduate school is an expensive investment, we hope that the degree will make graduates more attractive candidates for admission to graduate programs. A student who pursues law, for example, will bring a better understanding of how an engineer invents and how the intellectual property regime affords or constrains the application of that invention. A student who pursues medicine would bring an improved understanding of how to invent and develop a new device, such that they could commercialize that device much more quickly and cheaply (which is important, since few physicians stop practicing to develop such devices). A student who pursues government (or law) would understand the conflicting norms, ethics, and motivations of communities of scientists and firms in a marketplace. And finally, the student who stays in the lab for the rest of their career will bring an improved understanding of how their work in the lab influences the outside world (and how to direct that work to increase that influence). We hope that students who go onto graduate work in science and engineering might also be able to defer admissions to such programs while they complete the EST degree. To facilitate this, however, the research component of the degree will need to remain real. We obviously cannot predict how graduate departments will view a year in this degree, but we assume that the departments will view the degree more favorably, to the extent that the student can maintain active engagement with science and technology. Hence, we will need to carefully balance the technical and nontechnical components of the degree – this is consistent with our philosophy of giving the EST student the concise and minimal yet complete set of skills necessary to work within teams.

WHY CURRENT EDUCATIONAL CURRICULA ARE SUBOPTIMAL IN ENCOURAGING SCIENCE-BASED AND HIGH-TECHNOLOGY ENTREPRENEURSHIP Business, law, and clinical professionals rarely found successful hightechnology or high-science firms all by themselves (although we do not know of any comprehensive data on this). If one thinks of Notable firms such as Google, Genentech, Microsoft, Hewlett-Packard, Millenium, each

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were started by a scientist or engineer, often working with a more business or law or clinically-oriented partner. Even if the nontechnical partner has a technical degree in their background (and many do), they are usually too far from the idea-generating frontier in science or engineering by the time they become experts in business, law, or clinical practice. On the science and engineering side, the cliche´ of the crazy inventor without a clue about application or impact is understandable. Frequently focused on the details of mastering the technical problems, science and engineering specialists are sorely challenged to invent the technology and, at the same time, see the application, develop the product, garner resources, build an effective organization, and overcome institutional, cultural, and competitive barriers to adoption of innovation. It is the exceptional individual who can accomplish all these. These issues are only getting worse, with the explosion of information and educational demands in today’s world. It is getting increasingly difficult for the lone inventor or lone entrepreneur to succeed (Singh & Fleming, 2010). Hence, we believe that education in science and engineering and entrepreneurship must focus on (1) giving the technical student the minimal but complete skill-set and lexicon with which to communicate with the entrepreneurial community across the boundary of science and engineering and (2) exposing the student to other students of all types, so that they together can exploit immediate opportunities and develop the networks for future careers in science and technology entrepreneurship. The most obvious and reasonable counter-argument to our proposal is for a student to earn a business degree after a few years of technical work experience. This is still a very good – and possibly the best – option for the student who wishes to focus on the managerial side of science and technology management or develop opportunities for general management. This option is quite expensive in time and money, however, and it takes the student quite far from science and engineering and technical management. Many MBA curricula have also drifted away from science and technology, in response to students’ interests in finance or consulting. Although courses in technology strategy remain popular (at least at Harvard), courses that tackle the nuts and bolts of technology management and product development, let alone management of a scientific laboratory, have become less popular. However, if the MBA program teaches the theory and just as important, the experience of working in interdisciplinary teams, the MBA graduate is usually well prepared to work in an entrepreneurial team that includes scientists and engineers.

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Unless an MBA program is close to a school of engineering or science (e.g., Berkeley, MIT, and Georgia Tech), it is not always easy for the MBA student to find science and high-technology entrepreneurship opportunities. This is even more difficult if the MBAs lack opportunities to meet scientists and engineers. In addition to not providing immediate opportunities, lack of fraternization also makes it difficult for the MBA to build cross-disciplinary networks. We foresee limited overlap in student demand for MBA and EST degrees. We anticipate little overlap in applicant populations for the MBA and EST degrees, although some EST students might apply for the MBA a few years later. (Since EST graduates would have less business education than the undergraduate business major, we assume that business schools would still consider them attractive candidates.) Students are typically not admitted to MBA programs without work experience; we would foresee immediate matriculation for EST students. Currently, such science and engineering students often complete additional schooling or technical work and then apply to a MBA program. Many of these students who desire the MBA degree are not admitted to top MBA programs.1 This might be due to a bias against the technical applicant, possibly because such applicants are thought to (or do) lack communication skills and managerial talent. Or perhaps the school’s mission does not include the education of technical managers (e.g., if the school targets general managers). As a result, such students either attend a less prestigious program (often part-time) or avoid the MBA altogether. Although we mainly compare the EST degree to the MBA, it is also reasonable to contrast the degree with science and engineering curricula. We believe that the current structure of undergraduate and graduate technical degree programs does not provide sufficient background for most graduates to understand the impact and importance of their technical training. Many engineering programs have traditionally complemented a rigorous technical education with an additional year of broad liberal arts coursework, due to the challenges of packing in both curricula into four years. EST is not unlike such ‘‘3þ2’’ programs, except that the broadening education will be focused on how the student can apply their education to solve societal problems. (We believe that weakening the technical content of an undergraduate degree, in order to accommodate our proposal, would be a bad idea.) Even for a scientist or engineer who spends their entire career in the lab, we believe that the additional year will prove worthwhile. Rather than viewing this year as ‘‘wasted,’’ relative to purely technical work, we believe that it will greatly leverage the student’s efforts. Given that

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today’s students may well work for 50 years or more, this is a very sound investment. We believe this argument should hold for academic research scientists as well. Moving away from the applicant’s perspective, we believe that EST will also help universities in their primary mission of the generation and dissemination of knowledge. As we will detail later in text, we believe that every EST student should spend at least a summer in a university lab doing research (though internships in local high-tech firms would be a good option as well). Although the student’s contribution in that summer will probably be incremental, upon their return to school they will also be challenged to apply the lab’s larger research program for societal benefit. In the process of finding an application for their technical work, they will disseminate the university’s research. This argument is only a side benefit to the EST and not a primary motivation; most importantly, the EST degree should not be viewed as a free labor source for university technology transfer offices. Student expectations should also be managed, since much research has little immediate impact on the world; we should highlight the importance of the skill set and the importance of building that skill set with experiential challenges and collaborative efforts. EST should also encourage more undergraduates to pursue a science or engineering degree (we must obviously admit our bias in thinking this is a good thing, although much of that bias stems from a belief that scientists and engineers are good for knowledge-based economies). This will occur because the undergraduate will see immediate opportunities to apply that knowledge. This will hopefully provide some motivation for suffering through the endless problem sets. Although it would seem equally reasonable to train entrepreneurs in science and technology or scientists and engineers in entrepreneurship, the demand for science and engineering degrees from people with extant entrepreneurship background seems quite low.

EXTANT MODELS IN SCIENCE, ENGINEERING, AND ENTREPRENEURSHIP EDUCATION The existing degree program closest to our vision is the Stanford Engineering Management Master’s or Berkeley’s Management of Technology certificate program. The strength of these programs is their size, depth, and modularity; an engineer can assemble an outstanding education in entrepreneurship from a wide palette of available courses. On the basis of personal experience with

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the Stanford program, however, these degrees tend be stripped-down versions of the first year of the MBA curriculum, with varying degrees of more quantitative analysis and technology case contexts (see www.stanford. edu/dept/MSandE/cgi-bin/academics/index.php). (In general, these programs consist of modular course selections that are not integrated and still not geared towards the scientist and engineer who wishes to remain technical.) Furthermore, the student in these programs often views the degree as a MBA and rarely plans to return to the lab. The MIT Sloan MBA (widely acknowledged to be the premier technology management MBA) is moving away from technology management and the MIT LFM (Leaders for Manufacturing) program focuses on educating manufacturing and operations leaders. Some universities with engineering and business schools encourage cross-registration (such as Berkeley’s Management of Technology certificate; http://mot.berkeley.edu/) but again these are rarely integrated. Carnegie Mellon is undertaking a somewhat similar initiative (http://www.cit.cmu.edu/etim/overview.htm). Georgia Tech’s TI:GER program (Fleming, Quinn, & Thursby, 2006) is closest to our proposal in spirit, although it differs in details. TI:GER teams, typically consisting of a PhD student, two MBA students, and two law students, are formed around a doctoral student’s research program. The team actively develops the commercial applications of the research as it occurs. The advantages of this program include realistic exposure to the problems of commercializing research and team building, whereas disadvantages include distraction of the doctoral student (it is hard for many students to learn how to do research and lead a commercialization effort simultaneously). The latter problem can be mitigated with additional resources of money and time (like our proposal for a one-year master’s degree, an additional year before PhD graduation might provide adequate time). The Sloan Foundation has been supporting the development of educational programs for Professional Science Masters (PSM). The PSM has existed for about 10 years and is designed to provide students with advanced training in sciences without a PhD and pertinent business skills without an MBA. There are over 170 PSM programs at over 70 institutions around the United States. The goal of these programs is to place graduates in Science, Technology, Engineering and Mathematics (STEM) jobs. There have been over 2,700 graduates with PSM degrees. These programs usually consist of two years of advanced academic training in an emerging or interdisciplinary area with some additional profession component that might include workplace skills such as business, communications, or regulatory

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affairs. The basic idea is science plus some type of professional skill (e.g., negotiations, conflict resolution, and project management). The students in these types of programs usually have several years of work experience and are probably looking to these programs to improve their academic credentials and to enhance their career prospects. The EST master’s program that we are proposing has some distinct differences compared to many PSM programs. First, we are looking for students who have just completed a four-year college degree in engineering and/or science. These students will typically not have any significant professional experience. Second, the EST masters program will be just one year. Third, it will be less focused on professional skills than the societal implications of and opportunities for science and technology. Most importantly, the goal of the program is to give graduates (1) an understanding of how science and technology can improve society and (2) the tools with which to accomplish those improvements.

PHILOSOPHY, OBJECTIVES, AND EXECUTION OF A MASTER’S DEGREE IN ENTREPRENEURIAL SCIENCE AND TECHNOLOGY We currently foresee a few guiding design principles for the EST program. First, rather than replacing the professional degree in law or business, the EST program will only aspire to provide a basic interdisciplinary skill-set: the goal will be for an EST student to gain enough understanding of areas like law and business that s/he can collaborate effectively across interdisciplinary boundaries. Indeed, given the accumulation of knowledge and proliferation of specialties (Jones, 2008), the true Renaissance person who can do substantially more than this is probably a historical artifact. In his or her place, we propose that students must have enough understanding to ask and understand intelligent questions in various areas and at the same time, possess the communication and team-work skills to solve difficult problems in interdisciplinary teams. This brings up the second design principle: students need to learn to work in teams. We would be hard pressed to identify any important problem in today’s society that can be solved by one person. Such teams have the added benefit of supporting our third objective that students form life-long networks that make them more effective, wherever their careers take them. We hope to simultaneously encourage strong intra-program cohorts and at

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the same time, build external and interdisciplinary teams. Accomplishing this will require conscious balancing of the tension between intra-cohort cohesion and the benefits of working with professionals from other schools. It will also require development of innovative pedagogy that appeals simultaneously to the EST student and the business, law, medical, government, or public health student (the most obvious solutions being project courses that challenge teams to solve problems that touch upon multiple disciplines). We believe that a yearly cohort of 50 students would enable productive case discussions and provide enough diversity to allow intra-cohort networking. Beyond graduation, we can envision alumni events to renew relationships and update students on new thinking in science and technology entrepreneurship. Fourth, we hope that the degree will be at once experiential and pedagogically rigorous. To expand on this, we hope that students take and immediately apply what they have learned in their undergraduate education, using real research to solve real problems. We also hope that the pedagogy itself will be research based and hence avoid becoming overly focused on practical experience (while field work should inform research, and practitioners should be integrated into the program, the curriculum should not rely too heavily upon ‘‘war-stories’’ from successful practitioners – this often occurs when engineering schools create management programs without discipline-based research faculty). Fifth, we are assuming an elite population of students who are capable of accelerated learning. For example, this population should be capable of learning basic finance much more quickly than the typical MBA. As a result, the curriculum can be focused, compressed, and innovative. Such curricular innovation will require resources, however, for faculty to create new pedagogical materials, instead of simply leveraging marginally appropriate material from existing programs.

CURRICULUM POSSIBILITIES We envision a curriculum that would start in the spring term in the year before the degree. The spring term would introduce the students to various science and technology research opportunities. Although these opportunities might be predominantly from Harvard labs, they could, with careful oversight and direction, also come from local high technology firms or from the students themselves. We would envision a seminar series in the spring, with speakers from sponsoring labs and firms. The focus would be on

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big problems in society and how science and technology might address those problems. For example, health challenges would be appropriate, as well as global warming, environmental issues, security, governance and technology, or education. The seminar would also challenge the student to define and prioritize the objectives of science and technology research. This broadening challenge would help the student who has been focusing on narrowly defined and piece-meal assignments (in particular, problem sets and lab exercises) to grapple with the enormity of societal problems. The seminar would be a cornerstone of the program and would lay out its philosophy, objectives, and broad definition of science and technology entrepreneurship. Ideally, the students would come out of this seminar with the excitement that their work at the boundaries of science and society can greatly benefit the world. Following this spring term, the students would be expected to spend their summer doing science and technology research. Although this will probably occur on campus, it could also occur in a science or technology–based firm or in a government lab. All projects would be closely mentored by a faculty member. Ideally, this experience will provide the basis for a thesis and/or projects in their project-based learning classes. We are considering requiring an integrating paper, business plan contest entry, or founding of an entrepreneurial organization, in lieu of a formal thesis. The challenge will be in balancing this requirement with both the course requirements and hope that the student will stay close to science and technology. The best possibility of accomplishing this is to maintain flexibility; for example, a student going on to graduate work in science and engineering might submit original technical research and consider its impact upon society, while a student planning to pursue studies in government might propose a science and technology solution for a problem that is defined by a government or NGO. We intend to divide the main master’s year into approximately 240 sessions (the equivalent of four 30-session courses each term). The time between terms in January would be best spent in visiting and learning about the context of the science or technology application. We would count the prior spring term, the summer, and the January term as 15 sessions each, for approximately 285 sessions in total for the degree. We divide the content into fine-grained topics for expositional clarity – it is quite possible that the individual topics would be pulled together into more coherent and termlong courses. Developing the curriculum will obviously present tradeoffs in borrowing currently developed materials, as opposed to developing novel pedagogy. We suspect we will need to bootstrap our effort and borrow

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extensively, at least in the early years. However, we believe that it would be ultimately beneficial if customized and specific materials were developed for this program. In this way, for example, the student that goes on to the MBA will not see the same case that they discussed four years before. Hence, we would argue for the importance of stable funding and long-term investment in research and course development for the program. Our preliminary thinking on the content is as follows. Science and technology for solving societal problems (15 sessions). This seminar will discuss big problems facing the world and the potential for science and technology to solve them. It will give the opportunity for lab directors to advertise the research opportunities within their lab. Summer research internship (15 sessions). Students will work full-time over the summer in a campus, corporate, or government lab, under the direction of a science faculty member. In the fall, the student will take: Organizational behavior: working in teams (5 sessions). This course will consist of cases, such as HBS cases Henry Tam and Flextronics, and exercises and simulations, for example, Arctic Survival, Mt. Everest Expedition, and a simulation of the team dynamics that led to the Challenger launch decision. Interdisciplinary project course (15 sessions). This course is currently taught as ‘‘Commercializing Science.’’ It attracts students from seven Harvard schools and provides a true interdisciplinary challenge for all participants (students must self-organize across professions). Projects from this course have received funding and won business plan contests. For example, a nonprofit out of the Whitesides Lab, Diagnostics for All, won both the HBS and MIT business plan contest in 2008. Other entrepreneurial ventures have been started by student teams that met during the course. Alumni and outside mentors are often involved in coaching the student projects. The course gives the students real-time challenges in communication, interpersonal dynamics, and the joys and headaches of working across disciplines; ideally, it is an opportunity to apply what they are learning in the formal pedagogy, in organizational behavior, negotiation, and their spring seminar on applying science and technology to tackle big problems. University technology transfer (5 sessions). This course will complement the interdisciplinary project course and consist mainly of case discussions (e.g., HP CNSI), readings, and interaction with the Office of Technology Development.

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Intellectual property basics and strategy (15 sessions). This course will provide an overview of basic legal regimes relating to rights in information, including laws regarding patents, copyrights, trademarks, and trade secrets. It will explore the significance of such legal regimes for organizational enterprise and will highlight how the optimal strategy with respect to rights in information can depend on the technology at issue, the industry setting, timing in an organization’s life cycle, and organizational goals. The course will demand not only engagement with legal materials and factual scenarios but also completion of exercises in intellectual-property assessment (using the students’ own research). Accounting (10 sessions). We would foresee teaching the basics of accounting, including income statements, balance sheets, and term sheets. If resources are short, we might take advantage of online teaching materials (which have proven quite effective in teaching basic accounting). Finance theory (18 sessions). This course will discuss the time value of money, cost of capital, cash flow analysis and cash flow model of the firm, options theory and contingent claims, and possibly the capital asset pricing model (CAPM). The course will probably be more problem-set based than the typical MBA finance course. Non-profit entrepreneurship (10 sessions). This course will probably be taught concurrently or combined with commercial entrepreneurship. Topics will include models (such as nonprofit micro finance), philanthropic financing, and corporate social responsibility. Commercial entrepreneurship (10 sessions). This course will draw heavily from the business school curriculum in the first year entrepreneurship courses, covering aspects of opportunity identification, business model creation and analysis, valuation, realizing value, gathering resources, leaving your employer, and building teams. Materials might include HBS cases Beta Golf, ZipCar, YieldEx, E-Ink, Mason and Shepard, and Sitris Pharmaceuticals. Entrepreneurial finance (5 sessions). This course will include concepts such as funding processes, deal structures, valuation, sources of funding, staged investments and the value of creating options. Materials might include HBS cases Calera, 1366, Nantero, and C12. Marketing (15 sessions). Students will learn the most important elements of basic and entrepreneurial marketing. Concepts will include the diffusion and adoption of innovations, technology and new product launch, focus groups, conjoint analysis, and sales. Materials might include HBS case and exercise E-books, as well as HBS cases Cymbalta, Emotive, and American Well.

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The students will take the following courses in January and the spring term. January immersion (15 units). The students will spend three weeks working to apply their research, for example, in rural villages or hospitals, or by pitching plans to investors. Managing the technical professional and the ethos of science (10 sessions). Students will consider the motives of scientists and the institutions of science. A key (and unique) focus of the degree will be the understanding how these can co-exist with other institutions in society, such as capitalism and government. Readings will come from the sociology and economics of science and be supplemented by historical readings (such as the discovery of DNA) and case discussions. Science, government, and the law (15 units). This course might address issues relating to (1) the legal system’s use of scientific expertise in policymaking and litigation; (2) problems with policymaking under conditions of scientific uncertainty; (3) the application of scientific techniques and analysis to legal problems (e.g., in the development of empirical methods for the study of law or in subfields such as behavioral law and economics); and (4) ways in which science or technology can change approaches to lawmaking and governance [see, e.g., Beth Simone Noveck, Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful (2009)]. Ideally, law students will be part of this course. Legal challenges in entrepreneurship (5 sessions). This course might address some selection of questions of entity organization, contracts, employment law, regulation, and taxation. The subject matter of this course will overlap with that of the ‘‘Regulatory processes of science and technology’’ course. International issues in science and technology (15 sessions). Although the curriculum remains unclear, we would hope to include students and professors from the School of Government and other appropriate departments. Regulatory processes of science and technology (5 sessions). We would hope to involve colleagues and students from the medical school in this course, as well as in the ethics course. Ethics of science and technology (10 sessions). Although the curriculum remains unclear, we would hope to include students and professors from the Medical School. Negotiation and biases in decision-making (12 sessions). Students will probably take the business school course on negotiations. There is an opportunity to directly integrate the course with the business school offering, as there is value in learning how to negotiate with diverse individuals.

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Operations, prototyping, and manufacturing (7 sessions). Students will model a simple production line, gain exposure to supply chain and distribution concepts, and consider scale-up challenges. Course materials might include HBS cases Donner and National Cranberry, as well as HBS’s variability simulation. They will be exposed to tools for product and project management, such as portfolio analysis. Technology Strategy (15 sessions). This course will blend technical and strategy perspectives by analyzing three to four industries in depth (a precursor version is already taught). Students will gain detailed understanding of the technology that undergirds an industry’s economics and at the same time, learn how to analyze that industry from a business strategy perspective. This course will be taught jointly between engineering and the business school. In addition to the courses listed earlier, various meta-themes will run throughout the curriculum, for example, modeling and decision making. Fig. 1 gives very approximate breakdowns of the sources of the materials for the degree.

Fig. 1.

Estimated Contribution to Master’s Degree in Entrepreneurial Science and Technology, by Professional School.

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HOW WILL WE MEASURE SUCCESS? It will be important to closely track the careers of graduates, into their initial jobs or graduate school, and through their ultimate impact on society. We would be heartened to see graduates taking early leadership positions in entrepreneurship, nonprofits, industry, government, and academia. However, we suspect it will be 30 years before we have definitive results, which we would again measure as leadership in many societal sectors. This metric is still secondary, however, to the ultimate goal of the program, fostering application of science and technology to improve the health, economic, and environmental well-being of society. We close by indulging speculations of possible careers: an R&D manager at a corporation deciding which projects to continue, a student forming an Internet start-up fresh out of school, a lawyer arguing for changes in patent law, a DOE program manager selecting grant applicants, an applied scientist developing a new interdisciplinary lab, an engineering professor who wants his research used by practitioners, a social entrepreneur persuading investors to fund the eradication of malaria, a foundation head deciding where to fund philanthropic venture capital, an FCC governor thinking through bandwidth licensing, and a congressperson voting on climate change legislation. The possibilities are very exciting.

ACKNOWLEDGMENTS We thank our colleagues who gave their expertise in each of their respective fields and offered curriculum advice – Accounting: Srikant Datar; Entrepreneurship and Entrepreneurial Finance: Joe Lassiter and Bill Sahlman; Finance: Carliss Baldwin; Marketing: Elie Ofek; Organizations and Teams: Jeff Polzer. Andy Garmin and Cherry Murray provided general feedback. We surely did not get all of their ideas right and the mistakes remain ours.

NOTE 1. On the basis of the first author’s experience in writing letters of recommendation for science and engineering students that apply to elite MBA programs, very few of these very strong students are accepted into top programs.

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REFERENCES Fleming, L., Quinn, J., & Thursby, M. (2006). InfoVision (A): Technology Transfer at Georgia Tech. Harvard Business School Case 605064-PDF-ENG. Jones, B. (2008). The burden of knowledge and the death of the renaissance man: Is innovation getting harder? Review of Economic Studies. Singh, J., & Fleming, L. (2010). Lone inventors as sources of technological breakthroughs: Myth or reality? Management Science, 56, 41–56.

NAVIGATING THE ISSUES OF MULTIDISCIPLINARY STUDENT TEAMS SERVING UNIVERSITY SPIN-OFFS Sean M. O’Connor ABSTRACT Improving the commercialization of university research has become a national priority. Most existing programs focus on training and supporting faculty and students to be the entrepreneur. However, programs are also needed to train and support those who will serve the entrepreneur. This chapter asserts that professionals with specific expertise in serving entrepreneurs are a critical, yet overlooked, part of the ‘‘innovation ecosystem’’ necessary to commercialize university research. It provides an overview of the Entrepreneurial Law Clinic at the University of Washington, which provides a multidisciplinary teaching, research, and service platform that assists University spin-offs while developing the next generation innovation ecosystem. Bringing together law, business, and engineering students to work with tech transfer licensing officers and faculty researchers to spin off a university technology involves many challenges. Yet, it can be done and the benefits are manifold. This chapter outlines three key issues for this kind of program. First, who is the client: the tech transfer office or the faculty researcher? Second, how to mediate Spanning Boundaries and Disciplines: University Technology Commercialization in the Idea Age Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 21, 183–205 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1048-4736/doi:10.1108/S1048-4736(2010)0000021011

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among the different visions for how to commercialize the technology through the spin-off – including whether the technology is ready for commercialization or needs to undergo further translational work. And third, how to ensure that all the different students are being properly supervised and that all project members are keeping appropriate confidentiality toward the technology and business plans. The chapter shows how the missteps, conflicts, and confusion that naturally arise for each team project actually provide the best teaching moments for team members, supervisors, and faculty alike.

INTRODUCTION Improving the commercialization of university research has become a national priority.1 This involves the speed, quantity, and nature of the commercialization. Furthermore, in keeping with one of the original goals of the Bayh–Dole Act,2 an emphasis on the commercialization of federally funded university research through licenses to small businesses – which include start-ups – is warranted.3 Small businesses and entrepreneurs are also a primary engine of economic growth in the United States.4 Thus, any discussion of reforms to the university research commercialization process should include start-ups and small businesses. Although many existing small businesses can play a role in this commercialization process, start-ups organized around the university technology can most naturally focus on the technology, which will often be at a very early stage. Significant research and development (R&D), often including ‘‘translational’’ research, usually need to be done to transform the university innovations into deliverable products or services. Although mature, large businesses may have better resources to do this, they may not be as focused on the technology and perhaps will be more willing to abandon it if commercialization efforts run into ay obstacles. A start-up that has been formed and financed for the express purpose of commercializing that particular technology, however, will often have powerful incentives to keep working with the technology even if obstacles arise. At the same time, university licenses to local start-ups can provide greater local economic development impact than licenses to businesses outside of the region. The challenges for this model, however, are that start-ups often need more external resources than do large, established businesses. The latter can usually provide most or all of the necessary financing, research facilities and expertise, management

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experience, accounting, legal, and other critical professional services and infrastructure support. Accordingly, efforts to increase the amount of university research commercialization through local start-ups need to also improve the local innovation ecosystem that includes independent, external sources of professional services and infrastructure support. Many universities have implemented technology entrepreneurship programs to foster commercialization of university research through spin-off businesses.5 Most of these existing programs focus on training and supporting faculty and students to be the entrepreneur. This is obviously a critical and foundational component to develop a local innovation ecosystem. However, programs are also needed to train and support those who will serve the entrepreneur as part of the innovation ecosystem. Professionals with specific expertise in serving entrepreneurs are a critical, yet overlooked, part of the ‘‘innovation ecosystem’’ necessary to commercialize university research. Because these professionals will often work together to support entrepreneurs and start-ups, it is also crucial to develop multidisciplinary professional school programs that help participants coordinate their activities in serving entrepreneurs. Furthermore, many of these services will center on support for specific events such as a financing deal, license transaction, merger or acquisition, or initial public offering (IPO). In some of these, the external professional service provider, such as a lawyer or business consultant, will direct the execution of the process. Thus, professionals in the innovation ecosystem must also develop strong project management skills. This chapter provides an overview of the Entrepreneurial Law Clinic (ELC) at the University of Washington (UW) that provides just this kind of multidisciplinary teaching, research, and service platform to assist UW technology spin-offs while developing the next generation of professionals for the innovation ecosystem.6 Bringing together law and business students to work with tech transfer licensing officers and faculty and student researchers to spin off a university technology involves many challenges. Yet, it can be done and the benefits are manifold. This chapter outlines three key issues for this kind of program. First, who is the client: the tech transfer office or the faculty researcher? Second, how to mediate among the different visions for how to commercialize the technology through the spin-off – including whether the technology is ready for commercialization or needs to undergo further translational work. And third, how to ensure that all the different students are being properly supervised and that all project members are keeping appropriate confidentiality toward the technology and business plans. The chapter shows how the missteps, conflicts, and confusion that naturally arise

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for each team project actually provide the best teaching moments for team members, supervisors, and faculty alike. The section ‘‘University of Washington Entrepreneurial Law Clinic’’ introduces ELC, which the author founded and which provided the learning and research environment for the issues addressed in this chapter. The section ‘‘Who Is the Client?’’ works through difficult questions about who is the Clinic’s client when the technology to be commercialized is owned by the University. The section ‘‘Mediating among Different Commercialization Plans’’ discusses the need to mediate among different, often competing, visions as to how best commercialize the technology among the various stakeholders and team members. Following from the issues of previous section, ‘‘Supervising Different Types of Professional Students’’ goes further into the general issues of coordinating and supervising students from different professional schools and disciplines. The section ‘‘Conclusion’’ then concludes the chapter with a summarization of both the lessons learned and benefits earned by developing a clinic such as the ELC.

UNIVERSITY OF WASHINGTON ENTREPRENEURIAL LAW CLINIC ELC was founded in 2006, after a successful test pilot the year before. Although the focus of this chapter is on the UW spin-off service component, ELC covers the full range of small business, entrepreneurship, and nonprofit organization counseling that one might expect from a business law clinic. The touchstone slogan of ELC is ‘‘Promoting economic development by facilitating entrepreneurship.’’ Accordingly, its mission is threefold: (i) to promote economic development in Washington State by assisting entrepreneurs who face significant economic barriers to success through preventative legal and business consulting services that minimize risk and reduce operating costs; (ii) to provide real-life education to UW law and business students through counseling entrepreneurial businesses; and (iii) to provide meaningful pro bono opportunities for transactional business, intellectual property (IP), and tax lawyers, as well as business consultants. Although ELC is run primarily from UW Law School, it is a joint venture with the Center for Innovation and Entrepreneurship at the UW Foster School of Business (See http://www.foster.washington.edu/centers/cie). As suggested earlier, ELC serves four main categories of clients: technology entrepreneurs, microenterprise/small business owners, social

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entrepreneurs/nonprofits, and UW technology spin-offs. Its main goal is to deliver ‘‘preventive’’ legal and business counseling so that none of these innovators run into roadblocks based on preventable legal or business issues. ELC’s goal is that no business fails because a form was not filed correctly, IP rights were compromised, etc. ELC student teams and supervisors are expert at providing ‘‘lifecycle’’ counseling that not only analyzes where the business is today, but also where it will likely be in one to five years, and the pros and cons of the various paths that it can take. This enables first-time entrepreneurs to engage in the kinds of sophisticated business and legal planning that successful serial entrepreneurs usually undertake before launching a new venture. Each ELC team generally has three law students and one or two business students. The law student slots are divided into the core entrepreneurship specialties of business law, IP, and tax, with additional emphasis on employment, regulatory, and other law as needed. The business student slots are either general business consulting, or assigned according to specialties the client needs, such as marketing, accounting/finance, or operations. The student teams are then assigned supervising attorneys and business consultants from the local Seattle community, with each supervisor specializing in the field in which the student is working (e.g., an IP attorney supervises the IP student team member). ELC currently fields seven teams each year, with around 20 law students and 10 business students, serving approximately 30 clients in the aggregate each year. Former students have now become both supervisors to new students and in some cases entrepreneurs themselves. It works closely with almost every major law firm in the local community, as well as with numerous community development services and economic development government agencies at the Federal, state, and local level. Although there have been business law clinics around the United States for more than a decade, they primarily focus on small businesses and microenterprise such as retail shops or small service firms. They also generally use the traditional law clinic model in which one or two in-house directors supervise all students and projects. This provides close supervision for the students and a carefully controlled environment. However, it limits the size of the clinic to generally 10 or fewer students (depending on the law student clinic practice rules of the state where the clinic is located). Some clinics have experimented with having a single external law firm or legal aid organization supervise students. But the author pioneered a new model for ELC in which a broad roster of local lawyers and business consultants is used to staff any particular project and team. This allows many more clients

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and students to interact in ELC than in a traditional clinic. It also taps the strong entrepreneurship-focused professional services community in the Seattle area to deliver experienced, high-quality legal services and business mentorship to ELC clients and students. Supervisors are instructed by ELC full-time faculty and staff on how to supervise the students and what constitute the pedagogical goals and approved services of ELC. The author also pioneered ELC’s multidisciplinary clinic model that emulates the structure and services of technology and entrepreneurship focused law and consulting firms. Such firms often have industry-focused practice groups comprised of specialists in all the areas needed for, say, emerging biotechnology companies. Although these diverse specialties may not be required by a small ‘‘mom and pop’’ business, they will be needed for technology entrepreneurship clients. Accordingly, ELC uses a flexible approach to staffing teams. Traditional small business clients may be assigned a team with only one or two members, while technology start-ups will be assigned a full team. Further, each team’s specific expertise is tailored to the client’s industry space. For example, a biotechnology start-up would be assigned an IP team member who has both law and science training in the life sciences – and often in the specific field that the start-up is developing. The biotechnology start-up may also be assigned an additional team member with expertise in Food and Drug Administration (FDA) regulatory procedures. Another key innovation pioneered by the author is the legal and business audit (the ‘‘ELC Audit’’) that serves as the ELC’s primary deliverable to clients. The ELC Audit is an interactive analysis of the entrepreneur’s business model and legal business planning that culminates in a written memo summarizing findings and recommendations (ELC Audit Memo). The process simulates a due diligence investigation that a potential investor or acquirer would perform, but it is done on a confidential basis for the entrepreneur client’s eyes only. Thus, the entrepreneur gets a window into how potential investors, lenders, and acquirers would view the legal and business health of the venture. It is also similar to a management consultant analysis and report, except that it has many more details and recommendations on legal issues than that kind of analysis would normally contain. The ELC Audit helps the entrepreneur to revise the business model as necessary, as well as to take steps to remedy any legal deficiencies before they become an actual problem. Specifically, the ELC Audit will help the entrepreneur think through: choice of entity issues [e.g., corporation vs. limited liability company (LLC)]; the pros and cons of different types of financing (e.g., debt vs. equity) and the corporate/securities law issues and structures needed for

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each [e.g., the likely need for authorized preferred stock where venture capital (VC) financing is sought]; tax issues [e.g., decisions to file as an ‘‘S Corporation’’ or ‘‘C Corporation’’ with the Internal Revenue Service (IRS) and ‘‘entity level’’ vs. ‘‘pass through’’ tax structures generally]; IP issues (e.g., what is patentable and how to determine ownership, assignments, and licensing); employment law issues (e.g., employees vs. independent contractors); and regulatory issues as needed. The foregoing is not meant to be exhaustive, but rather to give a sense of the major areas of inquiry for each ELC Audit. When the ELC Audit is complete, the team writes up the ELC Audit Memo, which is reviewed by both the team’s supervisors and ELC leadership, before it is delivered to the client. In many cases, the ELC Audit and Memo constitute the sole service that ELC provides to the client, for reasons stated later in text. The default ELC engagement letter in fact only obligates ELC to perform this service and deliver the ELC Audit Memo. After that, the engagement normally terminates. In some cases, such as where the entrepreneur and business have very limited means, then ELC may agree to take on some additional specific tasks for the client, such as forming an entity or filing a trademark registration application with the U.S. Patent and Trademark Office (USPTO). In the technology entrepreneurship space, there are good reasons to limit the ELC engagement to the ELC Audit and Memo. First, tech start-ups that are going to close a major angel or VC financing round should be using a private law firm. Most of the tech-oriented law firms offer discounted, deferred, or other alternate compensation arrangements to promising tech start-ups that are at the serious financing stage. There is little need for a law school clinic to put itself into a position of ‘‘competing’’ with these firms. Furthermore, because of the reasonable availability of alternate compensation arrangements from these firms, finance-ready start-ups cannot be said to lack the means to afford legal representation – a key test for whether a law clinic should be providing free legal services. Second, although many tech entrepreneurs think they should just launch into forming the company, hiring employees, etc., without consulting an attorney, many of the critical missteps that plague these companies as they grow and seek major financing could have been avoided if they had sought legal counsel. Understandably, these entrepreneurs are usually concerned about the high cost of legal fees and may not be able to pay them. Also, they may be too early stage to be attractive enough for the tech law firms to offer alternate compensation arrangements. Thus, they often decide to wait until ‘‘real’’ legal issue arises to retain counsel. But by then it is often too late, and the cost of fixing the

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problem in litigation changes to the governance structure, or renegotiation of key contracts will likely be much more than what it would have cost for the ‘‘preventive’’ legal services at the beginning of the venture. These costly mistakes are most often made by first-time entrepreneurs. ELC’s primary focus on the ELC Audit allows it to both provide a complementary service to the private tech law firms and encourage early stage entrepreneurs to slow down and figure out what is really going on with their venture before committing money and resources to ill-advised arrangements. Once in the process, however, the critical analysis of the ELC Audit can help speed up the entrepreneur’s thinking about her proposed venture, so as to see more clearly whether it is truly viable or not. In some cases this can encourage beneficial ‘‘fast failure,’’ in which the entrepreneur can move on quickly to another promising venture without wasting substantial time and resources on one that she now sees is unlikely to succeed. The ELC Audit benefits the student team members because they get to work with the entrepreneur on the comprehensive legal and business planning process. Students who take jobs with large law firms after graduation will often only see small pieces of deals or other legal work for their first year or so. Yet, the partners they are working for will be looking to see which young lawyers have the capacity to see the big picture and ultimately become trusted advisors to clients working on major legal and business strategy issues. Those are the lawyers who will truly be on ‘‘partner track.’’ Thus, the ELC Audit process gives them an introduction to this kind of big picture strategizing and counseling so that they can be reflecting on it – and discussing it with their supervising partners – even when they are only responsible for a small part of the deal in their early years. Finally, the ELC Audit also benefits the supervising attorneys and business consultants who volunteer their time to ELC on a pro bono basis. For the attorneys, it helps to ensure that ELC overall is providing legal services to those who cannot afford them – hence qualifying all the work donated by supervising attorneys as pro bono for purposes of meeting national and state bar association recommendations. It also provides a rare opportunity for corporate, IP, and tax attorneys to use their expertise in a pro bono context. Most pro bono opportunities lie in criminal or sometimes civil litigation contexts, or in resolving disputes with Federal, state, or local government agencies. Yet, most major law firms have roughly half or more of their attorneys working in transactional corporate, IP, or tax capacities. For these attorneys to engage in pro bono work normally means that they have to step outside of their areas of expertise to take on, say a deportation

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case, or a death row appeal. Although there are some benefits for these attorneys to do this, it more often simply acts as a disincentive for them to engage in any significant pro bono work. ELC has unlocked the pro bono potential of many of these lawyers at local firms, much to the delight of the lawyer, the firm, and the pro bono coordinator at the firm. For both attorneys and business consultants, ELC provides a meaningful way to give to UW as a state university and to give back to their alma mater for the many attorneys and consultants who are UW alumni. At the same time, both attorneys and business consultants have found that participating in ELC can be a ‘‘feel good’’ business development vehicle as well. Many of them will engage in some degree of free client development anyway, and for those focused on the start-up space, it is commonplace that most of those hours will go to entrepreneurs whose ventures will not go anywhere. ELC gives these professionals a place to vet entrepreneurs while counting the hours as pro bono. Yet, if the venture looks promising enough, the professional has a new client. ELC actively encourages this because, again, it is focused on the early stage ELC Audit process. The more promising the venture, and the better developed it is, the less likely it is that ELC should be giving it free legal services. ELC’s aim, then, is to take the ‘‘diamonds in the rough’’ or ‘‘not ready for prime time’’ entrepreneurs and their ventures and polish them up to the point where it will be clearer to all whether the business has real potential. Thus, far from ‘‘competing’’ with law firms, ELC has been welcomed as an important part of the start-up development process, with law firms actually referring clients to ELC who are too early stage for the firm to work with. Because of all the foregoing, ELC has been extremely popular with its constituencies. There is always a waiting list of clients – ELC does not operate on a walk-in basis but rather schedules in clients in advance for the ELC Audit and Memo process. ELC has a roster of dozens of highquality attorneys and their firms ready to supervise student teams. And each spring, dozens of great law students apply for the limited spots available in ELC for the coming year. ELC has had somewhat more limited success on the business school and consultant side, but this in many ways was due to institutional constraints that have now been largely eliminated (see Supervising Different Types of Professional Students). In recent years, the number of business students has increased substantially. The number of business consultant supervisors has also increased, but could use more work. ELC’s work with UW spin-offs began in earnest only a couple of years ago. Such work was part of the original business plan of ELC (the clinic was itself a highly entrepreneurial venture with no funding, space, or personnel

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when the author designed and launched it). But initially, UW central administration and its risk management group did not allow ELC to work with any UW students, faculty, or staff. This prohibition extended far beyond work on UW technology spin-offs and appeared to include even work with faculty, staff, or students on outside projects. Thus, early on, ELC was not allowed to work with a Law School staff member on legal and business planning for his outside rock band. The administration’s concerns seemed to center on the potential conflict of interest if ELC counseled UW faculty, staff, or students in ventures that would in turn be adverse to, or negotiating against, the UW, such as in the technology spin-off context. Despite clear assurances from the author, Law School leadership, and the former head of what was then named UW TechTransfer, that ELC had a clear plan for avoiding such conflicts by making it clear to UW faculty, staff, and student clients that ELC could not help or represent them in any legal actions adverse to the UW, the risk management group conditioned ELC’s certificate of coverage from the UW’s self-insurance plan on ELC not representing any such clients. However, after a year of further lobbying and support-building, combined with a change in the risk management group’s policy that eliminated the need for UW programs to obtain a formal certificate for self-insurance coverage, ELC was allowed to work with UW faculty, staff, and students, including on UW technology spin-offs. Once ELC was allowed to work with UW tech spin-offs, this service became quite popular, both with faculty researchers and UW TechTransfer. The latter had also recently initiated its LaunchPad program that focused on developing spin-offs. ELC was a natural match. Furthermore, while TechTransfer had worked with the Business School and to some degree the Engineering School on programs involving students teams, all these focused on the teams being the entrepreneur. This had some drawbacks, such as when other prospective licensees appeared, or when TechTransfer decided the student team was not taking an approach with the technology that was inline with TechTransfer’s plans for the technology. In some cases, the student teams had already gone out to the community soliciting entrepreneurs and angel or VC financiers. These external players were then unhappy to discover that they had not been dealing with the actual decision maker on licensing the technology, because the students had no authority to execute assignments or licenses on UW’s behalf. ELC possessed none of these drawbacks because the student teams were there simply to serve the entrepreneur. However, this did lead to the question of who ELC’s client was in working with faculty researchers and TechTransfer’s LaunchPad project. This question is the subject of the following section.

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WHO IS THE CLIENT? General Issues Business and entrepreneurship law clinics frequently confront the question of ‘‘who is the client.’’ There may be multiple owners or founders, and the clinic needs to decide whether it represents one or more of them. Even where there is a single owner or founder, once a legal business entity such as a corporation is formed, that entity is its own legal person that technically needs its own legal counsel. Furthermore, when a clinic agrees to work with an individual who is going to form a legal entity during the representation, the clinic must decide whether it is representing the individual or the business-to-be-formed. In all these cases, the clinic’s insistence of representing only one player in the venture can be vexing for the owners and/or founders. Depending on professional responsibility rules in the state in which the clinic is operating, the attorneys may be able to have the parties sign a form acknowledging the conflict of interest where the clinic will represent more than one of the parties involved and waiving any potential claims those parties may have based on the combined representation. Nonetheless, the conversation must still occur with the attorneys satisfying themselves that the parties have truly understood and waived the conflict. It is easy to see the potential conflict where multiple owners or founders are involved. Although the individuals may believe they all share the same vision for the business and their roles in it, things can change quickly. Interests that seemed well aligned at first can change with changes in personal circumstances or even with changes in the business or its economic environment. Even more fundamentally, sometimes multiple individuals will seek representation from a business law clinic without having fully resolved who will be owners or partners and who will be employees. Or, the individuals may not have worked out their relative equity stakes in the venture. Furthermore, as the venture and its legal planning unfolds, the various individuals may realize that they have different exit strategies, and this can prompt discussions about whether the venture continues on the departure of some or all of the founders, and, if so, how equity stakes will be redistributed amongst the remaining founders or sold to the new owners of the business. Founders and any early stage investors may choose to memorialize their decisions on these issues in a ‘‘buy-sell agreement’’ or partnership agreement. However, the negotiation and execution of this contract is technically an adversarial legal event, even as it might be done quite amicably among the parties. This underscores the potential conflict of

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interest for the attorneys or clinic that has taken on representation of the multiple founders. Less obvious can be the potential conflict of interest for an attorney representing a single founder and her business entity. Small business owners and entrepreneurs just starting a business often view the venture as an extension of themselves. This can be true if they operate as a sole proprietorship or general partnership. They may know that forming a legal entity such as a corporation or LLC will help them in some general way. But they may not specifically know that the legal entity limits their personal liability for torts and contract breaches of the business. Just as importantly, they also usually do not know that treating the legal entity as an extension of themselves, or as an alter ego, can destroy that legal privilege of limited liability. This is because aggrieved other parties can seek to ‘‘pierce the corporate veil’’ in tort or contract actions against the business. If the court agrees, then the owners of the business will have unlimited personal liability for any damages or other legal remedies the court may award. The rationale behind this is that the special legal entity formed by action of the state must be treated and respected as its own legal person. If the owners do so, then the legal person of the corporation or LLC bears its own liabilities for actions done on its behalf. Passive owners – such as shareholders who are not also directors, officers, or employees of a corporation – thus stand to lose only the capital they have invested in the legal entity. Aggrieved claimants who have been awarded damages or other relief in court may not demand compensation or other remedies directly from passive owners. Individuals who take an active role in directing, managing, or working for the legal entity, will also generally have no liability for the entity’s actions, or even their own actions, so long as they were done lawfully on behalf of the legal entity. However, such individuals may have some liability for their own actions if they violate specific statutory law, were unlawful, or otherwise intentionally aimed at causing the harm that is being redressed. Accordingly, an important function that business law clinics perform is to educate owners and founders about these issues and the need to treat the entity as a separate legal person. Taking this principle of entity personhood seriously, however, means that the attorneys and individuals involved must act as if there is an additional ‘‘person in the room’’ when dealing with company business and relationships. For example, the founder may want to execute an employment agreement with the entity for her role as an officer or other employee. Like the negotiation of the buysell agreement or partnership agreement mentioned earlier, the negotiation and execution of an employment

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agreement is technically an adversarial legal event between the founder/ employee and the entity. Because their interests may not be aligned now or in the future – especially when other owners or employees join the company – then the entity really needs to have its best interests represented by counsel. Of course, as a practical matter, if the entity is a single person corporation or LLC, then it is hard to imagine how an attorney could be retained to represent the entity alone, as against the other attorney representing the owner. Who would say what the entity’s interests were? Without further belaboring the almost metaphysical aspect of these types of questions, the main point is simply that the owner must understand the seriousness of respecting and treating the entity as a separate legal person from herself. Furthermore, as soon as any other natural persons are involved, then a conflict waiver should likely be signed by at least the natural persons, perhaps with someone also signing on behalf of the entity, where the attorney or clinic intends to represent all of them. Nonetheless, once the venture gets to the point of having operations, multiple employees, and/or outside investors, then it should likely have its own counsel. But at this point, the owners and other insiders must understand that the entity’s counsel is not their personal counsel and cannot be held liable to represent their best interests, as against the entity’s interests. For its part, counsel to the entity must always make this clear to the natural persons acting on behalf of the entity. The business law clinic must then decide who is the client in advance of engaging in any attorneyclient relationship. It can offer to represent the entity (if one is formed), the to-be-formed entity, one of multiple founders, or possibly the owner of a single-person entity with no employees, with no conflict waiver. Or it can offer to represent multiple founders, or even multiple founders and the entity, with a conflict waiver, where permitted by state attorney professional responsibility rules. Once the decision is made as to whom the client is, then the clinic can begin performing legal services – hopefully with a formal engagement or representation later in place to establish the scope of representation and parties’ respective rights and obligations.

Special Issues in Working with University Researchers and Technologies When a law school clinic seeks to work with faculty researchers, and/or the technology transfer office at its own university, to assist in planning or launching a spin-off to commercialize university technology, an extra level

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of potential conflicts arise. This is because the clinic, as a part of the university, can neither represent the faculty researcher as against the university, nor the external business entity that will receive the spun-off technology as against the university.7 This conflict is not necessarily the same as the professional responsibility conflict arising in the situations described earlier, where the attorney is representing multiple parties in a legally adversarial event. Law school clinics generally do not represent their universities in attorneyclient relationships. But, a conflict of interest nonetheless arises because the clinic is a kind of insider at the university. Although many of the tasks that the clinic could perform for the faculty researcher or external company will not involve positions adversarial to the university, the core technology transfer license negotiation between the spinoff and the university is technically a legal adversarial action. Likewise, any negotiation by the university researcher against the university regarding ownership of IP or technology that the researcher developed, or over her relationship with the university in regard to her involvement with an external entity, would be adversarial activities. Thus, the clinic can either seek a waiver from the university for these kinds of representations or limit its representation of the researcher or external entity to other matters not adversarial to the university. The author is unaware of any business or IP law clinics housed at universities that have secured such a waiver. Even where the clinic commits to limiting its representation to activities that are not adversarial to the university, the university’s administration may still oppose the clinic’s representation of the researcher external spin-off company.8 The clinic could instead seek to represent the university in the matter of the spin-off. It is unclear what advantage this would give the university, as it will likely have its own regular in-house or external counsel that would cover negotiations with the spin-off company. At the same time, most universities established enough to have professional schools and clinics would be unlikely to be the sort of nonprofit organization qualifying for pro bono legal services. Because of the foregoing, few if any business or IP law clinics that are part of a university formally represent their university in forming spin-off companies. Accordingly, another ELC innovation was to establish a new kind of relationship with UW researchers and UW’s technology transfer office (recently renamed the ‘‘Center for Commercialization’’ or ‘‘C4C’’9). ELC does not formally represent UW researchers, C4C, or the external spin-off. Instead, it is authorized by C4C, on a case-by-case basis, to act as a consulting agent to C4C’s New Ventures unit (formerly ‘‘LaunchPad’’) to help that unit and any UW researchers who developed the technology and

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are interested in participating in its commercialization, to work through the legal and business planning issues involved in spinning-off the technology to a start-up formed around the technology. Thus, two decisions have to be made: C4C must decide that the particular technology will be licensed out to a start-up formed to commercialize the technology (a ‘‘UW spin-off’’); and C4C must authorize ELC to work as consultants on the spin-off process with New Ventures and any involved UW research personnel. At the same time, in the event that UW researchers contact ELC first with a request for help in commercializing technology they have developed, ELC has committed to UW and C4C that it will immediately direct the researchers to C4C for the appropriate invention disclosures and vetting of the technology for potential commercialization. ELC will not discuss specific ownership issues as between UW researchers and UW. However, ELC personnel, including the author, will discuss general university vs. researcher ownership issues in publications and lectures. ELC personnel have taken to describe the relationship with UW and UW researchers in the spin-off context by analogy to grant funding agreements that specify a principal investigator (PI). In the latter, the contract is formally between only the university and the grant funding organization. But the PI often signs the agreement as well, or is named in it, as someone who has specific rights and duties under the agreement. Likewise, when ELC works with C4C and New Ventures on a spin-off, the researchers are beneficiaries of what is essentially a consulting engagement. But the researchers are not a party to the agreement and are not law clients of ELC.

MEDIATING AMONG DIFFERENT COMMERCIALIZATION PLANS In the university context, many science and technology innovations are very early stage, and not yet ready to be produced for the market in the form of products or services. They may be patentable or otherwise protectable under some form of IP, however. The question then is who will do the further work needed to develop a product or service based on the innovation, and under what assignment or license terms from the university. This question is often answered at one level by determining just how early stage the innovation may be. One coarse distinction used is the difference between translational stage innovations and commercialization stage innovations. The former are

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science or engineering breakthroughs that clearly have commercial implications, but which are primarily new insights into an important phenomena. For example, a breakthrough in understanding how a specific cellular or molecular process leads to development and growth of certain malignant tumors clearly has important diagnostic and therapeutic implications. New ways of detecting whether the process is occurring in a patient could lead to new diagnostic tools in the clinical setting. Similarly, research on ways to disrupt this process could lead to therapeutics that would halt or slow the growth of such tumors. But, until such translational research is performed, there is no product or service in sight. The challenge for the translational stage is that it is generally the most risky and may involve many false starts as radically different visions of products or services based on the breakthrough scientific insight are explored. Tech transfer professionals will often refer to translational stage innovations as ‘‘cool science’’ that have no current commercialization potential. This can often be hard for researchers to hear, especially if they know that publication of the breakthrough is likely to garner them accolades in the academy. But this is not a judgment by the tech transfer officer on the merits of the innovation qua science, but only a judgment as to whether a licensee is likely to be found for whatever IP can be procured on the innovation as it currently stands. By contrast, commercialization stage innovations are those that solve a particular problem that directly enables a product or service to be created or enhanced. Although these are less risky as a general matter – because the product or service is at least identified – they can still require substantial R&D before a manufacturer or service provider can offer products or services to the market. This is because what may have worked in the lab, with substantial resources focused on experimental tests, may not be scalable for mass manufacturing or delivery to the market, or it may simply be too expensive to deliver per unit or service engagement, given what the market may be willing to pay. Furthermore, there may be sizeable regulatory approval or compliance costs that must be borne by the commercializing party. Nonetheless, with an identified product or service and the ability to rely on industry averages for scale-up and regulatory compliance, a prospective licensee can at least make an educated guess as to the value of the technology and the costs to bring it to market. The foregoing is clearly an oversimplified sketch of the issues involved in assessing university research results and seeking practical applications for them. Yet, it suffices to begin introducing the challenge of mediating among different commercialization visions help by different stakeholders. For example, on the one hand, the researcher who develops a translational

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stage innovation may still fervently believe that there is a ready market of licensees for IP covering it (and that such IP will be forthcoming or available if the tech transfer office simply pushes ahead with obtaining it). The tech transfer officer, on the other hand, may not believe that the innovation is patentable and/or that no company in the industry would license it, whether as know-how protected only as a trade secret or even if it were patentable. The debates can get still more intense when the innovation is in the commercialization stage and competing commercialization or dissemination visions arise within and across the researchers, tech transfer staff, prospective licensees (including external entrepreneurs), and, in some cases, student teams or others who have been given permission to explore commercialization pathways for the innovation. This can be one of the challenges of using student teams that are being trained to be entrepreneurs to work with the university’s technology. Without clear ground rules and set expectations as to who the ultimate decision maker is, with regard to actual licensing or assignment of the IP covering, the technology and the proper role of the student team, frustration, and bad feelings can arise. Yet, an even more fundamental debate can ensue among the stakeholders with regard to the ‘‘best use’’ of the technology, especially given most universities’ commitment to some form of a research-based public interest mission. In essence, there are three common university research commercialization or practical dissemination pathways: (i) dissemination through publication or low/no cost nonexclusive licenses; (ii) exclusive licenses to large established enterprises; and (iii) exclusive licenses to small or medium enterprises (SMEs), including start-ups. As a general matter, those stakeholders who view the university’s primary mission as teaching and basic research can be more inclined to favor (i). These individuals may feel that the university has no business getting directly involved in any commercialization activities. Or, they may feel that the university should not favor any one private firm with an exclusive license, especially where the research was funded by Federal or state agencies. Those who favor (ii), at least in a particular instance, may feel that it is too risky to license the technology to an SME (because the SME may not have the resources or experience to adequately commercialize the technology) or that a better financial deal can be struck with a large business. Where the latter is a concern, the individual may feel that one of the chief goals of technology transfer is to maximize revenues on the university’s IP, perhaps with the laudable intent of generating significant income that can be used to support increased research at the university. Those who favor (iii) for a particular technology may also prioritize local SMEs because they see the

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university’s technology transfer mission as having regional economic development as one of its main objectives. Or, they may believe that an SME will be able to be more innovative with commercializing the technology and/or focus on it as a priority (whereas it might wind up being neglected or even suppressed at the large established company). The point of this discussion is not to sketch out every variation or attempt to argue for one viewpoint over another. Rather, the purpose is to show that many viewpoints can be held by those involved. The ELC Audit can help informally mediate among these competing views by making express the various expectations of the stakeholders and realities of the technology. Although ELC teams do not formally mediate disagreements, they can help the parties think through the options and find compromises where possible. At the same time, however, because ELC is not a dispute resolution mechanism, strongly competing views among UW researchers, C4C staff, and other stakeholders involved in a UW technology can offer a real challenge to ELC and the student teams. Because ELC is not formally representing any of the stakeholders, its job is both easier and harder. Easier, because it does not have to take an advocacy position on behalf of one stakeholder as an attorney. Harder, because ELC has no binding role in the process, other than to act as a consultant. Notwithstanding the foregoing, because ELC is generally only involved in UW technologies that will be commercialized through a spin-off to be formed around the technology, any decisions by C4C that take the technology in a different direction – such as a license to an existing, mature business – will have the practical effect of ending ELC’s involvement on that technology.10

SUPERVISING DIFFERENT TYPES OF PROFESSIONAL STUDENTS Although the challenges of working with UW technology stakeholders when there are competing commercialization visions can be intense, the multidisciplinary nature of ELC teams adds its own set of potential conflicts and culture clashes. Differences between and among law students and business students with different training and focus areas set the stage for misunderstandings about process and content of the ELC Audit and Memo, as well as other services ELC may provide. The author and other ELC staff have learned much from these conflicts, even as they were expected in the author’s original design of ELC and intended to be a valuable learning

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experience for the students. As mentioned in the discussion of ELC in section ‘‘University of Washington Entrepreneurial Law Clinic,’’ one of the main objectives of ELC was to introduce students to the issues involved in working in the kind of multidisciplinary teams they will be involved in once they are working with start-ups in the real world. The remainder of this section discusses issues for each specific category of students and why ELC does not routinely involved science or engineering students on the teams. Law students still constitute the largest core of the ELC teams. On the one hand, they are the easiest for ELC staff to directly control. On the other hand, they present significant risks for themselves, ELC, and the client. They are easier for ELC staff to control because: (i) they enroll directly in the ELC course established in the Law School (business students enroll in separate course established in the Foster School of Business); and (ii) they must get a certification from ELC when they later seek admission to practice law that they did not engage in any breaches of conduct or professional responsibility and have the moral character and fitness to be a member of the bar. Thus, any unauthorized disclosure of confidential or sensitive client information, unethical conduct, or even poor judgment by them could have serious repercussions for their chances of becoming a licensed attorney. At the same time, they are ultimately giving legal advice that clients may rely on. If that advice is wrong, the client could wind up with legal liabilities it did not expect, or it could lose valuable property rights (such as patents), or suffer other legally detrimental harms. ELC does supervise the students in this regard, of course, and the final ELC Audit Memo and other deliverables must be reviewed by supervising attorneys and ELC staff. Nonetheless, comments made in conversation with clients, or statements made in deliverable documents that are not caught on review, might still either be wrong or be susceptible to misinterpretation by the client. Another challenge with law students is that each team member will have a different area of expertise and may either misunderstand another’s role or suggestions, or believe they know the other’s field and can second guess them. Finally, although ELC does not formally represent a client in its consulting for C4C on UW technology spin-offs, the risk can actually increase over the situations when ELC represents regular clients. This is because a student could give the impression to any of the individuals it interacts with on the C4C projects that they or ELC are giving formal legal advice or establishing an attorneyclient relationship. The challenges with business students are essentially inverted from those of the law students. The business students’ advice is not as fraught with potentially critical legal implications. If it is wrong, it may harm the

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venture’s prospects, but it is unlikely to result in legal harms or loss of valuable property rights. At the same time, the lack of a licensing process for business consultants, managers, and other major MBA career paths means that improper actions by the business students will not carry as significant repercussions as those for law students. And because the business students will generally be privy to the same confidential and sensitive information of the client or UW technology, other mechanisms for ensuring that no unauthorized disclosure or use of that information occurs had to be established. This is further compounded because the culture of law and business schools differ as the nature of the professionalclient relationships in law and business can differ. In law, the professional has an obligation of keeping confidences for all clients regardless of whether a specific agreement to do so is reached. It comes with the attorneyclient relationship itself and is enforced by attorney professional responsibility rules. In business, there is no automatic client confidentiality obligation. For any confidentiality obligation to be enforceable, it must have been expressly requested and agreed to, preferably in a written nondisclosure agreement (NDA) or confidentially agreement. Accordingly, ELC instituted a policy requiring participating business students to sign an NDA both with each student’s specific clients and with ELC. The latter protects the confidences of clients of other students, which may be disclosed within nonpublic class discussions for educational purposes. An additional challenge with business students seems to stem from the traditional lack of a clinical component to most MBA programs. For ELC to work effectively, it requires students to sign up for the full academic year, with exceptions in advance for special circumstances. This is true of many law school clinics across the country. But business students have tended to drop out unexpectedly over the course of the academic year at a significantly higher rate than law students. More importantly, those that do often seem surprised when confronted with the difficulties it imposes on ELC and the unacceptability of it from ELC staff’s perspective. This seems to have been changing somewhat as the Foster School of Business instituted an experiential learning requirement recently (and added the counterpart course to the Law School ELC course as one that satisfies the Foster School’s experiential learning requirement). This change in the Foster School’s administrative policies was quite beneficial and turned around a situation where there were substantial disincentives to MBA students to participate in ELC to one in which there are significant incentives to participate. Before the change, MBA students had to enroll in the Law School ELC course, as one of very limited elective courses that could be

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taken outside of the Foster School. Furthermore, a full year enrollment in ELC might then use up all the elective courses the student could take outside of the Foster School. After the change, MBA students could enroll in a course within the Foster School – thus not using up limited elective courses outside of Foster – and furthermore, this course was one of a relatively small number of courses that would satisfy the new experiential learning requirement. A final challenge with having business students participate in ELC is the relative lack of supervisors. Because there is not the same formal pro bono expectations and requirements for business people, there is less incentive for business people to look for pro bono opportunities. This seems to have also influenced the culture of business schools generally, in which there appears to be less emphasis on serving the public interest than there is in law schools generally. This is not meant as a rap on business schools, but rather as an observation of a phenomenon that has implications for multidisciplinary clinics such as ELC that need both faculty and student participation from business schools. At the same time, ELC has benefited greatly from the existence of the Center for Innovation and Entrepreneurship at the Foster School and its excellent leadership who have consistently championed the value of ELC to faculty and students there since even before ELC was formed. One seeming omission in ELC’s teams are science or engineering students. Particularly when ELC is serving C4C as well as outside technology entrepreneurs, it would seem that science or engineering students would be quite valuable. However, the reality is that science and engineering students are generally better served (and utilized) in acting as budding entrepreneurs. In other words, they are usually better suited to programs that train students to be the entrepreneur. In programs, such as ELC, that focus on training students to serve entrepreneurs, the clients include the scientists and engineers who have developed the technology. Although science or engineering students as ELC team members could be helpful to the other team members in understanding the technology at issue, the author’s experience was that many of them would second guess the client’s technology in ways that were counterproductive. In some cases, when ELC experimented with including science or engineering students, the independent analysis and critique of the technology was welcomed by the client. But in other cases, it became a real point of contention. None of this means that science and engineering students cannot interact with ELC. Rather, the interaction takes on the most natural form that it will in the real world – science and engineering students are often part of the UW researcher teams and/or

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business plan competition teams that are served by ELC as consultants to C4C. Additionally, both the Law School and the Foster School of Business each have a significant number of science and engineering trained students – many with advanced degrees – that then participate in ELC. These students are staffed on projects involving technology in the student’s area of expertise. In this way, ELC does get the benefit of having science and engineering trained students on the teams. But these students understand their role to be primarily that of the lawyer or business consultant, who happens to also understand the technology, and not that of the technology analyst.

CONCLUSION ELC has been a highly successful – and highly challenging – endeavor. The author engaged in a bootstrapped entrepreneurial venture to form a new kind of law clinic that would emulate the professional service firms environment that supports entrepreneurship. These firms are a critical part of the innovation ecosystem, especially for technology clusters based around major research universities. Yet, little attention has been given to their role. Even less has been given to how to develop the next generation of these professionals and their methods. ELC, then, serves as the starting point for a broader research, teaching, and service program in professional services for entrepreneurs at UW. ELC is, of necessity for its mission and vision, a multidisciplinary clinic. It is a joint venture with the Center for Innovation and Entrepreneurship at the Foster School of Business and partners with C4C and the Institute for Translational Health Science (ITHS, http:// www.ITHS.org) at UW. Using the many lessons learned in the clinic, the author is currently engaged in developing a new law, business, and entrepreneurship research and teaching program around ELC as its central service component.

NOTES 1. See, e.g., Office of Science and Technology Policy, Commercialization of University Research Request for Information, 75 Fed. Reg. 14476 (March 25, 2010). 2. P.L. 96-517, 94 Stat 3015 (1980) (codified at 35 U.S.C. yy 200–212). 3. 35 U.S.C. y 202(c)(7)(D). 4. See, e.g., Small Business Administration, Office of Advocacy, The Small Business Economy: A Report to the President (U.S. G.P.O., 2009).

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5. See, e.g., Georgia Institute of Technology, College of Management, TI:GERs (Technological Innovation: Generating Economic Results), available at http:// tiger.gatech.edu/. 6. See University of Washington School of Law, Entrepreneurial Law Clinic, available at http://www.law.washington.edu/clinics/entrepreneurial. 7. University ‘‘spin-offs’’ are different than private sector spin-offs. Although the private sector entity such as a corporation, LLC, or partnership can literally sell off or divest itself of a business unit or division (together with associated personnel and assets), a university – existing as either a nonprofit corporation or a state agency – cannot. Instead, the university can only assign or license any IP rights it has in the technology to an external entity. The university may help or guide the formation of an external entity to which it will then assign or license the IP. In some cases, the faculty, student, or staff researchers involved with developing the technology may choose to also work for the new entity on either a full-time or part-time basis, or as an independent contractor consultant. But the university will generally have no ability to direct these individuals to do so, or to let them go from their current university positions solely on this basis. 8. This was initially true in the case of ELC. See supra Part II. 9. See University of Washington, Center for Commercialization, ‘‘UW Tech Transfer Becomes UW Center for Commercialization’’ available at http://depts.washington. edu/uwc4c/aboutus/Docs/UWTT-becomes-Center-for-Commercialization.pdf 10. ELC has performed some freedom-to-operate and IP landscape analyses on technologies at different stages for C4C. Thus, sometimes ELC is still involved with UW technologies even if the particular technology will not be licensed to a spin-off company.

ACKNOWLEDGMENTS The author thanks Marie Thursby, Margi Berbari, and Stuart Graham for inviting him to participate in the 2010 TI:GER Kauffman Workshop, and the Workshop participants for helpful comments on the presentation that formed the basis of this chapter.