Cognitive Approaches to Entreprenuership Research, Volume 6 (Advances in Entrepreneurship, Firm Emergence and Growth) (Advances in Entrepreneurship, Firm Emergence and Growth)

CONTENTS LIST OF CONTRIBUTORS vii COGNITIVE APPROACHES TO ENTREPRENEURSHIP RESEARCH Jerome A. Katz and Dean A. Shephe...

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CONTENTS LIST OF CONTRIBUTORS

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COGNITIVE APPROACHES TO ENTREPRENEURSHIP RESEARCH Jerome A. Katz and Dean A. Shepherd

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ORGANIZATIONAL LEARNING BY NEW VENTURES: CONCEPTS, STRATEGIES, AND APPLICATIONS Benyamin Bergmann Lichtenstein, G. T. Lumpkin and Rodney C. Shrader

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ENTREPRENEURIAL FIT: THE ROLE OF COGNITIVE MISFIT Keith H. Brigham and Julio O. De Castro

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THE ROLE OF REGRETFUL THINKING, PERSEVERANCE, AND SELF-EFFICACY IN VENTURE FORMATION Gideon D. Markman, Robert A. Baron and David B. Balkin

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THE SELF-DETERMINATION MOTIVE AND ENTREPRENEURS’ CHOICE OF FINANCING Harry J. Sapienza, M. Audrey Korsgaard and Daniel P. Forbes

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EXTENDING THE THEORY OF THE ENTREPRENEUR USING A SIGNAL DETECTION FRAMEWORK Jeffrey S. McMullen and Dean A. Shepherd

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A TRANSACTION COGNITION THEORY OF GLOBAL ENTREPRENEURSHIP Ronald K. Mitchell

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THE IMPACT OF ENTREPRENEURIAL EXPERIENCE ON OPPORTUNITY IDENTIFICATION AND EXPLOITATION: HABITUAL AND NOVICE ENTREPRENEURS Deniz Ucbasaran, Mike Wright, Paul Westhead and Lowell W. Busenitz

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OPPORTUNITY DEVELOPMENT: A SOCIO-COGNITIVE PERSPECTIVE Alice De Koning

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THE DOMAIN OF ENTREPRENEURSHIP RESEARCH: SOME SUGGESTIONS Per Davidsson

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LIST OF CONTRIBUTORS David B. Balkin

Leeds College of Business, University of Colorado, Boulder, USA

Robert A. Baron

Lally School of Management and Technology, Rensselaer Polytechnic Institute, USA

Lowell W. Busenitz

Michael F. Price College of Business, University of Oklahoma, USA

Keith H. Brigham

Jerry S. Rawls College of Business Administration, Texas Tech University, USA

Per Davidsson

Jönköping International Business School, Sweden

Julio O. De Castro

Leeds School of Business, University of Colorado, Boulder, USA

Daniel P. Forbes

Carlson School of Business, University of Minnesota, USA

Benyamin Bergmann Lichtenstein

Department of Management, University of Hartford, USA

G. T. Lumpkin

Department of Managerial Studies, University of Illinois at Chicago, USA

Jerome A. Katz

Department of Management, Saint Louis University, USA

Alice de Koning

J. Mack Robinson College of Business, Georgia State University, Atlanta, USA

M. Audrey Korsgaard

Moore School of Business, University of South Carolina, Columbia, USA vii

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Gideon D. Markman

Terry College of Business, University of Georgia, Athens, USA

Jeffrey S. McMullen


Ronald K. Mitchell

Faculty of Business, University of Victoria, Canada Jointly appointed Professor, Guanghua School of Management, Peking University, PR China

Harry J. Sapienza

Carlson School of Business, University of Minnesota, USA

Rodney C. Shrader

Department of Managerial Studies, University of Illinois at Chicago, USA

Dean A. Shepherd


Deniz Ucbasaran

Nottingham University Business School, UK

Paul Westhead


Mike Wright


COGNITIVE APPROACHES TO ENTREPRENEURSHIP RESEARCH Jerome A. Katz and Dean A. Shepherd Cognition has always been central to the popular way of thinking about entrepreneurship. Entrepreneurs imagine a different future. They envision or discover new products or services. They perceive or recognize opportunities. They assess risk, and figure out how to profit from it. They identify possible new combinations of resources. Common to all of these is the individual’s use of their perceptual and reasoning skills, what we call cognition, a term borrowed from the psychologists’ lexicon. While cognition has been central to the way people in general describe entrepreneurship, it has been only sporadically used as an approach in entrepreneurship research. Worse, in many of those early efforts, cognitions were stipulated theoretically, and rarely checked. This led to ideas such as the belief in the economic literature that entrepreneurs were great risk-takers. Only when checked empirically against the harsh reality of entrepreneurs’ self-reports did researchers find that entrepreneurs in fact did not demonstrate a higher-than-average risk-taking propensity. David McClelland (McClelland, 1961; McClelland & Winter, 1969) and later Robert Brockhaus (1980) showed that entrepreneurs tended toward moderate risk-taking. Even this finding endured revision in the 1990s when researchers such as Arnold Cooper (Gimeno, Folta, Cooper & Woo, 1997) discovered that entrepreneurs perceive situations as less risky than objectively warranted. Just as risk-taking went through several revisions and refinements, so too did other elements of the entrepreneurial process such as opportunity recognition, attribution, self-efficacy, creativity and innovation. Much of this effort to revise

Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 1–10 © 2003 Published by Elsevier Science Ltd. ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06001-X

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JEROME A. KATZ AND DEAN A. SHEPHERD

and refine entrepreneurial cognitions began in the 1980s. Gartner (1985) argued persuasively for models of entrepreneurship (which he defined as organization creation) that included or dealt with at least two or more of four potential dimensions: person, firm, environment, and process. While some of these elements had been considered individually before (e.g. process models were discussed by McClelland, 1961; Shapero, 1975), the explicitly multi-level model Gartner proposed was seen as the most comprehensive to date. Although not intended per se as an attack on personological approaches, Gartner’s arguments had a chilling effect on personological research by the late 1980’s when Gartner published two more articles (Gartner, 1988, 1989) which persuaded many of the editors and reviewers in the field that a new, more inclusive and rigorous approach to individual level studies was needed. While the purely personological approaches common in the entrepreneurship research of the 1970s and 1980s would typically fail to consider multiple dimensions, cognitive process models, which often triangulate aspects of the entrepreneur, perceived elements of the environment, and use a process to tie these together (often with additional ties to the emerging firm), posed greater promise as a direction for future research. The model of organizational emergence published during this period by Katz and Gartner (1988) demonstrated among other things how individual level phenomena like cognition (e.g. enactment and intentional processes) could lead to the emergence of new entities at the organizational level, one of the most detailed cross-level synthesis ever developed in the research literature. Despite this, Gartner’s challenge of multi-level, multi-dimensional entrepreneurship research resulted in something of an inadvertent hiatus in individual-level research. Efforts by several individuals lead to the resumption of individual-level research with a stronger cognitive basis. Perhaps the 1980s could be called “The Age of the Conference” for the field of entrepreneurship. While the first “state of the art” conference began at Baylor in 1980 (Kent, Sexton & Vesper, 1982), and the first marketing-entrepreneurship conference was begun by Gerry Hills in 1982 (Cooper, Hornaday & Vesper, 1997) the late 1980’s saw a set of conferences emerge that held profound impacts on the cognitive approach to entrepreneurship research. One of these was the Gateways To Entrepreneurship Research Conferences at Saint Louis University, organized by Robert Brockhaus and Jerome Katz. Rather than inviting papers as a ticket of admission, the Gateways Conferences identified topics, and participants would discuss these, with the goal of generating new research and publication to come from the Conference. The Conferences provided the material for the first two volumes of the series you are reading now, covering topics such as demographic approaches to entrepreneurship, individual level entrepreneurhsip and firm-level entrepreneurship. That first Gateways

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Conference in 1987 exposed a rather well-known social psychologist named Kelly Shaver to entrepreneurship, including a fateful sushi dinner with William Gartner that would lead to a longstanding collaboration that later became central to the cognitive approach in entrepreneurship. Ray Bagby organized a January 1991 conference at the University of Baltimore on “Interdisciplinary Conference on Entrepreneurship Theory,” in which Frank Hoy and Jerome Katz supported Bagby as resident experts and discussion leaders. This resulted in two special issues of Entrepreneurship: Theory & Practice in 1991 and 1992 edited by Lanny Herron, Deborah Smith-Cook and Harry Sapienza (1991). Included in these special issues were papers by Shaver and Scott (1991) and Gartner, Bird and Starr (1992) that continue to be cited today as seminal works in anchoring the modern cognitive approach in entrepreneurship. Amid these efforts, Gartner felt that the opportunity was right to promote a new generation of individual-level studies of entrepreneurial processes. His intention was to hold a “theoretical shoot-out” (Gartner, personal communication, 2003). The initial result came in the form of a pair of special issues of Entrepreneurship: Theory & Practice published in Fall 1992 and Winter 1993 with the common theme “Thus the theory of description matters most” (Gartner & Gatewood, 1992). Included in these special issues were papers on intention (Bird, 1992), psychosocial cognitive models of choice (Katz, 1992), information processing (Hansen & Allen, 1992) and group emergence perspectives (Katz, 1993). The 14 articles in these special issues crossed all levels of analysis, but it was clear that the individual-level approach was still of tremendous interest to the research community, and that the congitive approach would be one of the major vehicles for the new generation of studies. Capitalizing on this observation, Gartner took the lead in developing yet another special issue of ET&P this one in Spring 1994 with the theme “Finding the entrepreneur in entrepreneurship,” co-edited by Gartner, Kelly Shaver, Elizabeth Gatewood and Jerome Katz (1994). That special issue came as an effort to restart empirical research on individual-level entrepreneurship. Central to this special issue was to be the direction empirical research in entrepreneurship would take, and here the impact of Kelly Shaver cannot be underestimated. One of the developers of modern attribution theory, Shaver was well versed in rigorous individual-level research approaches, and was to become a tireless networker bringing entrepreneurship researchers and cognitive theorists together. Gartner, Shaver and Gatewood had been developing and testing attributional models with samples drawn from Gatewood’s Small Business Development Center clientele (e.g. Gatewood, Shaver & Gartner, 1995), making these three a natural team for the special issue. Katz, who had worked with social psychologists and entrepreneurship researchers at Harvard, MIT and Michigan was added to aid in

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bridging the fields. Together, these four sought out exemplar papers, using current cognitive theory in rigorous and novel ways. The 1994 Special Issue, building on the theoretical models introduced in the 1992 and 1993 ET&P special issues, did much to reintroduce individual level empirical studies to the field of entrepreneurship research. This was evident in models with a strong cognitive element, such as the event model of Krueger and Brazeal (1994) and the competency model of Chandler and Hanks (1994), but also in more personality based approaches such as motivational model proposed by Naffziger, Hornsby and Kuratko (1994). By this point, the field had come full circle, with individual-level research based on stronger conceptual foundations and more rigorous empirical approaches, which had been Gartner’s goal. While in the prior generation of individual-level approaches personological approaches predominated, the new generation of individual-level research would have more of a cognitive orientation. It is worthwhile noting that this very fundamental change in the way research was conceived and performed was done largely as an effort by very junior professors. While Shaver, Bagby and Hoy were already senior in their fields, Gartner, Katz, Gatewood, Bird, Carsrud, Sapienza, Smith-Cook, Herron, Chandler, Hanks, Hansen, Krueger, and Brazeal were all assistant professors at the time of these conferences and special issues of the late 1980s and early 1990s. It is possible that the field of entrepreneurship in those days, with a less evolved infrastructure, was easier to move than it is today, but it is also fair to say that today there are more resources, more outlets for publications, and more venues to make ideas heard than there were 10 or 15 years ago. Arguably the potential for junior faculty to transform a field of inquiry still very much exists today. What is needed is will to achieve, a willingness to network, and above all a shared vision of transformations that will improve the discipline. The ten years since the publication of these special issues have continued to be a period of tremendous growth in the sophistication of individual level research, with even greater discussion and debate on individual level approaches than ever seen in the field. The cause for much of this came from the development of a survey for nascent entrepreneurs by the Entrepreneurial Research Consortium. The survey, later called the Panel Study of Entrepreneurial Dynamics, was intended as the standard-setter for key variables and measures in entrepreneurship research. Built by over 120 researchers from more than 30 institutions worldwide (Reynolds, 2000), the research teams developing measures included many of the most active individual-level researchers in entrepreneurship. The space limitations inherent in the survey meant that variables and measures received one of the most detailed, profound, public and critical assessments ever attempted in the field. As a result, a new distillation of key concepts and measures in individual-level processes emerged, and because of the widespread


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membership and involvement in the ERC/PSED process, these concepts and measures became among the most widely disseminated and best understood scales ever developed. Today the impact of the ERC/PSED effort is evident in the sophistication, elegance, and rigor of the measures used in entrepreneurship research. The ERC/PSED was invented and pioneered through the truly monumental efforts of Paul Reynolds, who had worked at Wharton, Michigan, Marquette, Babson and the London Business School at different phases of the research process. Reynolds’s contribution of vision and determination will probably become a legendary example for research entrepreneurship in academia, and his contributions to all phases of the research cannot be overstated. But like so many great entrepreneurial ventures, even those started by an individual often only come to fruition by the efforts of a team, and for the ERC, and later the PSED, this came in two stages. Initially, Reynolds, Nancy Carter and William Gartner worked closely together with the first precursor to the ERC research stream (Carter, Gartner & Reynolds, 1996), with Gartner working to operationalize the multi-level variables he discussed in prior works. As this work proved to viability of a study of nascent or potential entrepreneurs, the potential for the ERC/PSED emerged. To govern this consortium of institutions and faculty, an Executive Committee was formed. Reynolds, Carter and Gartner were immediately elected to the Executive Committee, as were Candida Brush, Per Davidsson, Mary Williams and Kelly Shaver. The resulting ERC/PSED surveys in many ways came to embody the new generation of individual-level cognitive research that Gartner and Shaver worked so hard to develop and showcase in other venues. With the past decade of concentrated focus on individual level models of entrepreneurial cognition in both theory, research and instrument development, the popular and academic conceptualizations of the entrepreneur as a person driven by cognitions have neatly come together. Today entrepreneurship researchers actually study concepts like opportunity and vision, which are immediately recognizable to the general public as characteristic of entrepreneurs. Despite the face validity of cognitive models of entrepreneurship to the general public, the specifics of the theories, instruments and research efforts themselves are focused on more demanding forms of validation, linked to a more demanding research community and the action arms of governments and business funding organizations eager to increase the number of business starts, especially among high-growth ventures. In developing this volume of the Advances in Entrepreneurship, Firm Emergence and Growth series, determining which of the many cognitive advances in entrepreneurship research to present posed a significant problem of choice.

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With an established foundation for cognitive approaches to entrepreneurship, we took the opportunity in this volume to look forward and showcase the work of those who we believe will build on this foundation and lead future research on cognitive approaches to entrepreneurship. The second chapter is “Organizational Learning by New Ventures: Concepts, Strategies, and Applications” by Benyamin Lichtenstein, Tom Lumpkin and Rod Shrader. We asked this team to contribute to Volume 6 for a number of reasons. First, Benyamin is a passionate advocate of entrepreneurship research on knowledge and learning, and brings a unique and refreshing perspective to the field. Both Tom and Rod have already made a substantial contribution to the entrepreneurship literature – Tom at the intersection of strategy and entrepreneurship and Rod at the intersection of international business and entrepreneurship. In this chapter, Benyamin, Tom and Rod categorize the organizational learning literature into Behavioral, Cognitive, and Action learning, and suggest a number of ways in which new ventures could be more successful at learning than larger and older organizations. They also explore three entrepreneurial contexts where learning might be particularly important and match them to the categories of learning. Benyamin, Tom and Rod provide an extensive prescriptive/implication section, in which they detail tactics for both enhancing entrepreneurial learning and studying entrepreneurial learning. The third chapter is “Entrepreneurial Fit: The Role of Cognitive Misfit” by Keith Brigham and Julio De Castro. We invited Keith and Julio to contribute to this book because their work acts as a counterweight to the conventional wisdom of the 1990s that research on the stable personal characteristics of entrepreneurs represented a dead end. In this chapter, they introduce the construct of cognitive misfit to the field of entrepreneurship within a Person-Organization fit (P-O fit) framework. They then group types of entrepreneurs according to their cognitive style and empirically test for misfit when interacting with organizational structure. They find that an entrepreneur whose cognitive style is mismatched with the firm context will tend to experience significantly more “negative” outcomes (higher burnout, lower satisfaction, and higher intentions to exit) than an entrepreneur who is more in fit. These findings have implications for certain types of entrepreneurs at different stages of firm growth and maturity. The forth chapter is “The Role of Regretful Thinking, Perseverance, and SelfEfficacy in Venture Formation” by Gideon Markman, Robert Baron and David Balkin. We approached Gideon to contribute a chapter because he is a talented and motivated young scholar developing important streams of entrepreneurship research at the individual level of analysis. In this chapter, he continues his productive association with Robert Baron and David Balkin. Robert has built a substantial reputation in the fields of psychology and social psychology and over


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the last five years has refocused his considerable skills and enthusiasm towards exploring entrepreneurial cognition and behaviors. David is also a leading scholar (in human resource management) who has recently turned his attention to the field of entrepreneurship. In this chapter, Gideon, Robert and David conduct a study to distinguish inventors who used their patents to start new ventures from those inventors who also created patents but remained within their existing organizations (as employees). The basis for the comparison is inventors’ tendency to engage in regretful thinking, their perceived capacity to persevere in the face of adversity, and their self-efficacy. The results are interesting, particularly the finding on regretful thinking. The fifth chapter is “The Self-Determination Motive and Entrepreneurs’ Choice of Financing” by Harry Sapienza, Audrey Korsgaard and Daniel Forbes. We are delighted to have these authors as contributors. Harry’s research typically breaks new ground and this chapter with Audrey and Dan is no exception. Audrey is a well-respected scholar of procedural justice and the application of her knowledge of that literature and different research methods to entrepreneurial issues has had a major impact on the field. Dan is a young scholar off to an impressive start. In this chapter, they develop a framework for understanding entrepreneurial financing choices by investigating the motives of wealth maximization and selfdetermination. Specifically, they focus on factors that influence entrepreneurs’ aversion to sharing decision control and their perceptions of decision control risk. They argue that venture stage, entrepreneurs’ experience and the business performance of past and current ventures influence decision control risk aversion, and that industry norms, reputation, and the procedural justice of interactions with financiers influence perceived decision control risk. The sixth chapter is “Extending the Theory of the Entrepreneur Using a Signal Detection Framework” by Jeff McMullen and Dean Shepherd. Jeff is completing his dissertation at the University of Colorado and will be an Assistant Professor at Baylor University from August 2003. We invited Jeff to contribute a chapter to this volume because we believe that he has considerable talent and will likely develop a number of highly impactful theories within the domain of entrepreneurship. In this chapter, he, with Dean Shepherd, propose that the decision to pursue opportunity requires concomitant consideration of belief (uncertainty) and desire (motivation). When they apply their framework to the better-known economic theories of the entrepreneur they demonstrate that these theories rely upon one construct or the other, and that a framework that includes both constructs provides the opportunity to integrate previously conflicting theories. The seventh chapter is “A Transaction Cognition Theory of Global Entrepreneurship” by Ron Mitchell. Ron is probably best known for his work on expert scripts and has been applying his knowledge and skills to developing our

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understanding of entrepreneurial knowledge. Ron is also a passionate advocate for further developing research on entrepreneurial cognition, e.g. he was the editor of a recent special issue of Entrepreneurship Theory and Practice on cognition and information processing. Ron presents a transaction cognition theory of global entrepreneurship. In this theory, he establishes a relationship between transaction cognitions – mental models guiding certain economic behaviors – and the success of transactions. Ron then subjects his theory to a rigorous assessment consistent with the philosophy of science. This chapter provides a path for future research on global entrepreneurship. The eighth chapter is “The Impact of Entrepreneurial Experience on Opportunity Identification and Exploitation: Habitual and Novice Entrepreneurs” by Deniz Ucbasaran, Mike Wright, Paul Westhead and Lowell W. Busenitz. The University of Nottingham has been a source of considerable (and high quality) entrepreneurship research. The primary drivers behind this reputation are Mike Wright and Paul Westhead. Deniz Ucbasaran represents the next generation charged with the responsibility of carrying the torch – a task at which she appears highly capable. Working with Deniz, Mike and Paul is Lowell Busenitz. Lowell’s research on the decision making of entrepreneurs has been truly innovative, e.g. he was the first to investigate the heuristics of entrepreneurs. In this chapter, Deniz and her colleagues utilize a human capital perspective to highlight cognitive and behavioral differences between types of entrepreneurs – habitual and novice. Of particular interest is their exploration of two broad categories of cognition – heuristic-based thinking and systematic thinking. The ninth chapter is “Opportunity Development: A Socio-Cognitive Perspective” by Alice De Koning. We invited Alice to contribute a chapter to this volume because she has a way of thinking about entrepreneurship that is new and refreshing. In this chapter, Alice links an entrepreneur’s cognitive process and social context in an interdependent process model of opportunity development. The process model is developed based on insights generated through two phases of exploratory field research with mostly successful multiple entrepreneurs and provides an explanation for ways in which people effect entrepreneurs’ thinking process and opportunity development. Her process model provides both insight into opportunity development and a means of connecting the streams of research on opportunity recognition and firm emergence. The tenth chapter is “The Domain of Entrepreneurship Research: Some Suggestions” by Per Davidsson. In Volume 3 of this series (1997), S. Venkataraman contributed a chapter on behalf of the Journal of Business Venturing, in which he made his case for a distinctive domain of entrepreneurship. We wanted to continue this discussion and so asked Per Davidsson to offer his perspective. In our opinion, Per is one of the leaders of the community of entrepreneurship


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scholars and is an editor for Entrepreneurship Theory & Practice in whose behalf he contributed this article. Per provides a provocative chapter that is bound to (we hope) stimulate further discussion among entrepreneurship scholars. These papers go into production at a particularly auspicious time for cognitive researchers. The 2000–2010 decade has been tagged as “The Decade of Behvior” by a consortium of more than four dozen scientific organizations worldwide (URL: http://www.decadeofbehavior.org/), and cognitive researchers are contributing much of the leading work in this effort. A month before this volume went to press, one of the 2002 Nobel Prizes in Economics was awarded to Daniel Kahneman, whose work (originated with Amos Tversky) focused attention on new forms of cognitive heuristics, revitalizing the field of cognitive science as a whole. These heuristics even made their way into entrepreneurship, initially through conceptual models such as the psychosocial cognitive model of entrepreneurship (Katz, 1992), and by now have diffused through the field to the extent that six of the papers in this volume (Lichtenstein et al.; Markman et al.; McMullen & Shepherd; Mitchell; Sapienza et al.; Ucbasaran et al.) cite the key works of Kahneman or Kahneman and Tversky. With such a background and grounding, this volume reflects an effort to explore in depth some significant portion of the full range of cognitive theory applicable in entrepreneurial settings. The volume is intended not just to help define the major cognitive initiatives of the present, but to provide an early, detailed introduction to the next generation of research and conceptual issues that define the growing cognitive orientation in entrepreneurship research.

REFERENCES Bird, B. J. (1992). The operation of intentions in time: The emergence of new venture. Entrepreneurship Theory and Practice, 17(1), 11–20. Brockhaus, R. (1980). Risk taking propensity of entrepreneurs. Academy of Management Journal, 23(3), 509–520. Carter, N. M., Gartner, W. B., & Reynolds, P. D. (1996). Exploring start-up event sequences. Journal of Business Venturing, 11(3), 151–166. Chandler, G. N., & Hanks, S. H. (1994). Founder competence, the environment, and venture performance. Entrepreneurship: Theory & Practice, 18(3), 77–89. Cooper, A. C., Hornaday, J. A., & Vesper, K. H. (1997). The field of entrepreneurship over time. Frontiers of Entrepreneurship Research, 1997 Edition. Babson Park, MA: Babson College. [http://www.babson.edu/entrep/fer/papers97/cooper/coop1.htm] Gartner, W. B. (1985). A framework for describing and classifying the phenomenon of new venture creation. Academy of Management Review, 10(4), 696–706. Gartner, W. B. (1988). “Who is an entrepreneur?” is the wrong question. American Journal of Small Business, 12(4), 11–31.

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Gartner, W. B. (1989). Some suggestion for research on entrepreneurial traits and characteristics. Entrepreneurship Theory & Practice, 14(1), 27–37. Gartner, W. B., Barbara, J. B., & Starr, J. (1992). Acting as if: Differentiating entrepreneurial from organizational behavior. Entrepreneurship Theory & Practice, 16(3), 13–32. Gartner, W. B., & Gatewood, E. (1992). Thus the theory of description matters most. Entrepreneurship: Theory & Practice, 17(1), 5–10. Gartner, W. B., Shaver, K. G., Gatewood, E., & Katz, J. A. (1994). Finding the entrepreneur in entrepreneurship. Entrepreneurship: Theory & Practice, 18(3), 5–10. Gatewood, E. J., Shaver, K. G., & Gartner, W. B. (1995). A longitudinal study of cognitive factors influencing start-up behaviors and success at venture creation. Journal of Business Venturing, 10(5), 371–391. Gimeno, J., Folta, T. B., Cooper, A. C., & Woo, C. Y. (1997). Survival of the fittest? Entrepreneurial human capital and the persistence of underperforming firms. Administrative Science Quarterly, 42(4), 750–783. Hansen, E. L., & Allen, K. R. (1992). The creation corridor: Environmental load and pre-organization information-processing ability. Entrepreneurship Theory & Practice, 17(1), 57–66. Herron, L., Sapienza, H. J., & Smith-Cook, D. (1991). Entrepreneurship theory from an interdisciplinary perspective: Volume I. Entrepreneurship Theory & Practice, 16(2), 7–12. Katz, J. A. (1992). A psychosocial cognitive model of employment status choice. Entrepreneurship Theory & Practice, 17(1), 29–37. Katz, J. A. (1993). The dynamics of organizational emergence: A contemporary group formation perspective. Entrepreneurship: Theory & Practice, 17(2), 97–102. Katz, J., & Gartner, W. B. (1988). Properties of emerging organizations. Academy of Management Review, 13(3), 429–441. Kent, C. A., Sexton, D. L., & Vesper, K. H. (1982). Enclyclopedia of entrepreneurship. Englewood Cliffs, NJ: Prentice-Hall. Krueger, N., & Brazeal, D. (1994). Entrepreneurial potential and potential entrepreneurs. Entrepreneurship: Theory & Practice, 18(3), 91–104. McClelland, D. C. (1961). The achieving society. Princeton, NJ: Van Nostrand. McClelland, D. C., & Winter, D. G. (1969). Motivating economic achievement. New York: Free Press. Naffziger, D. W., Hornsby, J. S., & Kuratko, D. F. (1994). A proposed research model of entrepreneurial motivation. Entrepreneurship Theory and Practice, 18(3), 29–42. Reynolds, P. D. (2000). National panel study of U.S. business startups: Background and methodology. In: J. A. Katz (Ed.), Advances in Entrepreneurship, Firm Emergence and Growth Volume 4: Databases for the Study of Entrepreneurship. Greenwich, CT: JAI Press. Shapero, A. (1975). The displaced, uncomfortable entrepreneur. Psychology Today, 9(6), 83–133. Shaver, K. G., & Scott, L. R. (1991). Person, process, choice: The psychology of new venture creation. Entrepreneurship Theory & Practice, 16(2), 23–45.

ORGANIZATIONAL LEARNING BY NEW VENTURES: CONCEPTS, STRATEGIES, AND APPLICATIONS Benyamin Bergmann Lichtenstein, G. T. Lumpkin and Rodney C. Shrader INTRODUCTION Organizational learning continues to be an important issue for all types of firms. Managerial accounts of organizational learning are in high demand; for example, Senge’s The Fifth Discipline (Senge, 1990a) has sold over 500,000 copies in the U.S. Studies exploring the nature of knowledge creation, intellectual capital, and knowledge management have been on the rise, with recent papers being published for academics (McElroy, 2000; Nahapiet & Ghoshal, 1998; Nonaka, 1994), and practitioners (Brown & Duguid, 1998; Fryer, 1999). According to some experts, the ability to transform information into knowledge through organizational learning is a critical success factor for all businesses in the current knowledge-based economy (Davis & Botkin, 1994; Lei, Slocum & Pitts, 1999). The importance of organizational learning should be especially strong for new ventures. Young firms, it can be argued, have a lot to learn and their ability to do so quickly and accurately is vital to their survival. Research has shown that older organizations have higher survival rates than newer ones (Carroll, 1983), due to their ability to encode learning into reliable routines (Levinthal, 1991).

Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 11–36 Copyright © 2003 by Elsevier Science Ltd. All rights of reproduction in any form reserved ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06002-1

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Insofar as small entrepreneurial ventures rely on proactivity and entrepreneurial strategy making (Dess, Lumpkin & Covin, 1997) their success will also be dependent on these qualities of organizational learning. Thus, the greater a learning orientation in a new venture, the greater the likelihood of its long-term success. Surprisingly, very little research on organizational learning has been carried out in new ventures, nor have researchers examined whether new ventures might be able to learn easier and more effectively than large organizations. Most of the empirical studies of organizational learning that have been conducted have focused exclusively on major corporations (Bowen, Clark, Holloway & Wheelwright, 1994), or on computer simulations of large organizations (Cohen, March & Olsen, 1972; Herriott, Levinthal & March, 1985; Lounamaa & March, 1987). Although insightful, little effort has been made to apply what has been learned from these studies to new ventures. A close look at the writing on organizational learning reveals that new ventures might be much more likely to learn than large companies. Entrepreneurial firms have an urgent need to learn due to three factors that are unique to the new venture experience. First, entrepreneurial behavior is the heart of new venture creation, and their ability to learn new behavior – particularly the way they learn to employ social skills – can be the key to success and performance (Baron & Markman, 2000). Second, the cognitive biases and heuristics that guide the earliest stages of new venture development (Busenitz & Barney, 1997) usually need to be “updated” to fit the emergent situation, calling for learning at every turn. Third, learning is a key to the creative process; this is especially important for nascent firms engaging in the creative course of opportunity recognition (Hills, Shrader & Lumpkin, 1999). This paper endeavors to link the organizational learning literature to the conditions facing new ventures by addressing three conceptual themes: (1) Why might new ventures be more likely to engage in successful learning than older, larger organizations? (2) Which contexts and cognitive arenas might be most impacted by learning in entrepreneurial firms? and (3) What processes, tools, and techniques of organizational learning in large organizations might be most successful in new and small ventures? We begin with a review of the literature on organizational learning which we summarize into three categories – Behavioral learning, Cognitive learning, and Action learning. These three categories become the organizing principle around which we propose distinct answers to the questions posed just above. Our goal is to integrate literature and provide useful suggestions to new firm owners and managers interested in extending the learning in their firms and creating learning organizations.

Organizational Learning by New Ventures

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ORGANIZATIONAL LEARNING: A SUMMARY OF THEMES The processes that contribute to learning outcomes are complex and occur on multiple levels of analysis (Argyris & Schon, 1978; Kim, 1993; Weick & Roberts, 1993). Many frameworks have been used to describe the qualities and characteristics of organizational learning, and these perspectives rarely acknowledge each other. Following earlier work by Lundberg (1995) and others, we categorize the organizational learning literature into three streams of scholarship: Behavioral learning, Cognitive learning, and Action learning. Each of these will be described briefly in the three subsections that follow. Behavioral Learning Many of the classic ideas about organizational learning are based on the assumption that organizations are goal-oriented, routine-based systems that respond to experience by repeating behaviors that have been successful and avoiding those that are not (Lundberg, 1995). This perspective has two manifestations. The aim of the first is primarily to describe the acquisition, distribution, and storage of information and knowledge in a firm (Huber, 1991; Leavitt & March, 1988; Walsh & Ungson, 1991). A second approach focuses on the adaptive learning concept that trial-and-error learning leads to routines and processes which confer selective advantage to the firm (Herriott et al., 1985; Levinthal, 1991; Van de Ven & Polley, 1991). Because of the emphasis on learning from repeated behaviors, this perspective is often referred to as behavioral learning. Behavioral learning focuses on the “antecedents and changes in organizational structures, technologies, routines and systems as the organization responds to its own experience and that of other organizations” (Lundberg, 1995, p. 7). These theories argue that organizational learning is an adaptive process and thus is triggered only by performance gaps or other signals of poor market performance (Cyert & March, 1963). In a similar way, since trial-and-error learning generates routines that tend to make an organization stable, it is only possible to spark major organizational change through significant externally-generated structural events such as the hiring of a new CEO (Tushman & Romanelli, 1985) or the approach of an impending deadline (Gersick, 1988). As such, the learning that occurs from a behavioral approach is primarily incremental (Levinthal, 1991).

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Cognitive Learning More recently, a perspective has arisen that focuses on the cognitive content of organizational learning and how changes in individual’s cognitive maps are transferred such that the organization’s cognitive maps are also changed (Bartunek, 1984; Brown & Duguid, 1991; Kim, 1993; Nonaka, 1994; Weick & Roberts, 1993). Here the focus is on the content of learning rather than on its behavioral outcomes, on the processes that improve the dissemination of data throughout a firm, and the utilization of that data to improve performance (Fryer, 1999). In a general sense, by putting the right processes in place, a learning organization can transform data into information, and information into knowledge, which can then be leveraged to generate learning in an organizational setting (Davis & Botkin, 1994; Kim, 1993). Organizational learning in this sense includes the process of exploiting externally-generated knowledge (Cohen & Levinthal, 1990) or transforming internally-stored knowledge (Garud & Nayyar, 1994) to increase the strategic assets of the firm. Since the assets in question are knowledge or “thought process” assets, this perspective is referred to as cognitive learning. These approaches connect to the resource-based view of strategy (Barney, 1991, 2001) by arguing that the knowledge creation process itself creates unique competencies with which the firm can compete. “Knowledge assets underpin competencies . . .. The firm’s capacity to sense and seize opportunities, to reconfigure its knowledge assets, competencies, and complementary assets . . . all constitute its dynamic capabilities” (Teece, 1998). As such, organizational learning leads to an increase in the “organization’s capacity to take effective action” (Kim, 1993, p. 43) as well as to the “mobilization of tacit knowledge held by individuals [that can] provide the forum for a ‘spiral of knowledge’ creation” (Nonaka, 1994, p. 34). Such learning, in turn, leads to greater firm effectiveness (Edmonson & Moingeon, 1994).

Action Learning In contrast to the other two frameworks, action learning approaches focus on the actual practice of correcting misalignments between what one says and what one does, in order to produce more effective action in organizational settings (Argyris, 1990; Senge, Kleiner, Roberts, Ross & Smith, 1994; Torbert, 1991). Learning in this sense becomes an ongoing process, built through a commitment to improve oneself in the context of improving the organization (Schön, 1983; Torbert, 1973). With the support of similarly committed individuals, a community of learning practice can be generated that may significantly impact the quality of communication,


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innovation, and team performance in a firm (Senge et al., 1994). According to this approach learning happens in “real time” – i.e. concurrent with the ongoing activities in the firm – thus this perspective is referred to as action learning. Among the numerous insights that have arisen through the research-practice of action learning is the distinction between first-degree, incremental learning and second-degree, “double-loop” learning (Argyris & Schon, 1978; Bartunek, 1984; Bateson, 1972). In first-degree learning, incremental modifications are made to organizational behaviors or tactics to improve the efficiency of organizing. Second-degree learning, on the other hand, examines the framework within which those actions are being done, to continuously ask whether the organization is pursuing the right goals through the right strategies (Torbert, 1991). Asking this type of reflective question requires a willingness to uncover hidden assumptions and face uncomfortable feelings (Argyris, 1990). Developing this awareness is a key goal of action learning, for it allows individuals and organizations to break through defensive routines that keep people from producing their best work, which can impact all areas of organizational life (Argyris & Schon, 1978). To some degree, action learning involves components of the other two perspectives. In order to examine assumptions and decide whether to improve or totally alter one’s course of action, cognitive learning is necessary. Similarly, the process of change that results – whether it involves incremental enhancements to budding routines, or a shift in strategy or overall direction – necessitates behavioral change. Action learning thus integrates the other two modes, while adding its own unique ability to recognize a mis-match between words and actions, the fortitude to face this mis-match, and the skill to take corrective action in real time. These three categories – Behavioral learning, Cognitive learning, and Action learning – provide a framework for exploring why new ventures might be highly likely to engage in organizational learning.

NEW VENTURES: OPPORTUNITIES FOR ORGANIZATIONAL LEARNING New and small ventures provide an ideal context for organizational learning in several ways. First, just as humans learn at a much faster pace in their formative years than later in life, new ventures should learn faster than more established firms. Second, as shown above, much of the literature on organizational learning focuses on how firms reexamine or break out of established patterns and routines. Because of their newness, routines and norms that might prevent learning or that might have to be unlearned may not yet be fully formed in new ventures. Third, young firms tend to have organic rather than formal structures, allowing for

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greater ease of communication among organization members and easier responses to change, two prerequisites of organizational learning. In the three subsections that follow, we will examine how each approach to learning might be useful for new ventures and highlight some of the vital qualities that can improve entrepreneurial success. We recognize that new firms are heavily influenced by their founder(s), whose cognitive orientation and decision-making style play a large role in all aspects of the firm. Thus in the following analysis we take a developmental approach, examining these three types of learning from the perspective of the entrepreneur and his/her core team. Given that all organizational learning is initiated and carried out by individuals (Argyris & Schon, 1978), our intention is to draw out the individual implications of each of the three perspectives on learning, and how each perspective reveals the advantages new ventures have over larger organizations for Behavioral, Cognitive, and Action learning.

Behavioral Learning in New Ventures The behavioral learning approach has been the one most represented in the organizational learning literature. Further, behaviorism is one of the founding movements of psychology (Skinner, 1938) and is central to theories of organizational motivation (Babb & Kopp, 1978). Behavioral learning generally equates learning with the establishment of stable habits or organizational routines, based on the performance outcomes of previous actions (Nelson & Winter, 1982). However, since the initial years of an emerging enterprise are marked by a lack of formal systems, structures, and roles, there are few consistent elements that can serve as a basis for incremental improvement of routines (Churchill & Lewis, 1983). At the same time, these issues point to the unique advantages that new ventures have for learning. Due to the rapid pace of organizing in new ventures, they hold the potential for a tremendous amount of trial-and-error learning for key individuals and for the founding team. Similarly, the development of new systems and structure requires an ongoing stream of organizational experiments, which can generate even more learning through trial-and-error mechanisms (Aldrich, 1999). Rather than having to implement a new organizational process that would encourage such experiments as might be necessary in a large company, experimentation is often the norm for entrepreneurs, providing great access to behavioral learning in the early stages of start-up. Additionally, whereas large companies will have already encoded their learning in routines, the flexibility and constant ferment in new ventures allows the learning captured by individuals to be spread throughout the organization before it is locked into specific Standard Operating Procedures.


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Adaptive learning, in which organizational behaviors are modified based on environmental feedback (Levinthal, 1991), is likely to be much faster and more responsive in new ventures, whose survival is dependent on the founding team’s ability to recognize and act on environmental feedback. Accomplishing this requires embracing failures with successes, and applying the insights from these experiences to all aspects of their work. In fact, the very lack of financial and labor resources in new ventures becomes another advantage for learning. Rather than engaging in long-term planning, entrepreneurs are more likely to act on their feet, thus increasing the trial-and-error learning in their firms. In the process of experimentation, the knowledge that is gained can be captured in the intellectual capabilities of its key members. According to behavioral theorists, learning has to be stored and retrieved for it to be useful to an organization. This presents many problems in large companies, which are often slow in eliciting the knowledge of their members, codifying this knowledge in useful ways, then indexing it in order to be utilized by others when facing similar circumstances (Huber, 1991; Kim, 1993). However, a new venture potentially bypasses this whole issue, simply because in its early years the essential knowledge is held by the entrepreneur and a small team, and any major problems are necessarily known to virtually all the key members. Thus, much of the combined learning in the organization can be accessed simply through calling a company-wide meeting. Out of this spirit of camaraderie and a mutual drive for survival, new ventures can use all learning to their advantage – whether sharing about successes or drawing lessons from mistakes (Petzinger, 1999). New ventures have been shown to be more likely to share this information internally as a way to learn and improve (Fryer, 1999). In contrast, large companies often believe it prudent to keep their failures secret, because they don’t want customers, competitors, media and even their own employees to hear about failures (Argyris, 1990). This hesitation can be the result of the codification of routines and structures which not only encode previous learning, but also can decrease the firm’s flexibility and openness. It is this flexibility and openness in new ventures that make them especially good at organizational learning. As such, a trial-and-error-type behavioral approach to learning may be enhanced in new ventures.

Cognitive Learning in New Ventures Whereas behavioral learning emphasizes the codification of experience in habits and routines, cognitive learning focuses on one’s internal frameworks for knowing – what have been called “cognitive schema” – and in how those frameworks can be transferred to others and leveraged to improve personal and organizational

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action (Kim, 1993). In large companies this transfer of knowledge can be hampered by bureaucratic formalization and control, which is necessary to maintain integration across multiple business units. In contrast, the limited staffing in new ventures requires that people take on responsibilities outside their immediate skill set – everyone becomes a “jack of all trades” – often resulting in an atmosphere of continuous sharing and change (Petzinger, 1999). This atmosphere encourages rapid dissemination of information, as well as a constant sharing and interpreting of multiple meanings about salient organizational events. Through these mental processes, and the creative conflicts they can engender, new information and knowledge is created; this is the essence of cognitive learning (Nonaka, 1988, 1994). According to Nonaka’s research, the three qualities for enhancing each individuals’ ability to create information and knowledge – intention, autonomy, and fluctuation – are much more likely to exist in small and new firms than in large corporations. These qualities are best supported in an environment of “creative chaos, which triggers the process of organizational knowledge creation” (Nonaka, 1994, p. 28). [T]he more chaos or fluctuation an organization has inside its built-in structure, the more likely it is to have a lively information-creation activity. Chaos is used here interchangeably with such concepts as freedom, fluctuation, randomness, redundancy, ambiguity, and uncertainty. A lively activity is created since the positive role of fluctuation or chaos widens the spectrum of options and forces the organization to seek imagination and new points of view (Nonaka, 1988, pp. 60–61).

This happens naturally in new ventures because few systems and layers of bureaucracy block the natural tendency toward autonomy, creative conflict, and the possibility to take-in and leverage chance information. Looking at new venture creation from a projects perspective reveals similar qualities of cognitive learning (Bird, 1994; DeFillippi & Arthur, 1998). In certain development projects, for example, “People learned from previous projects, advanced their skills during the course of their project, and applied what they learned to renew the company’s capabilities” (Bowen et al., 1994, p. 110). The typical organizational design in new ventures supports this type of cognitive learning process that extends organizational knowledge. To the degree that these knowledge-generating capabilities can be captured and understood, this success can extend an entrepreneur’s human capital, becoming a strategic capability for the firm as a whole (Brush, Green & Hart, 2001). This capability relates to insights from the resource-based view in strategy: Dynamic capabilities are most likely to be resident in firms that are highly entrepreneurial, with flat hierarchies, a clear vision, high-powered incentives, and high autonomy (to insure responsiveness.) The firm must be able to effectively navigate quick turns [and] must constantly transform and re-transform (Teece, 1998, p. 59).


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Developing these capabilities can rarely be done in a vacuum. Research on “communities of practice” reveals that the combined experiences found in larger companies results in a repository of accumulated wisdom (Brown & Duguid, 1991). What the young firm lacks in accumulated knowledge, however, it gains in high levels of collaboration and relative coherence. Further, entrepreneurs are often embedded in rich information networks, and their organizations often emerge through those networks. As such, cognitive learning done by founding individuals generally occurs across organizational boundaries, in a supportive “ecology of knowledge” (Brown & Duguid, 1998, p. 91). The entrepreneurial need to form multiple strategic alliances across all dimensions of the business means that a new venture may be better able to leverage these knowledge ecologies than a large organization. Similarly, new venture creation often involves absorbing knowledge and technology from previous experience and contacts, and integrating it in unique ways within the firm. The emphasis on survival in new ventures optimally generates an inherent openness to new ideas, in contrast to the “Not Invented Here” syndrome that can plague older firms. Thus, entrepreneurial ventures may have a higher absorptive capacity than larger firms, even though their levels of R&D spending may be proportionately lower (Cohen & Levinthal, 1990). With innovation playing such an important role among new entrants (Schumpeter, 1934/1959), the capacity for utilizing knowledge and technologies in new ways is quite high for new and small ventures. By contrast, as firms mature and grow, the economic pressures to internalize many functions often inhibits and slows their ability to learn. In all these ways, cognitive learning is especially important for new ventures.

Action Learning in New Ventures Perhaps the greatest potential for learning in small and new organizations utilizes the action learning framework. These tools for revealing individual’s underlying assumptions and reasons for acting can be used to support direct, honest communication and to mitigate misalignments between “espoused theory” and “theory in use” of leaders and all team members (Argyris, Putnam & Smith, 1985). In general, the creative ferment and drive for survival in new firms makes it a necessity for entrepreneurs and all members of their founding teams to engage in this kind of communication. This engagement can give a competitive advantage to young and small companies. One of the insights from action learning researchers is that organizational learning is facilitated when it is nurtured by a group of committed individuals (Isaacs, 1993; Senge, 1990a, b). The core qualities of learning in a team are

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easier to create in a small company that is literally a team, rather than in a large corporation which is made up of multiple groups in multiple divisions. For example, a new venture is constantly in the process of building shared meanings, and the work of creating the company is interlocked with uncovering assumptions and finding ways to resolve inevitable impasses (Gartner, 1993). Entrepreneurs in new ventures have a clear advantage over executives in more established firms, due to their smaller size and commitment to do whatever it takes to survive. Further, one of the hallmarks of action learning is the ability to engage in double-loop learning which can modify the underlying values and standards in an organization (Argyris & Schon, 1978; Torbert, 1991). While extremely difficult in any context, this type of transformative thinking/action may be easier in a young firm, whose founding team is less hampered by locked-in assumptions, and may still be identifying its primary goals (Sarasvathy, 2001). Whereas attempting this frame-breaking type of learning in large corporations is fraught with problems and is often unsuccessful (Kotter, 1995), renewal and transformation may be a natural developmental process in new ventures (Shuman, 1998; Simon, Houghton & Lumpkin, 2001). One of the greatest threats to the atmosphere of open-mindedness and honest interactions is the natural tendency that individuals have to avoid potentially embarrassing situations and threats (Argyris, 1990). In the face of the perceived political ramifications of those situations, executives in most large organizations develop organizational patterns that use defensive routines like “skilled incompetence” and “fancy footwork” in order to mitigate threatening situations (Argyris, 1990, p. 63). These defensive routines get stronger as they are reinforced over time, as individuals responsible increasingly believe that it is unrealistic or even dangerous to call attention to long-standing assumptions, let alone to work toward changing them. In the end, organizational rigidity and stickiness sets in, decreasing innovation, flexibility, and proactive behaviors. However, these tendencies are much less likely to take hold in new and small organizations, for many reasons. First, in the same way that new ventures have fewer rules and routines, individuals in new ventures are less likely to have set routines and habitual behaviors in their firm compared to members of larger corporations. Where defensive habits start to emerge in large firms, they can be identified and discussed much more rapidly in small ventures, where everyone is essentially in the same place and discomforts can be worked out in real time. Further, with the firm’s survival at stake there is little to lose in being honest in new ventures. Thus an optimistic, open atmosphere can be common in small and new companies, generating a group norm of frankness and clear communication. If the new venture starts with this type of open-minded attitude, defensive individuals may end up moving out of the organization. Moreover, persons naturally drawn to


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taking a protective stance are not likely to join new ventures in the first place, for their fear of experimentation prohibits them from working in an entrepreneurial atmosphere.

THREE CONTEXTS OF ENTREPRENEURIAL LEARNING As suggested above, new ventures learn in several different ways. The type of learning that may occur is likely to be a function of the context that an entrepreneurial venture is facing. Three particularly contexts that require a high degree of entrepreneurial learning are a positive use of behavioral skills, actively working through cognitive biases, and engaging in the creative process of opportunity recognition. These three contexts requiring entrepreneurial learning are organized below according to the three types of learning – Behavioral, Cognitive, and Action learning. Thus, the three subsections below briefly describe these contexts and address the different types of learning that might be beneficial to new ventures within them.

Developing Social Skills Since research in social and cognitive psychology indicates that social skills facilitate the attainment of important outcomes (Meeus, Engles & Dekovic, 2002), it is likely that entrepreneurs with strong social skills are more likely to be successful (Baron & Markman, 2000). Social skills consist of the ability to persuade, influence, and favorably impress others, perceptiveness in understanding others’ motives and concerns, and adaptability to different situations and people (Weber & Harvey, 1994). Such behaviors enhance the interactions that take place between entrepreneurs and the various constituencies they deal with, both within and outside of a new venture. Prior research suggests that social skills can contribute to entrepreneurial success by improving an entrepreneur’s ability to form effective founding teams, attract quality employees, and obtain funding (Baron & Markman, 2000, 2003). In addition, strong social skills contribute to the formation of social capital (Baron & Markman, 2000). That is, the ability to persuade, impress, and empathize with others generally opens doors for entrepreneurs by making them more confident and adaptable. Entrepreneurs without such skills often find it more difficult to make contacts, build a reputation, and capitalize on their social capital. Just as skills represent behaviors that entrepreneurs can use to advance

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their ventures, social capital is a valuable resource that can be leveraged to obtain more resources or acquire new knowledge (Nahapiet & Ghoshal, 1998). Thus, social capital can be an important source of competitive advantage. The social skills that are needed to generate social capital can be learned. The type of learning that is most likely to be involved in skills development is behavioral learning. As the descriptions above indicate, behavioral learning has two manifestations. The first involves the accumulation of knowledge. Once a particular social behavior is observed and deemed valuable, an entrepreneur can learn that skill. Training in social skills is readily available and has proven effective in modifying entrepreneurs’ social behaviors (Baron & Markman, 2003). A second aspect of behavioral learning involves trial-and-error learning. Founders seeking new venture funding often learn quickly that social ineptness can be a serious impediment to serious fund raising. By learning from their mistakes after being turned down a few times, entrepreneurs often find that it is social capital and social skills, as much as the quality of their business plan, that improves their chances of obtaining funding. Chris Barrett, founder of Metropolitan Talent Agency, exemplifies the power of gaining social skills that enact behavioral learning (Petzinger, 1999, pp. 212–215). The son of a shipyard owner, Barrett left that blue-collar world, enrolling instead in the High School of Music and Art in Queens, NY. After a number of secondary acting roles in secondary shows, he decided to learn the business of show business from the bottom up, becoming a junior talent agent in an established agency. Learning about behavioral motivation through the bonus system in the agency he worked for led him to found his own small firm based on different principles. Counter to industry standards, Metro Talent Agency developed a more creative bonus structure based on “bonus sharing” that compensated up to three different agents in a deal: the agent who originally signed the client, the one who was representing the client, and the one who actually landed the deal. He gave each agent veto power over any new client who wanted to be represented by the firm, and developed a data base that allowed every agent to access any information entered by any other agent in the firm – practices that were unheard of in the industry. This simple behavioral change resulted in an atmosphere of camaraderie and mutual gain; not surprisingly the agency expanded dramatically and successfully competes with the top agencies in the industry.

Overcoming Biases and Heuristics Entrepreneurs often launch businesses that initially falter because of “misperceiving” venture opportunities (Simon, Houghton & Lumpkin, 2001). However,


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ventures that get off to a shaky start do not necessarily fail. Examples of such early missteps are evident even among firms that later became highly successful (Collins & Porras, 1997). Research indicates that the cognitive processes and heuristic-based decision-making styles of entrepreneurs may lead them to make choices that later could be labeled mistakes (Busenitz & Barney, 1997). In this context, “heuristics” refers to non-rational decision rules or cognitive mechanisms that simplify an entrepreneur’s decision-making process. These simplifying approaches enable entrepreneurs to seize opportunities by providing decision-making short cuts in complex decision settings (Tversky & Kahneman, 1974). Thus, a venture launch that may have been avoided if more rational decision rules were used, propels the founding entrepreneur into a “corridor” of opportunities to establish the business on a more solid foundation (Ronstadt, 1988). The ability to make these types of start-up decisions may actually confer an advantage on entrepreneurs by making them able to undertake ventures in ways that other potential founders would be unwilling to attempt (Alvarez & Busenitz, 2001). On the one hand, opportunities that can easily be identified and analyzed are unlikely to confer any distinct advantage because they may be less rare and more imitable. On the other hand, unique insights or unanalyzable situations may push entrepreneurs to be more inventive and risk taking, yet simultaneously shield their creative opportunity from detection by competitors (Daft & Weick, 1984; Mosakowski, 1998). Even though many benefits may accrue to entrepreneurs who rely on biases and heuristics to make decisions about launching a start-up, ventures that are based on faulty assumptions must eventually be adjusted to fit environmental and market realities. The insights and information required to make such adjustments can be learned. Cognitive learning is the type of learning that is most likely to be involved in reassessing biases and heuristics. Cognitive learning occurs when there is a shift in the mental map that changes the way a problem or opportunity is perceived; no longer can the situation be viewed in the “biased” way it was seen before (Kim, 1993). In the case of a new venture that is based on a business model that has proven to be unworkable, the entrepreneurs’ vision or “theory of the business” (Drucker, 1994) must be altered for the venture to survive. This approach – wherein a venture is launched based on one set of assumptions and changes as new information alters the assumptions – may seem chaotic and uncertain. But it can also lead to the rapid creation of new knowledge and contribute to an organizational capacity to learn and act (Kim, 1993; Nonaka, 1994). For this to happen, founders and their young firms must foster collaboration and creativity as well as be flexible and willing to change (Garud & Nayyar, 1994; Nonaka, 1988).

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The e-commerce venture Autobytel provides an interesting example of this type of learning. Autobytel enjoyed early success by being one of the first car purchasing web sites (and the very first dot-com ever to advertise during the Super Bowl). Autobytel’s original business model involved providing an online referral service that linked customers (who use the service for free) to auto dealers (that paid $5,000 to sign up and average monthly fees of $900). But imitators soon followed, including the automakers themselves, and Autobytel began rapidly losing dealers. “The rules have changed,” said CEO Mark Lorimer, and the company had to find a new way to make money. It did so by reevaluating its competition and reframing the business it was in: specifically, Autobytel used its proprietary software to build a comparison shopping site for GM, thus changing from competing against the automakers to partnering with them. This shift in approach was required to survive, according to David E. Cole, an auto researcher at the Environmental Research Institute of Michigan: “Autobytel has good technology . . .. But it has to find a way to fit in” (Weintraub, 2001). By being willing and able to let go of its biases and alter its perspective, Autobytel found a way to effectively leverage its resources.

Successfully Recognizing Opportunities Opportunity recognition is a critical element of successful entrepreneurship (Venkataraman, 1997) that may be enhanced by effective learning. Prior theory and research suggests that opportunity recognition is a staged process involving multiple steps from the time an idea is generated to the point when a new venture is launched (e.g. Long & McMullan, 1984). Recently Hills, Shrader and Lumpkin (1999) linked the notion of a multi-staged process to a model of creativity that is often used in the study of individual creativity (Csikszentmihalyi, 1996; Wallas, 1926). Consistent with other scholars, their approach identified two broad phases of opportunity recognition. The first phase is a Discovery phase in which new venture insights are acquired either through a serendipitous event (Gaglio & Taub, 1992) or a deliberate search process (Bhave, 1994). The second is a Formation phase which refers to the selection, evaluation, and refinement processes involved in recognizing opportunities (deKoning, 1999; Timmons, 1994). A key feature of a creativity-based model of opportunity recognition is its recursive nature. Opportunity recognition is not limited to a singular “Aha” experience but it is a process in which insights are contemplated, new information is considered and knowledge is created. That is, the concept for a business, once discovered, must be formed into an opportunity through numerous iterations of evaluation, formation, and the discovery of new insights. This process – which


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involves adaptation and change and the conversion of information into knowledge – is a type of action learning. Because opportunity recognition occurs in the earliest stages of an entrepreneurial process, before routines are established or a culture has set in, it is likely to involve a highly intense level of learning. It can be argued that, until a viable opportunity is recognized, there is little else for the venture to learn. Therefore nearly all early entrepreneurial inquiry and creative activity is focused on learning the parameters and potential of an opportunity. One can learn how to create the conditions for this degree of inquiry and creative focus. Action learning describes the process by which new information is interpreted and assumptions are challenged continually, in a “double-looped” or multi-looped process. Action learning applies to both phases of opportunity recognition because specific insights and assumptions generated in both the Discovery phase and the Formation phase are recursively acted on and realigned. To do this effectively, entrepreneurs must question their assumptions and uncover hidden motives that may obscure how viable a given opportunity may be. Double-looped learning (Argyris & Schon, 1978; Torbert, 1991), though extremely difficult in any context, is easier in a newly forming firm that is not yet locked-in to a set of assumptions. This level of learning applied throughout the opportunity recognition process can be the impetus for an insight to be successfully identified and implemented. An example of action learning in the opportunity recognition process occurred at Stacy’s Pita Chip Co. In 1996, Stacy and Mark Andrus were operating a successful pita-wrap business in Boston that they wanted to grow. But customers kept asking for the baked pita chips they made every night from leftover pita bread and handed out free to customers. Although they loved the pita wrap business and it generated long lines of customers, people just kept asking for the low-fat pita chips. So when analyzing how to grow the business they were faced with a dilemma: chips or wraps. By examining industry trends they were able to re-think their initial assumptions, and decided that chips offered the stronger opportunity. “We thought we could get bigger faster with the chips,” said Stacy. They made a deal with a local distributor and, even though they had no production and little business experience, they launched a new business. Their willingness to examine assumptions and their ability to use accumulated knowledge in new ways helped them grow their pita chip revenues to $1.3 million in 2000 (Stuart, 2001). These examples of behavioral skills development, overcoming cognitive biases and heuristics, and successful opportunity recognition provide useful indicators of how the three different types of learning can manifest and support new ventures. In the next section, we will provide some general guidelines for stimulating learning by new ventures.

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TACTICS FOR ENHANCING ENTREPRENEURIAL LEARNING For young firms, the challenge is how to become learning organizations. New ventures that survive and grow will themselves eventually age and become more complex and routinized. It is critically important, therefore, for firms that want to be strong learners to take steps during their formative years to make learning a part of their organizational processes. New ventures are especially sensitive to founding conditions (Bygrave, 1989). Similar to the principle that strategies formed in the early years tend to guide firms for many years to come (Brush et al., 2001), young firms can design learning practices into the organization in order to establish learning as valued principle for firm growth and success. Therefore, in this section, we identify five learning practice that new ventures might engage in to create or enhance the learning environment of a young firm.

Create a Learning Atmosphere In a new venture, the job of creating a learning environment should be a key role of the firm’s founder(s) (Senge, 1990b). Usually the first to articulate a firm’s vision, founders and new venture leaders must then set forth a mission, interpret the environment in terms of the vision and mission, and make sense of events and circumstances that a new venture faces (Smircich & Stubbart, 1985). Once these aspects are articulated and set forth, the crucial distinction between regular firms and learning organizations is the latter’s ability to continuously align the vision and mission to organizational processes and culture (Collins & Porras, 1997). Alignment in this sense means continuously asking whether the tactics and operations in the new venture are appropriate given its strategy and purpose, and at a deeper level, whether the company’s strategy and purpose is on target (Torbert, 1991). Small firms are especially good at these alignment processes. One approach for generating a learning atmosphere in new ventures relies on creating “enabling conditions that promote a more favorable climate for effective knowledge creation” (Nonaka, 1994, pp. 27–29). One condition is “creative chaos,” which can be generated by emphasizing the critical nature of organizing in the formative stages of new ventures. By evoking a “sense of crisis” in the company, members focus attention on forming and solving new problems, thus engaging in cognitive learning. If supported by their leader’s use of action learning, employees will develop a strong sense of trust and willingness to engage in open, committed communication (Smith & Comer, 1994). Another


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condition is the creation of redundancy through cognitive learning, where newly created knowledge is rapidly disseminated throughout the firm, available to be used by others. Through a combination of new technology and close proximity, new ventures can consciously develop the processes for sharing knowledge that can grow with the company (Fryer, 1999; Petzinger, 1999). Systematic Debriefings The work of young firms is often organized around specific projects (Bowen et al., 1994). Projects provide a focus for sets of activities with a specific aim. Because they represent narrowly defined “pockets” of activity, they can be examined in terms of what was learned by the organization and organization members. By initiating a practice of systematically debriefing project processes and outcomes, young firms can create a learning environment through behavioral learning from experience, and cognitive learning that integrates member’s mental models (Ross et al., 1994). Although time consuming, project debriefing can significantly enhance performance on subsequent projects. Such a program, however, may be quite challenging to implement in young firms whose top managers are often hard-working multi-taskers. But the payoff in terms of both short and long term performance seems to support building this learning technique into the organization culture from the beginning. Shared Learning Under the guidance of Chief Learning Officer (CLO) Steve Kerr, General Electric has developed a practice of proactive learning among top managers. Managers of GE divisions are strongly encouraged and evaluated on their ability to find out about and apply best practices learned from other divisions. A similar sharing could also be applied to young firms. More broad than systematic debriefing with its project focus, shared learning practices focus on the learning and insights of a firm’s top managers and other “thought leaders.” In the GE model, managers have an annual meeting where they can “brag” about what they learned and ideas they “stole.” This, according to Kerr, is valued more highly than ideas developed within the division. Similarly, small firms can activate behavioral learning by publishing internal brag sheets or engaging cognitive learning by giving periodic “Who learned the most” awards. In very small firms (less than 10 employees), every member of the organization could participate in such learning competitions, perhaps gathering monthly for a Learning Lunch, or developing simple data bases of lessons learned and insights gained (Fryer, 1999).

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Avoid Heedless Interactions A complementary approach to organizational learning is “heedful performance,” which contrasts with routine-based habitual action (Weick & Roberts, 1993). In the process of interrelating in a heedful way, mutually shared fields of understanding are constructed; this in turn can generate a collective mind. In new ventures, this collective mind is not a luxury but is the essence of successful organizational action: The more heed reflected in a pattern of interrelations, the more developed the collective mind and the greater the capability to comprehend unexpected events that evolve rapidly in unexpected ways, [enabling the organization] to meet situational demands (Weick & Roberts, 1993, p. 366).

The cultivation of heedful behavior, especially in an atmosphere that supports cognitive learning and knowledge-creation can be crucial to successful learning. In contrast, key members of many organizations often exchange information and stories that are about the company but that do not aid the organization as much as potentially tear it down. Research by Weick and Roberts found that this kind of interaction can be quite destructive. As noted above, they label such interactions as “heedless.” They recommend instead “heedful” interactions, that is, processes that build up the organization by contributing to a collective mind. Moreover, utilizing the action learning format, individuals can be encouraged to notice when heedless relating is occurring in real time, and bring it up in a reflective way while it is actually happening. Doing so can not only avoid problems before they grow, but these types of interactions also build a level of trust and mutuality that can generate high integrity and strong success in new ventures.

Question Assumptions One of the aims of creating a learning environment is to generate conditions in which current practices and even their underlying assumptions can be challenged. Real organizational learning is enabled by fostering an action learning environment in which “double-loop learning” is supported at both the individual and organizational level. That is, individuals can question how their personal notions and beliefs are contributing to or detracting from firm effectiveness, and the firm’s values and mission are open for question as well. Both types of questioning are aimed at the same basic issue: “Am I, or are we, on the right track?” Developing this atmosphere begins by re-framing errors as guideposts of learning, and consciously encouraging the making of errors as evidence of success (Argyris et al., 1985). Then, individuals in new ventures can create a


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reflective atmosphere of action learning through several strategies that elicit first-order and second-order learning. First-order learning can be supported by: (a) making reasoning public, such that one’s assumptions and inferences are brought to the surface and tested; (b) initiating experiments and lines of inquiry, through which new alternatives are offered that expand the choices for dialogue and interaction; and (c) publicly reflecting on one’s personal reactions to others, where a genuine reflection of one’s behavior becomes an opening for individual and group learning (Argyris et al., 1985, pp. 297–302). Second-order learning is extended by: (a) publicly identifying and inquiring into dilemmas and apparent inconsistencies; (b) reflecting on actions and redesigning them; and (c) publicly examining one’s own and others’ responsibility for actions and outcomes (Argyris et al., 1985, pp. 312–315). Each of these approaches is ideally suited to new and small companies.

TACTICS FOR STUDYING ENTREPRENEURIAL LEARNING One basic premise of this paper is that learning in its various forms is more likely to occur in new ventures than in larger, older organizations. Within this basic research question can be found two associated questions: First, what exactly are the qualities and characteristics that make learning happen in small and new organizations – what does learning look like in entrepreneurial ventures? Second, what does “organizational learning” mean in nascent firms, i.e. before there is an organization per se? We will briefly highlight the potential of these questions to provide contexts for research in entrepreneurial learning.

Comparing Learning in New Ventures versus Large Organizations Much of our analysis of Behavioral, Cognitive, and Action learning revolved around how learning in each of these modes can be accentuated by new and small organizations in comparison to larger firms. Clearly, any of our assertions provide valid empirical questions for entrepreneurial researchers. For example, does trial-and-error learning happen more readily in organizations with greater structural flexibility, i.e. is there a positive correlation between minimal or organic structures and adaptive learning? Similarly, does knowledge retrieval happen more readily within entrepreneurial founding teams than through formal knowledge management systems in larger organizations? Likewise, do entrepreneurial firms exhibit cognitive learning more than established firms, through, for example,

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greater sharing of information, a greater breadth of strategic interpretations, or a greater concentration of diverse skills amongst founding teams? Is there evidence that small, new ventures learn from their mistakes more easily or more rapidly than large companies? Do small and new firms exhibit greater learning capabilities through collaboration, coherence and communities of practice than their larger competitors? Numerous additional new venture versus established company questions might arise to test implications of different approaches to learning. Three recent theoretical frameworks can be applied to this general research question. Perhaps the most likely is the role of effectuation (Sarasvathy, 2001) in learning, since many of the qualities of effectual thinking are directly implicated in behavioral, cognitive, and action learning. Another approach is Zahra and George’s (2002) recent re-conceptualization of Absorptive Capacity [ACAP], which focuses on the impact of acquisition, assimilation, transformation, and exploitation in the potential and realization of ACAP. The effect of these processes and their antecedents and moderators could be compared in small and new entrepreneurial ventures versus large corporate settings. Third, the recent Organization Science special issue on Knowledge, Knowing, and Organizations, presents multiple models and studies of knowledge creation, several of which could be expanded to study whether these types of cognitive learning occur more readily in small versus large corporate settings. Finally, many of the studies referred to already could be used in the same way.

Examining Learning Processes in Entrepreneurial Firms Whether or not young entrepreneurial firms do in fact learn more readily, better or faster than large organizations, a separate type of research question would explore the nature and practice of learning in entrepreneurial contexts. One set of questions involves the qualities, characteristics, and processes associated with learning in new ventures, particularly those that distinguish entrepreneurial ventures which do learn from those that do not, and to what degree that learning results in some measure of success or benefits in performance. One aspect of this question is an examination of changes in learning or learning style as a new venture grows in size and age, essentially correlating learning with some classic measures of structure, leadership, strategic development, and so on. A different set of questions are more process oriented, revolving around how learning happens in new ventures. What are the various forms or modes of learning in these contexts? What are the micro-processes that are associated with learning in any of its forms? How do these processes correlate with other known characteristics of organizing in new ventures? Very in-depth data would be required to uncover


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mental models, shared assumptions, and cognitive processes associated with learning, and some rigorous theoretical work to define what are researchable outcomes of learning in this context. Finally, are the qualities of action learning – which are fairly well understood in large companies – similar or different in new ventures, and within founding teams? This set of questions would explore the antecedents and precedents for avoiding the negative patterns and habitual routines associated with defensive learning mechanisms, as well as how an open, frank, clear communication style can be produced amongst entrepreneurial teams, and how that might change over time. In this vein, an action-research model might be appropriate, linking executive coaching and training to specific learning skills, such as minimizing gaps between espoused theory and theory-in-use, and how systematic debriefings can increase the capacity and usefulness of learning in entrepreneurial ventures.

The Levels-of-Analysis Issue: Learning in Nascent Firms A close reading of our essay reveals a thorny theoretical challenge in distinguishing learning in nascent firms from learning in established organizations. Is there “organizational learning” before there is a formal organization? More to the point, how do the processes we’ve identified as learning change as the pre-organization becomes a more established firm? Does the nature of learning differ when a single entrepreneur is enacting an opportunity versus when he/she is leading a small group of individuals in a “firm?” What are the key issues involved in transferring learning from the head of a founder to the actions of her/his founding team, and then again into the values and design of the organization that can fulfill the original conception and future ones as well? Three models may be helpful in pursuing this set of questions. First, Hills, Shrader and Lumpkin’s (1999) model of opportunity recognition as creativity envisions an iterative process leading from initial discovery to venture formation. This suggests that as the recognition process moves forward, the phenomenon shifts from being primarily individual-level to firm-level. A longitudinal qualitative research effort might begin to uncover how an entrepreneur’s behaviors and perceptions change over time as their business idea develops into a plan and potentially into a full-fledged organization. Second, Nonaka’s (1994) KnowledgeCreation spiral provides an iterative four-stage model for how learning moves from the individual-level to the group level, to an organization. This model, adapted to an entrepreneurial context, could provide insight into how a nascent firm turns into a learning organization. Finally, Kim (1993) makes a useful distinction between individual’s cognitive maps and organization’s shared schema. A close

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examination of how an entrepreneur shares her vision, and how that results in a collective culture, could offer many insights into the origin of organizational behavior, as well as an approach to learning that could be adapted to many different areas.

CONCLUSION We started this paper by asking: (1) Why might new ventures be more likely to engage in successful learning than older, larger organizations? (2) Which contexts and cognitive arenas might be most impacted by learning in entrepreneurial firms? and (3) What processes, tools, and techniques of organizational learning in large organizations might be most successful in new and small ventures? After reviewing organizational learning literature and categorizing it into three areas – Behavioral, Cognitive, and Action learning – we suggested many ways in which new ventures could be more successful at learning than larger and older organizations. We then identified three entrepreneurial contexts where learning might be particularly important – social skills, cognitive biases, and opportunity recognition – and we linked each context to one of the three areas of learning. Finally, we identified five specific tools that can be used to expand entrepreneurial learning and three issues for future new venture learning research. There are some limitations to our approach. First, a consistent typology of organizational learning has not appeared in the literature; ours is but one of many approaches. Further insights and implications might be drawn from considering other aspects of organizational learning. Second, one of the challenges for applying organizational learning to new ventures is in reconciling levels of analysis between the individual entrepreneurs who start firms, and the organization-level context of the learning literature. We acknowledge that the way entrepreneurs learn and the way new ventures learn may be different, and this is an important topic for future research. Overall our research suggests that new ventures offer a fertile ground for the best in organization learning to take root and grow. Chances for both short term survival and long term success, we believe, will be enhanced as new ventures adopt organizational learning practices. This paper has identified several approaches to organizational learning and demonstrated how they might benefit young and small firms. We hope that this orientation will be beneficial for entrepreneurs and other key members of new firms, scholars and those who want to understand how new ventures develop and grow, and our many colleagues who are striving to support the creation and fulfillment of entrepreneurial ventures.


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ENTREPRENEURIAL FIT: THE ROLE OF COGNITIVE MISFIT Keith H. Brigham and Julio O. De Castro INTRODUCTION The concept of fit is central to theories in both the fields of strategic management and organizational behavior. It is our contention that many key questions in the field of entrepreneurship might also be successfully addressed through a fit approach. For instance, why do entrepreneurs often make poor managers? And why must founders often be replaced by professional managers as their firms grow? The idea of misfit is implicit in both of these questions. A fit perspective may also be beneficial in better understanding specific entrepreneurial behaviors. For example, why does one entrepreneur start and grow multiple businesses over his or her career (serial) while another might be content with starting only one business (novice)? or Why does one entrepreneur continually strive to grow his or her firm while another is content to arrest development (lifestyle) at a certain level? All of these questions, and obviously many more, can be viewed and examined as questions of fit. In this chapter, we present research that employs a fit approach in the study of entrepreneurs. More specifically, we introduce the construct of cognitive misfit (Chan, 1996) to the field of entrepreneurship within a Person-Organization fit (P-O fit) framework. Based on data from 159 entrepreneurs and their firms, and using hierarchical regression analysis, we group types of entrepreneurs according to their cognitive style and test for misfit when interacting with organizational structure. Finally, we demonstrate significant relationships between cognitive fit/misfit and the outcomes of burnout, satisfaction, and intentions to exit the firm. The disordinal Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 37–71 © 2003 Published by Elsevier Science Ltd. ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06003-3

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(crossed) nature of these interactions suggests the areas where different types of entrepreneurs are more likely to experience negative outcomes, given the degree of structure in the firm. It is our contention that this research represents an important step by providing researchers with a means of placing the individual entrepreneur back into the entrepreneurship equation without the pitfalls and the limitations associated with many of the past psychological (trait) studies.

THE CONCEPT OF FIT The concept of fit has a great deal of intuitive appeal. However, while the theoretical basis is easy to grasp, measuring fit is often a difficult task. In this section, we will discuss how the concept of fit has been developed in different management areas. In the field of strategic management, the concept of fit plays a central role (Summer et al., 1990). The examination of fit between the firm and its environment, strategy, structure, processes, and resources and capabilities (e.g. Amit & Schoemaker, 1993; Chandler, 1962; Lawrence & Lorsch, 1967; Miles & Snow, 1984) has been an important endeavor in the field. In strategy, the levels of analysis of fit have usually consisted of combinations of firm or organizational level variables and environmental variables. The working assumption in these studies is that greater degrees of fit or congruence between these variables will be associated with greater firm performance. While the results of these studies have not always been consistent, there is general empirical support for the positive relationship between fit and firm performance. However, in the field of organizational behavior, most fit research incorporates individual level (person) variables. These person variables are then matched with some element of the individual’s work environment. The research falling under the overall domain of person-environment fit can be divided into four main categories. These include Person-Vocation (P-V) fit, Person-Organization (P-O) fit, PersonGroup (P-G) fit, and Person-Job (P-J) fit (Kristoff, 1996). Of these, the broadest level of the work environment with which a person may fit is at the vocational level (P-V fit), whereas a much more narrow focus looks at fit between the employee and job tasks (P-J fit). In this chapter, we are particularly interested in examining fit between individual entrepreneurs and their firms. The approach we will present falls under the P-O fit category. Recently, there has been a surge of interest in the study of P-O fit in the selection and organizational research fields. This is based largely on the recent ability of these types of studies to demonstrate empirical relationships between particular aspects of P-O fit and many relevant outcomes. Exploring the interaction between certain characteristics of the individual and the organizational environment is

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central to the study of Person-Organization fit. Basically, the P-O fit literature suggests that P-O fit occurs when there is congruence between certain attributes of the person and those of the organization or the work context (Chan, 1996). Conversely, a state of misfit exists when attributes of the person and those of the organization or work context are out of alignment. Personal attributes can include personality traits, values, cognition, beliefs, interests, and individual preferences. Organizational or work context attributes can include the climate, culture, norms, values, structure, strategic needs, and other expectations or demands in the work environment (Bowen, Ledford & Nathan, 1991; Bretz, Ash & Dreher, 1989; Chan, 1996; O’Reilly, Chatman & Caldwell, 1991; Rynes & Gerhart, 1990). In general, strong empirical support exists for the positive relationship between different facets of P-O fit and individual work attitudes. Greater degrees of fit have consistently been linked to greater individual satisfaction and organizational commitment. There is also a clear relationship between P-O fit and the outcomes of intentions to exit and turnover. Employees with low levels of fit with the organization will express higher intentions to exit or quit. Furthermore, several studies have demonstrated that these intentions are often realized (e.g. O’Reilly et al., 1991). Longitudinal studies have looked at both intentions to exit and actual turnover, and concluded that fit is a valid predictor of both intentions to exit and actual turnover. Prior research in this area also shows relationships between stress and P-O fit. This relationship is negative in nature with lower levels of fit being associated with higher levels of stress. Finally, there is also empirical evidence suggesting the positive relationship between P-O fit and individual measures of work performance. As we have discussed, most of the P-O fit literature has focused on individual outcomes. While the relationship between higher levels of fit and individual level outcomes appears clear and direct, the relationship between higher levels of fit and organizational level outcomes is less certain. While it might seem that an organization with high levels of internal P-O fit would realize positive organizational level outcomes, there is little supporting evidence. In fact, several P-O fit researchers have proposed that there may be a negative relationship between P-O fit within firms and organizational outcomes. These arguments are based on the idea that high levels of fit might lead to lack of innovation and strategic myopia. Conversely, lower levels of P-O fit, especially at the managerial level, might translate into greater heterogeneity and more positive outcomes. This presents an interesting paradox. Maximizing individual outcomes may actually serve to lower organizational outcomes. With the lack of empirical evidence supporting either side of this argument, it should suffice to say that the link between P-O fit and organizational outcomes is at best uncertain and in dire need of more investigation.

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In the entrepreneurship literature, the concept of fit is often implicit. For instance, managing a new venture through the stages of start-up to an ongoing, professionally managed business is generally problematic for entrepreneurs (Hambrick & Crozier, 1985). The extant literature on this subject attributes these transitional difficulties, in part, to a mismatch that arises between the owner/entrepreneur and the firm over time. This mismatch is generally attributed to a lack of fit between the individual entrepreneur’s abilities and characteristics and the varying nature of the firm as it progresses through the organizational life cycle. While these notions of “mismatch” and “fit” are generally accepted, little attention has been paid to the theoretical foundations that would explain the possible antecedents or salient variables which might explain or be employed to measure the varying degrees of fit or misfit between the owner/entrepreneur and his or her firm. There are two main objectives of this chapter. The first is to demonstrate how the construct of cognitive misfit combined in a P-O fit approach can be applied to help understand entrepreneurial behaviors and outcomes. The first part of the chapter presents the major constructs of cognitive misfit, cognitive style and work context. We discuss not only the development and measurement of these constructs, but also why they are particularly well suited for use in the study of entrepreneurial transitions and many other areas of entrepreneurship. The other main objective of this chapter is to present findings from a study which employed cognitive misfit within a P-O fit approach to examine entrepreneurial attitudes and outcomes. The second part of the chapter presents a brief overview of this study. We focus mainly on the method of operationalizing cognitive misfit and some measurement obstacles, the interesting findings, limitations, and future research directions.

COGNITIVE MISFIT The construct of cognitive misfit was first developed and introduced as a viable aspect of Person-Organization fit research by Chan (1996). Cognitive misfit refers to the degree of mismatch between an individual’s preferred and dominant decision-making style and the style demands (structure) of the work context. Whereas previously developed facets of P-O fit had included goals, values, ethics, climate, and particular personality characteristics, Chan argued that incorporating individual decision-making style at the individual level and structure at the organizational level was also a viable approach to examining P-O fit. In a study of 253 engineers, Chan showed that while cognitive misfit was uncorrelated with job performance, it was a valid predictor of actual turnover.

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The construct of cognitive misfit combines the individual’s cognitive make-up within a P-O fit framework. It is operationalized as the interaction of the individual’s decision-making style and the style demands of the work context. We have extended this approach into the field of entrepreneurship by defining the notion of mismatch in the entrepreneurial transition dilemma as a cognitive misfit problem between the individual entrepreneur’s dominant decision-making style and the varying demands of the new venture over time and at different levels of formalization and structure.

COGNITIVE PERSPECTIVE IN ENTREPRENEURSHIP In approaching the entrepreneurial transition problem as one of misfit between the entrepreneur’s cognitive make-up and the varying demands of the new venture over time, a central element is the individual entrepreneur. Despite the lack of success of personality and demographic variables in prior research and a movement towards external explanations of entrepreneurial activity, a number of scholars are now focusing on the potential role of cognitive factors and processes in studying entrepreneurship. The basic premise of this perspective is that key insights into distinguishing entrepreneurs from others and understanding entrepreneurial behavior may be attained through the study of how entrepreneurs think, process information, solve problems, make decisions, and make sense of the complex environments in which they operate. This perspective assumes that individual differences do exist both within entrepreneurs as a group and between entrepreneurs and other homogeneous groups of individuals. Utilizing the cognitive perspective would appear to be a relevant approach for tackling fundamental issues in entrepreneurship such as opportunity recognition, new venture creation, and managing growth. It is a marked departure from simply looking at personality as a direct predictor of entrepreneurial behavior. Employing a cognitive perspective in entrepreneurship research, Busenitz and Barney (1997) found that entrepreneurs and managers in large organizations employ different biases and heuristics (simplifying strategies used in making decisions) when faced with complex decisions. This study found significant differences between entrepreneurs and managers that personality based approaches had been unable to empirically demonstrate. Baron (1998) also uses a cognitive approach in presenting how entrepreneurs may be more prone to using certain cognitive biases and heuristics (counterfactual thinking, affect infusion, self-serving bias, and planning fallacy) than other types of individuals.

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These findings may be explained by the fact that entrepreneurs tend to operate in more uncertain and complex environments than do other individuals (e.g. managers in large organizations). The entrepreneur who regularly employs specific biases and heuristics may be better suited to navigate through the complex and uncertain environment in which he or she has chosen to operate. The notion of fit and misfit is implicit in this approach. Wright, Hoskisson, Busenitz and Dial (2000), suggest that dominant cognitive approaches may be advantageous or disadvantageous depending on the situation. This point is important. It is the interaction of the individual’s dominant decision-making style with the particular demands of a given situation that leads to varying degrees of fit and ultimately to either positive or negative outcomes.

PSYCHOLOGICAL VERSUS TRAIT APPROACH Our approach looks at the study of new ventures with a focus on the individual entrepreneur (Shaver & Scott, 1991). However, while the psychological approach to exploring new ventures has fallen out of favor (see Gartner, 1988) the theoretical basis for employing this approach is solid. While psychology does focus on the individual, it also assumes the interaction of the individual with external situations. Psychology combines external factors with internal processes; it can be defined by Lewin’s (1935) expression, B = f(P, E), where behavior is a function of both person and environment. Neither the person nor the environment alone is enough to sufficiently explain an individual’s behavior (Shaver & Scott, 1991). In the extant entrepreneurship literature, the broad psychological approach has been distorted by studies on the personality of the entrepreneur. The unsuccessful search for the personality profile of the successful organization founder is what psychologists would call a personological endeavor (Shaver & Scott, 1991, p. 25). These types of searches for consistency across multiple situations in personality traits went out of style in traditional psychology research over thirty years ago, when Mischel (1968) argued that behavior should be regarded as the consequence of person-situation interactions (Shaver & Scott, 1991, p. 25). It is important to distinguish how the cognitive perspective we employ in addressing the problem of entrepreneurial transition differs from previous attempts (trait research) to explain certain aspects of entrepreneurial behavior. Gartner (1985, 1988) asserts that the major thrust of most entrepreneurship research has been to prove that entrepreneurs are different from non-entrepreneurs. The logic behind these early trait studies was that if we (as researchers) could answer the question – Who is the entrepreneur? – Then we would gain an understanding of the phenomenon of entrepreneurship (personological approach).

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Gartner (1985, 1988) went on to state that with respect to personality traits, there is as much difference among entrepreneurs as there is between entrepreneurs and non-entrepreneurs, and that researchers should focus on the behavior of creating a new venture, not the personality of the founder (Gartner, 1988). We agree with Gartner that research should not continue to focus solely on the personality of the entrepreneur. However, we do believe that many researchers have taken his statement as a call to move away from any research that focuses on the individual entrepreneur. The inability of the personological (trait) approaches to provide adequate explanations of the entrepreneurial process has led to three fairly distinct responses (Busenitz & Barney, 1997). First is the argument that these previous failures were the result of improper methodologies (Ginsberg & Buchholtz, 1989; Stewart, Watson, Carland & Carland, 1999). Second, some researchers (following Gartner) have called for discarding the search for individual differences and have focused on external and/or economic explanations of entrepreneurial behavior (Aldrich, 1979; Amit, Muller & Cockburn, 1995). The third response, which we employ in this study, has been to focus on psychological and cognitive determinants of entrepreneurial behavior (Baron, 1998; Busenitz & Barney, 1997) through an interaction approach. We concur with Shaver and Scott, who stated that, The study of new venture creation began with some reasonable assumptions about the psychological characteristics of “entrepreneurs.” Through the years, more and more of these personological characteristics have been discarded, debunked, or at the very least found to have been measured ineffectively. The result has been to concentrate on almost anything except the individual . . . But none of these will, alone, create a new venture. For that we need a person, in whose mind all of the possibilities come together, who believes that innovation is possible, and who has the motivation to persist until the job is done. Person, process, and choice: for these we need a truly psychological perspective on new venture creation (1991, p. 39).

Whereas personological (trait) research has, at best, only provided marginal contributions, the potential of research employing a decision-making perspective appears to hold great promise. This is a truly psychological approach to studying entrepreneurs. By examining the interaction between the way entrepreneurs approach and make decisions with different situations and environmental factors, we may gain a better understanding of why certain entrepreneurs will behave differently than other entrepreneurs in a given situation. Again, research based on interactions is based on the tenet that behavior is influenced by the confluence of the person and the situation (Chell, Haworth & Brearley, 1991). Since many of the demographic and trait studies focused solely on the individual, it is not surprising that they showed low correlations with behaviors. Similarly, while situational factors are clearly integral components of the entrepreneurial process (e.g. Herron & Robinson, 1993; Shapero, 1984), all individuals will not become entrepreneurs

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under comparable circumstances, suggesting that some dimension of the individual must be in play. Hence, focusing solely on situational factors to explain the entrepreneurial process would appear to be a clearly under-defined approach. If one adheres to the tenet that behavior is best understood by studying the person and the situation, then investigating the psychology of the entrepreneur should be a primary line of inquiry in entrepreneurship research (Goldsmith & Kerr, 1991). The study of entrepreneurial cognition and decision-making is a much more robust and promising approach in studying entrepreneurs than the focus either on personality or situational factors alone. We believe that these approaches provide a theoretically sound and promising means for placing the individual entrepreneur back into a key role in the study of the entrepreneurial process.

COGNITIVE STYLE In this section, we will provide an overview of cognitive style. The section begins by providing some basic definitions and assumptions. This is followed by a history of the development of the cognitive style construct within several distinct areas of psychology. Finally, we focus on the two decision-making style models that have been used to operationalize cognitive misfit, distinguishing them from the family of “learning styles” that are also classified under the broad heading of cognitive style. The construct of cognitive style is widely recognized as an important determinant of individual behavior (Sadler-Smith & Badger, 1998). It has been defined as an individual’s preferred and habitual approach to organizing, representing, and processing information (Streufert & Nogami, 1989), a built-in and automatic way of responding to information and situations (Riding & Rayner, 1998), individual differences in the way people perceive, think, solve problems, learn, and relate to others (Witkin, Moore, Goodenough & Cox, 1977), and an individual’s characteristic modes of perceiving, remembering, and problem-solving (Messick, 1984). Cognitive style is a high-order heuristic and can most easily be conceptualized as the way the individual’s brain is “hard-wired.” This is a consistent approach that people employ when they approach, frame, and solve problems. Cognitive style has certain common characteristics: (1) it is a pervasive dimension that can be assessed using psychometric techniques; (2) it is stable over time; (3) it is bipolar; and (4) it describes different rather than better thinking processes (Sadler-Smith & Badger, 1998). The term cognitive style has become widely used, and many models and descriptions fall under the broad classification of cognitive style. A review of style research and the development of the construct of cognitive style will help

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to distinguish the decision-making models used to operationalize cognitive misfit from the many other models that fall within the broad domain of cognitive style. The contemporary field of cognitive style can trace its roots to four main areas in psychology. These include perception, cognitive controls and processing, mental imagery, and personality (Rayner, 2000). The term “style” has been used in the psychology of individual differences to describe psychological structures or observed behaviors associated with typical forms of functioning. The term “style” in the study of individual differences is related to various aspects of the individual’s performance, including cognition, behavior, motivation, communication, learning, teaching, and organizational behavior (Rayner, 2000). Beginning in the 1950s, many psychologists began generating descriptions of thinking and learning that were significantly related to individual differences. Table 1 provides a brief summary of some of these early attempts at identifying style dimensions and the creation of style labels to describe the nature of individual differences in cognition. It is reasonable to conclude that in the development of cognitive style, a lack of conceptual agreement over basic terminology in these early studies created a blurred understanding of the construct of cognitive style. However, these early works do demonstrate a clearly established cognition-centered approach to the study of individual differences (Griogorenko & Sternberg, 1995). Acknowledging these previous studies is important, not only for understanding the foundations of cognitive style, but also the vast number of distinct labels and models that continue to create confusion in the field to this day.

Table 1. Early Cognitive Style Dimensions/Labels. Style Dimension/Labels

Author(s)

Tolerant – Intolerant

Klein and Schlesinger (1951), Klein, Riley and Schlesinger (1962). Pettigrew (1958), Bruner and Tajfel (1961), Kogan and Wallach (1964), Messick and Kogan (1963, 1966). Allard and Carlson (1963), Harvey, Hunt and Schroder (1961), Schroeder, Driver and Struefert (1967). Allard and Carlson (1963), Bieri (1966), Harvey et al. (1961), Messick and Kogan (1966). Kogan and Moran (1969), Kogan and Wallach (1964, 1967). Cohen (1967).

Broad – Narrow Categorization Conceptual Integration – Integrative Complexity Cognitive Simplicity – Cognitive Complexity Risk Taking – Cautiousness Splitters – Lumpers

Source: Adapted from Rayner (2000, p. 121).

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While numerous researchers continued to create new labels and models of cognitive style, others began to recognize that the diversity of style theory was unproductive and misleading. Lewis (1976, pp. 304–305) stated that, “In my opinion, the right thing to do is to focus . . . on the search for individual differences which are basic, in the sense that they underlie (and to that extent, explain), a whole range of more readily observable differences.” The urgent need to rationalize and synthesize theory was echoed by many researchers and is prevalent in the literature (Curry, 1983; Griggs, 1991; Griogorenko & Sternberg, 1995; Rayner & Riding, 1997; Riding & Cheema, 1991). An attempt at categorizing cognitive styles was made by Messick (1984), whose research identified 19 key models in the field. He went on to argue that there needed to be a clear distinction between cognitive styles and cognitive abilities. Later, Riding and Cheema (1991) proposed that style models could be organized into two cognitive style families, which they called a Holistic-Analytic group and a Verbal-Imager group. Key models within the Holistic-Analytic dimension are presented in Table 2. In their typology, Riding and Cheema (1991) argued that over 30 different labels and models of style that had been identified by previous researchers could be classified based on this typology. Furthermore, they identified a third group of models of style that existed in the field. They posited that this group of models was distinct from those that had investigated cognitive processes, but had still fallen under the broad heading of cognitive styles. This third group was known as learning styles, but should more accurately be described as learning strategies. In management circles, perhaps one of the most well-known learning style models is Kolb’s (1984) experiential model of learning. The theory is based on two orthogonal dimensions, which he labeled prehension (taking hold of experience either through concrete experience or abstract conceptualization) and transformation (manipulating experience either through reflective observation or active experimentation). Rayner and Riding (1997, p. 16) described Kolb’s theory and model as assuming a combination of “ ‘hard wiring’ (cognition) and ‘soft wiring’ (process) that reflects a less stable set of individual differences which can change over time.” Thus, while cognitive style models that fall into the Holistic – Analytic family are more cognitive centered and stable, learning styles may be viewed as a separate family of styles with less stable characteristics. The specific decision-making styles incorporated in the present study (and described in detail in the next section) fall under the Holistic – Analytic heading and should be viewed as somewhat related to, but distinct from, learning styles.

Dimension/Labels

Description

Author(s)

Field Dependency – Independency

Individual dependency on a perceptual field when analyzing a structure or form that is part of the field. A tendency to assimilate detail rapidly and lose or emphasize detail and changes in new information. The tendency to work through problem solving incrementally or globally and assimilate detail. Individual preferences for seeking familiarity or novelty in the process of problem solving and creativity. Adaptors prefer conventional, established procedures; innovators prefer restructuring or new perspectives in problem solving. Analysts favor a structured approach to problem solving and systematic methods of investigation; intuitivists prefer an open-ended approach to problem solving and random methods of exploration.

Witkin and Asch (1948), Witkin (1964).

Levelling – Sharpening Holist – Serialist Assimilator – Explorer Adaptors – Innovators

Analytic – Intuitive

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Table 2. Key Holistic – Analytic Models of Cognitive Style.

Klein (1954), Gardner, Holzman, Klein, Linton and Spence (1959). Pask and Scott (1972), Pask (1976). Kaufmann (1989). Kirton (1976, 1987, 1994).

Allinson and Hayes (1996).

Source: Adapted from Rayner (2000, p. 125).

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Recent comprehensive reviews of the Holistic – Analytic models within the cognitive style paradigm (Hayes & Allinson, 1996; Rayner, 2000; Rayner & Riding, 1997; Riding & Rayner, 1998; Sadler-Smith, 1998, 2000) suggest that: (1) There are a number of psychometrically sound measures of decision-making style (for example, Allinson & Hayes, 1996; Kirton, 1976; Riding, 1994); (2) There is empirical evidence demonstrating that the dimensions measured by these models are stable over time and independent of intelligence; (3) These measures of decision-making style interact with external factors affecting individual attitudes and behaviors. Sadler-Smith (2000, p. 193) concludes that “Unlike the field of ‘learning style,’ which is characterized by divergence and a paucity of robust measures, within the cognitive style field [decision-making style] there appears to be some convergence of models and frameworks.” Sadler-Smith and Badger (1998) have argued that there are two models of decision-making style from within this family that satisfy the criteria for a “style” and are suitable for use in organizational settings. These two models are presented in the next two sections.

Adaption – Innovation The first of these two models of cognitive style is Kirton’s Adaption-Innovation Theory (KAI) (Kirton, 1976, 1980). According to KAI Theory, individuals are viewed as falling on a continuum from extreme adaptor on one end to extreme innovator on the other. Adaption-Innovation as a cognitive continuum places an individual in a preferred mode of tackling problems at all stages (for example, from one’s view of what is the problem, what are the relevant data and information for its resolution, what is the appropriate solution, and how should it be implemented). As a decision-making preference, it is presumed to be unrelated to level of capacity such as I.Q. (Kirton, 1989). The KAI Theory proposes that: (1) an individual’s problem-solving style is stable and does not change with age or over time; (2) adaptors and innovators have different attributes, each of which, depending on the circumstances, could be advantageous or disadvantageous; (3) one of these sets of attributes comes naturally to an individual; the opposing set has to be learned as part of the individual’s coping behavior; (4) employing coping behavior is stressful, and when it is no longer required, there is a marked tendency to return to the preferred style; (5) forms of coping behavior include changing circumstances to suit the preferred style or forming a team whose combined preferences cover expected problem situations (Kirton, 1976, 1980).

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Adaptors tend to be conservative, viewing a problem and exploring solutions within generally accepted guidelines and frameworks. On the other end of the continuum, innovators see the existing guidelines and framework as being part of the problem and often incorporate radical or frame-breaking processes or ideas as part of their solution. Adaptors are characterized by precision, reliability, efficiency, discipline, and conformity. They seek solutions to problems in previously understood and accepted ways and appear impervious to boredom, maintaining high accuracy over long durations of detailed work. Conversely, innovators are typified by undisciplined thinking and tangential approaches to tasks and problem solving that cut across accepted paradigms (Kirton, 1989). They are risk-takers who challenge and tend to operate outside of the existing framework (Buttner & Gryskiewicz, 1993). In general, an adaptive cognitive style forms an ability to do things better while an innovative cognitive style forms an ability to do things differently (Kirton, 1989). KAI Theory is construed as a stable and preferred cognitive strategy for dealing with all stages of the problem-solving process. In the organizational context, research has confirmed that systematic differences in KAI mean scores exist across established functional groups. Specifically, the mean KAI scores of groups who operate in a relatively structured environment tend to fall on the adaptive side of the continuum. Examples of these adaptive groups include bankers, accountants, and those involved mostly in maintenance or production areas (Chan, Elliot, Ong & Long, 1995; Gul, 1986; Holland, 1987; Kirton, 1980). Alternatively, groups who operate in relatively unstructured environments have KAI means that are innovative in orientation. Examples of these innovative groups include those in marketing, personnel, planning, and research and development (Chan et al., 1995; Foxall & Hackett, 1994; Holland, 1987; Kirton & Pender, 1982; Lowe & Taylor, 1986). These findings suggest that that there is a link between the level of structure in a given organizational context and an individual’s preferred decision-making style and that individuals may self-select into environments that are more congruent with their dominant decision-making style.

Intuitive – Analytic In 1996, Allinson and Hayes developed the Cognitive Style Index (CSI). The impetus for constructing the CSI was that despite the growing interest in cognitive style among management researchers and practitioners, there was a shortage of valid and reliable measures that were convenient for use in organizational settings. The CSI measures the generic intuition-analysis dimension of cognitive style. Allinson and Hayes (1996) argue that while there have been a number of

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dimensions on which cognitive style has been differentiated [19 separate labels (Messick, 1984); 29 separate labels (Hayes & Allinson, 1994)], the superordinate dimension of intuition-analysis encompasses all of these. The CSI is based upon hemispherical differences in the brain as a possible source of cognitive style differences. Allinson and Hayes (1996) employ the term Intuition to describe “right brain” thinking (i.e. judgment based on feeling and the adoption of a global perspective) and Analysis for “left brain” thinking (i.e. judgment based on mental reasoning and a focus on detail). This idea of hemispherical differences stems from the work of Sperry (1968) and others in the 1960s on “split brain” patients. Management theorists have also been influenced by these findings and their potential. For example, Mintzberg (1976) claims that planning and management science, with their dependence on logic, require a rational (left-brain) cognitive style. However, management at the policy level, which involves dealing with uncertainty, requires a more intuitive (right-brain) style. Allinson and Hayes (1996) caution that the attribution of differences in analytical versus intuitive behavior to hemispherical differences in brain function should, in the absence of conclusive neurophysiological evidence, be treated metaphorically rather than literally. In general, intuitivists tend to be relatively nonconformist, prefer an open-ended approach to problem solving, rely on random methods of exploration, and work best with ideas requiring a broad perspective. Alternatively, analysts tend to be more compliant, favor a more structured approach to problem-solving, prefer systematic methods of investigation, and are especially comfortable with ideas requiring sequential analysis (Allinson & Hayes, 1996). There are obvious parallels between the Adaptive-Innovative dimension (KAI) and the Analytical-Intuitive dimension (CSI). Sadler-Smith and Badger (1998) assert that the respective poles for both KAI and CSI share a number of features. In an analysis of decision-making style models, they argued that the analyst (CSI) and adaptor (KAI) styles could, for convenience, be labeled “analytic” and the intuitive (CSI) and innovative (KAI) styles may be labeled as “holistic.” They concluded that both measures approach a fundamental analytic-holistic dimension of cognitive style and that there is more similarity than difference between the two measures.

WORK CONTEXT STYLE DEMANDS While certain dimensions of an individual’s cognitive style will remain stable over time (Hayes & Allinson, 1996; Kirton, 1980), the style demands of the new venture are variable as the venture grows. Thus, the potential for different degrees of cognitive fit and misfit between the stable style of the entrepreneur and the

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variable style demands of the organizational context are not only likely, but may be inevitable. From a cognitive misfit perspective, we are particularly interested in the style demands related to formalization, structure, centralization, and bureaucracy. Chandler (1962) theorized that organizations develop patterns of organizational structure in response to common growth and market challenges. Weber (1946, 1947) argued that large organizations are more bureaucratic than smaller ones and that the relative size of the administrative component should be proportional to the size of the organization. Blau (1974) argued that while it is true that more complex organizations have a larger administrative ratio than simpler ones and that large organizations do tend to be more complex, there is a point of economies of scale, which allows larger organizations to function with a proportionately smaller administrative staff. However, despite this possible exception, other aspects of bureaucratization do appear to be directly related to the size of the organization (Blau, 1974; Mintzberg, 1979). Many life-cycle stage models support this idea that the organizational style demands will change as the organization matures. In their review of the life-cycle construct, Hanks, Watson, Jansen and Chandler (1994) provide a synthesis of 10 different life-cycle models. All of these models propose that certain key dimensions of organizations will change with respect to age and size (e.g. levels of formalization, structure, and bureaucracy).

DECISION-MAKING STYLE AND WORK CONTEXT Previous work on cognitive style suggests that there is a predictable link between preferred individual problem-solving/decision-making style and structure and bureaucracy. According to Kirton (1976), “The individual who displays the highly adaptive decision-making style has been termed ‘organisation man’ (Whyte, 1957), and is suited to work within large institutions.” Kirton further states that “because adaptive man works within cognitive systems, he is also at home in bureaucratic ones . . .” (1976). Thus, the individual possessing an adaptive cognitive style and the resulting characteristic behaviors, similar to those of “organisation man,” is much better fitted to a more formalized and bureaucratic organizational work context and also more likely to be found there. Conversely, the individual who possesses an innovative cognitive style and exhibits the characteristic behaviors associated with that style is ill fitted for a bureaucratic organization. The policies and procedures that are an aid and comfort to the adaptive individual may be a cause of great discomfort and agitation to the innovative individual. In contrast to an individual with an adaptive cognitive style and behaviors, the innovator does not seek to reduce conflict or minimize

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risks, and solves problems at an accelerated pace and in a frame-breaking or unpredictable manner. Whereas this cognitive style and resulting behavior is a liability in organizational settings, it can be an asset in other environments. This is a key point. Adaptive or innovative styles will be more congruent, or better fitted, to different organizational work contexts. While the individual entrepreneur’s KAI type remains stable over time, the style demands of the new venture will vary according to the structure and size. In the same vein, Allinson and Hayes (1996) also theorized that individuals with different decision-making style preferences (i.e. analytic and intuitive) would have a strong preference for different work contexts. In organizational settings, analysts will subscribe to the bureaucratic norm and prefer work settings that are oriented towards careful routines, governed by logic, and clearly structured and organized. In contrast, intuitivists will prefer freedom from rules and regulations, and a work setting that is activity oriented, flexible, and unstructured. Allinson and Hayes (1996) presented correlation data in their initial validation study that demonstrated support for these claims.

COGNITIVE MISFIT AND COPING BEHAVIOR The construct of cognitive misfit is determined by the level of incongruence (misfit) between the entrepreneur’s preferred problem-solving/decision-making style and the style demands of the firm’s work context. When individuals are in a state of cognitive misfit, they will employ certain specific coping behaviors to handle the conflict between their preferred problem-solving style and the conflicting style demands being placed upon them. KAI Theory suggests that when experiencing cognitive misfit, innovative individuals may employ adaptive behaviors as part of their coping mechanisms, and vice versa. However, these coping behaviors are not sustainable, and there is a marked tendency for individuals to return to their preferred decision-making style (Kirton, 1976). Exhibiting coping behavior is a source of great stress and, according to KAI Theory, the individual required to sustain high levels of coping behavior (exhibiting behaviors associated with the non-preferred style) will eventually either: (1) change the circumstances to suit his or her preferred, dominant style or (2) form a team whose combined preferences cover expected problem situations. Thus, when the individual entrepreneur experiences high levels of cognitive misfit (the style demands of the work context are incongruent with his or her preferred cognitive style), coping behavior will be required. The greater the degree of cognitive misfit, the more coping behavior is required and, consequently, the higher amount of stress on the individual (Pervin, 1968). The relationship between

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cognitive misfit and coping mechanisms may help us to better understand why different types of entrepreneurs may exhibit predictable behaviors when faced with growing a new venture.

RESULTS FROM OUR INITIAL STUDY Having argued the rationale for using a P-O fit approach and developed the construct of cognitive misfit, we will now present some of the results from our first attempt at incorporating this framework with the study of owners/entrepreneurs. The specific hypotheses tested are based on the model in Fig. 1 and restated below.

Summary of Hypotheses Hypothesis 1a. Cognitive style moderates the relationship between the structure of the work environment and burnout. (SUPPORTED) Hypothesis 1b. For less structured work environments, more intuitive entrepreneurs will experience lower burnout than those who are more analytic, but for more structured work environments, more intuitive entrepreneurs will experience higher burnout than those who are more analytic. (SUPPORTED)

Fig. 1. Model of an Entrepreneur’s Cognitive Fit/Misfit.

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Hypothesis 2a. Cognitive style moderates the relationship between the structure of the work environment and satisfaction. (SUPPORTED) Hypothesis 2b. For less structured work environments, more intuitive entrepreneurs will experience higher satisfaction than those who are more analytic, but for more structured work environments, more intuitive entrepreneurs will express lower satisfaction than those who are more analytic. (SUPPORTED) Hypothesis 3a. Cognitive style moderates the relationship between the structure of the work environment and intention to exit. (SUPPORTED) Hypothesis 3b. For less structured work environments, more intuitive entrepreneurs will express lower intention to exit than those who are more analytic, but for more structured work environments, more intuitive entrepreneurs will express greater intention to exit than those who are more analytic. (SUPPORTED) Hypothesis 4a. Cognitive style moderates the relationship between the structure of the work environment and intention to grow the business. (NOT SUPPORTED) Hypothesis 4b. For less structured work environments, more intuitive entrepreneurs will express lower growth intentions than those who are more analytic, and the difference between the two groups will increase as the work environment becomes more structured. (NOT SUPPORTED) Hypothesis 5a. Cognitive style moderates the relationship between the structure of the work environment and percentage change in the number of employees. (NOT SUPPORTED) Hypothesis 5b. For less structured work environments, more intuitive entrepreneurs will experience less growth than those who are more analytic, and the difference between the two groups will increase as the work environment becomes more structured. (NOT SUPPORTED)

METHODS Sample The sampling frame consisted of companies listed in the 2000 Colorado High Technology Directory. Subsidiaries and not-for-profit companies were excluded from the sampling frame. Also excluded were those companies with no contact

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information or where the only listed contact held a title that clearly signified a lower level within the organization. From the total number of 1,791 companies listed in the directory, 1,294 were retained for inclusion in the sampling frame. Through the course of data collection, another 87 companies were removed for the following reasons: unable to be contacted (first contact letter was returned as undeliverable); business closed (included both voluntary and due to deaths); company was acquired or identified as a subsidiary (notified the author through phone, e-mail, or correspondence). This left a total number of 1,207 companies that had a possibility of responding to the mail questionnaire. Of these, 267 usable questionnaires were returned constituting an effective response rate of 22.1%. A time trend extrapolation test (Armstrong & Overton, 1977) was conducted as a check on non-response bias. Subsequent comparison of the two groups (early versus late respondents) through ANOVA tests indicated no significant differences between the groups on the explanatory or dependent variables used in this study. For the present study, a smaller sub-sample of the original data set was used. This set included only those respondents who currently had ownership in the firm and were involved in the day-to-day operation of the firm. Also, we included only firms with more than five employees, as those with fewer than five did not have sufficient structure to properly test for interactions. This left 159 owners/entrepreneurs in the current sub-sample for which the hypotheses in this study were tested.

Data Collection Data were collected through a mail questionnaire between March and April of 2001. For both construction and implementation of the mail questionnaire, the “Tailored Design Method” (Dillman, 2000) was followed as closely as possible.

Variables and Measures Cognitive Style Index: Intuition–Analytical. Sadler-Smith and Badger (1998) have argued that two models and subsequent measures of decision-making style are suitable for use in organizational settings and can be employed in field surveys – the Kirton Adaption Innovation Inventory (Kirton, 1976) and the Cognitive Style Index (Allinson & Hayes, 1996). We chose the more recent Cognitive Style Index because, as Allinson and Hayes (1996) argue, while there have been a number of dimensions on which cognitive style has been differentiated [19 separate labels (Messick, 1984); 29 separate labels (Hayes & Allinson, 1994)], the superordinate

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dimension of intuition-analysis appears to encompass all of these. The CSI measures the generic intuition-analysis dimension of cognitive style. The CSI consists of 38 items, each requiring the subject to respond on a trichotomous true-uncertain-false scale. In the present study (n = 159), the internal consistency and reliability of the CSI measure, as estimated by Cronbach’s alpha, was 0.86. This is consistent with those reported by Allinson and Hayes (1996) and other researchers. For our sample of entrepreneurs, the mean CSI score was 32.06 (S.D. 12.59). The mean for the sample was significantly more towards the “intuitive” side of the scale than the means reported for other groups (e.g. various types of managers, business school undergraduates, and teachers (Allinson & Hayes, 1996)). Despite the shift, the distribution of scores for the sample remained within acceptable limits for a normal distribution (skewness 0.261, S.E. of skewness 0.191; kurtosis −0.541, S.E. of kurtosis 0.379). Work Context Index. This is a composite variable created by first summing the standardized scores of the three structural variables – vertical differentiation, formalization, and specialization (detailed below). A higher score represents a more structured, formal and bureaucratic organizational context. The Cronbach’s alpha for this index was 0.70, and the inter-correlation range was 0.36–0.52. Both skewness and kurtosis were within acceptable limits. The variable Vertical Differentiation (Levels) consists of the total number of organizational levels within the firm (Dewar & Hage, 1978). Respondents were asked to count the total number of levels in the longest line between direct workers and the organization’s chief executive officer, including both of these levels (Pugh & Hinkson, 1976). This resulted in a range of scores from 1 to 6. Higher scores represent a higher degree of vertical differentiation. Scores were distributed normally. The variable Formalization was operationalized using a scale of eleven items. All eleven items were summed to create an index. The higher the score, the greater the degree of formalization of the organization. Hanks et al. (1994) employed this measure of formalization and reported a Cronbach’s alpha of 0.85. For this study, the Cronbach’s alpha was 0.88, and inter-correlations ranged from 0.15 to 0.75. Specialization was measured on a scale adapted from Pugh, Hinkson, Hinnings and Turner (1968). Respondents were given a list of twenty functional areas and asked to check those in which they have at least one full-time employee. The item is scored by totaling up the number of functions checked. This provides possible scores ranging from 0 to 20. A higher score indicates a greater degree of specialization. Cognitive misfit is a composite measure based on individual decision-making style preferences and work context style demands. The cognitive fit score was calculated by multiplying the individual’s centered CSI score by the centered

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Work Context Index score for that individual. The cognitive fit score is the interaction term between CSI and Work Context. In the present study, the person variable (i.e. decision-making style as measured by CSI) and the organization variable (i.e. Work Context as measured by WCI) represent the effects of the person and the organization. The person × organization product term (interaction) represents the degree of P-O fit on the expressed main effects (CSI and WCI). Dependent variables. Burnout has been defined as “a process in which a previously committed (individual) disengages from his or her work in response to stress and strain experienced in the job” (Cherniss, 1980, p. 18), and as “a state of emotional exhaustion caused by excessive psychological and emotional demands . . .” (Jackson, Schwab & Schuler, 1986, p. 30). It is theorized that burnout consists of three components: emotional exhaustion, depersonalization of others, and feelings of diminished personal accomplishment (Maslach & Jackson, 1981). We focused on the nine-item emotional exhaustion component of the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1986). The emotional exhaustion component is the most important of the three components (Rosse, Boss, Johnson & Crown, 1991). Participants are asked to indicate how often they have felt that way for each of the nine items. A mean of the nine items was used as the index for burnout. A higher score corresponds to a higher level of burnout. The Cronbach’s alpha was 0.90, and inter-correlations ranged from 0.35 to 0.70. Intentions to Exit was measured using four items each scored on a 7-point Likert-type scale. These four items were used by O’Reilly et al. (1991). A higher score corresponds to a greater intention to exit. The Cronbach’s alpha was 0.76, and the inter-correlations ranged from 0.21 to 0.84. Satisfaction has been measured using numerous different scales. For this study, we chose to use a measure of satisfaction first developed by Quinn and Staines (1979). They define satisfaction as “affective reaction to the job,” and the definition and measure is intended to refer to and measure what they label as “facet free job satisfaction” (p. 205). This is an established measure of satisfaction and is reviewed, in depth, by Price and Mueller (1986, pp. 220–223). Besides being an established measure, it has also been used in two studies (Eden, 1975; Naughton, 1987, using an earlier version) which compared the satisfaction levels between the self-employed and wage or salaried workers. This allows for the comparison of scores across somewhat similar groups. The total score for the measure was calculated by summing the scores for the five individual items. Higher scores correspond with higher levels of global job satisfaction. The Cronbach’s alpha was 0.74, and the inter-correlations ranged from 0.33 to 0.58. Growth intentions was measured using two items. Both of these items were similar to those used by Westhead and Wright (1998). Respondents were asked, “How would you prefer for the number of employees in the business to change over

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the next TWO years?” and “How would you prefer for the sales for the business to increase or decrease over the next TWO years?” For both of these items, participants indicated their response using a 7-point Likert-type scale ranging from 1 = 20% or more decrease, to 7 = More than double. The overall score was calculated by summing the scores for each of the two items. Observed scores ranged from 2 to 14, and the distribution met the criteria for normality. The Chronbach’s alpha for the two-item scale was 0.76, and the correlation between the two items was 0.62. Employee Growth reflects organizational growth for the firm’s most recent year of performance. It was calculated using both data from the 2000 Colorado High Technology Directory and self-reported employment data, based on the following formula: % Change in Number of Full-Time Employees =

Full-Time Employees 2001−Full-Time Employees 2000 Full-Time Employees 2000

This formula was used by Hanks et al. (1994) and is similar to employee growth formulas used in numerous previous studies.

Control Variables Following previous P-O fit studies examining similar dependent variables (e.g. O’Reilly, Chatman & Caldwell, 1991), we controlled for owners education, gender, and tenure with the firm. In the entrepreneurship literature, education (Cooper & Dunkelberg, 1987) and gender (Cooper & Artz, 1995; Sexton & Bowman-Upton, 1990) are frequently controlled for. While owners age is also frequently controlled for in entrepreneurship studies, and was asked for and collected, we chose to use tenure instead. Tenure was highly correlated with owner’s age, which made the inclusion of both variables problematic. Virany, Tushman and Romanelli (1992) have argued that CEO tenure should be controlled for in research seeking to relate CEO characteristics to firm performance. Given a choice between the two, tenure appears to be a more relevant variable to this study. In addition, it seems reasonable to expect that prior or concurrent business ownership could influence the individual’s level of burnout, intentions to exit, satisfaction, growth intentions, and employee growth. As a result, this was controlled for by using the dummy variable Serial, which was coded 0 for ownership in only one firm and 1 for ownership in two or more firms. The final control variable chosen for inclusion was one that measured firm performance. This variable is

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a subjective measure of performance in which the respondent is asked to rate the current profit performance of his or her firm versus the competition. Inclusion of this variable as a control is important since a goal of this study is to identify the relationship between cognitive fit/misfit and the dependent variables over and above what may be explained by the financial performance of the firm.

Data Analysis In order to test the hypotheses, we used hierarchical regression. To reduce the possibility of multicolinearity between the main effects and their interactions, the independent variables were centered (Aiken & West, 1991). First, the control variables were added. Next, the centered main effects (CSI and WCI) were entered as the second block. Finally, the interaction term (CSI × WCI) was entered as the third block. While the multiple regression equations described above will indicate whether or not an interaction is significant for a given criterion (dependent) variable, they do not provide much information on the true nature of the interaction. In order to reveal the true nature of the interaction, the suggested procedure is to plot the interaction (Aiken & West, 1991). We followed Cohen and Cohen’s (1983) recommendation to use values of the predictor variable at one standard deviation above the mean and one standard deviation below the mean. These values at plus and minus one standard deviation are then substituted back in to the modified regression equation and plotted to display the interaction. Following this procedure allows the hypotheses relating to the nature of the proposed interactions to be tested.

RESULTS Means, standard deviations, and intercorrelations for the variables used in the models are presented in Table 3. Of particular interest are the mean scores for several of the dependent variables. The mean scores for burnout and satisfaction were extremely high as compared to the reported means for other sample groups (wage or salaried employees) in other studies. Conversely, the mean score for intention to exit was very low as compared to employees in other studies. We further discuss the implications of these findings in the implications section of this chapter. The results of the hierarchical regressions are displayed above in Table 4. For burnout, the main effects model makes a significant contribution over and above the base model (R 2 = 0.035, p < 0.05). Within the main effects model,

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Table 3. Means, Standard Deviations, and Correlations.a Mean

S.D.

1

2

3

4

5

6

7

8

9

10

11

1. Burnout 2. Satisfaction 3. Intention to exit 4. Intention to grow 5. Employee growth 6. Cognitive style index 7. Work context index 8. PFMVS 9. Gender 10. Education 11. Tenure 12. Serial

22.65 20.42 9.81 9.50 0.43 32.06 0.00 5.10 0.95 4.21 14.78 0.60

12.59 4.89 5.81 2.36 1.83 12.59 2.37 1.51 0.22 1.16 8.55 0.49

−0.60 0.48 0.03 −0.09 −0.03 −0.16 −0.32 −0.14 0.04 −0.14 0.02

−0.62 0.01 0.07 0.01 0.11 0.47 0.24 −0.01 0.09 0.12

0.09 −0.02 −0.05 0.08 −0.36 −0.18 0.03 −0.15 0.04

0.06 −0.16 0.02 −0.06 0.01 0.25 −0.38 0.05

0.05 0.17 0.17 0.05 −0.05 −0.17 −0.05

−0.07 −0.05 −0.04 −0.05 0.10 −0.11

0.05 0.12 –0.02 –0.17 0.05

−0.01 −0.09 0.15 0.10

−0.11 0.03 0.11

−0.24 −0.01

−0.06

Note: Correlations greater than 0.16 indicate p < 0.05. a n = 159.


8

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Table 4. Results of Hierarchical Regression Analysis Regressing the Outcomes on CSI, WCI, and Their Interaction.a Variable

Burnout Base Model

Performance Gender Education Tenure Serial CSI score Work context CSI × Work context R2 2 R change an

Satisfaction

Intentions to Exit

Intentions to Grow

Employee Growth

Main effects

Interaction

Base Model

Main effects

Interaction

Base Model

Main effects

Interaction

Base Model

Main effects

Interaction

−0.32*** −0.15** −0.02 −0.12 0.08

−0.31*** −0.13* −0.04 −0.15* 0.09 −0.04 −0.19**

−0.30*** −0.14* −0.03 −0.17** 0.08 −0.06 −0.20*** −0.16**

0.47*** 0.25*** 0.07 0.05 0.04

0.47*** 0.24*** 0.07 0.07 0.04 0.05 0.09

0.46*** 0.24*** 0.07 0.04 0.05 0.06 0.10 0.12*

−0.37*** −0.20*** −0.05 −0.09 0.11

−0.38*** −0.21*** −0.04 −0.07 0.10 −0.06 0.07

−0.36*** −0.21*** −0.04 −0.04 0.08 −0.08 0.06 −0.22***

0.01 −0.04 −0.18** −0.31*** 0.01

0.01 0.04 0.18** −0.31*** −0.01 −0.12 −0.03

0.01 0.04 0.18** −0.31*** 0.01 −0.11 −0.03 0.02

0.15*** 0.15***

0.18*** 0.04**

0.21*** 0.29*** 0.03** 0.29***

0.30*** 0.01

0.32*** 0.02*

0.19*** 0.19***

0.20*** 0.01

0.24*** 0.05***

0.16*** 0.16***

0.17*** 0.01

0.17*** 0.01

Base Model

Main effects

0.21** 0.20** 0.06 0.04 −0.08 −0.07 −0.22*** −0.20** −0.09 −0.09 0.08 0.14*

0.08** 0.08**

0.11** 0.02

Interaction 0.20** 0.04 −0.07 −0.20** −0.09 0.09 0.14* 0.05 0.11** 0.01

= 159.

∗ Significant

at p < 0.10. at p < 0.05. ∗∗∗ Significant at p < 0.01. ∗∗ Significant

61

62


Fig. 2. Plot of CSI × WCI on (a) Burnout, (b) Satisfaction and (c) Intent to Exit.

the findings suggest that work context has a statistically significant influence on burnout. The negative sign of the standardized regression coefficient suggests that burnout was higher for those entrepreneurs with an organizational context that is less structured, less formal, and less bureaucratic. We hypothesized that cognitive style moderates the relationship between work structure and burnout. The interaction model makes a significant contribution over and above the main effects model (R 2 = 0.026, p < 0.05) and therefore provides support for Hypothesis 1a. The interaction was plotted to aid with interpretation and is displayed in Fig. 2a. As hypothesized, for the work context lower in formalization and structure, burnout was higher for more analytic individuals than for more intuitive individuals. Conversely, for the work context higher in formalization and structure, burnout was greater for more intuitive individuals than for more analytic individuals. Therefore, Hypothesis 1b was supported. For satisfaction, the main effects model does not make a significant contribution over and above the base model (R 2 = 0.010, p > 0.10). Within the main effects model, the findings suggest that neither work context nor cognitive style alone has a statistically significant association with satisfaction. We hypothesized that cognitive style moderates the relationship between work structure and satisfaction. The interaction model makes a significant contribution over and above the main effects model (R 2 = 0.015, p < 0.10) providing support for Hypothesis 2a. The interaction was plotted and is displayed in Fig. 2b. As hypothesized, for the work context lower in formalization and structure, satisfaction was higher for more intuitive individuals than for more analytic individuals. Conversely, for the work context higher in formalization and structure, burnout was greater for more

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analytic individuals than for more intuitive individuals. Therefore, Hypothesis 2b was supported. For intent to exit, the main effects model does not make a significant contribution over and above the base model (R 2 = 0.088, p > 0.10). Within the main effects model, the findings suggest that neither work context nor cognitive style alone has a statistically significant influence on satisfaction. We hypothesized that cognitive style moderates the relationship between work structure and individuals’ expressed intentions to exit their firm. The interaction model makes a significant contribution over and above the main effects model (R 2 = 0.047, p < 0.01) providing support for Hypothesis 3a. The interaction was plotted and is displayed in Fig. 2c. As hypothesized, for the work context lower in formalization and structure, intention to exit the firm was higher for more analytic individuals than for more intuitive individuals. Conversely, for the work context higher in formalization and structure, intention to exit the firm was greater for more intuitive individuals than for more analytic individuals. Therefore, Hypothesis 3b was supported. For growth intentions, the main effects model does not make a significant contribution over and above the base model (R 2 = 0.012, p > 0.10). Within the main effects model, the findings suggest that neither work context nor cognitive style alone has a statistically significant influence on growth intentions. We hypothesized that cognitive style moderates the relationship between work structure and individuals’ expressed intentions to grow their firm. The interaction model does not make a significant contribution over and above the main effects model (R 2 = 0.001, p > 0.10). Therefore, Hypothesis 4a was not supported. In addition, having found that the interaction was not significant, Hypothesis 4b was not supported, and plotting the interaction was rendered moot. For employee growth, the main effects model does not make a significant contribution over and above the base model (R 2 = 0.022, p > 0.10). Within the main effects model, the findings suggest that neither work context nor cognitive style alone has a statistically significant influence on growth intentions. We hypothesized that cognitive style moderates the relationship between work structure and employee growth. The interaction model does not make a significant contribution over and above the main effects model (R 2 = 0.002, p > 0.10). Therefore, Hypothesis 5a was not supported. In addition, having found that the interaction was not significant, Hypothesis 5b was not supported, and plotting the interaction was rendered moot.

DISCUSSION The empirical results indicate that when controlling for firm performance, entrepreneurial experience, and other demographic variables, there is a significant

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relationship between cognitive misfit and the individual entrepreneur’s reported levels of burnout (H1), satisfaction (H2), and his or her intentions to exit (H3) the firm. Furthermore, when these significant interactions are plotted and examined in detail (Fig. 2a–c), they reveal some interesting patterns. These disordinal (crossed) plots suggest that different types of entrepreneurs (analytic versus intuitive) will experience different outcomes, given the level of structure and formalization in their firms. An entrepreneur whose cognitive style is mismatched with the structure level of her/his firm will tend to experience significantly more “negative” outcomes (higher burnout, lower satisfaction, and higher intentions to exit) than an entrepreneur who is more in fit. This is an important finding. It suggests which types of entrepreneurs will experience greater difficulty in managing their businesses (from a cognitive conflict perspective) at different stages of growth and maturity. These results allow us to offer some prescriptive advice to practicing and nascent entrepreneurs with respect to where they are more likely to experience cognitive misfit and the associated negative outcomes as they attempt to grow their businesses. This is an important step towards better understanding the cognitive component of entrepreneurial transition difficulties. Closer examination of the plots reveals that while the interactions for burnout and satisfaction were as expected, the effects for analytical entrepreneurs are more marked than those of intuitive entrepreneurs. An initial explanation of that result could be that intuitive entrepreneurs might be more able to adapt than analytical entrepreneurs to less than desirable environments. Yet the results for intent to exit are similar for both analytical and intuitive entrepreneurs, meaning that while intuitive entrepreneurs might suffer less burnout and greater satisfaction than analytical entrepreneurs, they are as likely to want to exit the venture when in misfit. Further research is needed on the nature of analytical and intuitive entrepreneurs and the implications of misfit for organizations. The significant relationships between cognitive misfit and burnout, satisfaction, and intentions to exit support our contention that it is important to look at interactions in the study of entrepreneurship. Individual or firm level variables alone are not sufficient to explain the dynamic nature of the questions of real interest in the field. It is the interaction of the person (entrepreneur) and the place (firm) that yields significant insights and may offer a better understanding of questions such as the entrepreneurial transition dilemma, serial entrepreneurship, and lifestyle entrepreneurs. Focusing on the ways that entrepreneurs think and make decisions in combination with relevant firm level or environmental level variables allows us as researchers to keep the individual entrepreneur in the equation without falling into the personological trap that was indicative of so many of the past “trait” studies.

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We failed, however, to uncover significant relationships between cognitive misfit and growth intentions (H4) and percentage change in employee growth (H5). With respect to growth intentions, we proposed that individuals would desire to avoid cognitive misfit and, as a result, would seek to either grow or arrest development of their businesses in the direction that fit with their dominant style. While we expected that individuals would want to be in cognitive “fit,” this non-finding does highlight an interesting paradox. The highly intuitive entrepreneur is best suited for the early stages of the business; however, growing the business will likely lead to increased cognitive misfit. It seems plausible that the desire to achieve growth far outweighs the possible negative consequences of cognitive misfit (of which the highly intuitive entrepreneur may have little or no awareness). Further research is needed on this point, as it should provide better explanatory power to the executive limit scenario and its limitations. Finally, the relationship between cognitive misfit and percentage change in employee growth was not significant. This was the only outcome variable that was at the firm and not the individual level. This is clearly related to the issue of levels of analysis in entrepreneurship research. It is possible that the ability of the entrepreneur to influence the growth of the firm over a one-year period was too small to detect. Also, numerous confounding variables may influence employee growth. Method and measurement issues have to be examined in depth when attempting to link entrepreneur and firm level variables, and that link remains a big concern for entrepreneurship research. However, despite this non-finding, we believe that whenever possible, entrepreneurship researchers should examine possible links to traditional performance measures.

Implications for Scholars In this chapter, we have further validated and extended the construct of cognitive misfit as a viable facet of P-O fit. We have extended the traditional P-O fit approach beyond employees and some aspect of their job or organization to owners and their businesses. This not only adds validity to the P-O fit approach, but demonstrates its ability to be used to address fundamental questions in the field of entrepreneurship. This is a multidisciplinary and multilevel approach that allows researchers to include the individual entrepreneur in the study of entrepreneurship, while avoiding the limitations and traps of earlier studies using psychological variables. One problem with employing a multidisciplinary approach such as the one in this study is that many of the measures were developed for employees within organizations and not for owners/entrepreneurs. The mean scores for several of the dependent variables indicate that owners/entrepreneurs are different than

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employees with respect to burnout, satisfaction, and intention to exit. New measures of these variables, scaled specifically for entrepreneurs, would allow us as researchers to capture much more of the true variance on these variables and would be a solid contribution to research of this type. The idea that the owner/CEO/entrepreneur transition dilemma is a problem of misfit is not new in the management literature. However, which individual and environmental variables might lead to this misfit and the nature of the relationships between these variables is very underdeveloped. We provide a framework that specifies the interaction of two of these variables (cognitive style at the individual level and work context (structure) at the firm level) as a potential contributing source of this misfit.

Limitations and Future Research This study uses intentions as a proxy for actual behavior. Whereas intentions have been linked to actual behavior in P-O fit studies (Chatman, 1991), it should be acknowledged that intentions do not always translate into actual behavior. A longitudinal design is necessary to determine if expressed intentions ultimately lead to a specific behavior. Also, the generalizability of the results to owners/entrepreneurs in other types of industries should not be assumed. In the end, the generalizability of the results of this study can only be determined through testing with different subjects and settings (Flanagan & Dipboye, 1980). Finally, and as is often the case with studies of this kind, despite the precautions undertaken and some comparative support, it is impossible to rule out common method bias. A number of alternative cognitive style models have been excluded from this study and could also be potentially relevant. Furthermore, the construct of cognitive misfit is only one facet of fit by which to explore many of the lingering questions in entrepreneurship. While this study finds that the construct of cognitive fit/misfit does hold significant explanatory power with respect to entrepreneurial behavior, it is likely just one component in what is ultimately a much more well-defined model of entrepreneurial behavior. Therefore, the results of this study point to a number of promising avenues for future research. Studies that combine both individual and situational factors through an interaction approach may hold great promise (Stewart, 1996). While this study focused on the interplay between individual decision-making style and the situational factor of work context, the examination of the interaction between decision-making style and other situational factors would appear to be a promising approach. Further, a longitudinal approach would allow us to examine the link between intentions and actual behaviors and outcomes.

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Moreover, if one looks at entrepreneurship as a career choice, then following entrepreneurs throughout their careers (possibly including multiple new business formations) seems to be an obvious and logical approach. Why does one entrepreneur start and grow multiple businesses over his or her career (serial) while another is content to only start one business (novice) and even arrest development (lifestyle) at a certain level? Cognitive misfit could add some substantial understanding with respect to these different types of entrepreneurs. Roure and Maidique (1986) found that experienced and well-balanced entrepreneurial teams influence organizational performance. The theory on decision-making styles explicitly states that one form of coping behavior is the formation of teams to handle non-preferred tasks or problems (Kirton, 1989). While the design of this study did not allow for the examination of entrepreneurial team compositions, this would appear to be a necessary area of investigation. We propose that the effectiveness of the entrepreneurial team could be examined by looking at the decision-making styles of the individual team members. Do well-balanced entrepreneurial teams (from a decision-making style perspective) outperform teams that are made up of members with similar styles? Do more experienced entrepreneurs build teams with members having similar or dissimilar styles to their own? Does having a team with a range of styles and different from that of the entrepreneur moderate or mediate the relationships found in this study? There is a large body of existing research on decision-making style and teams within organizations. Extending this research into the study of entrepreneurial teams is an important and very promising area for future research.

CONCLUSION If, as many researchers have argued, the individual entrepreneur is the most salient unit of analysis in entrepreneurship research and theory (Herron & Sapienza, 1992), then a more complete understanding of the entrepreneur is a necessary prerequisite for a more refined understanding of the process of entrepreneurship. A robust and comprehensive model of entrepreneurship must demonstrate how the predispositions and cognition of entrepreneurs are transformed into action (Shaver & Scott, 1991). The findings presented in this paper suggest that cognitive misfit is a useful construct in understanding entrepreneurial attitudes and intentions. Examining the interactions of entrepreneurs’ different decision-making styles and aspects of their firms allows us to avoid the limitations associated with focusing on only individual or firm variables to explain behaviors and organizational outcomes. We believe that this research represents an important step, not only in gaining a fuller

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understanding of the entrepreneurial transition dilemma, but also, in ultimately creating a more complete model of the entrepreneurial process.

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THE ROLE OF REGRETFUL THINKING, PERSEVERANCE, AND SELF-EFFICACY IN VENTURE FORMATION Gideon D. Markman, Robert A. Baron and David B. Balkin INTRODUCTION Shane and Venkataraman (2000) and Venkataraman (1997) suggest that the field of entrepreneurship seeks to understand how opportunities are discovered, created, and exploited, by whom, and with what consequences (italic added). Surprisingly and despite the fact that the person – the entrepreneur – is central to the creation of new ventures, entrepreneurship scholars are reluctant to explicitly include individual differences in formal models of new venture formation. For example, notwithstanding the important role that entrepreneurs play in forging new wealth and creating new jobs, research to identify cognitive processes, attitudes, behaviors, traits, or other characteristics that distinguish entrepreneurs from others who opt to work as employees remains somewhat marginal. Indeed, only very few studies on individual differences have been published in leading management journals. One possible explanation for this reluctance is that in the past researchers might have classified most individual differences as traits research and thus criticism spilled over to include all individual difference research, regardless of


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whether the focus was trait, cognitions, emotions, attitudes, behaviors, or other characteristics. The goal of this paper is to employ a rigorous methodology in order to assess the impact of several individual difference factors (factors possessing a firm foundation in existing theory and empirical findings) on individuals’ decision to use their inventions (i.e. patents) to start new ventures. Indeed, a more recent research stream suggests that individual differences might play an important role in entrepreneurship (Baum, Locke & Smith, 2001). To mention a few studies, Shane (2000) found that individuals from diverse technological backgrounds who assess the same technological invention (e.g. 3DP™ ) recognize and then develop very different business opportunities. Sarasvathy, Simon and Lave (1999) used verbal protocols to illustrate that entrepreneurs evaluate and process information differently from bankers. Additional evidence suggests that entrepreneurs, as compared with managers, may perceive and react to risk differently; entrepreneurs gathered significantly less information, utilized less formal techniques to analyze problems, and followed less rational decision processes than managers did (Busenitz, 1999; Busenitz & Barney, 1997). In contrast, Kaish and Gilad (1991) found that entrepreneurs spent considerably more time searching for information and paid attention to different risk cues than did executives of established firms. Studying several biases such as illusion of control and the belief in the law of small numbers, Simon, Houghton and Aquino (2000) suggest that entrepreneurs might not realize that certain tasks are beyond their control. Others noted that entrepreneurs tend to make quick decisions (Bird, 1988; Eisenhardt, 1989; Stevenson, Grousbeck, Roberts & Bhidé, 1999). New evidence also confirms that common cognitive scripts not only explain similarities in decision-making among entrepreneurs across cultures but also behavioral differences between entrepreneurs and non-entrepreneurs within countries (Mitchell, Smith, Seawright & Morse, 2000). Recently, longitudinal and cross-sectional research showed that, in the architectural woodworking industry, CEOs’ traits, skill, and motivation were significant (direct and indirect) correlates of ventures growth (Baum & Lock, 2002; Baum, Locke & Smith, 2001). Finally, entrepreneurs and non-entrepreneurs may react to environmental complexity in contrasting ways. Meyer and Dean (1990) report that managers replace founding entrepreneurs because the latter reach the “executive limit”; entrepreneurs fail to adequately reduce environmental complexity and thereby hinder subsequent venture growth. These more recent studies hint that older research may have yielded inconclusive findings or identified attributes that could not reliably distinguish entrepreneurs from non-entrepreneurs due to inappropriate methodology such as invalidate measures and inadequate statistical control (e.g. issues of direct versus indirect effects; cf. Baum & Locke, in press). Many past studies relied on

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psychometric scales of questionable validity or inappropriate sample populations. For example, what is the theoretical rationale for comparing entrepreneurs with managers (Busenitz & Barney, 1997), bankers (Sarasvathy, Simon & Lave, 1999), or students (Chen, Greene & Crick, 1998; Krueger, 1993)? Entrepreneurs build new businesses regardless of resource availability (Timmons, 1999); they erect their firms from the ground up, are normally highly vested in their new venture, and subsequently are liable for their firm’s success or failure. Managers and bankers, on the other hand, command and control established business propositions; they are agents not owners, and they are not as exposed to personal risks as entrepreneurs are. Keeping these contextual variations in mind, it is easy to understand why research on individual differences that is based on such samples might provide very inconsistent insights on entrepreneurship. The challenge of selecting appropriate control groups is related to the daunting question “who is an entrepreneur and who is not” (Robinson et al., 1991). Thus an important factor that hinders research in entrepreneurship evolves around procedures for sample selection. First, due to the aura surrounding economic growth and innovation, many studies implicitly share a common bias of over-selecting successful entrepreneurs. Second, despite the importance of technological innovation not many studies actually control for subjects’ ability to innovate and hence it remains unclear whether reported differences are due to group membership (e.g. entrepreneurs vs. non-entrepreneurs) or ability to innovate. Third, to our knowledge, few studies relied on homogenous groups of entrepreneurs and even fewer relied on random sampling techniques. Ignoring that large variations among entrepreneurs make comparisons within and across studies difficult, much research used convenient samples of entrepreneurs who work in diverse, frequently even unrelated, industries. Finally, researchers in entrepreneurship have a tendency to “handpick” their samples despite the fact that pre-study knowledge of group membership might have inadvertently introduced additional confounds and biases. The foregoing review suggests that although the field of entrepreneurship is interdisciplinary with roots in economics, sociology, management, and psychology, research on individual differences in entrepreneurship has not drawn extensively upon the findings and methodologies of social and cognitive psychology. This is somewhat disappointing because a number of key issues that entrepreneurship research tries to address focus on human cognitions, thoughts, and mental models (e.g. “Why do some persons but not others become entrepreneurs?” “What is it that makes some entrepreneurs so much more successful than others?”). Indeed, an important objective of this chapter is to augment the methodological standards currently used in research on individual differences in entrepreneurship by relying not only on constructs and processes borrowed from sister disciplines such as social and cognitive psychology, but also on their methodologies.

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For the past several years, Baron and his colleagues (e.g. Baron, 1998, 2000; Baron & Markman, 2000, in press; Baron, Markman & Hirsa, 2002; Markman, Balkin & Baron, in press; Markman & Baron, 2002) and others (cf. Baum & Locke, in press; Baum, Locke & Smith, 2001; Shane, 2000) have sought to augment methodological rigor and create closer conceptual links between entrepreneurship and cognitions by identifying well-established psychological constructs that seem relevant to understanding the characteristics and activities of entrepreneurs. Our research sought to extend this ongoing work. Building on emergent research on individual differences and entrepreneurship (including, Busenitz & Barney, 1997; Chen, Greene & Crick, 1998; Honig, 1998; Stewart et al., 1999, to name a few), we focus on three dimensions that appear to be particularly relevant for entrepreneurship research. The factors examined here are: (a) regretful thinking (an aspect of counterfactual thinking), thoughts regarding events and outcomes different from the ones that actually occurred (Baron, 2000); (b) perseverance – perceived ability to persist and overcome adversity and challenges;1 and (c) self-efficacy – our belief in our ability to perform certain tasks successfully (Bandura, 1997). As explained henceforth, we predict that since setbacks, challenges, snags, and disappointments characterize the process of new venture formation, persons who start new companies – as compared with ones who don’t – will recall more regretful thoughts, but will also perceive higher levels of capacity to persevere and selfefficacy. To reiterate, the goal of this chapter is to address the following question: Are patent inventors who start new companies (hereinafter called entrepreneurs) higher in terms of perceived regrets, perseverance, and self-efficacy than inventors who opt to work as employees for established organizations (hereinafter called non-entrepreneurs)?

THEORY AND HYPOTHESES Regretful Thinking: Thinking About Negative Outcomes Experiencing unintended detrimental consequences or imagining favorable outcomes that did not materialize is a frequent experience for most people. Such regretful thinking often occurs in response to information about unfavorable outcomes and unmet expectations, and frequently leads, in turn, to strong emotional reactions such as disappointment and blame (Zeelenberg et al., 1998). For instance, regretful thinking can be observed among Olympic athletes who win silver medals. Such athletes have been found to be less happy with their success than are athletes who receive bronze medals (Medvec, Madey & Gilovich, 1995). Research on counterfactual thinking explains this seemingly anomalous


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result in the following manner: Silver medal winners are unhappy because they imagined winning a gold medal (i.e. they imagined better outcomes than they actually received), while bronze medallists are happier because they imagined receiving no medal at all (i.e. worse outcomes than they actually received). Thus, like counterfactual thinking, regretful thinking is a cognitive representation of alternative consequences and they are activated automatically, particularly (though not exclusively) in response to misfortunes and disappointments (Baron, 2000). Regretful thinking is important because such strong sentiments may have profound effects on one’s mood, understanding of cause-effect relationships, decision-making, task performance and even personal health (Roese, 1997). For instance, thoughts may activate the same analgesic pathways that morphine does; placebos boost blood flow to brain areas that are packed with opiate receptors. In management, research shows that regret and blame are particularly vivid in contexts involving product failure (Creyer & Gurhan, 1997). Our basic prediction is that entrepreneurs, because they encounter potent market and technological obstacles, experience substantially more regrets than others who invent yet participate in a very limited way in the commercialization of their technologies. Championing a new venture can evoke strong emotions; capitalizing on poor opportunities (and the subsequent failure) or caving in to competition (and observing how others reap the rewards) can stir up strong regrets. At first glance, this prediction would appear to be directly contrary to findings reported by Baron (2000), who reported that entrepreneurs, as compared to non-entrepreneurs, experienced fewer life regrets and less intense regret over missed opportunities. However, there appear to be several reasons why Baron (2000) found fewer regrets in his group of entrepreneurs while we expect to find more (and more intense) regrets among entrepreneurs in the present research. First, Baron (2000) worked with a very different population than the one employed here. The entrepreneurs his research had all started their own businesses (Gartner, 1988), and so can reasonably be described as entrepreneurs. However, in many cases, these businesses did not involve ideas for new products or services; rather, they were largely “new variations on existing themes.” In the present research, in contrast, we focus on persons who have generated ideas for new products or services and, moreover, have obtained patents on these inventions. In short, they are all inventors, and to the extent they use their inventions to start new ventures, are entrepreneurs in the strongest sense of this term, as defined recently by Shane and Venkataraman (2000). We reason that people who have discovered an idea for a product or service feel a strong, personal involvement with this idea: after all, they have truly created it. For this reason, we suggest, they may be more likely to experience strong regrets following setbacks. In short, they are so “ego-involved” with their inventions, that they are more likely to experience regrets when they

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encounter obstacles while developing these ideas. In contrast, people who invent under the auspice of and for existing organizations may be less “ego-involved” with them; indeed, they have chosen to turn the inventions over to others. Thus, they would be expected to experience fewer or weaker regrets than entrepreneurs. A second reason why we predict different results in the present study than those obtained by Baron (2000) is as follows: in his research, Baron used a standard measure of counterfactual thinking – asking participants to list the three things they regret most in their lives. This kind of open-ended question leaves participants free to describe regrets over actions they had taken which had turned out badly (e.g. “I invested in a stock that went straight down.”) and actions they had not taken but wish they had (e.g. “I wish I had decided to pursue an MBA degree”). Research suggests that thinking about actions people wish they had taken but did not, can be especially unpleasant, so it is possible that the participants in Baron’s research suppressed reports of such events, thus lowering the total number of regrets reported. In the present research, we restrict our attention to regrets of decisions – actions taken that turned out poorly. As a result, participants may be more likely to report regrets than was true in the research by Baron (2000). And given the reasoning presented above (i.e. people who start new ventures remain ego-involved with their inventions), we predict that entrepreneurs will report more and more intense regrets than non-entrepreneurs. What about the pattern of regrets reported by entrepreneurs and nonentrepreneurs? If entrepreneurs are indeed more involved with their inventions than persons who invent for others, we might expect that the regrets they report will focus more on business decisions and other factors relating to their new ventures, while non-entrepreneurs will report a wider range of regrets (e.g. over education, career choices, etc.). This hypothesis too will be investigated. Overall, our reasoning concerning regretful thinking leads us to the following hypotheses: Hypothesis 1. Inventors who build new ventures based on their discoveries report a higher number of regrets than inventors who invent as employees for an existing company. Hypothesis 2. Inventors who build new ventures based on their discoveries report more intense regrets than inventors who invent as employees for an existing company. Hypothesis 3. The regrets of inventors who build new ventures based on their discoveries focus to a greater extent (than inventors who invent as employees for an existing company), on business decisions, but to a lesser extent on other decisions (e.g. ones pertaining to education, career choices, personal relationships).


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Perseverance Despite repeated assertions – particularly by economic paradigms – that at the core of entrepreneurship is “opportunities recognition” and “alertness” (cf. Kirzner, 1997), we maintain that entrepreneurship research must also assess key activities such as one’s conviction in his or her ability to convert discoveries into moneymaking services or products. The recognition versus execution debate is not new and many scholars remain adamant that the opportunity – and particularly the process of opportunity recognition – remains fundamental to wealth creation processes (Shane, 2000; Shane & Venkataraman, 2000). We fully agree, yet our interviews of patent inventors, technology transfer executives, patent attorneys, and entrepreneurs indicate that only small subsets of all discoveries are actually commercialized. A typical Silicon Valley VC firm, which receives over 5,000 unsolicited business plans a year and invest only in about ten, will earn industry admiration once even one of those unsolicited business plans becomes a successful venture. This inverse exponential relationship between discovered opportunities and tangible ventures hints that recognizing opportunity is perhaps necessary but clearly insufficient for entrepreneurship to take place. Because the recognition of opportunity is largely an intangible, cognitive process to be finally validated when the new venture or product is finally launched, a fundamental question in entrepreneurship research is not only who can discover opportunities, but also who can persevere to harvest them (Shane & Venkataraman, 2000). Business history is quite familiar with inferior products, services, and technologies that nevertheless outmaneuvered, out-marketed, and outsold superior counterparts or vice versa, where advanced products and breakthrough technologies that despite their pre-eminence were defeated by inferior counterparts. The Wintel (Microsoft operating system and Intel microprocessor) standard of personal computers became the dominant computing technology despite the fact that Apple’s MacIntosh technology provided user-friendlier interface. The technology to record video data on magnetic tape and the subsequent battle between Betamax and VHS is another example. To use another anecdote, although science fiction enthusiasts envisioned teleportation – dematerializing an object at one location, and sending the details of that object’s precise atomic configuration elsewhere, where it is reconstructed – as a real opportunity, for years it was thought to violate the Heisenberg uncertainty principle of quantum mechanics (Einstein, Podolsky & Rosen, 1935). Now, using a paradoxical feature of quantum mechanics known as the Einstein-Podolsky-Rosen correlation or EPR entanglement, it is known that quantum teleportation is possible – at least with photons (Bennett et al., 1993). The point is that despite the recognition of an opportunity (i.e. where time and space could be eliminated from travel), complex

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technological barriers, high and long-term risk, and intensive investment capital required to convert such opportunity into a reality daunt entrepreneurs, investors, and even nations. Like Thomas Edison, who accumulated 1,093 patents and noted that genius is 1% inspiration and 99% perspiration, we point that it is one thing to discover opportunities, but an entirely different matter to harvest them. To recap, we concede that opportunity recognition may be a precondition to entrepreneurial efforts, yet here we stress that it remains unclear who are the persons who not only recognize novel, useful, and non-obvious technologies, but also transform these inventions into establish new ventures. Because the actual pursuit of opportunities is very challenging (McGrath, 1995), we suspect that at least part of the answer to this question lies in human variability in terms of perceived capacity to persevere in the face of adversity. Although the concept of perseverance has been studied for many years, most research on this topic has focused on how beliefs, thoughts, and attitudes persist in light of new information and in spite of the discrediting of old “facts.” More recently, Eisenberger (1992), Eisenberger, Kuhlman and Cotterell (1992), Eisenberger and Leonard (1980), have extended this line of work to the domain of task performance and work persistence. For example, Eisenberger (1992) found that reinforced effort results in persistence that transfers to different tasks: the phenomenon of learned industriousness (Eisenberger, 1992) occurs when high effort on one task (e.g. solving complex anagrams) transfers to another (e.g. detecting differences between cartoon drawings). Eisenberger and Leonard (1980) found that high effort reduces disruptive responses such as frustration, blame, and anger produced by early failure, and thus leading to greater subsequent tenacity and persistence. Like Eisenberger (1992), we define perseverance as one’s capability to persist and endure in the face of difficulties, risks, and failure. We propose that because individuals experience varying levels of adversity, success is determined, to an important degree, by the extent to which individuals persevere despite what appear to be insurmountable obstacles, or in Stoltz’s terms (1997, 2000), adversity. A corollary of this is that perceived perseverance may be crucial – even if insufficient – for one’s success in entrepreneurial settings. Perceived capacity to persevere influences individuals’ courses of action, the level of effort they put forth while pursuing their endeavors, the length of their endurance and the level of their resilience in the face of lasting obstacles and repeated failures (cf. Eisenberger & Leonard, 1980). Perceived perseverance also influences how much stress and setbacks individuals experience while they cope with taxing situations, and the level of accomplishments they realize (Bandura, 1997). For instance, perseverant people find out ways to circumvent constraints or change them by their actions, whereas less diligent people are easily discouraged by impediments and unexpected challenges (Bandura, 1997; Eisenberger et al., 1992).


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Launching a new venture requires a high level of conviction in one’s ability to overcome challenges plus the successfully transformation of the new technologies into attractive commercial products or services. We propose that people who discover or invent similar inventions and are exposed to very comparable obstacles differ in the way they perceive adversity. This proposition begs the following question: What specific types of perseverance will be most useful to entrepreneurs? Although the answer to this interesting question depends, to an important degree, on the various situations entrepreneurs face, a careful review of available evidence (e.g. Stoltz, 1997, 2000) indicates that two constructs appear to be particularly relevant. These include, perceived control over adversity and perceived ownership of the outcome of adversity (regardless of what caused the adversity in the first place). As detailed below, we predict that technical inventors who create new ventures perceive higher levels of control over the adversity they face, and sense greater ownership regarding outcomes of the adversity.

Control: Perceived Control over Adversity People strive to control events that affect their life circumstances because doing so provides innumerable personal, financial, and social benefits (Lam & Schaubroeck, 2000). Being able to predict and control events fosters adaptive preparedness, whereas inability to exert influence over adversity breeds apprehension, apathy, and at times desperation (Bandura, 1986). Also, because actions are based more on what is perceived or believed than on what might be objectively true, alleged control is an important precursor to one’s level of motivation and actions. Specific perceived control over adversity is a major basis of action because people who believe they can attain certain outcomes have the incentive to act (Bandura, 1997). Perceived control over adversity – which is central to most human behaviors and the focus of this theory – should not be confused with general “locus of control”; the former refers specifically to control over adversity whereas the latter is a global measure of one’s ability to influence one’s own fate or outcomes (Stoltz, 1997). Perceived control over adversity influences the course of action, the level of effort put forth, and the length of perseverance and resilience in the face of obstacles, failures, or hardship. Perceived control over adversity also affects how much stress individuals experience while they cope with taxing environments, as well as the level of accomplishments they realize (Stoltz, 2000). In short, perceived control over adversity influences what individuals do and become and their motivation to act despite impediments. Theory and practice agree that when confronting setbacks, perseverant individuals intensify their effort and experiment with new actions, whereas those

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who are less perseverant quickly give up (Cervone, 1989). We note that the development and use of new technologies as a basis for new components or even end products is the outcome of intensive work conducted by determined, self-disciplined individuals (cf. McGrath, 1995). Indeed, our interviews with numerous patent inventors (entrepreneurs and non-entrepreneurs) indicate that their key challenge is to persevere until they thoroughly troubleshoot vexing technological obstacles. Since launching a new venture entails a combination of both technological and business obstacles, it stands to reason that entrepreneurs would probably have stronger perceived control over their adversity. Stated differently, launching a business requires strong perceived control over adversity along both technological and business value chains; entrepreneurs must not only convert their new technological discovery into working prototypes but also transform them into viable moneymaking products and services. While we suspect that all inventors perceive strong control over adversity related to their work, we expect entrepreneurs to perceive even stronger control over adversity because the survival and longevity of their young venture depends on their perseverance and determination to convert their new technology into a business. Hence the following hypothesis: Hypothesis 4. Inventors who build new ventures based on their discoveries tend to have higher perceived capacity to control adversity than inventors who invent as employees for an existing company.

Accountability: Perceived Ownership of the Outcomes of the Adversity Accountability, particularly in response to unfavorable events, might manifest itself as regret, disappointment, and blame (Roese, 1997). Some individuals, for instance, experience intense discontent when they fail to attain outcomes for which they have a strong mental image (Medvec, Madey & Gilovich, 1995). Such strong emotions are important because growing empirical evidence suggests that emotions have profound effects on perceptions and judgments (e.g. Forgas, 1995), understanding of cause-effect relationships, decision-making, and thus on task performance (Mandel & Lehman, 1996). Perceived accountability, despite the short-term negative affect it generates (e.g. sadness, disappointment, and blame), is offset by inferential benefits that prove advantageous on a longer-term basis. For example, when task performance is deemed inferior because of lack of effort (rather than ability), a causally potent antecedent has been identified; deploying additional effort will enhance future performance. Substandard execution and its associated negative affectivity alert us to a particular problem and prompt


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corrective thinking and action (Schwarz, Bless, Srtack & Klumpp, 1991). In short, once accountability “mobilizes” us to rectify negative outcome (Peeters & Czapinski, 1990), an adaptive reaction is established. Accountable individuals focus on actions and outcomes; they take steps to circumvent unpleasant events or center their attention on the outcomes of adversity regardless of its origin (Stoltz, 1997). Because entrepreneurship entails highly turbulent environments in which the process of transforming technological opportunities into innovative products or services rarely goes undisturbed, human variability in preference and reaction to setbacks and disappointments may be quite telling. The question, then, is whether entrepreneurs, in the presence of adversity and setbacks, perceive stronger accountability over adversity (regardless of its origin) than do non-entrepreneurs? Two rationales, both emanating from the interaction between persons’ preferences and situations (Markman & Baron, 2002), suggest that the answer to this question is yes. First, there is no strong reason to suspect that inventors differ on how hard they work on their discoveries. However, because it is public knowledge that launching a new venture requires resolve to overcome setbacks – including those impediments generated by others – persons with weak sense of ownership regarding adversity might shirk from becoming entrepreneurs. Conversely, individuals who take leadership over adversity are more likely to excel in entrepreneurial settings. Naturally, accountability to harsh circumstances and setbacks could intensify subsequent to becoming entrepreneurs and as such diminished ownership over adversity among entrepreneurs might increase business vulnerability. Agency theory confirms that owners are more committed to and accountable for producing commercial outcomes than their agents (Deckop, Mangel & Cirka, 1999). This suggests that accountability and business ownership are causally interrelated: highly accountable persons are likely to pursue entrepreneurial undertakings and those who are owners of their ventures likely to become more accountable. Second, studies report that lack of ownership, as captured by various excuses and rationalizations, is a self-protecting reaction aiming to reduce personal responsibility for taxing events (Schlenker, 1997; Schlenker et al., 2001). The goal is to sway others (self-included) that failure did not stem from one’s action as it might otherwise appear to be; and when self-fault is undeniable, that the “incident” is merely due to transient blemish rather than permanent attribute (e.g. carelessness rather than stupidity). Avoiding accountability minimizes negative repercussions, including reducing negative affect (e.g. guilt, shame, remorse), damage to reputation, and punishment for failures. Although failure is rarely intentional, individuals vary greatly in the way they see failure, and thus the prospect of failure guides many decisions (Schlenker et al., 2001). As entrepreneurial undertakings are notoriously difficult and the large majority of young businesses fail (Hamel, 2000; The State of Small Business: A Report to the President, 1995), it stands to

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reason that attitudes towards failure influence one’s likelihood of championing a new venture. Research confirms that lack of accountability and the assignment of blame are particularly vivid in contexts involving failure (Taylor, 1995) and that responsibility diffuses in proportion to group size (Forsyth, Zyzniewski & Giammanco, 2002). Keeping this in mind, it is apparent that work span and responsibility horizons of non-entrepreneurs and entrepreneurs are also unequal. Entrepreneurs are in charge of their business; its growth and the markets to be pursued, its competitiveness, and of course, failure. Entrepreneurship is an occupation in transition; entrepreneurs work with more diverse and interdependent stakeholders and they are accountable for deliverables that are outside their immediate function and control. The cross-functional nature of their work diverges from the traditional work and compartmentalized activities that are common in domains in which non-entrepreneurs work. The prospect of all-embracing vocation span and the open-ended responsibilities (for good, but also bad) suggest that persons seeking entrepreneurial careers probably hold themselves responsible and accountable for adversity they face. On the other hand, persons shirking from taking ownership over predicaments that are not their doings are rather unlikely to seek entrepreneurial undertaking. Thus, the interaction between persons’ preferences and contexts (cf. Markman & Baron, 2002), agency theory and the obvious implications of failure suggest that persons opting to become entrepreneurs will perceive stronger ownership over outcome of adversity than their counterparts. This reasoning is summarized by the following hypothesis: Hypothesis 5. Inventors who build new ventures based on their discoveries tend to have higher perceived ownership of the outcome of adversity that they encounter than inventors who invent as employees for an existing company. Although studies on individual differences recognize some unavoidable overlaps between perseverance and self-efficacy, evidence from theoretical and applied studies on human variability suggests that these constructs have unique features that merit their conceptual distinctness (Bandura, 1995, 1997; Nir & Neumann, 1995). Hence, to further theoretical development we conclude this theory section with additional discussion regarding self-efficacy. Testing for the unique effect of self-efficacy, rather than self-esteem or locus of control, is important because research has shown that the former is a robust predictor of superior task performance (cf. Bandura, 1997), and human variability in entrepreneurship (Baum et al., 2001, 2002; Chen, Greene & Crick, 1998). We reasoned that testing for the unique effect of perseverance, over and above self-efficacy, would provide discriminate validity between perseverance and self-efficacy.


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Self-Efficacy: Beliefs in our Ability to Effectively Accomplish Certain Tasks To briefly reiterate, self-efficacy involves the belief that we can organize and effectively execute actions to produce given attainments (cf. Bandura, 1997; Chen, Greene & Crick, 1998; Gist & Mitchell, 1992; Krueger & Brazeal, 1994). Self-efficacy impacts our perceived control, how much stress, self-blame, and depression we experience while we cope with taxing circumstances, and the level of accomplishments we realize. It also influences our courses of action, level of effort, our reaction to failure, and whether our thoughts are self-hindering or self-aiding (Bandura, 1999; Wood & Bandura, 1989). Vasil (1992) found that when the effects of experience, academic rank, and disciplinary affiliation are controlled, scholars high in self-efficacy excel. While many occupations call for high self-efficacy (Gist & Mitchell, 1992), performing innovative research resulting in patents is a good example since it is constrained by time, funding, and uncertain outcomes despite relentless intellectual effort. Moreover inventions are scrutinized, challenged, and frequently refuted before (and sometimes after) they attain patent status. Since the process of scientific discovery is strewn with technological obstacles, successful patenting rests heavily on strong self-belief (Bandura, 1999; Gist & Mitchell, 1992; Wood & Bandura, 1989). In short, self-efficacy is central to most human functioning, and since actions are based more on what people believe than on what is objectively true, thoughts are a potent precursor to one’s level of motivation, affective states, and actions. If self-efficacy impacts career undertaking, performance, and success would it also predict, or at the very least be related to, entrepreneurial pursuits? We think that it would because of three main reasons. First, people avoid careers and environments they believe exceed their capabilities (regardless of the benefits these may hold), but they readily undertake vocations they judge themselves capable of handling (Krueger & Dickson, 1994), and the higher their self-efficacy, the more challenging the activities they pursue. Second, because entrepreneurs operate at the crux of change, innovation, and market perturbation, they personally realize higher financial, technological, and legal liabilities and uncertainties. On the other hand, inventors “working-for-others” continue to operate in relative seclusion and predictability; they are less exposed to market resistance, competitors’ retaliation, or suppliers’ protest. Past research indicates that under taxing circumstances individuals with higher self-efficacy perform more adeptly (cf. Bandura, 1997). Finally, although some research has suggested that self-efficacy successfully differentiates entrepreneurs from non-entrepreneurs (Chen, Greene & Crick, 1998), as we noted earlier, such inferences stem from studies with students, managers, and occasionally with handpicked samples of entrepreneurs. We suggest that starting a new venture – obtaining external funding, recruiting

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key partners and employees, and overcoming what appear to be insurmountable business and technological obstacles – is substantially different than managing an existing operation or undergoing classroom simulations. Since self-efficacy reliably predicts the scope of career options considered, occupational interests, and personal effectiveness, we suggest that it will also be related to the pursuit of entrepreneurial activity. Thus our last hypothesis is as follows: Hypothesis 6. Inventors who build new ventures based on their discoveries have higher self-efficacy than inventors who invent as employees for an existing company.

METHODS We decided to focus on a sample of patent inventors because patents are a reasonable proxy for technological innovation, a precursor to newly developed product components, and an indication of technological capital (Balkin, Markman & Gomez-Mejia, 2000). Patents may also erect legal and technical barriers to rivals. For example, our interviews of patent inventors, technology transfer executives, and patent attorneys suggested that good patents may provide footholds to new technologies and therefore are an important source of competitive advantage. Finally, patents are an indication of inventive capacity that benefits society (Trajtenberg, 1990). To reduce selection biases commonly found in entrepreneurship research (see Markman, Balkin & Baron, in press, for a detailed discussion on this issue), while obtaining evidence on the hypotheses, we extracted a random sample from a list of 4,861 patent inventors, obtained from the U.S. Patents and Trademark Office. All 4,861 inventors were granted patents for their inventions during 1997 and 1998 for inventions encompassing surgery devices (patent classes 600, 601, 602, 604, 606, 607). Since the original list of 4,861 inventors included only minimal contact information (i.e. first and last name, city, and state), we used Visual Basic to scan the Nation Wide Phone Directory (NWPD) software that resides on CD ROMs and to retrieve complete contact information (e.g. full address and phone number) for each inventor. The Visual Basic output yielded 3,491 non-duplicated entries. Then, using Excel Spreadsheet and the “randomize” command function, we selected a random sample of 586 patent inventors. Then, in early 1999, all 586 patent inventors were contacted via telephone, were invited to participate in our study, and were sent a survey. Two weeks later we called all the non-respondents and then sent our second batch of survey mailing. We repeated the same procedure two to six additional


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times with all non-responding inventors. Although we sent surveys to a random sample of 568 patent inventors and received 233 surveys back (41% response rate), only 217 were usable. Hence, in contrast to many studies in entrepreneurship that compared samples of known entrepreneurs with a sample of predetermined non-entrepreneurs, we relied on a random sample of inventors, all of whom invent in the same technological space, at the same time period, and we did not know ahead of time, which inventors were entrepreneurs and which were not. Placed at the last section of the mail survey, a qualifying question asked inventors to indicate whether they used their invention to start their own business in 1997 or 1998. Such a qualifying question used successfully in previous studies including the Entrepreneurship Research Consortium (ERC project; Carter, Gartner & Reynolds, 1996). Of the 217 qualified inventors, 55 (25%) used their invention to start a new company and therefore were classified as entrepreneurs (coded as 1), whereas the remaining 162 (75%) did not, and thus were classified as non-entrepreneurs (coded as 0). This relatively high rate of entrepreneurship (25%) may be attributable to the monopolistic nature of patents. Unlike traditional businesses, start-ups anchored in patents enjoy substantial technological protection, competitive insulation, some legitimacy, and a relatively wider window of opportunity (Rivette & Kline, 2000). Before outlining the study’s procedures and operations, it is worthwhile to reemphasize four things that distinguish this study from others. First, classification of participants, in this case, patent inventors as entrepreneurs or as non-entrepreneurs, was made only after the surveys were collected and data were coded. Second, restricting the assessments of entrepreneurs and non-entrepreneurs to inventors who invent in the same technological space at roughly the same time period (1997–1998) provides a stronger test of hypotheses outlined above. Third, although the source of the primary data collection was based on self-reported surveys, we also verified data consistency through phone interviews and crosschecks with the U.S. Patent and Trademarks Office website (e.g. patent count). Fourth, we made an attempt to account for non-response bias. To this end, we compared our random sample with 46 inventors who refused to return their surveys back on age, formal education, annual income, and number of patents developed. Data from non-responding inventors – obtained via phone calls – and analysis showed no significant differences between the two samples. Finally, because of potential covariation between opportunities and individuals, Shane (2000) advocates that studies on individual differences control for the characteristics of the opportunity. To alleviate this predicament we took three remedial steps: First, using a random sample of inventors implies that the characteristics of the opportunities may also be randomly distributed between the two groups. Second, our AQ measures were not tied to the opportunity, but instead were based on the same hypothetical scenarios described

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henceforth (Table 1). And finally, inventors in this study invented highly related patents (surgery devices), which to some extent represents a set of opportunities with similar characteristics.

Procedures and Operational Measures Inventors were asked to complete a short questionnaire consisting of several scales adapted from widely used measures of regretful thinking (Baron, 2000), perseverance (Stoltz, 1997, 2000), and self-efficacy (Maurer & Pierce, 1998). Unlike previous research that treated regretful thinking as a unidimensional or homogenous construct, we assessed three aspects of regretful thinking, including a quantitative, qualitative, and magnitude measure of inventors’ regrets. The quantitative and qualitative measures were based on inventors’ responses to an open-ended question: “think about your life and career and list the decisions that you regret most.” This question generated two different dimensions of regretful thinking. First, we counted the quantitative measure of regrets by adding up the decisions that inventors regretted most. Second, a content analysis of the same decisions by two independent raters had identified six types of regrets (e.g. business opportunities, career, education, investment and finance, personal value, and relationships). Interrater consistency was high; in 92% of the cases they were in complete agreement. This was the measure of the qualitative nature of regrets. Finally, on the next page of our survey, participants were also asked to indicate, on a seven-point scale, how much regret they had experienced regarding the decisions they had just listed (1 = little regret; 7 = much regret). This was the measure of the magnitude of regrets. The instruments to measure regretful thinking are depicted in the Appendix. The measure of perseverance consisted of a 40-item scale that was developed and validated by Stoltz (1997, 2000) with more than 100,000 participants from diverse organizations in a variety of industries. Each item consisted of a statement representing hypothetical events (e.g. “you apply for a job change and don’t get it”; “you fail to meet the deadline on a major project”) followed by two questions, each representing the dimensions described earlier (i.e. control and ownership). The respondents’ task was to indicate, on a five-point scale, the extent to which the statements represented them (see Appendix). Factor analysis showed that the two constructs, composed of eight items each, were reliable (e.g. control: ␣ = 0.77 and ownership: ␣ = 0.81). Following Stoltz’s recommendation (1997, 2000) we also created one additional variable – a composite of both scores of perseverance. Despite the fact that self-efficacy measures have generally relied on scales relating to specific tasks, some research calls for broader measures, particularly

1. Groupb 2. Regrets’ strength 3. Count of regrets 4. AQ – control 5. AQ – ownership 6. Self-efficacy 7. Age 8. Education 9. Innovation 10. Incomec

Mean

S.D.

1

2

3

4

5

6

7

8

9

0.25 5.77 2.87 3.40 3.99 6.01 48.11 18.94 13.21 11,8273

0.42 1.21 1.62 0.59 0.51 0.99 10.81 3.22 16.37 83,845

0.10 0.16* 0.19** 0.17* 0.17* 0.03 0.23** 0.03 0.04

0.02 0.16* 0.23** 0.14* −0.07 0.08 0.10 0.16*

0.11 0.17** −0.01 0.12 0.00 −0.13* −0.06

0.41** 0.18** 0.04 −0.07 0.03 0.18**

0.18** 0.06 −0.08 −0.04 0.12

0.03 0.01 0.05 0.11

−0.04 −0.01 0.04

0.24** 0.10

0.10

= 217. refers to entrepreneurs versus non-entrepreneurs. c Income is annual earnings in dollars. ∗ Correlation is significant at the 0.05 level (2-tailed). ∗∗ Correlation is significant at the 0.01 level (2-tailed).

aN

b Group


Table 1. Means, Standard Deviations, and Correlations among Study Variables.a

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when the vocations under consideration have little in common or require a very diverse set of skills (cf. Bandura, 1997). Since starting ventures requires human capabilities in many and different domains and validated self-efficacy scales for patent inventors are not yet available, we used a general scale. Therefore, perceived self-efficacy in this study was measured in terms of the belief about what one can do under different conditions with whatever skills one possesses (Chen, Gully & Eden, 2001; Eden & Aviram, 1993). This measure was an eight-item, seven-point scale (1 = strongly disagree; 7 = strongly agree) that was used successfully in previous research (Maurer & Pierce, 1998). Items in this scale included such statements as “I am strong enough to overcome life’s struggles,” “I can handle the situations that life brings,” and “I usually feel I can handle the typical problems that come up in life” (␣ = 0.89). All eight measures of self-efficacy are depicted in the Appendix. Consistent with previous research on individual differences in entrepreneurship, the survey obtained additional control variables such as age, education (measured by years of formal education), and personal annual income for 1998. Since all the inventors in our study worked on novel, non-obvious, and useful technologies, an additional control variable that our study brings to this line of research is a measure of one’s inventive capacity as captured by the number of patents granted to each inventor (Griliches, 1990; Romer, 1996).

Analyses A MANOVA examined the relationship between entrepreneurs and non-entrepreneurs (as the fixed factor) on a set of five dependent variables: magnitude of regrets; number of regrets; control over adversity; ownership regarding outcomes of adversity; and self-efficacy, where age, years of education, innovation (i.e. number of patents), and income included as covariates. Content analyses and discriminant analysis examined the relationship between entrepreneurs and non-entrepreneurs on all qualitative data regarding inventors’ regretful decisions.

RESULTS Table 1 presents means, standard deviations, and correlations for all the inventors, regardless of group membership. As shown in Table 1, the average inventor in this study was 47 years old, had more than 19 years of formal education, and at the time of the survey had been granted over 13 patents. In 1998, the average inventor earned approximately $118,000 a year. Entrepreneurs and non-entrepreneurs were closely matched on education, age, income, and innovations, and the inventors


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who also became entrepreneurs started their firms with two cofounders and raised, on average, $6 million to build their company. The results of the MANOVA revealed statistically significant differences between entrepreneurs and non-entrepreneurs on the sets of dependent variables (Pillai’s trace = 0.05, F = 3.80, p < 0.02). The size of the multivariate effect of entrepreneurship on the five dependent variables, as indexed by partial eta squared, was 0.09. Univariate ANOVAs confirmed that entrepreneurship had significantly stronger regrets (F = 6.01, p < 0.05), but the two groups did not differ on the number of regrets (F = 1.44, p = ns). Univariate ANOVAs also revealed that group membership (i.e. entrepreneurs vs. non-entrepreneurs) was significantly related to perceived control over adversity (F = 8.03; p < 0.005) and perceived ownership regarding outcomes of adversity (F = 4.07; p < 0.05). Specifically, means perceived control over adversity and perceived ownership regarding outcomes of adversity were significantly higher for entrepreneurs than for non-entrepreneurs. Finally, and consistent with predictions made elsewhere, but with different samples (Chen, Greene & Crick, 1998), we too find that entrepreneurs tend to have significantly higher self-efficacy (F = 5.27; p < 0.02). As described earlier, a content analysis of the qualitative measure of regretful thinking identified six types of regretful decisions, including business opportunities, decisions regarding career, education, investments, personal values, and personal relationships. A discriminant analysis suggested that entrepreneurs regret more decisions regarding business opportunities whereas non-entrepreneurs list more regrets about education and career decisions (Chi-square = 30.84; p = 0.01). The discriminant function accounted for 78% of the between group variability. Thus, findings reported in Tables 2 and 3 provide support for all hypotheses except for Hypothesis 1; the difference in entrepreneurs’ and nonentrepreneurs’ regret count was not significant. That is, Hypothesis 1 was not Table 2. MANOVA Analysis: Dependent Variable Meansa for Entrepreneurs and Non-Entrepreneurs. Dependent Variable (H1) Number of regrets (H2) Magnitude of regrets (H4) AQ-control (H5) AQ-ownership (H6) Self-efficacy

Entrepreneurs

Non-Entrepreneur

F-value

␩2

3.21 6.15 3.65 4.20 6.30

2.90 5.67 3.32 3.90 5.90

1.44 6.01* 8.03* 4.06* 5.41*

0.01 0.03 0.05 0.03 0.03

Multivariate effect: Pillai’s trace = 0.09, F = 2.80∗ , ␩2 = 0.09. a Means adjusted for covariates: age, education, annual income, and number of patents. ∗ p < 0.05.

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Table 3. Discriminant Analysis: Structure Matrix of Regret Variables.a Function Business opportunity Education Career Value Relationship Investment a Dependent

0.85 −0.43 −0.35 0.12 0.03 −0.01

Wilks’ Lambda 0.94 0.99 0.98 1.00 1.00 1.00

F

Sig.

23.58 4.00 6.07 0.45 0.02 0.58

0.001 0.05 0.01 0.50 0.88 0.45

function: Entrepreneurs versus non-entrepreneurs.

supported because although, on average, entrepreneurs reported a slightly a higher number of regretful decisions than non-entrepreneurs did, the difference was not statistically significant. On the other hand, since the two groups differed on the magnitude of their regrets, Hypothesis 2 was supported; entrepreneurs reported significantly stronger regrets than non-entrepreneurs did. Additionally, and as predicted by Hypothesis 3, entrepreneurs reported primarily regrets concerning business opportunities whereas non-entrepreneurs reported mainly regrets about their career and education. Entrepreneurs, as compared with non-entrepreneurs, also tend to have significantly higher perceived control over adversity and a greater sense of ownership regarding the outcome of adversity regardless of the origin of adversity. Hence, Hypotheses 4 and 5 were supported. Finally, since entrepreneurs reported significantly higher self-efficacy, Hypothesis 6 was supported too. To recap, the data offered support for Hypotheses 2–6, but Hypothesis 1 was not supported. Table 2 shows the adjusted means for the five dependent variables broken-down for entrepreneurs and non-entrepreneurs. The discriminant function of the structure matrix and the test of group means for the qualitative data are reported in Table 3.

DISCUSSION As we stated at the beginning of this chapter, much research in entrepreneurship is using tighter theoretical views and better empirical testing to address the question of individual differences (e.g. Baron, 1998, 2000; Baron & Markman, 2000, in press; Baron, Markman & Hirsa, in press; Busenitz & Barney, 1997; Chen, Greene & Crick, 1998; Krueger, 1993; Markman, Balkin & Baron, in press; Markman & Baron, 2002). We tried to extend this ongoing work by assessing the relationship between regretful thinking (Baron, 2000), perceived capacity to persevere (Stoltz, 1997, 2000), and self-efficacy on the one hand, and new venture formation on the


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other. Findings confirmed that those inventors who start new ventures tend to have specific regrets (i.e. relating to business opportunities), have significantly stronger regrets, higher perceived control over the adversity and perceived ownership of the outcome of adversity that they face, and finally, stronger self-efficacy. Unlike previous research that shows that entrepreneurs – as compared to others – engage in limited counterfactual thinking (cf. Baron, 2000), we found that entrepreneurs and non-entrepreneurs report almost identical numbers of regretful decisions. Our research found that not only do entrepreneurs experience stronger regrets; they also experience regrets over different kinds of decisions than their counterparts (i.e. business opportunities vs. career and education decisions). We attribute these findings, which diverge from the ones reported by Baron (2000), to our unique and random sample of patent inventors and our qualitative measures of regrets, which were different from the ones employed by Baron, and focused on regrets over decisions rather than over life events of missed opportunities. As noted earlier, the different populations employed and the contrasting measures gathered from participants were expected to generate contrasting patterns of results. While the finding that entrepreneurs regret mostly decisions about business opportunity has high face validity (e.g. pursuing opportunities is at the core of many entrepreneurial activities), a key question is why did non-entrepreneurs, but not entrepreneurs, regret career and education decisions? Though this question should be fully addressed by further research, we offer the following explanation. Job autonomy, particularly in inventive capacity, dramatically influences how incumbents perceive their work and experience career-related regrets. Unlike entrepreneurs, non-entrepreneurs work and invent for their employers, and as such they may encounter stronger barriers to career mobility, limited discretionary power, and of course, restricted autonomy. A career plateau – the point at which advancement is improbable – can occur to many astute inventors despite years of experience. Good engineers have strong problem-solving skills in their respective technical domains; however, to become executives or managers they need new skills in management, decision making, business acumen, working well with a diverse workforce, as well as foresight and perseverance. Thus, one’s early decision to become a skilled scientist or engineer may limit the subsequent likelihood of being groomed for leadership roles and succession to the top management team (Daily, Certo & Dalton, 1999). As are a result, non-entrepreneurs in our sample may have many bases for experiencing regrets over their past decisions. While this explanation is quite plausible, this proposition is beyond the scope of our study and thus awaiting further empirical testing. Research in social, cognitive, and applied psychology shows that self-efficacy reliably predicts personal effectiveness under diverse tasks and careers (cf. Bandura, 1997), and new studies confirm that entrepreneurs tend to be higher in self-

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efficacy than other persons (Chen, Greene & Crick, 1998). We tried to extend such results to patent inventors and our findings confirm that inventors who start new ventures have significantly higher self-efficacy. Perhaps even more interesting was our finding that after controlling for self-efficacy, inventors’ perceived capacity to persevere continues to distinguish between entrepreneurs and non-entrepreneurs. Again, results show that, over and above self-efficacy, perceived perseverance is related to new venture formation; each of the three constructs – self-efficacy (␩2 = 0.03); control over adversity (␩2 = 0.05); and ownership of outcome of adversity (␩2 = 0.03) – accounted for unique variance that was not captured by the other constructs. Since past research suggests that perceived capacity to persevere predicts personal effectiveness under diverse tasks and careers (cf. Bandura, 1997; Eisenberger, 1992), we were curious whether perseverance explains some variability in inventors’ annual earnings – a crude proxy of personal success. To this end, we performed a simple stepwise hierarchical regression, in which we regressed annual income first on all the control variables (age, education, innovation), and then on self-efficacy and finally on perseverance as captured by an aggregate measure of perceived control over adversity and ownership of the outcome of adversity. This post hoc analysis showed that highly perseverant patent inventors earn significantly more than patent inventors whose perseverance was very low (adjusted R 2 = 12%; F = 4.40; p < 0.01). To give a concrete example, the annual earnings of patent inventors who’s average perseverance score was in the top 20% was approximately $128,692 vs. $93,933, which was the annual earnings of inventors whose perseverance score was in the bottom 20% – almost $35,000 per year difference. Thus, higher perseverance scores – as measured across all patent inventors – were related to higher personal income. Naturally our ad hoc analysis provides a simple initial assessment of the link between perceived perseverance and personal success. Such link should be the subject of future empirical research, which includes wage determination variables.

Implications, Limitations, and Future Research This study has important implications for research, theory, and practice. It provides guidance for future research on individual differences in the context of new product development and innovation, and it makes contributions to our understanding of individual differences in the context of entrepreneurship. For example, it shows that even among persons who discover novel, useful, and non-obvious technologies, those who ultimately undertake the daunting task of creating new ventures appear to have higher perseverance and higher self-efficacy. However, and despite very


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favorable coverage by the popular press, it appears that the entrepreneurial journey yields particular, and at times stronger, regrets. Despite the fact that regretful thinking is an important cognition in decisionmaking, until recently very little research has focused on this topic in the context of entrepreneurship (cf. Baron, 2000 as an exception). For example, researchers have noted that decision makers anticipate and take into account the possibility that their decisions may produce regretful thoughts (e.g. Loomes & Sugden, 1986). As such, future research on entrepreneurs’ regretful thinking could be helpful in understanding how these thought processes and cognitions affect decision and actions. For instance, regretful thinking, which is primarily associated with the presence of negative outcomes, together with people’s natural tendency to avoid disappointment, could explain why some people, but not others, reject the possibility of starting a new venture. Others showed that people are generally risk-averse and that this tendency is stronger under conditions of possible gains than possible loss (Kahneman & Tversky, 1982). To extrapolate from the work of van Dijk et al. (1999), one reason for this tendency could be that opting not to start a new business might limit disappointments and regretful thinking. In other words, increased anticipation of disappointment and regrets might motivate risk-aversive career paths. Playing it “safe” allows us to expect less, obtain what we expect more easily, and therefore avoid the perils of becoming chagrined with disappointments and regretful thinking. Clearly, regrets are interesting cognitions that await further investigation in the context of new venture formation. The study’s focus on perseverance and the evidence that people are not victims of their adversities strike a hopeful note. That is, unlike relatively stable personality and trait characteristics, perceptions of adversity are somewhat open to modification. As perseverance enables human action, at least to some extent people are the architects of their own destinies (Bandura, 1986). Assuming all else equal, one’s reaction to adversity is – with the appropriate education and training – improvable (Stoltz, 1997; Waldroop & Butler, 2000). For example, developing perceived control and accountability is accelerated when individuals alter the reasons they assign for their successes and failures. When people change their explanations for why important and impactful outcomes occurred, they improve their expectations for positive outcomes in the future (Mifflin & Schulman, 1986; Seligman, Reivich, Jaycox & Gillham, 1995). Seligman and his colleagues (1995) suggest that providing individuals with such tools and skills can help them transform helplessness into mastery that bolsters self-efficacy and perseverance. Teaching individuals to challenge their thoughts and assumptions can “immunize” them against adverse impact of setbacks (Eisenberger, 1992). Improving one’s perseverance reduces the risk of helplessness as it boosts performance, improves physical health, and increases self-reliance in the face of new challenges (Stoltz, 1997).

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At another level, evidence also suggests that a “can-do” attitude rubs off; that being around dynamic individuals who keep adversity in perspective is infectious (cf. Smith & Muenchen, 1995). This suggests that entrepreneurs can use their pattern of thinking (e.g. “can-do”) to inspire and motivate their partners and others who work with them. Finally, since perceived perseverance is significantly associated with personal success, we may want to – assuming all else equal – give strong considerations to tenacious entrepreneurs and agents. A corollary of the fact that perseverance is both augmentable and functional is that it may be worthwhile to assess this construct among future entrepreneurs. For example, investors such as venture capitalists may improve their odds if they consider technical inventors’ perceived perseverance. Similarly, in the context of corporate entrepreneurship, managers may assess intrepreneurs’ levels of perseverance to identify early career track of technical people to become champions of new business units. Of course, these suggestions are tentative and awaiting further empirical testing and validation. Before concluding, several limitations to this study must be addressed. First, although dividing patent inventors into entrepreneurs and non-entrepreneurs simplifies the methodology, it is an oversimplification. In reality, particularly over time, inventors may “migrate” from working for others to working for themselves and vice versa. Some inventors may be building their own start-up – which may or may not be tied to their patent – while holding employment elsewhere. Also, the survey did not collect data on the organizations in which participants work, and it is possible that some inventors who were classified as non-entrepreneurs actually work for start-up firms. In other words, the division of participants into two dichotomous groups may fail to capture a richness that ranges between what we categorically termed entrepreneurs and non-entrepreneurs. While the use of two dichotomous groups proved quite revealing, assessment of more than two groups was beyond the scope of our study and thus awaiting further empirical testing. A second limitation stems from the reliance on patent count as a proxy of innovation. Researchers note that the distribution of patent quality is highly skewed toward the low end with a long, thin tail into the high-value end (Trajtenberg, 1990). Our interviews with technical inventors, chief technology officers (CTOs), intellectual property attorneys, and technology transfer directors reveal that fewer than 10% of patents are commercialized. This is not surprising because many patents have no market value until they are combined with several other patents (e.g. Gillette’s Mach 3 is protected with over 30 patents!). Additionally, some inventors may file for patents for intellectual reasons, others to passively “protect” technology share, and yet others use patents to strategically position their invention in a particular technological space. Also, patents may allow inventors to cajole rivals into alliances, partnerships, and concessions that are not of the rivals’ initial liking. In short, as patent count is an imprecise proxy of innovation, we suggest


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that future empirical research try to distinguish between high- and low-quality innovations. As we hinted earlier, future empirical studies should also attempt to measure and control for additional factors relating to individual differences (e.g. locus of control, need for achievement, level of motivation, self-esteem, and so on), contextual factors (e.g. work environment) and various outcomes variables (e.g. starting a new business, performance, success, etc.). Unfortunately, a key challenge to field researchers is the unavoidable tradeoff between casting a broad empirical net and response rate. Highly inclusive survey instruments with a myriad of psychometric scales are also lengthy and time-consuming, and while such surveys may work in classroom settings, they are prohibitively difficult to justify to inventors and scientists or to persuade them to respond. One final weakness of our study, which is mainly due to its cross-sectional design, relates to uncertainty regarding causality. Since data were collected after inventors began building their new ventures, it is unclear whether founding a new firm increases one’s regrets, perceived control and ownership of adversity, and self-efficacy, or whether scoring high on these dimensions leads one to found a new venture. Two points – and a rich research stream on the causal efficacy of human thought (cf. Bandura, 1995) – suggest that at least perseverance and selfefficacy are more likely to precede the act of new venture formation than to be the result of it. First, it is important to recall that we relied on a general, rather than specific, measure of self-efficacy. Since general self-efficacy is the result of lifelong experiences; is quite stable by the time individuals are adults (Bandura, 1997); and since we obtained data from entrepreneurs shortly after they had launched their new ventures, such short-term business activity, in and of itself, probably did not elevate one’s self-efficacy in any meaningful way. Second, success and failure in diverse activities and over prolonged periods of time shape one’s perceived control over adversity and ownership over outcomes of adversity (Stoltz, 1997, 2000). As explained above, since our entrepreneurs launched their new ventures only a few months before we surveyed them, it is unlikely that these relatively short-term activities had already altered inventors’ perseverance levels in such a significant magnitudes. Clearly, only longitudinal methods or experimental research design will fully address this question, but in the meantime, and for the reason outlined above, we suspect that differences in perseverance and self-efficacy may contribute to the decision to become an entrepreneur rather than the opposite. Notwithstanding these limitations, it is important to recognize the recent resurgence of interest in individual differences in the field of entrepreneurship (Baron, 1998, 2000; Baum et al., 2001; Busenitz & Barney, 1997; Chen et al., 1998; Ensley et al., 1999; Honig, 1998; Sarasvathy et al., 1999; Stewart et al., 1999, to name a few). In this context, this study adds some value as it was based on a random

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sample of inventors, all of whom had invented patents in the same technological space, at the same time period (1997 and 1998), and we – the researchers – did not know ahead of time which inventors were entrepreneurs and which were not. Collecting income information is another dimension that separates this study from its predecessors. Our findings suggest that perceived capacity to persevere in the face of daunting and formidable obstacles may be related to personal success such as annual earnings. For example, highly perseverant patent inventors enjoyed almost $35,000 more in annual earnings than less perseverant inventors. Furthermore, since perceived perseverance explains additional variance that was not captured by self-efficacy, our study expends our knowledge of individual differences; it introduces new constructs that we believe merit further empirical testing. In closing, we found that entrepreneurs, as compared with non-entrepreneurs, report particular and stronger regrets, significantly higher levels of perceived control and accountability over adversity, and higher levels of self-efficacy. We also found that, among our inventors, perseverance is related to personal success as measured by annual earnings. To the extent that perceived perseverance is vital in life, we suspect that Confucius was right when he suggested that our greatest glory is not in never failing, but in rising every time we fail.

NOTE 1. Some entrepreneurship research addressed the issue of persistence and tenacity (cf. Baum & colleagues, 2001, 2002; McGrath, 1995) and others have used persistence as an outcome. However, such research neither measured the underlying psychological factors that make one persist nor made distinctions between types of perseverance.

ACKNOWLEDGMENTS We gratefully acknowledge that this research was funded in part by the 2001 John Broadbent Endowment for Research in Entrepreneurship at Rensselar Polytechnic Institute. The opinions (and errors) are the authors’ and not the grantor’s. We also thank the participants of the Lally-Darden retreat (2002) and three anonymous reviewers for their insightful comments and suggestions.

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APPENDIX Sample Items Used to Measure Perseverance Inventors used a 5-point scale to indicate the extent to which they agreed with each of the statements following the situations described below. Situation: Someone you consider important is not receptive to your ideas. Control: How much control do you feel 1 = no 5 = complete you have in this situation? control control Ownership: To what extent do you feel 1 = not 5 = completely responsible for dealing with the responsible responsible at all outcome(s) of this situation? Situation: You apply for a job change and don’t get it. How much control do you feel 1 = no 5 = complete you have in this situation? control control Ownership: To what extent do you feel 1 = not 5 = completely responsible for dealing with the responsible responsible outcome(s) of this situation? at all Control:

Situation: You fail to meet the deadline on a major project. How much control do you feel 1 = no 5 = complete you have in this situation? control control Ownership: To what extent do you feel 1 = not 5 = completely responsible for dealing with the responsible responsible outcome(s) of this situation? at all

Control:

General Self-Efficacy Scale Please indicate the extent to which you agree with each of the following statements (circle one number for each item). Strongly Disagree = 1

2

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1. I am strong enough to overcome life’s struggles 2. At root, I am a weak person 3. I can handle the situations that life brings 4. I’m usually an unsuccessful person

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7 = Strongly Agree

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APPENDIX (Continued ) 5. I often feel that there is nothing I can do well 6. I feel competent to deal effectively with the real world 7. I often think that I’m a failure 8. I usually feel I can handle the typical problems that come up in life

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Regretful Thinking Looking back over your entire life, please list the things that you regret most:

Little Regret = 1

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Considering these things you listed on the previous page, how regretful are you?

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7 = Much Regret

6 3

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THE SELF-DETERMINATION MOTIVE AND ENTREPRENEURS’ CHOICE OF FINANCING Harry J. Sapienza, M. Audrey Korsgaard and Daniel P. Forbes Capital structure decisions are very much dependent on the owner’s personal preference for risk-taking, rather than on a strict evaluation of relative costs for different financial possibilities (Petty & Bygrave, 1993). From the small business owner’s point of view, staying in control and remaining independent can be challenged when and if he/she admits an external financier into the business (Bhide, 1992), in that the external financier may be perceived to interfere with the visions of the small business manager. The fear of losing control over the business certainly influences the owner/manager’s attitudes towards and use of external financial sources (Ang, 1992; Winborg, 2000).

INTRODUCTION Take the image of the entrepreneur as a driven accepter of risk, an individual (or set of individuals) hungry to amass a fortune as quickly as possible. This image is consistent with the traditional finance theory view of entrepreneurial startups, one that assumes that profit maximization is the firm’s sole motivation (Chaganti, DeCarolis & Deeds, 1995). Myers’s (1994) cost explanation of the pecking order hypothesis (i.e. entrepreneurs prefer internally generated funds first, debt next, and external equity last) incorporates this economically rational view of entrepreneurs’ financing preferences. According to this view, information asymmetry and Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 105–138 Copyright © 2003 by Elsevier Science Ltd. All rights of reproduction in any form reserved ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06005-7

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uncertainty make the availability of external financing very limited and the cost of it prohibitively high. To compensate, entrepreneurs must give up greater and greater control in order to “buy” funds needed to achieve the desired growth and profitability. Indeed, Brophy and Shulman (1992, p. 65) state, “Those entrepreneurs willing to relinquish absolute independence in order to maximize expected shareholder wealth through corporate growth are deemed rational investors in the finance literature.” Undoubtedly, cost and availability explanations of financing choices are valid for many new and small businesses. However, many entrepreneurship researchers have long been dissatisfied with the incompleteness of this perspective. Many entrepreneurs wish to accomplish some things other than to amass wealth. Many want to create something, to shape an organization as they see fit. Indeed, for some, the primary goal is to be the architect, the creator and/or commander of the new organization. For some, being in command is merely the means to attain wealth; for others, being in command is the goal, and wealth generation is the means. We propose that wealth maximization and self-determination are the two primary motives driving entrepreneurial financing choices, that the different degrees of each motive that exist across firms affect their financing choices, and that trade-offs between the two (when necessary) are complex and dynamic. Our contribution in this chapter is to develop a framework for understanding how entrepreneurial firms’ drive for self-determination mixes with wealth creation motives to explain financing choices. Our main focus is on explicating the factors that influence the drive for self-determination1 and the perceived risk of losing control of decisions. Some entrepreneurship theorists have implied that the wealth maximizing explanation of financing decisions in small or new firms is inadequate. For example, Cressy et al. (1996) and Storey (1994) both observe that even when outside funds are available, fear of risking independence causes some entrepreneurial firms to avoid external funds. Others have also attributed financial structure decisions to risk-taking attitudes as well as need for independence (e.g. Ang, 1992). Citing Barton and Matthews (1989) and Levin and Travis (1987), Chaganti et al. (1995, p. 8) argue that the preferences and personal goals of managers play a more potent role in small, new firms than they do in large, established ones. Several articles demonstrate that financing in entrepreneurial businesses is “different.” For example, Van Auken and Doran (1989) show that newer firms use more debt than older firms; Chaganti et al. (1995) show that newer firms prefer internally-generated over external equity to a greater extent than older firms; Van Auken and Holman (1995) show that financing strategies of private firms change as the firms develop. In short, a good amount of literature exists which suggests that entrepreneurial firms’ finance structuring choices do not conform to the expectations of traditional finance theory (Winborg, 2000). If these empirical observations are valid, how do we explain the choices made by entrepreneurial firms?

The Self-Determination Motive and Entrepreneurs’ Choice of Financing

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What we wish to accomplish in this chapter is to highlight the importance of the motive of self-determination, or “decision control,” in the financing choices of managers of new firms. We propose a more complex view of financing decisions that acknowledges the potentially mixed motives of wealth maximization and self-determination2 : one in which the pursuit of wealth and control are not independent of one another, one in which past history and future expectations play significant roles, and one in which the relative balance between the two motives is salient. For business start-ups, the wealth or profit motive is an assumption that requires little justification. Therefore, our focus here will be on the decision control motive, including its antecedents and consequences. We highlight what the consideration of this motive explains in the financing decisions of entrepreneurs beyond what might be explained by the more parsimonious single-motive model. Our central thesis is that, controlling for the strength of the wealth creation motive, choices among type of financing (e.g. internally generated funds, debt, outside equity) is determined by entrepreneurs’ drive for self-determination and their perceptions of the risks to self-determination posed by sources of finance. An additional element that this second motive introduces is emotion, which may place limits on rational-economic decision making. The remainder of the chapter is organized as follows. First, we briefly review the literature pertinent to understanding the financing choices of startup firms. We then outline the scope of our theorizing, our key assumptions, and the relevant theoretical perspectives. In order to develop ideas in depth we focus on growth-seeking firms wherein the potential for conflict between wealth and control motives may be greatest. Our theoretical framework begins with a brief overview of agency theory, decision theory, and organizational justice. We then develop our views of how growth-seeking firms choose between debt and equity to fuel further growth and how, once a type of financing has been picked, they choose among specific providers of the same type of financing. We develop this final area of theorizing around the concepts of risk perceptions and willingness to take risks. Here, however, the risk is not economic risk but the risk of sharing or giving up control. We conclude with a discussion of the boundaries of our theorizing and the implications of our work for practice and future research.

LITERATURE ON THE FINANCING STRUCTURE OF ENTREPRENEURIAL FIRMS Entrepreneurial finance has been the subject of considerable research attention by scholars in economics, finance and management. However, much of the existing literature has focused on the role of financing providers, or the “supply” side of

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the entrepreneurial financing process. For example, some of the more prominent literature streams in the area of entrepreneurial finance are those devoted to the provision of particular kinds of financing, such as venture capital (e.g. Amit, 1998; Manigart, 1994), bank loans (e.g. Hancock & Wilcox, 1998; Riding & Swift, 1990), “angel” investments (e.g. Freear, Sohl & Wetzel, 1994; Haar, Starr & MacMillan, 1988) and public market or “IPO” financing (e.g. Bruton & Prasad, 1997; Deeds, DeCarolis & Coombs, 1997). One of the better known contributions in this literature is the “pecking order hypothesis” (Donaldson, 1961; Myers, 1994), a concept borrowed from general corporate finance theory, which maintains that entrepreneurs generally prefer internal financing to debt and debt to external equity. Economic factors form the basis of this ordering, according to the theory, as entrepreneurs are held to favor financing options that cost the least and pose the least risk to financial rewards. Thus, according to this theory, entrepreneurs are reluctant to accept external equity because of its accompanying threat of wealth dilution, and they agree to accept it primarily out of a belief that the financial opportunities made available through it exceed its financial costs. These greater costs for small and new firms arise out of the business and agency risks inherent in dealing with startups (Winborg, 2000). The absence of performance history for the venture and skill verification for the entrepreneurial teams leads to greater perceived risks of incompetence and opportunism. Additionally, because executing due diligence is as costly if done on a small firm as on a large one, it is relatively more expensive for suppliers of capital to process funding for new firms. The risks are lower for providers of debt to the extent that collateral exists. This view sees economic factors driving the ordering of financing structure for new firms. Research examining entrepreneurial financing decisions from the perspective of capital providers does suggest a role for non-economic factors. For example, cognitive biases have been shown to be present in bank lending (Riding & Swift, 1990) and venture capitalists’ decision processes (e.g. Zacharakis & Shepherd, 2001; Zacharakis & Meyer, 1998). Steier and Greenwood (1995, p. 350) observed that personal references play a stronger role in venture capitalists’ financing decisions than do business plans and they concluded that “funding is neither fully reasoned nor divorced from wider contexts.” Sapienza and Korsgaard (1996) provided some evidence that investors’ decisions to provide additional funding is affected by their perceptions of the procedural fairness of the entrepreneurial team. While these studies do provide evidence of some role for factors beyond pure economic reasoning in capital providers, each also appears fully consistent with the presumption of a dominant economic motive in the behavior of capital providers.


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Although less research attention has been paid to the “demand” side of the entrepreneurial financing process, some research does exist. For example, some attention has been paid to “bootstrapping,” the practice whereby entrepreneurs finance their businesses without formal sources of external financing and rely instead on the use of internal funds or the careful management of customer and creditor relationships (e.g. Van Auken & Neeley, 1996; Winborg & Landstrom, 2001). Another stream of literature has examined ventures’ resources and capital structure, either in descriptive terms (e.g. Bates, 1997) or as determinants of performance (e.g. Chandler & Hanks, 1998). Consistent with the capital supply-side literature, researchers collecting data from entrepreneurs tend to interpret financing structure as determined by financing availability or costs. However, a significant proportion of this literature introduces an interesting concept not found in supply-side analyses: many mention the possibility that mixed or non-economic motives may be driving entrepreneurial choice. Some researchers of entrepreneurial financing explicitly or implicitly reject the constrained choice and profit maximization explanations. Chaganti et al. (1995), for example, complain that the prevailing paradigm erroneously ignores factors such as owners’ values or goals. Winborg (2000) showed that entrepreneurs who sought financing to achieve higher growth sought more external funding; interestingly, he also found that those who professed a “need” for external financing also held more positive attitudes toward it. Winborg details at length the great efforts entrepreneurs use to avoid external sources of funding but concludes that “in some situations, external finance is used despite a negative attitude and despite a fear of the consequences” (2000, p. 55). One of our central aims in this paper will be to try to unravel what this “fear” is about (beyond economic loss) and what encourages or discourages entrepreneurs from facing this fear. Although the fear of loss of control or, alternatively, the drive for independence has been frequently mentioned as a key motivator for entrepreneurs (e.g. Ang, 1992; Chaganti et al., 1995; Storey, 1994), few attempt to sort out whether an observed drive for self-determination is a means to achieve economic ends or a separate end in itself. For example, Mishra and McConaughy (1999) portray the “risk of the loss of control and the aversion to debt” of family firms simply as fear of bankruptcy. Debt may also cause loss of flexibility, and equity may cause a loss of managerial control (Chaganti et al., 1995); yet it is unclear in these portrayals whether this fear may be simply reduced to economic terms. Closer to our intentions, Chaganti et al. posit that some entrepreneurs are motivated by economic gain for themselves or their families and that others are motivated by their “desire [for] control over their own affairs and [to] avoid dependence on others” (1995, p. 9). They provide some support for the argument that those motivated to a greater extent by economic gain seek a different mix

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of external to internal financing than those motivated to a greater extent by self-determination. In summary, the literature has focused on the supply side of financing for new firms. The supply-side logic has justifiably been one of economic rationality, though some evidence exists that bounded rationality and perhaps even relationships or politics play a role. The literature is more scant on the demand side and generally follows the paradigm applied to suppliers. The concept of mixed motives for entrepreneurs has received some attention in the demand-side literature, but no over-arching framework exists.

Scope and Premises of our Analysis We do not view new firms as neatly fitting into “wealth-maximizers” versus “selfdetermination-maximizers.” Indeed, often the two motives will be intertwined and will affect one another directly and indirectly through the choices they provoke. Instead, we believe that an understanding of new venture financing structure will be enhanced by developing a theoretical framework that incorporates the powerful motive of self-determination alongside that of wealth creation. In order to develop this framework, we focus on the antecedents and consequences of the self-determination motive in new firms that possess at least enough of a wealth creation goal to provoke the consideration of external sources of financing. Critical to our arguments is the concept of self-determination as described in the psychological theory of intrinsic motivation (Deci, 1980; Deci & Ryan, 2000). We define self-determination as the desire to pursue activities of deep personal interest (Deci & Ryan, 2000). As such, self-determination appears to be highly similar to the concept of autonomy, that is, independence or control over one’s action. Indeed, theory suggests that autonomy is a fundamental and central need underlying self-determination, but there is more to this motivation than the need to control. Theorists (Deci & Ryan, 2000) suggest two additional fundamental needs that drive self-determination: competence and relatedness. The need for competence is the drive to accomplish goals or outcomes and to have an effect on one’s environment. The need for relatedness involves the need to form connections and relationships with other people. Self-determination involves the joint satisfaction of at least one of these needs along with the need for autonomy. Thus, self-determination involves experiencing a sense of accomplishment or mastery in an endeavor over which one has control. Self-determination and well-being may also be achieved through autonomy in conjunction with secure relatedness to others. Of the two, research has provided more support for the importance of the joint effects of autonomy and competence for motivation.


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According to this conceptualization, self-determination is an end in itself rather than a means to achieve desired economic ends. However, the drive for selfdetermination can be complementary with economic goals and may thus enhance economic outcomes for the entrepreneur. That is, an entrepreneur who is driven to succeed and make an impact on the target industry and who also desires to maintain a large measure of control or ownership over the firm is apt to be in a position to financially benefit from his or her successes. However, a direct, positive relationship between self-determination and economic gain is unlikely, as research suggests that motivational factors such as need for achievement have a weak direct effect on venture success and are likely to be dependent on a number of other factors (Gartner, 1989). Moreover, as our framework will show the motivation for selfdetermination may lead entrepreneurs to make sub-optimal financing decisions. We assume that most individuals or groups of individuals who start new firms are motivated both by a desire to create wealth and to experience the satisfaction of directing the enterprise as they see fit. We also assume that the strength and mix of these two motives vary across new venture teams. The type of financing sought and accepted by entrepreneurial teams is affected by the mix of wealth and self-determination motives. If one motive predominates over the other, such decisions will be relatively easy and consistent over time. For example, an entrepreneur who seeks above and beyond all else to be her own boss may forego any outside source of financing and content herself with a “lifestyle” business. If the wealth accumulation motive predominates, entrepreneurs will behave according to the assumptions of standard finance theory and seek whatever type of financing will help achieve wealth, regardless of self-determination effects. However, if both motives are strong or weak the choices become more difficult. Entrepreneurs who seek rapid growth but also desire absolute control over venture decisions, for example, face the difficult task of balancing these two drives. The framework we develop is based on several premises. First, we assume that entrepreneurs have at least some level of choice in terms of financing options. Although financing choices may be severely constrained by such factors as venture stage and asset base, even the most constrained have some level of choice (Winborg, 2000). Furthermore, since we will focus on entrepreneurs who have at least some level of reasonable expectation of external financing, constraints in our framework are important but do not absolutely determine financing structure. For example, Smith (1999) showed that, even in the difficult area of venture capital financing, entrepreneurs have choices among venture capitalists as well as other alternatives. Second, we assume that, all else equal, entrepreneurs will prefer less costly and less control-threatening sources of financing. However, all else is not equal and that is what our framework is about.

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Table 1. Goals and Financing Preferences. Wealth Accumulation Drive Low Self-Determination Drive High Bootstrapping Debt only for survival Low

High

Early stage: angels, VC Late stage: debt, VC VC and angels Depending on venture stage and size of funding needs

We assume that different types of financing hold different implications for growth potential and pose different risks to the entrepreneur’s retention of decision control. The interplay between growth goals and self-determination concerns on financing choices is illustrated in Table 1. Generally speaking, we assume that using only internally generated funds (e.g. entrepreneurs’ equity and sales revenues) limits the potential rate of wealth accumulation, even though it may not limit total accumulation in the long run. At the same time, internally generated funds provide the greatest protection for the self-determination motive. Debt allows greater potential for growth than working from internally generated funds alone, but this potential is limited by the payback requirement. Debt introduces some threat to self-determination as banks and other lenders include covenants regarding the acceptable actions of the firm (Mishra & McConaughy, 1999). Arms-length external equity3 provides the greatest potential for rapid growth as it allows new firms to take bold strategic actions without any short-term payback requirements; at the same time, external equity poses the greatest threat to the entrepreneur’s decision control both because of wider-sweeping control rights typically granted to equity holders and because of the potential active involvement of such providers in the strategic decisions of new firms. Among external equity sources, there is some reason to believe that private individuals providing equity pose a lower perceived threat of wresting decision control than do venture capital firms (Shepherd & Zacharakis, 2000). Face validity for this expectation is reflected in the popular term “angels” for private investors and the pejorative “vulture capitalists” for formal venture capitalists. Kotkin (1984) provided some empirical support in documenting the reluctance of smaller firms to seek venture capital for fear of loss of control. It is worth noting that these assumptions are consistent with the pecking order hypothesis but provide a significant refinement. That framework suggests that entrepreneurs move from internal sources to debt to equity as a function of the costs and benefits of each, discounted in some manner by the financial risks of


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each source (Donaldson, 1961; Myers, 1994). In contrast, our framework suggests that entrepreneurs may choose financially less-attractive options if they are also strongly driven by the self-determination motive and if such options pose a certain level of perceived risk to their own retention of decision control. Whereas finance theory is based on an analysis of economic costs, our explanation is rooted in an assessment of the psychological costs associated with these various financing forms. A final clarification that our framework offers is that it provides a basis for understanding the choices entrepreneurs make among financing sources of the same type. The pecking-order hypothesis is silent, for example, on how entrepreneurs might choose among venture capitalists (or, e.g. among banks) offering essentially the same valuation or terms. We will argue that such choices are determined by the perceived threat to self-determination that specific financiers or financial firms represent to an entrepreneur as well as by the overall value that specific entrepreneurs place on self-determination. Smith’s (1999) assessment of why some entrepreneurs chose lower valuation alternatives even when some higher valuation choices are available is consistent with our premise. In summary, our framework contributes to theory on new venture financing by suggesting that entrepreneurs’ preferences for types of financing and for particular sources within types are influenced by two psychological factors that have been largely overlooked by prior analyses. The first of these is the overall value that entrepreneurs place on retaining self-determination, and the second is an entrepreneur’s perception of the threat that a specific type or source of financing poses to the entrepreneur’s self-determination. We call this threat to self-determination “decision control risk,” and we characterize the overall value that entrepreneurs place on self-determination as an aversion to that risk, or “decision control risk aversion.” We now develop our theoretical framework for new venture financing choices by drawing on and then extending existing agency, decision-making, and organizational justice theory. The development centers around factors related to risk-taking propensity and risk perception.

THEORETICAL FRAMEWORK Agency Theory and Risk Agency theory frames the process of organizational governance as a relationship between principals and agents in which principals assign to agents the responsibility to carry out certain activities on their (the principals’) behalf. The focus of agency theory is on how the principal-agent relationship is structured so as to protect the principal’s interests against the self-interests of the agent. According to the

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theory, principals face two sorts of agency risks: adverse selection, the tendency for the agent to misrepresent him or herself to the principal, and moral hazard, the tendency for the agent to act opportunistically. Agency theory highlights the importance of goal conflict and information asymmetry in determining the level of agency risk to be expected in a given exchange. Specifically, agency risk is thought to arise to the extent that principals and agents possess divergent goals and it is difficult or expensive for principals to monitor agents (Eisenhardt, 1989; Jensen & Meckling, 1976). The theory specifies that agency risks will influence the principal’s efforts to monitor and control the agent and that the measures used to monitor and control the agent will influence agent compliance. As past literature has pointed out, investors seek to minimize the agency risks they face through the imposition of controls, incentive alignment, and monitoring (Sahlman, 1990). In the context of corporate governance, the roles of principal and agent are typically assigned to investors and managers, respectively. This role assignment reflects the separation of ownership from control that characterizes most large firms. However, the dynamics of ownership and control in new venture contexts are different from those in large firm settings for several reasons. First, many entrepreneurs hold significant ownership stakes in their firms in addition to their managerial positions. These ownership stakes often go far beyond those that largefirm managers accumulate through incentive-based compensation plans. In fact, it is not uncommon for entrepreneurs to hold a majority or a plurality of their firms’ equity shares. Second, the control exercised by entrepreneurs is often greater than that exercised by managers in larger firms in part because managers have more discretion in smaller and younger firms (Hambrick & Finkelstein, 1987) but also because entrepreneurs often have important historical connections to the firm. To the extent that the entrepreneurs have founded their firms or played some other instrumental role in the early stages of the firms’ development, they may have significant non-financial investments in their firms. Additionally, they may have significant intellectual capital invested in the firm or feel an emotional attachment to employees, customers or products. Finally, entrepreneurs are essentially in the role of both agent and principal. Before approaching venture capitalists for financing, entrepreneurs are not only managers but also owners of the firm. Whereas in most large firms, investors hire managers through the board of directors, new venture entrepreneur/managers effectively hire their owners. Thus, the entrepreneur, in their role of principal, faces agency risks associated with the investor in the role of agent (Cable & Shane, 1997; Gifford, 1997). Specifically, as Gifford (1997) points out, venture capitalists may shirk by choosing not to invest the time or capital necessary to grow the venture because of opportunity costs (e.g. alternative investments that they perceive to be more promising) or information asymmetry (e.g. they


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may hold negative information about the venture’s prospects that is unavailable to entrepreneurs). In addition to the agency risks entrepreneurs face in their role as principal, their dual roles involve assuming a risk with regard to the loss of control over the venture, which we believe has profound practical as well as psychological implications for the entrepreneur. As the new venture takes on outside investors, the entrepreneur increasingly assumes the subordinate role of agent. As agents, entrepreneurs are vulnerable to exploitation by investors. Moreover, as the entrepreneur takes on the role of agent, his or her stature as a principal is diminished. Clearly, these risks have practical and economic implications, but they are also distinct in that they involve psychological issues pertaining to the intrinsic value that entrepreneurs may place on control and social psychological issues pertaining to interpersonal trust and other aspects of relationships that facilitate the sharing of control. Theory on organizational justice suggests that persons in the agent or subordinate role are keenly sensitive to the potential for exploitation and the need to protect their self-interests (Korsgaard & Sapienza, 1999). Moreover, this theory, which will be discussed further below, indicates that agents are concerned not only with exploitation in the material sense, but in terms of humiliation and loss of dignity. In summary, agency theory provides insight into the risks entrepreneurs face regarding sharing or ceding control to outside investors. Agency theory views risk as an objective characteristic of the context and sees agents’ risk preferences as stable; however, individuals’ assessments of risk are not always in line with objective risk, and their tolerance for risk may vary substantially (Sitkin & Pablo, 1992; Wiseman & Gomez-Mejia, 1998). In the following section, we review research on behavioral decision making to identify factors that contribute to risk perceptions and preferences. This literature examines general factors associated with decision making and risk taking, independent of the decision content. Although this perspective is typically applied to economic risk, we believe that understanding the factors contributing to risk perceptions and preferences have implications for entrepreneurs’ concerns regarding sharing control and self-determination.

Decision Theory: Risk Propensity and Risk Perception In their influential review of the factors affecting risk taking behavior, Sitkin and Pablo (1992) identified two key factors, risk propensity and risk perception. Risk propensity refers to the general tendency of the individual to take or avoid risks, whereas risk perceptions are the individual assessments of risk inherent in a situation. Sitkin and Pablo’s model specifies that the impact of objective risk and

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other contextual factors on risk taking behavior are mediated by these two factors. Subsequent research by Sitkin and Weingart (1995) supported this contention. Several individual difference and contextual factors influence risk propensity and risk perception, which we believe have implications for how entrepreneurs handle risk regarding the sharing of control. The role of risk propensity in entrepreneurship has received considerable empirical attention. The findings of a recent meta-analysis indicate that entrepreneurs, especially those whose primary goal is growth as opposed to income, are significantly more risk seeking than non-entrepreneurs or managers (Steward & Roth, 2001). Other studies, however, suggest that the role of risk propensity is less clear. For example, Palich and Bagby (1995) failed to find a difference in risk propensity between entrepreneurs and managers, but they did find significant differences in risk perceptions and labeling of risk. Similarly, Forlani and Mullins (2000) found that the decision to start a new venture was not directly related to risk propensity but rather to risk perception. Ambiguity regarding the impact of risk propensity on risk taking in entrepreneurship may be in part due to the focus on risk propensity as a stable trait. That is, it is possible that entrepreneurs may not be dispositionally risk seeking, but may be more or less risk seeking as a function of experience and context. Consistent with this view, Sitkin and Pablo (1992) point to two sources of risk propensity in addition to individual differences: inertia and outcome history. Inertia refers to the habitual handling of risk over time; that is, persons who have previously adopted a risk-seeking strategy continue to make risky decisions out of habit or precedent. Outcome history refers to the extent to which the person has been successful in risk-seeking decisions in the past. Those who have been successful in the past are likely to use risk-seeking strategies in the future, whereas those who have been unsuccessful become more risk averse (Thaler & Johnson, 1990). This evidence is consistent with the threat rigidity hypothesis wherein individuals are apt to be more conservative after experiencing a failure or threat (Chattopadhyay, Glick & Huber, 2001; Staw, Sandelands & Dutton, 1981). Both inertia and outcome history suggest that past behavior and past experience are likely to have important influences on entrepreneurs’ risk-taking with regard to funding options and sharing control. Because negative performance outcomes can undermine self-determination (Chattopadhyay et al., 2001), outcome history may be a particularly relevant variable. As noted above, risk perceptions are also an important predictor of decision making (Forlani & Mullins, 2000; Palich & Bagby, 1995). It is important to note that managers and entrepreneurs do not appear to define risk as it is described in economic theory (Forlani & Mullins, 2000). In the economics perspective, risk is narrowly defined as the uncertainty or probability associated with a given


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outcome. In contrast, managers and entrepreneurs consider three elements of risk: the uncertainty of outcomes, the potential losses, and the magnitudes of potential gains and losses (Forlani & Mullins, 2000; Sitkin & Pablo, 1992; Yates & Stone, 1992). That is, people tend to view a decision as risky if it has a low probability of success, if there are potential losses, or if the potential losses are significant. Research on risk perceptions suggests several factors influence individuals’ assessments of risk. One important determinant of risk perceptions is risk propensity, which is thought to influence attention to and the weighting of negative versus positive outcomes, thereby leading to overestimation of the potential for success or failure. Consistent with this view, Forlani and Mullins (2000) and Sitkin and Weingart (1995) found that risk propensity led to lower assessments of risk, which in turn led to riskier decision making. These findings have implications for how entrepreneurs’ prior experience with various funding options may affect both their willingness to share control and their beliefs about the extent to which they will actually cede control. A second influence on risk perceptions is problem framing. Research shows a consistent effect of framing such that persons facing a decision framed in terms of losses are more risk-seeking as compared to persons facing a decision framed in terms of gains (Kuhberger, 1998). Framing effects should be distinguished from outcome history effects. The effect of framing occurs when a given future event is framed in terms of risks of loss versus gain (Kuhberger, 1998). In contrast, outcome history, which leads individuals to label a situation as a threat or opportunity, has quite the opposite effect, in that persons are more risk-seeking when a positive past history induces them to label a situation as an opportunity ( Slattery & Ganster, 2002; Thaler & Johnson, 1990). Kahneman and Tversky (1979) speculated that framing effects result from individuals’ valuing the avoidance of negative outcomes more than the attainment of positive outcomes of comparable magnitude; in other words, they perceive that there is more at risk when faced with loss (Sitkin & Pablo, 1992; Sitkin & Weingart, 1995). Thus, individuals view incurring a loss as riskier than is failure to achieve a gain. Thus, the framing of financing options in terms of growth/wealth (gains) versus self-determination goals (losses) may influence entrepreneurs’ risk perceptions of these options. Experience, independent of the quality (success or failure) of the experience, also plays a role in shaping risk perceptions. Research suggests a curvilinear relation between experience and risk perceptions (Sitkin & Pablo, 1992). Very inexperienced and very experienced individuals tend to be more confident in their ability to succeed than moderately experienced persons. Moreover, moderately experienced people tend to have more accurate and stable assessments of success. Inexperience leads individuals to rely on incomplete information, whereas a high degree of experience tends to lead to the illusion of control. Thus, the relationship

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between experience and risks perceptions has implications for how optimistic entrepreneurs are regarding financing options as well as the type and extent of information they attend to in making funding decisions. To summarize, research suggests that the willingness to face the risks of sharing control depends on the entrepreneur’s risk propensity and perception of risk. The more entrepreneurs tolerate risk and the lower they perceive it to be, the more likely they will choose a funding option that cedes some control to investors. Situational factors such as prior patterns and success in risk taking influence propensity and perception of risk. Entrepreneurs’ specific performance history and experience with financing choices also influence how they perceive the risks of decision sharing. Furthermore, entrepreneurs’ evaluations of financing sources may be affected by how others view or frame the risks versus opportunities inherent in these different sources. Whereas research on risk and decision making has focused mainly on economic risk, our interest is in the risk of ceding selfdetermination. Nonetheless, we believe that similar judgment processes are likely to operate. For example, the framing effect has been obtained in non-economic settings, such as personal decisions (Kuhberger, 1998). At the same time, research suggests that individuals are less apt to employ utility maximizing strategies for non-economic motives (Frisch & Clement, 1994). Self-determination and the sharing of control are essentially social-relational concerns rather than economic concerns, which we believe are influenced by a unique set of factors that extend beyond strict economic considerations and are governed by processes other than utility maximization (e.g. Bazerman, 1993). These factors and processes are addressed in theories of procedural justice, which are discussed below.

Procedural Justice and Perceived Risk Theories of organizational justice address the causes and consequences of the fairness of decision processes and outcomes. There are two main forms of justice, distributive justice, which are evaluations of the allocation of resources, and procedural justice, which are evaluations of how fairly allocation decisions are made. Procedural justice is an important perception because it exerts a powerful influence on reactions to decision making and the quality of relations between exchange partners. People are more willing to accept a decision when they perceive decision procedures as just, even when the outcome of the decision is unfavorable to them (Korsgaard et al., 1995; Schaubroeck et al., 1994). Managers are more likely to cooperate with a strategic decision (Kim & Mauborgne, 1993) and employees are more likely to cooperate with their employers (Konovsky & Pugh, 1994; Moorman, 1991) if they perceive the decision-making procedures to be just. In


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contrast, when procedures are unjust, employees are likely to engage in noncompliant or retaliatory behavior (Greenberg, 1990; Skarlicki & Folger, 1997). Moreover, the effect of procedural justice extends beyond a single decision; perceptions of procedural justice also exert a strong influence on the perceiver’s trust in the other party (Korsgaard et al., 1995; Sapienza & Korsgaard, 1996). Given that trust may lead to lower perceived risk (Das & Teng, 2001), these findings suggests that procedural justice may play an important role in entrepreneurs’ perceptions of decision control risk. Moreover, perceptions of procedural justice also affect individuals’ willingness to enter (Busenitz, Moesel, Fiet & Barney, 1997) and remain in the exchange relationship (Sapienza & Korsgaard, 1996). Thus, procedural justice has potential value for understanding how interactions with potential investors may affect entrepreneurs’ decisions about obtaining funding. Aspects of both formal decision-making procedures and interpersonal interactions influence the perception of procedural justice. The relevant formal aspects of decision-making procedures include the opportunity for input in the decisionmaking process, judgments based on the evidence, correctability or refutability of the decision, and consistent application of the procedure (Folger, Konovsky & Cropanzano, 1992; Leventhal, 1980). In addition to formal procedural issues, perceptions of procedural justice are influenced by interactions with the decision maker, which is often referred to as interactional justice (Tyler & Bies, 1990). There are two main facets of interactional justice: interpersonal sensitivity, or the extent to which individuals are treated with respect and dignity, and informational justice, or the extent to which individuals are given adequate and timely information regarding the decision procedure and outcome (Greenberg, 1993; Greenberg & Cropanzano, 1997). Both procedural and interactional factors give rise to judgments of procedural justice, although the importance of particular factors may vary somewhat depending on the decision context (Greenberg & Cropanzano, 1997). Clearly, procedural justice affects economic value. If exchanges between two parties are governed by unbiased and legitimate procedures, the parties can expect to receive what they are due in the long run, even if a given exchange is not favorable. Simply put, fair procedures help ensure the fair allocation of outcomes. However, the value of procedural justice extends beyond the protection of material self-interest. Theories of procedural justice, such as the relational model (Tyler & Blader, 2000) and fairness heuristic theory (Lind, 2001), suggest that individuals have two main concerns in exchange relationships. First, they are concerned with the prospect that their contributions will not be reciprocated (i.e. the other party will exploit the relationship). Second, individuals are concerned that, if they become psychologically invested in the relationship, they are in danger of rejection by the other party or by the group. This latter concern is thought to be the more powerful

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motive (Lind, 2001) and to lead individuals to be sensitive to cues regarding their status and value in the exchange relationship. The power of the identity motive is rooted in social identity theory, which concerns how individuals come to define themselves in terms of their memberships in groups and relationships to others. An individual’s self-concept contains many facets or aspects that serve to define the self. Social identity is that aspect of a person’s self-concept that is determined by her/his membership in a particular group. Threats to social identity involve threats to status and loss of dignity or “face” and are closely linked to procedural justice (Tyler & Blader, 2000). Being treated in a procedurally just fashion signifies that the individual’s status and dignity is preserved. In contrast, unjust treatment can be an assault to one’s dignity and call into question one’s status. We believe that procedural justice will have important implications for how entrepreneurs respond to potential financiers. The fairness of initial interactions with a potential financier sends signals regarding the financier’s willingness to directly or indirectly share decision making control and should thus influence entrepreneurs’ perceptions of decision control risk associated with a given funding source. Equally important, entrepreneurs’ sense of self-determination is likely to be influenced by identity concerns that are triggered by unjust treatment in initial interactions. Recall that self-determination is a joint product of one’s sense of autonomy and competence. To the extent that an entrepreneur’s autonomy is limited by taking on outside investors, the importance of maintaining a sense of competence should be greater. The greater the decision control risk, the more strongly the entrepreneur will respond to threats to identity, for such threats may undermine his or her sense of competence and worth. Because procedural justice signals status and respect, entrepreneurs are likely to be sensitive to procedural justice factors to the extent that they possess a strong self-determination motive. Consequently, fairness in initial interactions with potential financiers will likely influence entrepreneurs’ risk perceptions and preferences regarding financing.

A FRAMEWORK FOR CONSTRAINED FINANCING CHOICES In this section we consider some of the most fundamental factors that limit entrepreneurs’ choice sets and discuss ways in which their beliefs about the importance of decision control influence the choices they make within those sets. These choices and the factors affecting them are illustrated in the decision tree presented in Fig. 1. Entrepreneurs appear to face a bewildering array of financing options. Yet the number of choices that are economically rational for a given


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Fig. 1. Financing Choices Decision Tree.

entrepreneur to pursue at a given point in time is limited. An underlying premise of our framework is that economic rationality is not the only salient form of rationality. Choices will also be constrained for some by the extent to which they increase the prospects for realization of self-determination.

Internal Versus External Financing Perhaps the most fundamental financing choice entrepreneurs face is whether to seek external financing at all. In some cases, entrepreneurs will be unable to obtain external financing because of inadequate revenue history or collateral. Our attention will be devoted to how choices are made by those entrepreneurs who have a reasonable option to pursue external funding. We posit that choices will be influenced at least in part by entrepreneurs’ willingness to take decision control risks. Virtually all forms of external financing require the entrepreneur to sacrifice some degree of decision control that could be retained if all financing were internally

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obtained (Winborg, 2000). In addition, our premise that entrepreneurs possess at least some minimal level of aversion to sharing decision control implies that the fear of sharing decisions is salient in a very wide range of circumstances. However, because significant and rapid growth for early stage ventures is very unlikely without any use of external financing, entrepreneurs who hold significant growth goals will be pushed toward use of external financing even though they may find it frustrating to their self-determination purposes (Winborg, 2000). Conversely, those for whom self-determination is a predominant motive will be pushed toward bootstrapping (i.e. avoidance of external financing) even if it frustrates wealth goals.

Debt Versus Equity To the extent that entrepreneurs have chosen to pursue external financing, they must determine which type of external financing to pursue. The most significant distinction that can be made among alternative financing types is that between debt and equity. Two factors that affect the availability of debt and equity are entrepreneurs’ growth aspirations and the stage of the venture’s development. The stage of a venture’s development especially affects whether or not debt is available. Lending decisions are determined largely by perceptions of the borrower’s ability to repay the loan, and an assessment of this ability is frequently based on historical evidence that the borrowing firm generates sufficiently stable cash flows to support repayment. However, ventures in a very early stage of development will not yet have consistently generated cash flows and will be unable to document their ability to repay a loan. Therefore, ventures in the very early stages of development may have to confine their search for external financing to a search among equity sources. And even this choice may be constrained to angel investors unless the venture possesses a unique and clearly protectable technology (Ehrlich, DeNoble, Moore & Weaver, 1994). Later stage ventures, by contrast, may be able to choose between debt and equity, depending on the size and stability of the cash flows they generate. Growth aspirations affect the availability of equity because equity investors seek to realize a rate of return on investment only available through rapid growth (Sahlman, 1990). Entrepreneurs who have relatively modest growth aspirations must therefore confine their search to debt sources (or attempt to deceive investors). Entrepreneurs with high growth aspirations for their firms, by contrast, may choose between debt and equity. Here, the relative strength of aversion to decision sharing and the perceived threat of decision sharing risk become salient. When the willingness to take this decision sharing risk is extremely low,


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entrepreneurs will accept the greater business (economic) risks of debt and may eschew equity even when that offers a higher expected return. In short, when entrepreneurs are able to choose between debt and equity, their level of decision control risk aversion will influence their choice. It will do so because entrepreneurs are likely to perceive a significant difference between the decision control risks associated with equity and debt financing. Equity generally involves a more significant sacrifice of entrepreneurial decision autonomy than does debt. Lenders may impose loan “covenants” or similar restrictions on the firms’ operations, but these restrictions differ from the involvement of equity investors in several ways. Loan restrictions are generally contractual restrictions that are agreed upon up front by the investor and the borrower. As, such, they are explicit in their formulation and limited in number and scope to the terms that are included in the loan contract. In addition, they tend to focus on short-term operational issues that affect that firm’s management of its cash flow. Equity investors, by contrast, by virtue of their ownership stake, can exert a far more wide ranging influence on entrepreneurs’ strategic decisions. Although the specific terms of their involvement, such as the number of representatives they can appoint to the board, may be contractually determined, the actual degree of influence they will exert often remains somewhat unclear at the time an investment is made. Their influence can turn out to be greater than the entrepreneur initially anticipates, and it can increase over time. In addition, equity investors are more likely to attempt to influence long-term, “big-picture” strategic decisions of the firm, and it is this kind of influence that entrepreneurs with a high level of decision control risk aversion are most reluctant to give up. In summary, entrepreneurs are likely to perceive that higher levels of decision control risk are associated with equity financing rather than with debt financing. The influence that this perceived risk exerts on a specific entrepreneur’s choice of debt or equity will depend on the level of decision control risk aversion that that entrepreneur possesses. All other factors being equal, entrepreneurs with high levels of decision control risk aversion will prefer debt to equity because taking on equity investors generally involves a sacrificing a greater degree of strategic decision autonomy. On the other hand, those entrepreneurs with low or moderate levels of decision control risk aversion may choose either debt or equity, depending on the strength of their aversion and on the influence of other decision factors. In fact, all things equal, they are likely to prefer equity, because they may actually prefer to share decision control with an equity investor than to assume the additional risk of business failure imposed by loan-related covenants and restrictions. We emphasize that the type of decision control risk aversion to which we refer is distinct from the influence of economic self-interest. Decision control risk aversion, in our terms, reflects the degree of intrinsic value that entrepreneurs place on

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the sense of self-determination associated with retaining control over the strategic decisions of the firm. This intrinsic value may correspond to entrepreneurs’ understandings of their own economic self-interest, but it need not do so. Entrepreneurs may also experience internal conflict between the value that they place on selfdetermination and their perceptions of their economic self-interest. In the context of the choice of debt-versus-equity, this kind of conflict is quite likely to occur. For example, an entrepreneur may perceive that her own economic prospects would be more favorable if she took on an equity investor, because an equity investment could provide valuable advice and fuel growth that is more ambitious and more rapid than debt financing could. However, if the same entrepreneur had a sufficiently high level of decision control risk aversion, she might well choose to forgo equity financing because she perceives debt financing to have a lower level of decision control risk. Should some entrepreneurs wish to maintain decision control solely because they believe that they can make superior economic decisions, we would view this as a component of wealth drive rather than self-determination. We expect that different entrepreneurs will place different values on the economic opportunities associated with debt and equity and will have different levels of decision control risk aversion; these differences help explain their choices.

Choosing Among Alternative Investors of the Same Type As with entrepreneurs choosing between debt and equity, entrepreneurs choosing among alternative equity investors may find their decision control risk aversion to be in conflict with their economic interests. For example, an entrepreneur may perceive that a greater economic payoff can be obtained by accepting financing from Investor A than from Investor B. This may be because Investor A can offer more capital or superior supporting services, such as networking and publicity, or because Investor A offers more favorable investment terms with regard to issues of valuation and dilution. However, Investor B may still be preferred under such circumstances, if Investor B is thought to present a lower decision control risk. If the difference between the decision control risks posed by alternative investors is great enough, and if an entrepreneur’s level of decision control risk aversion is great enough, then a choice that runs counter to the entrepreneur’s economic self-interest will occur. We expect that the greatest effects of fear of loss of decision control will occur for entrepreneurs whose choice is restricted to equity because of inadequate collateral or an insufficient revenue stream. Among those who may consider either equity or debt, the most decision-control risk averse can choose debt to ameliorate control concerns. However, those who choose equity in order to promote growth (e.g. early


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stage, high-potential ventures) will include a broader mix of individuals: Those who are the most decision-control risk averse may feel “forced” to choose growth, while others for whom decision control is less important may not share this sense. Self-selection among individuals able to choose between debt and equity will cause the set in the former group to be composed of those more averse to decision control risk and those in the latter to be less so. Even within these two groups, however, there will be variation. Within equity financing, early stage entrepreneurs may have a choice between angels and venture capitalists. For some, extremely high cash needs may render angels an economically infeasible choice; for some, insufficient development of a proprietary technology may rule out venture capital. However, when there is a choice, we expect those who most fear decision sharing will favor angels because of the reputation that venture capitalists have for greater control demands (Kotkin, 1984; Shepherd & Zacharakis, 2000). Assuming equal cost of financing, entrepreneurs will choose between the two types of equity financing (angels and venture capitalists) whom they perceive to pose the least threat to their self-determination. Even when the economic costs are not equal across a given set of equity financing options (e.g. a set of venture capitalists), entrepreneurs may pick higher cost deals if their decision control risk aversion is sufficiently high. As mentioned earlier, Smith (1999) presented evidence that entrepreneurs often choose less economically favorable deals when given a choice among alternative investors. One explanation could be that other compensating economic factors existed, such as investor reputation. Alternatively, the reason could be the explanation we have offered here: entrepreneurs choose investors who elicit the lowest level of fear of loss of control. Entrepreneurs choosing among debt financiers will, like those considering only equity, favor those lenders whom they perceive to pose a lower decision control risk, and the extent to which they do so will depend on their overall level of decision control risk aversion. However, we expect that the influence of decision controlrelated factors on a choice among debt financiers will be weaker than the influence of these factors on a choice among equity investors. This is because the perceived differences in decision control risk among equity investors may vary more than those among debt financiers, who are generally perceived to pose less decision control risk. In summary, we expect that once a type of financing has been selected (based on the entrepreneurs’ relative mix of wealth motive and self-determination motive and the situational factors that constrain choice), the self-determination motive will play a role in choosing the particular financier. The strength of this role is likely to be greatest in those “forced” to choose among equity investors because of the combination of wealth and situational factors. In the next section we explain

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the factors influencing control risk aversion and perceived threat of loss of selfdetermination.

DETERMINANTS OF DECISION CONTROL RISK AVERSION AND RISK PERCEPTIONS Table 2 summarizes the factors affecting decision control risk aversion and perceived threat of loss of control. The table shows that entrepreneurs’ levels of decision control risk aversion are influenced primarily by the past performance of their ventures as well as their own past experiences with decision sharing while their levels of perceived decision control risk are influenced by framing factors influenced by industry norms as well by provider-specific factors, including reputation and procedural justice. These factors are discussed in greater detail in the sections below.

Determinants of Decision Control Risk Aversion An entrepreneur’s level of decision control risk aversion refers the degree to which he or she is averse to sharing control of the firm. This construct is concerned with control over “big picture” issues pertaining to the strategic direction of the firm, not everyday operational issues. Although entrepreneurs may also desire control over mundane issues, and that desire may be related to a desire for strategic control, the two desires may also arise from separate sources. The determinants that we focus on are factors pertaining to the entrepreneur’s prior experiences as an entrepreneur and organizational factors pertaining to the

Table 2. Factors Affecting Decision Control Risk Aversion and Perceived Decision Control Risk. Decision Control Risk Aversion Good Current Venture Performance Good Performance of Past Ventures Good Past Experience with Control Sharing

Effect Increases risk aversion Increases risk aversion Decreases risk aversion

Perceived Decision Control Risk Framing Effects: Salience of growth opportunities vs. decision control loss Procedural Justice of Initial Interactions

Effect Decreases perceived risk Decreases perceived risk


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recent performance of the current venture. Clearly, there are other factors that are likely to influence an entrepreneur’s level of decision control risk aversion. For example, differences in education, demography or other aspects of individuals’ personal backgrounds have been shown to be related to peoples’ beliefs and cognitive processes (e.g. Taylor, 1975; Wally & Baum, 1994), and such factors probably influence entrepreneurs’ levels of decision control risk aversion as well. However, in light of the newness of theory in this area, we will focus here on several basic determinants pertaining to entrepreneurs’ professional experiences and the histories of their ventures. The recent business performance of entrepreneurs’ ventures is one factor that is likely to affect entrepreneurs’ levels of decision control risk aversion. Entrepreneurs whose current ventures are successful are, like most managers, likely to attribute much of that success to their own control of the ventures (Bettman & Weitz, 1983). Such entrepreneurs are likely to have confidence that their ventures will continue to be successful as long as they retain control of them, and their belief in the promise of continued economic success is likely to contribute to their desire to retain control. However, the influence of venture success on their desire to retain control is not mediated solely by economic factors. Several psychological factors are likely to bear on this desire as well. First, as indicated by research on the self-serving bias (Miller & Ross, 1975), individuals are likely to attribute performance successes to their own efforts and abilities and to blame external forces for their failures. Thus, entrepreneurs are likely to attribute the ventures’ recent success to their own control. The success of these individuals’ ventures reinforces beliefs in their own abilities, which should give rise to strong feelings of personal satisfaction with the experience of being in control. They will be reluctant to give up the sense of control that makes these beliefs possible, because sharing control of their ventures will dilute the degree to which they can claim credit for the ventures’ future success. Conversely, entrepreneurs whose ventures are performing poorly are likely to feel relatively little satisfaction with and responsibility for their ventures and, accordingly, will have lower levels of decision control risk aversion. Second, entrepreneurs who attribute their recent success to their own control are likely to feel a strong sense of personal responsibility for their firms and the people involved in them. For these entrepreneurs, the control they exercise over their ventures is likely to be infused with a variety of personal feelings, such as loyalty and affection. For example, entrepreneurs whose firms have been successful may feel a sense of paternalistic responsibility to their employees and customers. One of the reasons entrepreneurs are likely to value control is that it enables them to make strategic decisions that benefit these constituencies for whom they may have strong feelings – decisions that may conflict to some extent with those that would

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be prescribed by purely economic analyses. Thus, entrepreneurs may be averse to sharing control of their decisions because they feel an obligation to continue to manage their firms in ways that reflect the strong emotional investments they have in their firms. Similar reasoning is likely to apply to the performance of previous ventures founded by serial entrepreneurs. For these individuals, the feelings of personal satisfaction and responsibility they derive from the management of their current ventures are likely to be strongly influenced by the performance of previous firms that they have managed. These past experiences are likely to guide the way they think about issues of control sharing in their current ventures. Although the performance of the current venture may still be relevant to the level of decision control risk aversion levels in such circumstances, the impact of current performance may actually be overridden by prior entrepreneurial experiences if such experiences have been particularly extensive or vivid. That is to say, entrepreneurs whose past venture experiences were sufficiently positive may seek high levels of decision control even if the performance of their current ventures is average or poor. Those who experienced only failure in the past may attribute it to need for outside help and may be more willing to listen to others. Another factor that is likely to bear on entrepreneurs’ levels of decision control sharing aversion is their satisfaction with past experiences they may have had with decision sharing itself (Cyert & March, 1963). Such experiences will inform their beliefs about the intrinsic value of decision control. Individuals will be less averse to sharing decision control if they had a positive experience and more averse to sharing decision control if that experience was negative. In determining whether to interpret a past control-sharing experience positively or negatively, entrepreneurs will look to both economic and non-economic data. If prior control-sharing experiences yielded poor business performance, that negative experience is likely to negatively color the way entrepreneurs think about the entire experience of control-sharing. It should be emphasized that the perspective of such entrepreneurs will not only be soured by the fear of further economic loss but also by the feelings of frustration and dissatisfaction that they are likely to associate with such experiences (Forgas & George, 2001). In addition, however, even if prior control-sharing experiences yielded average or even above-average economic outcomes, entrepreneurs will draw on their recollections of the non-economic aspects of those experiences in formulating positive or negative opinions about the intrinsic value of retaining versus sharing control. These aspects are independent of the economic aspects of past experiences, and the opinions they imply about the intrinsic value of decision control may or may not correspond to the implications of past economic performance.


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Consider, for example, an entrepreneur looking back on a single past venture experience in which financing was obtained from an equity investor who shared significant decision control and which was, in the end, highly successful from an economic standpoint. The entrepreneur might conclude that in spite of the economic success that was realized, the psychological distress associated with sharing decision control was very great. An entrepreneur might form such an opinion if she recalled high levels of stress or anxiety being associated with strategic disagreements or strong feelings of humiliation or frustration being associated with the experience of having to compromise certain business principles or abide by strategic decisions with which she disagreed. If these recollections are sufficiently strong, they could override the positive opinions about control-sharing that the economic success might otherwise suggest and lead her to formulate a generally negative opinion about the experience of control-sharing and, hence, a high level of decision control-sharing aversion. Thus, the intrinsic value that entrepreneurs place on retaining decision control is likely to be influenced by the degree to which their prior decision control-sharing experiences are positive or negative. And although the economic success of past ventures in which decision control was shared is likely to figure into such decisions, it is by no means either the sole determinant nor necessarily the most important one.

Determinants of Perceived Decision Control Risk As the above analysis suggests, entrepreneurs’ need for self-determination can factor into financing choices in terms of entrepreneurs’ aversion to losing decision control. Additionally, self-determination concerns may influence financing choice in terms of perceptions of the risk of losing decision control. As discussed earlier, several contextual factors contribute to risk perceptions. In the context of entrepreneurs’ financing decisions, these factors are likely to reveal themselves in a number of unique ways. Below, we address how two of these factors, framing and procedural justice, are manifested in entrepreneurs’ search for financing. Generally, framing a decision in terms of potential losses leads decision makers to be more risk seeking than if the decision were framed in terms of gains. That is, people tend to choose riskier options when attempting to avoid losses than they would when attempting to attain gains. In the context of financing, the most salient potential gains of various options are the funding itself and the growth potential for the entrepreneurial firm. A significant material loss is the loss of wealth (e.g. loss of equity); however, we also believe that self-determination concerns are potential losses. Specifically, these losses involve the entrepreneurs’ loss of control over the firm and their own actions as CEOs. With the potential loss of

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control, entrepreneurs may also lose the sense of responsibility for the firms’ successes and failures, and consequently, lose the opportunity for achievement and demonstrating competence. The threat of these losses is greater in external equity financing options, and particularly with venture capital. When these potential losses are made salient (as opposed to potential wealth and growth gains), entrepreneurs are likely to take risks in attempt to avoid such losses. Thus, they may have an inflated sense of optimism about their ability to obtain and successfully work with alternative sources of financing. In other words, entrepreneurs are more likely to seek private individual equity financing, debt-based financing or bootstrapping than venture capital when threats to self-determination – as opposed to opportunities for growth – are made salient. Self-determination concerns, and the attendant threat of decision control loss, may be made salient in a number of ways. First, industry norms may help to create an image of a financing type wherein growth (i.e. gains) or control (i.e. losses) is more or less prevalent. For example, in certain sectors of industry (e.g. biotech) wherein some venture capital-backed firms have experienced spectacular success, the choice between venture capital and other options is apt to be couched in terms of growth opportunities. Moreover, the history and reputation of specific providers may influence the frame of choices among financing sources of the same type. For example, if a particular venture capital firm has a reputation for replacing members of a firm’s entrepreneurial team, the choice between it and other venture capital options may be framed in terms of potential loss of decision control. The perceived risk of working with particular financiers is also influenced by the perceptions of procedural justice in interactions with potential providers of financing. Research indicates that people evaluate interactions they have with exchange partners from the perspective of justice (Lind, 2001). Procedural justice, in turn, signals a person’s standing in the exchange relationship and informs on the exchange party’s character (i.e. trustworthiness, benevolence, etc.). In the case of entrepreneurs, such assessments arising from initial interaction with potential financiers would likely inform on the risk of losing control and status in the firm. Consequently, when initial contact between a financier and an entrepreneur is marked by fair treatment – such as consistent application of standards, open, two-way communication, and respect – the entrepreneur is likely to view decision control risk as relatively low. In summary, we argue that two factors, framing and procedural justice, are likely to influence perceptions of decision control risk. Industry and reputation influence the framing of financing options as decision control loss versus growth opportunities because these factors affect the salience of self-determination concerns. Procedural justice in interactions with particular financiers influences risk perceptions because it informs on potential threats to self-determination. It


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is also likely that these factors influence the salience and prospects of economic losses as well, such as loss of potential wealth associated with loss of ownership or opportunism. To some extent, risk seeking associated with framing and procedural justice is attributable to both self-determination and economic concerns. However, we feel that a consideration of both economic and psychological losses and applying the concept of framing and procedural justice provides insight into how entrepreneurs may choose riskier and, in some cases, economically suboptimal financing options.

DISCUSSION We set out in this chapter to give more explicit recognition to a phenomenon that has been noted by some previous researchers but that remains under-appreciated in the entrepreneurship literature generally: that entrepreneurs are often motivated to start new businesses in order to realize a dream of being creators and controllers of their own destinies. When this dream does not also incorporate a goal of accumulating as much wealth as possible, little internal conflict exists regarding how the dream should be financed, and entrepreneurs will “bootstrap” in order to sustain operations. However, when the dream includes a vision of rapid wealth accumulation alongside one of self-determination, realization involves a financing dilemma. Our primary purpose here was to build a theoretical framework that helps to explain the impact of the self-determination drive in entrepreneurs who also seek wealth; the focus of this development was on factors that influence entrepreneurs’ aversion to sharing decision control and their perceptions of decision control risk. In brief, we argued that venture stage, entrepreneurs’ experience and the business performance of past and current ventures influence decision control risk aversion, and that industry norms, reputation, and the procedural justice of interactions with financiers influence perceived decision control risk. From the perspective of practice, one of the key implications is that a model of entrepreneurs’ selection criteria may be derived from our analysis. The venture capital literature is replete with evidence of how equity investors rate investment opportunities and advice about how they should rate them (Smart, 1999); the criteria for banks are also well-understood (Winborg, 2000). However, our analysis suggests that explaining entrepreneurs’ preferences is much more complex because of the mix of motives involved. Our analysis suggests that entrepreneurs consider the economic costs and benefits as well as the decision sharing costs and benefits of financing type and individual financier. While it is useful for investors or lenders to know that experience and venture performance influence entrepreneurs’ willingness to take decision control risks, what is particularly

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useful for investors is the knowledge that investors can influence perceptions of decision control risk. For example, investors can frame financing opportunities in ways that appeal to the self-determination motives of specific entrepreneurs. Entrepreneurs, for their part, may misrepresent their levels of decision control risk aversion to investors, for example by persuading investors that they do not place a high value on self-determination in order to help obtain financing. Entrepreneurs may do this in order to mollify the concerns of an investor with whom they are negotiating if they also believe that, in the end, they will be able to resist the investor’s efforts to influence decision control or if they view the financing arrangement as simply a short-term fix or a springboard to other opportunities that pose less decision control risk. In addition, it is conceivable for entrepreneurs to be unaware of their own levels of decision control aversion. They might consciously desire to make financing choices on a strictly economically rational basis and yet be influenced by self-determination concerns. This assertion is consistent with procedural justice theory, which suggests that individuals are not consistently rational and calculative in managing their exchanges with others (Lind, 2001). Instead, persons often rely on their perceptions of procedural justice as a signal of the exchange partner’s trustworthiness. Thus, even in attempts to make the most economically beneficial financing decision, entrepreneurs may be influenced by the fairness of initial interactions with potential investors in ways that may have little to do with economic returns. Thus, our analysis highlights the risks involved in ignoring self-determination as a motive in financing decisions. These risks affect both investors and entrepreneurs. Investors who fail to consider the importance of self-determination may misinterpret the motives of entrepreneurs. This misinterpretation can lead to miscommunication and, ultimately, can cause investors to negotiate poorly with entrepreneurs or to work ineffectively with them after an investment has been made. It is clear, therefore, that a key task of angels and venture capitalists is to assess the genuine levels of decision control risk aversion possessed by the entrepreneurs whose ventures they are considering financing. Because entrepreneurs’ motives may be obscured, ascertaining these motives will likely require investors to conduct in-depth and explicit discussions about decision control issues with entrepreneurs and perhaps to research entrepreneurs’ past decision sharing experiences with people capable of serving as references on such matters. Our analysis also suggests that entrepreneurs themselves need to spend some time reflecting on what their “true” motives are with regard to self-determination in order to diminish their chances of deceiving themselves. Entrepreneurs who have both high growth aspirations and a high aversion to decision control risk face a real dilemma that highlights the importance of accurate self-understanding with


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regard to this issue. Entrepreneurs should seek to obtain that understanding by talking with more experienced entrepreneurs about issues of decision control or searching their own past personal and professional lives for clues about the value they actually place on self-determination. From a theoretical standpoint, our analysis provides some much-needed clarity and structure to our understanding of how entrepreneurs’ motives for self-determination influence their financing decisions. Clearly, models that account only for economic interests (or, for that matter, only for psychological factors) provide an incomplete picture of how entrepreneurs choose to finance their businesses. Both types of factors can figure prominently in these decisions, and models that account for both should be better able to predict financing choices. An additional theoretical implication of the perspective we put forth here is that entrepreneurs’ financing decisions are complex and interesting from a cognitive standpoint, because psychological factors may interact with economic ones in ways that lead them to make choices that they otherwise would not or should not make. For example, entrepreneurs’ perceptions of their own economic prospects may be influenced by the value they place on self-determination, causing them to underestimate the economic benefit that external financing might provide. Alternatively, entrepreneurs may be driven by economic ambitions to accept financing alternatives that are incompatible with the equally strong motives they may have for self-determination, perhaps because they erroneously believe that they can resist or manage the process of sharing decision control. It should be noted that in developing our analysis, we have focused on the entrepreneur as an individual decision maker. This focus has simplified the presentation of our ideas in a constructive way, but we also acknowledge this simplification as a limitation or boundary condition of our analysis. In the real world, of course, major new venture management decisions such as those surrounding the choice of financing are often made by groups of people, not a single entrepreneur. For example, Sitkin and Pablo (1992) suggest that social and work context may shape risk perceptions. Group composition, leadership and culture, and organizational control systems may all influence the perceived risk of an option. Given evidence of risky shift and groupthink, we speculate that groups composed of highly similar individuals are apt to be more extreme in their risk perceptions. Leadership, culture, and control systems can create an environment in which risk-taking is either supported or discouraged. This last class of factors suggest that entrepreneurs’ confidence in their ability to maintain control of the venture after obtaining financing may depend upon their social environment, that is, the composition of the top management team, the entrepreneurs’ network of associates, mentors, or role models, and so forth.

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In some cases there are several individuals who could plausibly claim to be entrepreneurs within a single firm, and in other cases entrepreneurs have surrounded themselves with others with whom they effectively share decision control within the venture, either by virtue of equity ownership or trust. Accounting for the ways in which group dynamics might interact with the phenomena we have described in this chapter – for example, by considering how individuals’ potentially differing levels of decision control risk aversion might be reconciled or considering the implications of team size or composition on the way venture management teams perceive the decision control risks associated with various financing choices – would represent valuable extensions of our work, and we encourage future researchers to pursue such issues. Future researchers might also consider how trust might relate to the issues of self-determination discussed in this chapter. For example, trust might enable entrepreneurs to agree to use external financing even in instances in which they perceive that a high degree of decision control risk is associated with the type or source of financing that they choose. We would suggest that, in these cases, trust does not reduce the risk of decision control, but it can increase an individual’s willingness to take a specific risk. Thus, when entrepreneurs trust a given individual or institution, it may reduce the influence that their overall levels of decision control risk aversion have on choices involving those specific financing alternatives. In summary, our analysis has attempted to show how entrepreneurs’ motives for self-determination can influence their financing decisions. We hope that our discussion will spur further contributions in this area by other researchers and will be useful to educators, investors and entrepreneurs themselves.

NOTES 1. By drive for self-determination (alternatively, fear of loss of decision-making control) we intend something other than the fear of bankruptcy. Mishra and McConaughy (1999) labeled family firms’ more conservative use of debt as a fear of loss of control; however, this position stops short of clarifying the difference between the fear of loss of economic value and fear of loss of decision-making rights. We argue here that for some the intrinsic value of being the decision maker cannot be compensated for by economic wealth. 2. In the best of circumstances, entrepreneurs can maximize wealth without endangering self-determination or can maximize self-determination without endangering wealth. We assume, however, that maximizing one typically poses some risk to the other. 3. We do not explicitly consider loans or minor equity received from family and friends in this discussion. Most frameworks tend to consider such sources as internally generated funds, but such sources may be far murkier in practice. To the extent that no formal obligations are attached to such sources, they operate like internally generated funds; to the extent that explicit payback or decision rights are attached, they operate like debt or equity.


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Slattery, J. P., & Ganster, D. C. (2002). Determinants of risk taking in a dynamic uncertain context. Journal of Management, 28, 89–106. Smart, G. H. (1999). Management assessment methods in venture capital: An empirical analysis of human capital valuation. Venture Capital, 1, 59–82. Smith, D. G. (1999). How early stage entrepreneurs evaluate venture capitalists. Frontiers of Entrepreneurship Research, Proceedings of the Kaufmann-Babson Entreprneurship Research Conference, University of South Carolina, 289–303. Staw, B. M., Sandelands, L., & Dutton, J. (1981). Threat rigidity effects in organizational behavior: A multilevel analysis. Administrative Science Quarterly, 26, 501–524. Steier, L., & Greenwood, R. (1995). Venture capitalist relationships in the deal structuring and postinvestment stages of new firm creation. Journal of Management Studies, 32(3), 337–357. Steward, W. H., & Roth, P. L. (2001). Risk propensity differences between entrepreneurs and managers: A meta-analytic review. Journal of Applied Psychology, 86, 145–153. Storey, D. (1994). Understanding the small business sector. Routledge, NY. Taylor, R. (1975). Age and experience as determinants of managerial information processing and decision making performance. Academy of Management Journal, 18, 74–81. Thaler, R. H., & Johnson, E. J. (1990). Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice. Management Science, 36, 643–660. Tyler, T. R., & Bies, R. J. (1990). Beyond formal procedures: The interpersonal context of procedural justice. In: J. Carroll (Ed.), Applied Social Psychology and Organizational Settings (pp. 77–98). Hillsdale, NJ: L. Erlbaum Associates. Tyler, T. R., & Blader, S. L. (2000). Cooperation in groups. Philadelphia, PA: Taylor & Francis. VanAuken, H., & Doran, M. (1989). Small business capitalization patterns. Journal of Applied Business Research, 2, 15–22. VanAuken, H., & Holman, T. (1995). Financial strategies of small public firms: A comparative analysis with small private firms and large public firms. Entrepreneurship: Theory & Practice, 20, 29–41. Van Auken, E., & Neeley, L. (1996). An examination of nontraditional financing by small firms. Proceedings of the USASBE National Conference. Wally, S., & Baum, J. (1994). Personal and structural determinants of the pace of strategic decision making. Academy of Management Journal, 37, 932–956. Winborg, J. (2000). Financing small businesses: Developing our understanding of financial bootstrapping behavior. Ph.D. Dissertation. Scandinavian Institute for Research in Entrepreneurship, Halmstad University, Sweden. Winborg, J., & Landstrom, H. (2001). Financial bootstrapping in small businesses. Examining small business managers’ resource acquisition behaviors. Journal of Business Venturing, 16, 235–254. Wiseman, R. M., & Gomez-Mejia, L. R. (1998). A behavioral agency model of managerial risk taking. Academy of Management Review, 23, 133–153. Zacharakis, A., & Meyer, G. (1998). A lack of insight: Do venture capitalists really understand their own decision process? Journal of Business Venturing, 13, 57–76. Zacharakis, A., & Shepherd, D. (2001). The nature of information and overconfidence on venture capitalists’ decision making. Journal of Business Venturing, 16, 311–332.

EXTENDING THE THEORY OF THE ENTREPRENEUR USING A SIGNAL DETECTION FRAMEWORK Jeffery S. McMullen and Dean A. Shepherd INTRODUCTION Shane and Venkataraman (2000) suggest “the field [of entrepreneurship] involves the study of sources of opportunities; the processes of discovery, evaluation, and exploitation of opportunities; and the set of individuals who discover, evaluate, and exploit them” (p. 218). However, the study of the judgment required for opportunity evaluation has been greatly overshadowed by interest in opportunity recognition and to a lesser extent opportunity exploitation. This is surprising considering the number of economic theories of the entrepreneur that recognize sound judgment as a principal quality of entrepreneurship (Cantillon, 1755; Kirzner, 1973; Knight, 1921; Mises, 1949; Say, 1840; Schumpeter, 1934; Shackle, 1955). In fact, the first recognized theory of the entrepreneur defined the entrepreneur as someone who exercises business judgment in the face of uncertainty (Cantillon, 1755/1931, pp. 47–49). Similarly, Knight (1921, p. 271) suggests that the essence of entrepreneurship is judgment, born of uncertainty, and argues that it is this judgment that delineates the function of entrepreneur from that of manager. He goes on to point out that the function of manager does not in itself imply entrepreneurship but that a manager becomes an entrepreneur when he exercises judgment involving liability to error (Knight, 1921, p. 97). However, the judgment referred to by these theorists is not just any


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form of judgment, it is judgment exercised in the decision of whether to take action. Because entrepreneurship is concerned with how new and appropriate ideas are implemented (Amabile, 1997), whether they be new firms (Gartner, 1988), new products or processes (Schumpeter, 1934, p. 66; Smith & DiGregorio, 2002, p. 130), entry into new markets (Lumpkin & Dess, 1996) or the reorganization of entire industries (Schumpeter, 1934, p. 66), entrepreneurship is consistently represented by two primary elements: novelty and action. We argue, therefore, that entrepreneurship requires judgment because (1) entrepreneurship cannot exist without action (Kirzner, 1997; Shane & Venkataraman, 2000); (2) action is always uncertain (Mises, 1949); and (3) the navigation of uncertainty mandates the exercising of judgment (Knight, 1921). Moreover, because entrepreneurial action is action in regard to something new, or “novel action” (Smith & DiGregorio, 2002, p. 130), it possesses even greater uncertainty than routine actions, and therefore may necessitate even more judgment. Finally, because new products, processes, etc. are how businesses reinvent and reinvigorate themselves, this entrepreneurial judgment is integral to an organization’s effectiveness and sustained performance, making it a cornerstone of effective strategic management (Schendel & Hofer, 1979) as well as new venture creation (Shane & Venkataraman, 2000). Why then has the judgment required for opportunity evaluation received so little attention by entrepreneurship scholars? We propose three primary reasons: (1) difficulties associated with interpreting existing economic theories of the entrepreneur; (2) lack of a conceptual framework capable of rigorous empirical examination; and (3) unfamiliarity by most entrepreneurship enthusiasts with the empirical methods necessary to test such a framework. This chapter seeks to advance the field of entrepreneurship by addressing each of these points in detail thereby helping to fill the void they represent and enabling scholarly advancement on a number of previously intractable fronts.

INTERPRETATIONS OF ECONOMIC THEORIES OF THE ENTREPRENEUR The entrepreneur of economic theory is a function not a personality (Hebert & Link, 1988, p. 154), and for better or for worse a function appears to have theoretical meaning only in relationship to its respective system. This observation has two significant implications regarding the proper interpretation of economic theories of the entrepreneur: one scope restricting and the other scope expanding.

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Scope Restricting Implications of Entrepreneur as Function Recognition that the entrepreneur is a function meaningful only in relation to the system suggests that economic theories of the entrepreneur may be bound by their unit of analysis (which counter-intuitively is the economy not the individual). That is, they appear to be inappropriate for the study of the entrepreneur independent of the system in which he or she operates. Unfortunately, this observation exposes a necessary restriction to the use of economic theory by management scholars. However, recognition of this restriction also clarifies numerous empirical contradictions and theoretical frustrations. For example, the recent scholarly interest in opportunity recognition has produced a number of attempts to utilize Kirzner’s (1973) theory of entrepreneurship within the management literature. Kirzner’s (1973) entrepreneur fulfills the function of an arbitrageur who moves the economy toward equilibrium by rectifying discrepancies in supply and demand. This process is made possible through a concept that Kirzner labels entrepreneurial alertness. However, within Kirzner’s work there is an unwitting dilution of the concept of entrepreneurial alertness from a description of a behavior that is necessary for the economy to function properly to a description of a psychological characteristic common to successful entrepreneurs. We say “unwitting” because Kirzner frequently refers to his mentor’s belief that, “The economist must never be a specialist. In dealing with any problem he must always fix his glance upon the whole system” (Mises, 1949, p. 69). If Kirzner truly subscribed to this perspective, then the economy would be the theoretical unit of analysis and the entrepreneur would be viewed as the means for explaining why the system functions properly. However, Kirzner’s later work frequently violates this perspective. For instance, in his early writings Kirzner (1973, 1979, 1980) argues: Entrepreneurial alertness is not an ingredient to be deployed in decision-making; it is rather something in which the decision itself is embedded and without which it would be unthinkable (1980, p. 11).

He goes on to suggest that entrepreneurial alertness is what happens when the market presents a profitable situation that is successfully exploited by an individual who “fits” the necessary profile (1973, p. 74; 1980, p. 13). In essence, entrepreneurial alertness is a less mathematical, and therefore more approachable, exposition of probability theory in which the market presents an objective opportunity for someone possessing the necessary knowledge. Entrepreneurial alertness ensures exploitation of this opportunity and consequently perpetuates the market system. To use an analogy, alertness is like a radio trivia contest. Although you or I may not know the answer to the trivia question asked by the disc jockey, someone

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inevitably does. Therefore, an objective opportunity is argued to exist. The first individual who knows the answer and calls in to claim the prize is the exhibitor of Kirzner’s (1973) concept of entrepreneurial alertness. Accordingly, entrepreneurial alertness has no meaning a priori. By definition, it can only be said to exist post hoc. For example, Kirzner (1980) notes that, . . . faulty and inadequate entrepreneurship must be interpreted, therefore, not as evidence of the absolute scarcity of entrepreneurial alertness . . . but as evidence that the alertness costlessly available has somehow remained latent and untapped (p. 13).

Therefore, alertness can only be said to exist for the successful radio caller (and possibly to a lesser degree for those who knew the answer, tried to call, but were not as fast as the winner). The caller who got through but provided the wrong answer cannot be said to have exercised alertness. Thus, alertness is a configural concept in which an objective market opportunity is only an opportunity for those possessing the necessary attributes. Therefore, because this alertness is a product of the market, it is problematic for it to be discussed as a universal attribute of entrepreneurial individuals independent of the system in which they operate. In his later work, Kirzner (1982, 1997, 1999) broadens his concept of entrepreneurial alertness as an effort to soften (1982, pp. 156–157) the more deterministic stances taken in his earlier work (1979, p. 9). In doing so, he obfuscates the configural nature of the “alertness” concept by discussing it more in terms of a quality observable in characteristics such as prescience, boldness, self-confidence, creativity, and innovative ability (Kirzner, 1999). Although this exposes alertness as nothing more than judgment, it also leads the reader to believe the concept is separable from the market context. However, we argue that it is not. This judgment is continually discussed in terms of how well the entrepreneur’s envisaged future corresponds to the realized future (Kirzner, 1982, p. 156). Regardless of the degree of linguistic ambiguity employed, the “accuracy” benchmark implied by this “correspondence” is clearly suggestive that objective market opportunities exist that some people will accurately identify while others will not (Addleson, 1995). This suggests that, although the foundation of Kirzner’s argument has not changed, his explication of “entrepreneurial alertness” has digressed into a more confusing exposition of entrepreneurial perception. Based on the above interpretation of Kirzner’s concept of entrepreneurial alertness, we can now understand why studies building on his work have faced a number of theoretical and empirical frustrations. For example, building upon Kirzner’s (1979, 1985) concept of entrepreneurial alertness, Gaglio and Katz (2001) propose that “H1: In any given market situation, alert individuals are more sensitive to signals of market disequilibrium than non-alert individuals” (2001, p. 100) and “H9: Alert individuals are more sensitive to the profit potential of


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ideas and events than non-alert individuals” (2001, p. 104). Our interpretation of Kirzner suggests that these hypotheses are tautological (true by definition) once one recognizes that “alert individuals” have to be “more sensitive to signals of market disequilibrium” and “more sensitive to the profit potential of ideas and events” than non-alert individuals in order to be labeled “alert” in the first place. It appears that Kirzner’s theoretical ambiguity has lead to insufficient recognition that the entrepreneur (and his/her alertness) is a function inseparable from the system. Entrepreneurial alertness is not a quality or attribute of individuals rather it is a configuration of profitable environmental condition and applicable knowledge possessed by the individual. Similar frustrations have been found on the empirical front. For example, in an altered replication of Kaish and Gilad’s (1991) survey, Busenitz (1996) finds no evidence of any difference in the degree of Kirznerian “alertness” between entrepreneurs and managers. According to Kirzner’s theory a person becomes an “entrepreneur” (can be said to have performed the entrepreneurial function) because he or she has experienced “entrepreneurial alertness.” Therefore, the defining factor that delineates entrepreneur from non-entrepreneur (or manager) is “entrepreneurial alertness.” Any other criteria for discrimination would be inconsistent with Kirzner’s definition of entrepreneur. Therefore, like Gaglio and Katz (2001), Busenitz (1996) may have been investigating a form of “alertness” but we argue that this quality or attribute is not the same “entrepreneurial alertness” as that conceptualized by Kirzner in his early, theoretically consistent work in which the entrepreneur is a function of the economy rather than a psychological/cognitive profile or personality trait. What is important to note is that Kirzner’s (1973) theory of entrepreneurship does not constitute an empirical theory (Popper, 1959; Smedslund, 1978) for the individual because it is true by definition and therefore unfalsifiable (e.g. Greve, 2001; Popper, 1959), but it is a falsifiable theory when the unit of analysis is the economic system and not the entrepreneur. To illustrate this point we use a football analogy. Imagine that the economy is a football team, and the entrepreneur is the quarterback. The objective of the economic theories of the entrepreneur is not to explain why one quarterback’s statistics are better than another’s. Rather, their objective is to explain what makes a football team function properly (or perform well). The entrepreneur is like a quarterback in that each theorist believes that the team will not move without his actions. Some theorists focus upon the passing skills of the quarterback and argue that these constitute his primary function in determining the success of the team. Others point to his smooth handoffs to the running back. Some emphasize the ability to react to the defense and call better plays at the line of scrimmage. Finally, others suggest that it is some nebulous leadership quality that defines an effective quarterback. Regardless of which attributes are considered

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definitive of the quarterback, the theorists agree that the function of the quarterback is integral to the team and that his actions greatly influence its performance. However, evidence for the theory of the quarterback is observed through the performance of the team. Thus, the performance of the quarterback irrespective of the team tells us little about the quality of the theory’s explanatory or predictive power. It neither confirms nor denies the theory because it no longer recognizes the quarterback’s function within the team. Similarly, however, knowing the team’s performance without knowing exactly what the quarterback did or did not do also tells us little about the persuasiveness of a particular theory of the quarterback, for there is no way to know whether it was the passing skills, handoffs, play calling, or leadership that made the difference. Although these observations impose limitations upon the scope of the theory of the entrepreneur, they also present potential for the advancement of the theory of the entrepreneur on two different fronts. The first is analogical and recognizes that the function of the entrepreneur may be applicable to systems other than the economy (discussed in the section that follows). The second is empirical and concerns the use of methods that are relatively novel to entrepreneurship researchers, such as experimental methods (further suggestions offered in the discussion section below).

Scope Expanding Implications of Entrepreneur as Function Realization that the entrepreneur is a function meaningful only in relation to the system suggests unexplored potential for these theories. For example, because economists who were interested in explaining the mechanics of the economy developed the original theories of the entrepreneur, the system in which the entrepreneurial function was discussed was always the economic system. However, this does not mean that the economy is the only system in which the entrepreneurial function has meaning. Rather, it suggests that because the entrepreneur represents a function and not a personality, the role he or she fulfills is potentially as applicable to the effectiveness of other systems (e.g. industries, strategic groups, organizations, and even teams) as it is to the economy. In essence, the entrepreneur of economic theory is like the center of a wheel. Although the size of the wheel may change, the center fulfills the same function whether that wheel is for a bicycle or a tractor. Therefore, if the function of the entrepreneur within the economy is implementing new combinations (Schumpeter, 1934), then, perhaps, it is the entrepreneur that fills this function within the organization as well. Similarly, if it is the entrepreneur’s function to bear the uncertainty that is intrinsic to exercising judgment (Cantillon, 1755;


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Knight, 1921; Mises, 1949) then why would this be any different within the organization? Because economic theories of the entrepreneur (especially those proposed by “Austrians”) are notorious for neglecting the firm in their haste to discuss economics in terms of individual action (Casson, 2002), it would appear that their concepts are readily transferable. However, the challenge then becomes reconciling seemingly irreconcilable theories. Although a daunting prospect, this challenge may not be as ominous as it first appears.

SHARED ASSUMPTIONS OF ECONOMIC THEORIES OF THE ENTREPRENEUR Regardless of how disparate some economists may argue their theories of the entrepreneur to be, the fact remains that they share a number of assumptions that make them more similar than dissimilar. The most important amongst these are (1) an emphasis on the entrepreneur as a function of the economic system (as discussed above); (2) a shared ontology (or worldview); and (3) a focus upon one behavioral aspect of entrepreneurial action to the detriment of others.

Shared Ontology With a few exceptions (most notably G. L. S. Shackle), most economic theory devoted to the subject of entrepreneurship has been firmly rooted in a social realist ontology. Ontology is “the assumptions about existence underlying any conceptual scheme or any theory or system of ideas” (Flew, 1979, p. 256). Because social realists suggest that there is a “real” world “out there” apart from the flawed apprehension of it (Lincoln & Guba, 2000, p. 176), their ontology, or worldview, consists of a belief in an objective reality. In contrast, social constructivists believe that there is no social reality other than that negotiated by society’s members (Lincoln & Guba, 2000). Consequently, their worldview consists of multiple interpretations and mutual consensus. Accordingly, they argue that concepts like money do not exist beyond the meaning given to them through the mutual interpretations of the members of society.1

A Focus Upon Only One Behavioral Aspect of Entrepreneurial Action Another commonality allowing the possible reconciliation of economic theories of the entrepreneur is a general realization that the function fulfilled by the

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entrepreneur is one that consists of mindful or “purposeful” behavior. In other words, each of these theories considers entrepreneurial behavior to be intentional behavior, or “action” (Greve, 2001). Whether attempting to achieve a profit by deriving “new combinations” through creativity (Schumpeter, 1934), “seeing” a way in which factor markets could be coordinated to produce personal profit (Kirzner, 1973), or making a decision under uncertainty that requires a willingness to bear uncertainty that others would not (Knight, 1921; Mises, 1949), entrepreneurship is discussed as an act of will and the product of a priori entrepreneurial judgment. Certain psychological characteristics are argued to be conducive to this entrepreneurial judgment, and even the economists who continually profess that the entrepreneur is a function and not a personality often fall victim to conjecture and hypothesizing in regards to the type of person who would choose to fill the role (e.g. Schumpeter admittedly commits this economic sin). However, most economic theories are descriptive, behavioral theories with little to no discussion of why a person might possess more, or better, perception or motivation than another person. Self-professedly, these theorists are interested only in what the entrepreneur does to ensure the effectiveness of the economy. Again, this speaks to our earlier point that the economist is interested in why the economy works, not in why the individual who is filling the entrepreneurial function is more insightful or motivated than his or her fellow human beings. When psychological characteristics are mentioned, however, they should be, and frequently are, discussed as necessities of the function, not determinants of the actors who will fill them. More importantly, however, these psychological characteristics revolve around one central concept, the decision to engage in entrepreneurial action, which we define as the attempt to profit personally within a market context by creating something that is novel to the actor. This definition has an intention (i.e. “to profit”), a behavior (i.e. “the attempt”), and a number of social rules or qualifiers regarding both the intention (e.g. “personally”) and behavior (e.g. “within a market context,” “by creating something,” “that is novel to the actor”) that, when taken together, enable categorization of the action as “entrepreneurial,” rather than merely “innovative,” “opportunistic,” or “creative.” In addition, these various components constitute an “action” as stipulated by action theorists (see Greve, 2001 for an eloquent articulation of this position).2 With these assumptions in mind, the question then becomes whether there is a framework capable of embracing social realism, a “function” as subject, the many disparate behavioral aspects constituting this function, and the psychological aspects comprising the judgment responsible for this entrepreneurial action. We argue that Signal Detection Theory is just such a framework.


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AN INTRODUCTION TO SIGNAL DETECTION THEORY (SDT) Modern Signal Detection Theory (SDT) evolved from the development of communications and radar during the first half of the twentieth century. During World War II engineers developed the principles of SDT in an attempt to discern a signal from a “noisy” background (Peterson et al., 1954; Tanner & Swets, 1954). Green and Swets (1966) introduced SDT to psychology for a similar purpose, to explain detection experiments in which weak visual or auditory signals had to be distinguished from a “noisy” background (MacMillan & Creelman, 1991). In essence, SDT is a meta-theory designed to provide a conceptual framework for analyzing decisions made under uncertainty. As testimony to its effectiveness, SDT is now being adopted with increasing frequency by scientists in other fields that place a premium on minimizing error through the development of assessment, prediction, and decision systems with high accuracy and discriminatory power – i.e. information retrieval, knowledge testing, psychiatric diagnosis, and medical detection and decision-making (McFall & Treat, 1999; Swets, 1996). Like many economic theories of the entrepreneur, SDT presumes that nearly all reasoning and decision-making takes place in the presence of some uncertainty. Through its precise language and graphic notation, SDT allows analysis of decision making in the presence of uncertainty by addressing both worlds of human experience: the internal world of thought, feeling, and value and the external world of the environment. Accordingly, it appears well suited to examine the entrepreneurial decision of whether to pursue an opportunity, defined as a situation believed to present a feasible and desirable course of action. But, perhaps the best way to introduce the fundamentals of SDT is through an example. To apply SDT, one must assume that the aim of the individual is to discriminate accurately between two mutually exclusive states, the presence or absence of a signal. SDT is illustrated through experimental methods requiring repeat decisions. The simplest form of SDT experiment is referred to as a yes-no experiment, because it requires “yes” and “no” responses (i.e. “Yes,” I believe this to be a signal or “No,” I do not believe this to be a signal). Although the theory allows for more complex experiments, we will apply SDT to the phenomenon of entrepreneurial action as a yes-no experiment in the interest of space and clarity. Four Potential Outcomes Imagine that an individual is presented a faint signal. The individual must now determine which of two mutually exclusive states the signal belongs. If the signal is

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believed to exist, then it is classified as signal plus noise. If not, then it is categorized as noise alone. Because the decision-maker has two choices (“yes” or “no”) and there are two stimulus classes (signal present or signal absent), potential for four outcomes exists: hit (signal present and person says “yes”), miss (signal present and person says “no”), false alarm (signal absent and person says “yes”), and correct rejection (signal absent and person says “no”). Hits and correct rejections are desirable, whereas false alarms and misses are not. The same decision structure applies to the entrepreneurial phenomenon. If one chooses to act upon a possible signal, then there is the potential for a “false alarm,” i.e. an error of commission. That is, the prospective entrepreneur may decide to pursue what is believed to be an opportunity only to realize that he or she was sadly mistaken. In contrast, if one chooses not to act, then there is the potential for a “miss,” i.e. an error of omission. In this scenario, the prospective entrepreneur chooses to forgo what could possibly be an opportunity only to find that his or her assessment was wrong and that he or she did indeed miss the opportunity. Scholars in management and economics have embraced the above conceptualization of the entrepreneurial decision. For example, Shane and Venkataraman (2000, p. 220) state, An entrepreneurial discovery occurs when someone makes the conjecture that a set of resources is not put to its “best use” (i.e. the resources are priced “too low,” given a belief about the price at which the output from their combination could be sold in another location, at another time, or in another form). If the conjecture is acted upon and is correct, the individual will earn an entrepreneurial profit. If the conjecture is acted upon and is incorrect, the individual will incur an entrepreneurial loss (Casson, 1982).

Casson’s (1982) conceptualization of the entrepreneurial decision of whether to act upon a possible opportunity is the decision illustrated in Fig. 1. Such a decision requires individual judgment regarding (1) whether a signal exists and (2) whether that signal is deemed worthy of action.3,4 Fortunately, SDT provides an ideal framework for detailed examination of this kind of decision.

Fig. 1. The Entrepreneurial Decision.


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Three Elements of Signal Detection Theory SDT encompasses three independent components comprising the individual’s decision-making process: signal strength (conceptualized in terms of information quantity), uncertainty, and motivation. Signal strength (conceptualized in terms of information quantity). SDT argues that more information is always desirable because it increases decision accuracy, i.e. the likelihood of obtaining either a hit or a correct rejection while reducing the likelihood of an outcome in the two error cells. This is conceptually analogous to turning up the volume on an auditory signal to ensure enhanced discriminability. Thus, the more information a decision maker possesses about a signal, defined as current information that changes one’s belief about the value of a future state (Fiet, 1996), the less confusion there is in its classification. Uncertainty. Individuals face two kinds of noise factors that contribute to their uncertainty regarding whether a signal exists: internal noise and external noise. Many possible sources of external noise exist. Using opportunity evaluation as an example, external noise may take the form of uncertainty in market demand, feasibility of production, competitive response, attainability of necessary resources, etc. This noise is frequently, but not always, reducible. SDT posits that with the proper training and practice, people can learn what cues to look for, and that with additional effort, they can acquire more, and more reliable, information. Thus, through practice people can enhance the quality of their perceptions. However, due to time constraints, search costs, cognitive limitations, ignorance, and impatience, omniscience is never achieved. Although the individual makes every effort possible to reduce the external noise, there is little to nothing that can be done to reduce internal noise, which refers to the noisy process of encoding what the individual observes. Numerous factors contribute to people’s dispositions that could dramatically alter their ability to discern a signal. Among these are attention, pressure, stress, mood, and age, to name a few. External noise and internal noise contribute to internal response, which is best described as an individual’s impression of whether a signal is present or absent. Internal response is capricious and inherently noisy. Even when there is no signal present, there still will be some internal response in the individual. Motivation. In addition to the acquisition of more information, individuals may choose to forgo additional data collection and rely instead upon their own judgment. The point at which this tradeoff is made is called the criterion. In fact, because different individuals feel that the different types of errors are not equal, they may exhibit different biases in establishing this criterion. For example, two individuals may be presented the same amount of information. The first person may classify the signal as present. Although she/he realizes that this may result in a false alarm,

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committing a miss error may be a far more costly mistake. In contrast, a second person that is presented the same amount of information may choose to classify the signal as absent. For this person, missing a signal may be more palatable than committing a false alarm error. Thus, people can share equal access to information but choose dramatically different actions based upon the criterion they employ.

GRAPHICAL REPRESENTATION OF SDT AS APPLIED TO ECONOMIC THEORIES OF THE ENTREPRENEUR Figure 2a and b shows a graph of two hypothetical internal response curves. The curves on the left are for the noise-alone, and the curves on the right represent the signal-plus-noise. Noise-alone reflects the status quo or routine environment of the decision maker in which no signal is present. Noise is always present. Signal-plus-noise represents an introduction of a signal to the decision maker’s

Fig. 2. Internal Response Probability of Occurrence Curves for Noise-Alone and for Signal-Plus-Noise.


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otherwise routine environment. The internal response for the signal-plus-noise case is generally greater (situated farther to the right) than the internal response for the noise-alone case, but because a distribution (a spread) of possible responses exists, the two curves may overlap. When the two curves overlap, there is uncertainty around whether a signal is truly present.5

Signal Strength and Disequilibrium Economics Graphically, more information will have the effect of shifting the signal-plusnoise curve to the right, further away from the noise-alone curve. Figure 2a shows two sets of probability of occurrence curves. Less overlap exists between the two curves when more information is present. As a result, the subject’s choices are less difficult, and the participants are able to pick a criterion that produces a nearly perfect hit rate with almost no false alarms or misses. Regardless of whether the actor starts in the midst of a dynamic market process or in a static equilibrium state, both scenarios would conceive a close proximity between means to indicate there is little information present to assist in the assessment of whether the signal is indeed present. In contrast, increasing distance between the means is representative of an increasing amount of information to assist in assessing whether a signal is present. For example, compare an individual that hears from a number of friends about problems they are having with an existing technology with an individual that has information from 5,000 people about problems that are having with the same technology. In the first example, the means are close and in the second example the means are further apart. Statistically speaking, signal strength is analogous to effect size when contemplating the power of a study.

Uncertainty, the Austrians, and G. L. S. Shackle There is another aspect of the probability of occurrence curves that determines detectability of a signal: the spread of the curves. For example, consider the two curves in Fig. 2b. The separation between the means is the same for the curves on the right as it is for the curves on the left, but the curves on the right are much skinnier. Accordingly, the signal is more discriminable when there is less spread (less noise/uncertainty) in the curves. This is primarily attributable to the existence of higher quality information or a better understanding of the cues upon which one should focus his or her attention. As a result of this quality, the signal is clearer. This produces less overlap between the two probability of occurrence curves and enables

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perceptual accuracy. It is analogous to the reliability of a measure – the greater a measure’s reliability the more predictive it can be of a dependent variable. It is this element of SDT to which Austrian economists devote the majority of their efforts. Mainstream Austrian Approaches. Even strong proponents of the rationalist perspective within Neoclassical Economics recognize that cognitive constraints of human beings prevent omniscience (e.g. Stigler, 1967, p. 291), but such was not always the case. Through the efforts of a number of economists critical of equilibrium theory, unreasonable expectations of human capabilities regarding decision making have been slowly exposed. Perhaps, this is best developed in the writings of Frederick A. Hayek (1945, 1948), Ludwig von Mises (1949), and Israel Kirzner (1973, 1979, 1982, 1985, 1997, 1999), whose work is heavily reminiscent of the philosophical and methodological approach put forth by Max Weber (1947). Accordingly, a cornerstone of Austrian economics has been the emphasis of the subjectivity of knowledge and the need to study economic action from the vantage point (interpretation) of the actor. These Austrian economists suggest that the entrepreneur is distinguishable from the non-entrepreneur by the lack of spread in his or her distribution around the signal plus noise mean. In other words, the entrepreneur has a skinny distribution rather than a flat, widely spread distribution, owing to superior perception. We re-emphasize the word “perception.” Because most Austrians share a social realist ontology in which the signal-plus-noise mean is conceived of as an “objective” opportunity awaiting recognition and exploitation, the emphasis is not on some enduring psychological difference (or “trait”) capable of discriminating entrepreneurs from non-entrepreneurs. In fact, the Austrians do not care “who” exploits the opportunity; only that “someone” does, once again re-emphasizing the behavioral nature of these theories of the entrepreneur. This “someone,” they argue, fulfills the behavioral requirement of opportunity exploitation because he or she possessed the appropriate knowledge, i.e. that knowledge which is required for accurate opportunity recognition and considered to be a prerequisite for entrepreneurial action. Therefore, the Austrian approach is concerned primarily with epistemology. These theorists believe the economy is a dynamic process that draws its momentum from the actions of individuals who possess the knowledge necessary to recognize the potential of new situations created from the consequences of predecessors’ decisions and actions. It is this assumption that underlies Hayek’s (1945) emphasis upon “local” knowledge, or knowledge of time and place, and explains why Austrians are typically libertarians. If one shares their conception of entrepreneurship, then the obvious recommendation for creating a vibrant economy is to allow as many people as possible the freedom to exploit an opportunity when noticed. Only through the large numbers that a free society provides can one take advantage


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of the probability that the right individuals will be in the right place at the right time to exploit the opportunity presented by the market process. Both Hayek and Mises so deeply believed in this platform that they often produced aggressive but eloquent critiques of the arguments made by advocates of formally planned economies (e.g. Hayek, 1960). Because the discovery and pursuit of new means-ends frameworks does not appear to happen equally to two individuals with comparable information (Kirzner, 1973, p. 227), many Austrians have begun to hypothesize about the psychological properties apparently required by the entrepreneurial function. For example, Harper (1998, pp. 248–251) discusses the necessity of a high internal locus of control. However, it seems that these theorists have strayed from the course. Rather than a disciplined description of the properties inherent to the entrepreneurial function, they are now searching for psychological traits that would enhance the likelihood that an individual would fill this function. This is primarily attributable to similar transgressions by Schumpeter (1934) and Kirzner (1979). For instance, Kirzner (1979) states “The truth is that the ability to learn without deliberate search is a gift individuals enjoy in quite different degrees. It is this gift surely, that we have in mind when we talk of entrepreneurial alertness. Entrepreneurial alertness consists, after all, in the ability to notice without search opportunities that have been hitherto overlooked” (p. 148). Now, let us juxtapose this with Kirzner’s earlier statement within the same book: This entrepreneurial alertness is crucial to the market process. Disequilibrium represents a situation of widespread market ignorance. This ignorance is responsible for the emergence of profitable opportunities. Entrepreneurial alertness exploits these opportunities when others pass them by. G. L. S. Shackle and Lachman emphasized the unpredictability of human knowledge, and, indeed, we do not clearly understand how entrepreneurs get their flashes of superior foresight. We cannot explain how some men discover what is around the corner before others do. . . . As an empirical matter, however, opportunities do tend to be perceived and exploited. And it is on this observed tendency that our belief in a determinate market process is founded (p. 9).

Kirzner’s first comment refers to a perceptual gift that appears to represent a trait (i.e. an enduring, individually determined gift or ability) capable of delineating entrepreneurs from non-entrepreneurs even after the function of entrepreneur is fulfilled and they have settled into a managerial capacity. But, the above passage is closer to how entrepreneurial alertness should be conceived if Kirzner wishes to remain consistent with his economic argument, which is grounded in a behavioral description of the entrepreneurial function. In fact, the last two sentences of the above passage remind us of why entrepreneurial alertness is a configural concept. After all, if entrepreneurs are defined by the function they fulfill, and the nature of that function is to recognize and exploit opportunities intrinsic to the market,

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then it is inconsistent to speak of all individuals with narrow distributions as possessing entrepreneurial alertness but possessing less of it than the entrepreneur who actually exploited the opportunity presented by the market. This would suggest that anyone who recognizes the opportunity is an entrepreneur regardless of exploitation, unless: (1) entrepreneurial alertness is no longer synonymous with the accuracy definitive of the entrepreneurial function; or (2) an “opportunity” is not singular but instead representative of a set of similar possibilities. By expanding the scope of entrepreneurial alertness to all those who perceive the market opportunity, Kirzner unwittingly introduced the need to address motivation. For without concurrently addressing motivation, Kirzner’s theory is no longer one of entrepreneurship because there is no guarantee of exploitation. Instead, it becomes merely an exploration of entrepreneurial perception. Recently, Smith and DiGregorio (2002) have introduced an alternative, and perhaps more theoretically consistent, approach to the knowledge construct in which they propose a more nuanced picture of Austrian economics. They present a theory of entrepreneurial action that shares the Austrian assumption that different people possess different stocks of knowledge. However, they subdivide this knowledge into knowledge of markets, customers, and technologies, and argue that Koestler’s (1964) concept of bisociation is then responsible for integration of these concepts and the realization of new possibilities, or “new combinations” (see Kirzner, 1999 for a reconciliation of his theory with Schumpeter’s in which he shows the two agree on this bisociative element inherent to the entrepreneurial function). Finally, Smith and DiGregorio address motivation, if only cursorily, to reiterate Amabile’s (1996) beliefs that intrinsic motivation is more conducive to creativity, or in the case of their theory, bisociation. We, too, share the Austrians’ and Smith and DiGregorio’s view that people possess different stocks of knowledge. Furthermore, we too agree that some of these stocks are more appropriate than others for the recognition of particular signals and that this prior knowledge may enable, or at least enhance, the potential for recognizing signals. But, for signal recognition to become opportunity recognition, knowledge must generate the belief that action is warranted. Because knowledge is frequently defined as properly justified true belief (Audi, 1995, pp. 233–235), it would seem that today’s prior knowledge is comprised of yesterday’s properly justified true beliefs. Likewise, in regards to the decision to act, tomorrow’s knowledge begins with today’s beliefs, for which time partially provides a litmus test. Therefore, prior knowledge and current information contribute to one’s beliefs regarding the envisaged future, but the strength of these beliefs is diminished by uncertainty. Lipshitz and Strauss (1997, p. 150) note that, “Uncertainty in the context of action is a sense of doubt that blocks or delays action.” Thus, for a


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belief to produce action, it must be held with enough conviction to overcome such uncertainty. For example, I may watch someone push a wheelbarrow on a tightrope across Niagara Falls, and if asked, I may respond that I sincerely believe that the tightrope walker could put someone inside that wheelbarrow and successfully cross the Falls again. But when asked if I believe it enough to climb into the wheelbarrow, my decision would quickly reveal the uncertainty underlying my belief. The point is that, in the context of action, the concepts of knowledge, information, and uncertainty congeal to create “belief.” Whether someone acts depends upon their beliefs, and these beliefs are a function of their knowledge and uncertainty surrounding it. A tight distribution may tell a story of applicable knowledge held with reasonable certainty, but it is the belief that such a scenario generates that is of primary interest. Regardless of the justification and conviction surrounding a belief, this chapter emphasizes that increases in knowledge alone (no matter how appropriate and how diverse), will not produce opportunity recognition or entrepreneurial action without the concomitant consideration of motivation. However, because knowledge is potentially a prerequisite for successful entrepreneurial endeavor and because it serves to diminish the uncertainty underlying a belief, one would expect it to be highly correlated with the propensity for entrepreneurial action. Accordingly, we suggest the following proposition: Proposition 1. Strength and quality of information contribute to one’s “belief” regarding whether a signal is present. Decreases in the uncertainty underlying this belief are positively correlated with the propensity for entrepreneurial action. Alternative Austrian Approaches. Before moving to the motivation construct, however, an emerging alternative to the social realism of mainstream Austrian economics deserves attention. This movement finds its roots in the psychological, anti-equilibrium approach taken by Shackle (1955, 1972) and his emphasis upon enterprise (Shackle’s term for entrepreneurship) as a product of imagination. Inspired by Shackle’s rejection of the social realist, and somewhat deterministic (dis)equilibrium paradigm, recent efforts have taken a hermeneutic turn. Hermeneutics is the theory or philosophy of interpretation. Championed by Dilthey, hermeneutics is . . . the imaginative but publicly verifiable reenactment of the subjective experiences of others. Such a method of interpretation reveals the possibility of an objective knowledge of human beings not accessible to empiricist inquiry and thus of a distinct methodology for the human sciences. One result of the analysis of interpretation in the nineteenth century was the recognition of “the hermeneutic circle,” first developed by Schleiermacher. The circularity of interpretation concerns the relation of parts to the whole: the interpretation of each part is dependent on the interpretation of the whole (Audi, 1995, p. 323).

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For example, Addleson (1995) and Maki (2001) propose an alternative paradigm of Austrian research. They argue that equilibrium should be replaced with an emphasis upon determining “understanding.” Addleson (1995) builds his argument partially on Giddens’ (1977) concept of the double hermeneutic, which states: . . . the methodologist has two levels of understanding or interpretation to think about. One level pertains to the theorist’s understanding – the nature of the world that he identifies and describes in his theory. This level of the double hermeneutic is common to all enquiry. Then there is a level that is peculiar to social science. The focus here is on the individuals whose social conduct is the object of analysis (1995, p. 18).

Addleson (1995) suggests that knowledge is merely a starting point not the answer for why opportunities are “perceived” and pursued. His argument is surprisingly similar to concepts of sensemaking introduced to management by Weick (1993, 1995) and flourishing in the work of members of the managerial and organizational cognition division within the Academy of Management (Chattopadhyay, Glick & Huber, 2001; Dutton & Jackson, 1987). Accordingly, a brief digression to discuss whether and how SDT’s uncertainty element can incorporate these arguments appears merited. The first and perhaps most important thing to note is a change in language. The term perception is replaced with interpretation. No longer is the discussion one of whether the individual perceives an objective reality accurately, now discussion revolves around an interpretation of information. In other words, the theorist is interested in how one “understands” or “makes sense of” a situation.6 What we wish to point out is that the work of Shackle and critical psychologists such as Weick can be incorporated into the SDT framework presented, but only if a signal is defined as information (e.g. a new technology) rather than as an objective opportunity. Accordingly, the distribution around the signal represents the strength of one’s awareness to the information. This, however, does not yet imply the infusion of meaning. In other words, the actor merely recognizes the existence of information but has not attributed this information value. In contrast, the distribution around the signal for the actor in mainstream Austrian economics represents the clarity with which that actor has recognized an objective market opportunity (a signal possessing inherent value). The narrower his or her distribution, the better his or her perception. Whether conceived of as perception or interpretation, the actor of both ontological perspectives has yet to become an entrepreneur. To become an entrepreneur, the signal must be infused with value or meaning in such a way that action is the outcome of judgment. For this to happen there must be consideration of a second element of judgment known as motivation and represented by SDT’s criterion.


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Motivation and J. A. Schumpeter Perhaps the simplest strategy that a person could adopt in evaluating a potential signal is to pick a criterion location along the internal response axis. Whenever the internal response is greater than this criterion the decision-maker responds “yes” and a signal is said to be present, but whenever the internal response is less than this criterion the decision is “no” and the individual passes on the potential signal. The vertical lines in Fig. 3 indicate an example criterion. Figure 3 (top) indicates the two possible outcomes when a signal exists (i.e. “hit” or “miss”) based upon the location of the person’s decision criterion. Hits correspond to signal-plus-noise

Fig. 3. Decision Outcomes for Internal Response Probability of Occurrence Curves for Noise-Alone and Signal-Plus-Noise.

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Fig. 4. Effect of Shifting the Decision Criterion.

when the internal response is greater than (to the right of) the criterion. When the internal response is below (to the left of) the criterion, a miss occurs. Figure 3 (bottom) indicates the two possible outcomes when a signal does not exist – either a false alarm or a correct rejection – depending upon the location of the decision criterion. False alarms correspond to noise-alone when the internal response is greater than (to the right of) the criterion, and a correct rejection when the internal response is below (to the left of) the criterion. Suppose that an individual chooses a lax criterion (Fig. 4, top), so that the response is “yes” to almost everything. That person will almost never miss a signal when it is present and will therefore have a very high hit rate but more false alarms as well. Thus, there is a clear cost to increasing the number of hits, and that cost is paid in terms of false alarms. If the individual chooses a strict criterion (Fig. 4, bottom) then the response is “no” to almost everything and therefore there


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will rarely be a false alarm, but also many missed signals. There is no way for a person to set the criterion to achieve only hits and no false alarms. Given the uncertainty surrounding the decision, there is always overlap between the noisealone and signal-plus-noise curves, and it becomes inevitable that some mistakes will be made. However, individuals have some say in the type of error they will tolerate, as reflected by where they choose to locate their criterion. Scholars do this through their choice of alpha values, which determines the acceptable trade-off between type I and type II errors.

J. A. Schumpeter The location of the criterion, or bias exhibited by the actor, is central to much of the non-Austrian economic theory on entrepreneurship. Schumpeter (1934) and Knight (1921), like the Austrians, took a behavioral approach to theorizing about entrepreneurship, but instead of starting with exploitation and arguing that the entrepreneur is defined by opportunity recognition (i.e. the Austrian approach), these scholars sought to explore what was necessary in the exploitation portion of the equation. In fact Schumpeter (1934) notes: It is no part of [the entrepreneur’s] function to “find” or to “create” new possibilities. These are always present, abundantly accumulated by all sorts of people. Often they are also generally known and being discussed by scientific or literary writers. In other cases, there is nothing to discover about them, because they are quite obvious. To take an example from political life, it was not at all difficult to see how the social and political conditions of France at the time of Louis XVI could have been improved so as to avoid a breakdown of the ancient regime. Plenty of people as a matter of fact did see it. But nobody was in a position to do it. Now, it is this “doing the thing,” without which possibilities are dead, of which the [entrepreneur’s] function consists (p. 88).

Accordingly, the bearing of uncertainty emerged as a contender for the attribute definitive of the entrepreneurial function and was embraced implicitly by Schumpeter and explicitly by Knight. However, both argue that bearing uncertainty is merely something that the entrepreneur must do either as a means of “innovating” (as in Schumpeter’s theory) or as a means of “profiting” (as in Knight’s theory). The theoretical attribute of being willing to bear uncertainty, however, is merely a behavior and does not necessarily have anything to say about the motivation for such behavior (a point made by Schumpeter prior to his own exposition into the subject (1934, pp. 90–91)). Typically, economists seek to avoid transcending from the “what” into the “why” of entrepreneurial activity and even Schumpeter is apologetic before committing his own transgressions (p. 90), but in the end he yields to temptation:

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We shall finally try to round off our picture of the entrepreneur in the same manner in which we always, in science as well as in practical life, try to understand human behavior, viz. by analyzing the characteristic motives of his conduct. Any attempt to do this must of course meet with all those objections against the economist’s intrusion into “psychology” which have been made familiar by a long series of writers (p. 90).

So what does Professor Schumpeter have to say about the motivation behind entrepreneurial action? Quite a lot. Some right, some debatable, but all interesting. For example, Within given social circumstances and habits, most of what people do every day will appear to them primarily from the point of view of duty carrying a social or a superhuman sanction. There is very little of conscious rationality, still less of hedonism and of individual egoism about it, and so much of it as may safely be said to exist is of comparatively recent growth. Nevertheless, as long as we confine ourselves to the great outlines of constantly repeated economic action, we may link it up with wants and the desire to satisfy them, on condition that we are careful to recognize that economic motive so defined varies in intensity very much in time; that it is society that shapes the particular desires we observe; that wants must be taken with reference to the group which the individual thinks of when deciding his course of action – the family or any of the group, smaller or larger than the family; that action does not promptly follow upon desire but only more or less imperfectly corresponds to it; that the field of individual choice is always, though in very different ways and to very different degrees, fenced in by social habits or conventions and the like: it still remains broadly true that, within the circular flow, everyone adapts himself to his environment so as to satisfy certain given wants – of himself or others – as best he can. In all cases, the meaning of economic action is the satisfaction of wants in the sense that there would be no economic action if there were no wants. In the case of the circular flow, we may also think of satisfaction of wants as the normal motive.

Schumpeter focuses his subsequent discussion on enduring dispositions or “traits,” which we believe is inconsistent with his own argument that the entrepreneur is a function and should be discussed as such. Thus, instead of discussing motivation in the same contextually specific manner in which he discussed the behavior, he moves from transitory function to enduring personality. His use of “typical” as an adjective describing the entrepreneur frequently indicates this shift in approach from the conduct or behavior intrinsic to the function of the entrepreneur to a speculation of the type of individual most likely to fill that function. Looking back over the development of the field of entrepreneurship, it appears that Schumpeter’s foray into motivation, coupled with this theoretical shift from the transitory to the enduring, is possibly responsible for fostering early enthusiasm in “traits” research by psychologists and management scholars. For example, responding to the previous passage, Schumpeter (1934) offers hypotheses regarding entrepreneurial personality such as, . . . the typical entrepreneur is more self-centered than other types, because he relies less than they do on tradition and connection and because his characteristic task – theoretically as well as historically – consists precisely in breaking up old, and creating new, tradition (pp. 91–92).


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Experience teaches . . . that typical entrepreneurs retire from the arena only when and because their strength is spent and they feel no longer equal to their task (p. 92). . . . there is the dream and the will to found a private kingdom, usually, though not necessarily, also a dynasty. . . . Closer analysis would lead to discovering an endless variety within this group of motives, from spiritual ambition down to mere snobbery (p. 93). Then there is the will to conquer: the impulse to fight, to prove oneself superior to others, to succeed for the sake, not of the fruits of success, but of success itself (p. 93). Finally, there is the joy of creating, of getting things done, or simply of exercising one’s energy and ingenuity. . . . Our type seeks out difficulties, changes in order to change, delights in ventures (pp. 93–94).

However, Schumpeter finally notes that these are not motives capable of discriminating entrepreneurs from non-entrepreneurs because these same motives “may in principle be taken care of by other social arrangements not involving private gain from economic innovation” (p. 94). Failure to heed this comment set the quest for the distinctly entrepreneurial trait into motion. But far more damaging to the study of the psychology of the entrepreneur is the failure to realize that motivation does not have to be a study of personality traits. Rather, like behavior, motivation can be transitory and theorized about in such a manner (e.g. see Higgins’ (1997) work on Regulatory Focus Theory). Regardless of why the entrepreneur is motivated to implement new combinations, it is clear that the entrepreneur of Schumpeter’s (1934) theory has a very lax criterion, meaning that the entrepreneur has a bias for the new over the routine and therefore a willingness to bear the uncertainty intrinsic to all action, and accentuated in novel action. Although Schumpeter (1934) preferred to stick with description of entrepreneurial conduct, or the behavioral element of the entrepreneurial function, he definitely recognized the existence of a process preceding this behavior, a process including motivation and worthy of deeper analysis. He notes: What other [motives] could be provided and how they could be made to work as well as the “capitalistic” ones do, are questions which are beyond our theme. They are taken too lightly by social reformers, and are altogether ignored by fiscal radicalism. But they are not insoluble, and may be answered by detailed observation of the psychology of entrepreneurial activity, at least for given time and places (p. 94).

F. H. Knight and Others Schumpeter, however, was not the only economist who chose to describe the behavior inherent to entrepreneurial action. Professor Frank H. Knight (as well

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as, Austrian, Ludwig von Mises) also recognized that entrepreneurship required a willingness to bear uncertainty. Knight delineated uncertainty from risk, arguing that uncertainty had unknown probabilities, was inestimable, and therefore uninsurable. Risk, in contrast, was none of these things. This distinction takes on meaning in relation to the entrepreneurial function because Knight argued that the entrepreneur receives his profit because of his or her willingness to bear an uncertainty that others would not. This willingness allows for the achievement of residuals not attributable to the three factors of land, labor, and capital. Accordingly, the entrepreneurial function as visualized by Knight mandates a lax criterion, which ensures the classification of a signal as an opportunity and the bearing of uncertainty. Uncertainty, and the judgment it necessitates, provides the foundation for most of Knight’s theorizing regarding the function of the entrepreneur. Knight (1921) points out “Any degree of effective exercise of judgment, or making decision, is in a free society coupled with a corresponding degree of uncertainty-bearing, of taking the responsibility for those decisions” (271). He expounds: With uncertainty present, doing things, the actual execution of activity, becomes in a real sense a secondary part of life; the primary problem or function is deciding what to do and how to do it. The two most important characteristics of social organization brought about by the fact of uncertainty have already been noticed. In the first place, goods are produced for a market, on the basis of an entirely impersonal prediction of wants, not for the satisfaction of the wants of the producers themselves. The producer takes the responsibility of forecasting the consumer’s wants. In the second place, the work of forecasting and at the same time a large part of the technological direction and control of production are still further concentrated upon a very narrow class of the producers, and we meet a new economic functionary, the entrepreneur (p. 268).

Given this argument, it becomes fairly simple to map Knight’s entrepreneur on to SDT’s conceptual framework. After all, his theory already conceptualizes the entrepreneur in terms of decision making. Because this decision concerns something unprecedented and unique, it requires the entrepreneur to exercise judgment regarding whether the profit potential compensates him for the psychological cost associated with bearing the uncertainty involved in its pursuit. Thus, uncertainty and the willingness to bear uncertainty both contribute to judgment. Knight (1921) explicates: Uncertainty thus exerts a fourfold tendency to select men and specialize function: (1) an adaptation of men to occupation on the basis of kind of knowledge and judgment; (2) a similar selection on the basis of degree of foresight, for some lines of activity call for this endowment in a very different degree from others; (3) a specialization within productive groups, the individuals with superior managerial ability (foresight and capacity of ruling others) being placed in control of the group and the others working under their direction; and (4) those with confidence in their judgment and disposition to “back it up” in action specialize in risk-taking (p. 269).


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It seems evident also that the system would not work at all if good judgment were not in fact generally associated with confidence in one’s judgment on the part both of himself and others. That is, men’s judgment of their own judgment and of others’ judgment as to both kind and grade must in the large be much more right than wrong (p. 269). The statement implies that a man’s judgment has, in an effective sense, a true or objective value (pp. 269–270 fn).

In a noble effort, Knight’s theory encompasses many of the issues accentuated by both the Austrians and Schumpeter. However, it, too, is behavioral in nature. Even though Knight devotes a considerable amount of time and thought to the concept of judgment, he discusses the location of the criterion merely in terms of perceived profit potential. As for signals and the uncertainty surrounding them, Knight discusses them more as a function of managerial need (i.e. forecasting future sales) than as opportunities awaiting evaluation or exploitation. In conclusion, for all his discussion regarding uncertainty and judgment, Knight’s theory contemplates judgment primarily in terms of the location of the criterion. He makes some acknowledgment to differences in knowledge but not in reference to the discernment of opportunity. Rather, this knowledge is discussed more in terms of explaining why uncertainty produces specialization within the firm. For example, Knight argues that employees are promoted to management upon proving the quality of their entrepreneurial judgment. That is, they not only exhibit the foresight necessary to navigate uncertainty effectively, but also, they possess the confidence to back it up with action, which requires the bearing of this uncertainty. Thus, ascending the firm hierarchy is closely tied to a reputation for specializing in making tough decisions, such as those requiring entrepreneurial judgment. Recently, several other economists have begun to explore determinants of the location of the criterion. In particular, Baumol (1993a, b) and Casson (1995) have begun to embrace the notion that the supply of entrepreneurs is not finite but rather a function of environment and therefore capable of manipulation by policy makers at the economic or organizational level. The argument is that it is the expected payoff structure that determines whether individuals will seek to fulfill the entrepreneurial role. Therefore, if making an organization more entrepreneurial were the goal, then the means would involve increasing the pecuniary rewards associated with entrepreneurial action. Casson (1995) takes this a step further and ties it to business culture. Therefore, it seems that organizational commitment to rewarding and expecting novel action is representative of what scholars loosely refer to as an entrepreneurial culture and can take shape in the cognitive and regulative mechanisms frequently discussed by institutional theorists (Scott, 1995).

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Although the motivation represented by the criterion is clearly an important element of the decision to act entrepreneurially, it alone is incapable of predicting whether entrepreneurial action will occur. After all, it takes the concomitant consideration of belief (uncertainty) and desire (motivation) to derive entrepreneurial action. Therefore, knowing the location of the criterion and just how willing to bear uncertainty a person might be, does not guarantee that a signal will be perceived or interpreted as an opportunity. Accordingly, we suggest the following proposition: Proposition 2. One’s belief (and underlying uncertainty) regarding the presence of a signal requires an evaluation. This evaluation is a function of desire (motivation) such that the individual must value the expected payoff associated with the successful action enough to prefer action and the possibility of committing an error of commission to inaction and the possibility of committing an error omission. Therefore, increases in the payoff structure of action (relative to inaction) are positively correlated with the propensity for entrepreneurial action. In summary, SDT provides a means of examining a decision under uncertainty. This decision requires an individual to classify an object, such as a signal, as present or absent. Action is then dependent upon: (1) the degree of uncertainty underlying the belief of whether the signal is present; (2) the desirability of acting on that signal as expressed through the individual’s motivation and his or her strategic inclination in coping with that uncertainty; and (3) the necessary environmental conditions. The underlying uncertainty or “noise” is a function of the quantity and quality of information, frequently referred to by scholars as perception or interpretation and expressed as both the distance between means and the spread of the distributions. The strategic inclination of the decision-maker is represented by the location of the individual’s decision criterion.

ENTREPRENEURIAL ACTION AS SYNTHESIS We will now demonstrate how SDT allows the reconciliation of these economic theories of the entrepreneur and how the concomitant consideration of the constructs of belief (uncertainty) and desire (motivation) explain the likelihood of entrepreneurial action. As a means of achieving this end, it is beneficial to introduce a tool called the ROC (Relative Operating Characteristic) curve used by SDT to convey graphically both constructs simultaneously.


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The ROC Curve ROC curves (Fig. 5) capture both a decision-maker’s decision bias and ability to discriminate a signal from noise. The curves are plotted with the hit rate on the vertical axis and the false alarm rate on the horizontal axis. If the criterion is strict, then both the false alarm rate and the hit rate will be very low. As the criterion becomes more lax, the hit rate and the false alarm rate both increase. Therefore, the ROC is comprised of a number of points, each of which reflects a different decision criterion. As one moves from left to right along the curve, one is witnessing the effect of making the criterion more lax. Also, for any reasonable choice of criterion, the hit rate is always larger than the false alarm rate. As a result, the ROC curve is bowed upward. A straight line reflects a pure chance event. The ROC curve characterizes the choices available to people. They may set their criterion anywhere, but any choice that they make will result in a hit and false alarm rate somewhere on the ROC curve.

Theoretical Reconciliation of the Theory of the Entrepreneur Using SDT’s ROC Curve Although we have applied various elements of the SDT framework to economic theories of the entrepreneur, the ROC curve allows synthesis within one diagram of the insights of these economists while simultaneously exposing differences and potential limitations of their theories. Because the theories of Schumpeter (1934), Kirzner (1973), and Knight (1921) are the most original of the economic theories of the entrepreneur and the most influential within both the economics and management literature today, we will use them for illustrative purposes. Schumpeter’s innovator and the ROC curve. The uncertainty faced by Schumpeter’s entrepreneur is high for all those who would contemplate action. In other words, no one possesses knowledge that allows for the perception of less uncertainty than everyone else because everyone is considering novel, radical innovations in the form of new combinations (1934, p. 66). Schumpeter does recognize individual differences in the ability to generate new combinations, but as shown earlier he emphasizes that “doing” is what delineates entrepreneurs from non-entrepreneurs. This assumption prevents explicit recognition of perceptual differences amongst market actors and attributes differences in the likelihood of entrepreneurial action primarily to differences in entrepreneurial motivation. In effect, Schumpeter’s (1934) theoretical conceptualization views various actors as different points on the same ROC curve (see the d = 1 curve for an example). Who will act entrepreneurially, therefore, is merely a question of who sets their

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Fig. 5. The ROC (Relative Operating Characteristic) Curve.


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criterion farthest to the right. This accounts for why Schumpeter’s entrepreneur must be an “adventurer,” “empire-builder,” “leader,” or any one of a number of other terms embracing the notion of assuming exceptional levels of uncertainty.7 What the assumption fails to account for is why a number of “real world” entrepreneurs are often risk averse individuals who are far from adventurous gamblers but instead are conservative creators of wealth who act only when the “perfect” opportunity presents itself. In other words, Schumpeter fails to account adequately for “low-levels” of entrepreneurship such as starting service firms. Although these individuals are dismissed as not truly being entrepreneurs (i.e. their actions are not radically innovative enough to qualify as entrepreneurial), it would seem that this is a distinction that arises from conceptual limitations rather than theoretical justification. Kirzner’s alert arbitrageur and the ROC curve. Entrepreneurial motivation becomes a given in Kirzner’s theory, while the emphasis is placed on alertness to entrepreneurial opportunities (1973). In essence, all of the individuals contemplated by Kirzner are points at the far right side of an ROC curve. What differentiates the individual that acts entrepreneurially from those that do not is the quality of his or her perception. In other words, Kirzner’s entrepreneur is an individual with a d of four while his or her peers are on the ROC curve at a d of one. To make this point, Kirzner focuses upon an arbitrage opportunity where uncertainty is nearly abolished through the individual’s alert realization while walking down a street that an apple can be bought for $5 and sold to baker of apple pies a block later for $6. This means that at the moment of opportunity recognition, the entrepreneur is exercising extreme levels of knowledge in which uncertainty is absent and the probability of occurrence curves do not overlap. Although Kirzner has come under criticism from fellow Austrian economists for eliminating uncertainty from the equation (see Kirzner, 1982 for criticism and response), in all fairness it should be noted that Kirzner did this to make a point. He sought to eliminate uncertainty to show that opportunity is more than mere speculation and that entrepreneurship requires the individual to “recognize” that an opportunity for profit does exist (Kirzner, 1973, p. 86). However, because there is no way to eliminate time from the equation, uncertainty is still present even in arbitrage, and this “recognition” remains merely a highly probabilistic “guess” or well justified belief. Kirzner’s efforts crystallize the importance and subjectivity of perception but at the expense of the development of entrepreneurial motivation. However, when the probability of occurrence curves overlap heavily, the importance of motivation becomes difficult to ignore – Schumpeter’s work is testimony to this. What is important, however, is that a second pillar of entrepreneurial action is accentuated, a pillar which is communicable through SDT’s framework.

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Knight’s (or Mises’) uncertainty bearer and the ROC curve. Both Knight (1921) and Mises (1949) emphasize the role of uncertainty in the economy and both theorists consider the entrepreneur to be the bearer of this uncertainty. Although there is no evidence that Mises was directly influenced by Knight, his explanation of the entrepreneurial function is not substantially different from Knight’s (Hebert & Link, 1988, p. 130), and therefore no distinction will be made when discussing this profile. Both these theorists conceptually recognize the overlap of the probability of occurrence curves and the existence of a decision criterion. Therefore, they sufficiently embrace the concept of uncertainty, and they recognize that entrepreneurs must be willing to tolerate this uncertainty to act. However, because both are concerned with economic behavior as it relates to their explanations of how the economic system works, neither examine why the decision criterion is located where it is. This observation is not limited to the efforts of Knight and Mises. As mentioned before, both Schumpeter and Kirzner also fail to contemplate motivation other than cursorily because they believe such study to be the domain of psychology and unnecessary to the explanation of how the system operates. Ironically, though, each theory conceptualizes the entrepreneur as an economic actor who uses judgment to deal with novel and complex problems. Casson (2002) makes a similar observation: Judgment is a capacity for making a successful decision when no obviously correct model or decision rule is available or when relevant data is unreliable or incomplete. Cantillon’s entrepreneur needs judgment to speculate on future price movements, while Knight’s entrepreneur requires judgment because he deals in situations that are unprecedented and unique. Schumpeter’s entrepreneur needs judgment to deal with the novel situations connected with innovation (p. 4).

Although judgment is central to entrepreneurship, it is given insufficient attention because of the economists’ focus upon the behavior of the entrepreneur. If the focus of the theory is upon explaining why the economy operates as it does, and the entrepreneurial function completes this explanation, most economic theorists consider profit to be an adequate answer to the question of why a particular individual steps into this role and fulfills the function. Even for Schumpeter, the attributes discussed are intended to be in relation to those required by the function, not by the individual that decides to fulfill that function. This introduces an interesting opportunity to bridge the gap of the study of the entrepreneur as function within the economy with the study of entrepreneur as personality. Unlike economists, management scholars are typically concerned with the actor rather than an actor. In fact, the concept of strategic management can be contemplated as an attempt to understand why a firm was first to match the desired characteristics of the current economic system so that one can advise the firm on


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how best to ensure that they fill this function in a perpetually changing future of never-ending evolutionary market process (see Rumelt, 1979, pp. 197–198). At the heart of this dilemma is motivation. Because entrepreneurship can only occur if one is willing to act, and one can only be willing to act if he or she is willing to tolerate the potential of making a mistake, it appears that a key to either enacting the future or positioning one’s self appropriately is through discovering the composition of the constructs of belief (uncertainty) and desire (motivation) and developing these accordingly. Only by recognizing that both constructs together create entrepreneurial action can one realize that different compositions can produce identical behavior. Thus, Proposition 3. Entrepreneurial action arises from a belief-desire configuration (or “intention”). Conducive environmental conditions are then required to allow this intention to be converted into behavior. Belief is a function of one’s certainty regarding the presence or absence of a signal, whereas desire is a function of how personally motivating the belief is.

DISCUSSION We believe that an SDT framework provides a basis for extending the theory of the entrepreneur yet there are still a number of important issues that need to be addressed, including debate over the nature of opportunities and challenges in empirically testing this chapter’s propositions.

The “Nature” of the Signal Perhaps the most controversial element in this chapter is the “nature” of the signal. This speaks to a potentially divisive question concerning the philosophy of social science, specifically, one of ontology. Within the ever-increasing circle of entrepreneurship researchers, two camps appear to be emerging. First, there are those who view opportunities as “objects” possessing an existence regardless of whether an entrepreneur identifies and exploits them (Shane & Venkataraman, 2000, p. 220). For these individuals, discussion of a signal as representative of an “objective” opportunity is non-threatening and consistent with their worldview. However, there is a second camp whose argument is equally persuasive. These scholars argue that opportunities are often enacted phenomena with no existence independent of the individuals who envision and/or exploit them (Gartner et al., working paper, p. 4). For these scholars, discussion of a signal as representative

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of an “objective” opportunity undermines their worldview. They fear that the language that accompanies this realist ontology is reflective of an artificially constructed framework super-imposed upon the phenomenon by scholars to help them make sense of it (p. 3). They argue that the consequence of such a framework is that it obfuscates scholarly understanding of the phenomenon as it actually occurs. For example, by using words such as “discovery,” which they argue are not used by people when confronting the phenomenon in its natural state, scholars distort findings by introducing their own conceptions of the phenomenon to be explained. Although this debate is integral to “good” science, it remains diluted as a discussion of the nature of opportunity and unrecognized for what it is, a philosophical debate about the nature of the social world and of human action (McMullen & Corbett, 2003). However, recognizing the foundation of the debate allows one to address it. There are two ways to tackle this issue. The first is pragmatic and satisfactory for applying the SDT framework using experimental methods. The second is theoretical and must be discussed to extend the framework to the study of the phenomenon within natural settings. The pragmatic solution to SDT’s conceptualization of the nature of the signal. Perhaps the easiest way to address the philosophical nature of the signal is to avoid it. Arguably, this is precisely what many psychologists, such as Simon, Tversky and Kahneman, and Giegerenzer, have done. Using experimental methods or controlled tasks, the researcher is able to observe individual behavior within an “objective,” somewhat microcosmic, reality. For example, Simon’s work using the game of chess or Tversky and Kahneman’s framing experiments, both examine judgment and decision-making in regard to an “objective” answer (i.e. one that is logically right or wrong). Historically, the quality of the individual’s decision has been determined by equating “good” judgment with “rational” judgment as determined by statistics, or probability theory (Hastie, 2001, p. 660). Accordingly, these tasks are not subject to multiple interpretations. In essence, the item to be judged in such a study is similar to an auditory signal, which the researcher controls and which he or she can be certain either exists or does not exist. Moreover, motivation is a non-issue because the motive of all participants, i.e. to make the “right” decisions, is known by the researchers. This same approach can be used to apply the SDT framework to the decision of whether to engage in entrepreneurial action. Because one can control within an experiment the amount of information presented to the individual, the researcher can control the distance between the means in such a way as to ensure substantial distance or close proximity. Thus, regardless of whether an “opportunity” in the real world ever objectively exists, one can create an experiment in which an opportunistic situation does undoubtedly exist (i.e. the potential for arbitrage).


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However, whether a situation can be classified as an opportunity if an individual recognizes it and deems it unworthy of exploitation, and whether an arbitrage opportunity is an “entrepreneurial” opportunity, are both important issues to which we will return in a moment. On a final note, we wish to point out that SDT can also be used to examine a change in someone’s preference regardless of “accuracy.” That is, SDT can determine which cues under which conditions influence one to be more or less likely to act regardless of whether that action is or is not “wise.” Therefore, whether SDT is best used by attempting to address “accuracy” is also debatable. What we do wish to reemphasize is that, through controlled experiments, the distance between the means (and therefore the “objective existence” of the signal) can be determined without necessarily taking a philosophical stance regarding the objectiveness of the social world. The theoretical solution to SDT’s conceptualization of the nature of the signal. The presence or absence of the signal poses the greatest challenge for the use of SDT when conceptualizing entrepreneurship in the “real world.” However, the previous argument provides some potentially valuable insight into how several issues might be addressed. First, it begs the question, “Is there a difference between opportunity for someone and opportunity for a particular actor, and if so, how would this relate to the philosophical nature of the signal?” Because economic theorists seek to explain the mechanics of the system, they concern themselves primarily with discussion of opportunity as “opportunity for someone.” Although this implies an “objectiveness” to the nature of the opportunity, it does not imply that it is an opportunity for everyone that sees it. For example, Kirzner (1999) states: . . . the seer who can imagine how the world might be improved by a radical innovation, but who lacks the needed boldness and initiative (to shoulder the risks which he would have to assume in order actually to introduce this innovation to reality in a world fraught with uncertainties) – has in fact not yet really discovered an available, attractive opportunity for innovation. If he has not seen that opportunity in so shining a light that it drives him to its implementation in spite of the jeering skepticism of others, and in spite of the possibility of its ultimate failure – then he has not really “seen” that opportunity (p. 13).

Thus, the situation is deemed feasible but not desirable. Alternatively, the presence of a signal could mean only that there is something within the environment that was not there before – for any one of a host of reasons (i.e. a change in technological abilities, a change in consumer preferences, etc.) – and that for someone possessing the right type of knowledge and desire, an opportunity exists. Two levels are at work here. First, there is a potentially objective stage in that an opportunity for someone (i.e. a signal) exists owing to exogenous factors. Ignoring debate of whether this is an opportunity, one could concede that something (i.e. a

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technology) now exists that did not exist before and that this technology enables new commercial possibilities. Second, there is an interpretive stage, in which there is an individual assessment of whether the opportunity for someone is an opportunity for him or her. To answer this question one must exercise judgment regarding whether a stimulus (i.e. signal) could be effectively exploited by him or her (to be discussed in greater detail in the next section). At this point, it seems possible for social realists and social constructivists to coexist peacefully. Social realists would discuss this stage in terms of perception, (i.e. Who will “accurately” recognize the potential of this technology, and why?) whereas social constructivists would discuss the stage in terms of the attribution of meaning or sensemaking (i.e. Will the actor notice this new technology, and what will it mean to him or her? How does he or she come to make sense of it in such a way as to deem it an opportunity, a threat, or merely a distraction?). One solution is that the general definition of signal, i.e. current information that changes one’s belief about the value of a future state (Fiet, 1996), could be defined more narrowly for examination in natural settings, i.e. as a technological invention that allows for profit possibilities generally thought to be impossible or cost prohibitive prior to its existence (see Shane, 2000, for an example). Perhaps, this approach merely represents a sheepish sidestepping of philosophical concerns by shifting the nature of the question from “what is entrepreneurial discovery?” to “what is scientific discovery?” but given that philosophers have debated the nature of existence for thousands of years, this will have to do for the time being. Therefore, it is our assumption that an objective reality exists at least in relation to what is and is not currently technologically possible. This position, however, implies nothing regarding the nature of the social world. For example, Hebert and Link (1988, pp. 121–123) point out that even G. L. S. Shackle, a radical subjectivist, recognizes limitations on what is possible. In fact, it is Shackle’s conception of bounded uncertainty that we wish to embrace in relation to the strength of the signal (or distance between the means): If a man can set no bounds to what may follow upon any act of his own, he evidently looks upon himself as powerless to affect the course of events. There are, indeed, two views of history which would compel him to acknowledge his own powerlessness. If history is determinate, he cannot alter its predestinate course. If history is anarchy and randomness, he cannot modify this randomness nor mitigate the orderlessness of events. It is only a bounded uncertainty that will permit him to act creatively (1966, p. 86).

Therefore, it seems that there are limitations on that which is possible at any moment in time. But, just as time changes, so do these limitations on what is possible. Imagination perpetually tests these limitations, sometimes deeming them unsatisfactorily prohibitive and, consequently, action ensues. Other times they appear insurmountable and inaction is believed to be the wise choice. But,


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regardless of the judgment exercised and the subsequent decision made, life is full of stimuli that direct attention, some of which are compelling to many, like the obvious possibilities that would accompany the creation of a teleporter, and some of which are compelling to few, like the slowly developed possibilities of the internet prior to the creation of web browsers. In both cases, a signal exists, but whether and how this signal is recognized is the domain of Signal Detection Theory’s uncertainty element.

METHODOLOGICAL POSSIBILITIES FOR TESTING THE FRAMEWORK To investigate empirically the possibilities arising from this framework, scholars must use a research method that can either simultaneously test the information about the signal (strength and quality) and motivation (in terms of the level of preference for an error of omission relative to an error of commission) on entrepreneurial action or be able to test one of the constructs while controlling for the other. SDT has been primarily tested using experiments. Experiments provide researchers the opportunity to control and manipulate the system and to capture the decision-maker’s opportunity assessment and willingness to act. For example, the quantity of information can be increased about a signal to decrease an individual’s uncertainty about whether a signal represents an opportunity. In laboratory settings, motivation has typically been operationalized by manipulating the financial rewards associated with the cells of the payoff matrix, i.e. varying the costs associated with an error of omission and those for an error of commission. For entrepreneurship research, these costs would represent the expected costs of missing a good opportunity (e.g. from loss of market share) and the expected costs of false alarm (e.g. the investments in new equipment and/or loss of market reputation). These examples represent motivating cues from the environment; however, as we point out above individual differences are likely to be important. Operationalizations of motivation that captures differences between individuals are likely to arise from the choice of motivation theory. For example, regulatory focus theory offers an existing instrument to capture an individual’s preference for an error of omission over an error of commission. Experimental methods are typically high on internal validity yet raise questions of external validity. Testing our framework in the field using, for example, a survey methodology will complement research on entrepreneurial action that relies on experiments by offering greater external validity although there are a number of challenges in doing so. The philosophical question about the nature of an opportunity is highlighted in the section above. It obviously has implications for empirical

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investigations. A survey methodology could capture an individual’s perception of his/her stock of knowledge through measures of self-efficacy adapted to the task of discriminating whether a signal exists or not and the task of effectively exploiting opportunities. A survey could also include existing measures of human capital to capture an individual’s “actual” stock of knowledge, especially as it relates to interpreting signals from the environment and in exploiting opportunities effectively in the market place. For motivation, items from existing measures of regulatory focus could be used to capture an individual’s predilection towards entrepreneurial action. These existing measures could be adapted to capture the individual’s perception of environmental cues and their motivating influence. Alternatively, scholars could capture the decision maker’s subjective assessments of the values in each of the pay-off cells and or more directly ask them to state what they believe are the costs of omission and costs of commission. Although a survey methodology has a number of limitations in capturing data on decision-making (e.g. self reporting and retrospective reporting biases (Shepherd & Zacharakis, 1997)), it can collect data on the tangible outcomes of those decisions. For example, the number of novel products and processes introduced; the number of new market segments or new geographical regions entered; the number of prototypes being tested; the number of new patents generated; etc. Ideally these measures would be triangulated with objective secondary data. Whether conducting empirical research in the laboratory or in the field on the system’s influence on the entrepreneurial action of individuals or firms, there are a number of challenges facing scholars who wish to test empirically the framework proposed in this chapter. Those that are able to meet these challenges will make an important contribution to the field of entrepreneurship and likely contribute to the literature on the psychology of action.

CONCLUSION We hope to have conveyed the potential benefits associated with applying a Signal Detection framework to the theory of the entrepreneur. By recognizing that the entrepreneur of economic theory is a function and not a personality, we have argued that these theories are as applicable to the firm as they are the economy. However, because of their behavioral nature, they merely describe what it is that entrepreneurs do to ensure the vitality of the system. For these theories to become prescriptive and helpful to firm managers and policy makers, the antecedents to these behaviors must be understood such that managers can either identify the right people to hire or know how to manipulate the environment in such a way as to induce the desired behavior. We hope to have shown that SDT enables rigorous theoretical


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conceptualization and empirical investigation of these antecedents within the very framework capable of reconciling the many theoretically identified behaviors that constitute dimensions of entrepreneurial action. Using this SDT framework, we have illustrated that the decision to pursue opportunity requires one to recognize a signal and classify it as both feasible and desirable. Because feasibility is a belief possessing varying degrees of uncertainty regarding whether a signal exists, and because desirability represents an evaluation of whether the payoff expected is worth the psychological (and other related) costs that would be expended in its pursuit, it appears that the decision to act entrepreneurially not only requires judgment but also that it is a member of the familiar expectancy-value (belief-desire) models common to both judgment and decision making research (Hastie, 2001) and action theory (Greve, 2001). This observation suggests that “opportunity” and “action” are both concepts that require concomitant consideration of belief (uncertainty) and desire (motivation). However, our application of SDT to the better-known economic theories of the entrepreneur has exposed their reliance upon one construct or the other. This not only impairs scholarly understanding of entrepreneurial judgment, it also suggests that some explanations of why the economic system works may be incomplete. For example, it seems that Austrians like Hayek who focus upon knowledge asymmetries as a primary explanation of why new firms emerge may have identified only half of the puzzle. If one shares the view that knowledge asymmetry is a probabilistic approach to explaining firm emergence then it seems that a second probability distribution must also be acknowledged, one regarding desire/motivation. SDT suggests that it is not enough merely to know that profit is possible, one must also want to pursue it. This suggests a slightly more complex foundation for an entrepreneurial theory of the firm. In the end, we hope to have “alerted” the reader to the theoretical and empirical benefits of applying SDT to economic theories of the entrepreneur. Given that entrepreneurship often seems to be an elusive concept incapable, or at least unconducive, to rigorous theorizing and empirical investigation, we feel confident that the reader will share our enthusiasm in regard to the potential that SDT presents in advancing scholarship in the emerging field of entrepreneurship.

NOTES 1. The fact that economists share this social realist ontological perspective is often obfuscated by labels such as “subjectivist” that are frequently embraced by “Austrian” economists and meant to distinguish them from Neo-classical economists. Although the term “subjectivist” would seem to suggest social constructivist sympathies, such is not

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the case. The subjectivism referred to by leading Austrian theorists, such as Ludwig von Mises or Israel Kirzner, is not in reference to ontology but rather epistemology, or the theory of knowledge. Although the “subjectivist” distinction may be helpful in delineating “Austrians” from Neo-classical economists, who build theories around individuals capable of objectively interpreting (making purely rational choices in) this objective reality, it is misleading. These Austrian economists are indeed referring to different interpretations when they use the term “subjectivism,” but these different interpretations are in reference to epistemological limitations suffered to a greater or lesser extent by individuals within an economy who are attempting to interpret an objective reality accurately (Addleson, 1995). Therefore, “Austrian” economists may not share the same epistemological stances as more “objective” Neo-classicists, but they still share the worldview that there is an objective reality that is more or less accurately depicted by certain members of the economy. 2. It should be noted that the core action components (i.e. “the attempt to profit”) were augmented by one “qualifier” at a time and only when deemed absolutely necessary to delineate “entrepreneurial” from potential substitutes. The logic behind this approach is as follows: if “entrepreneurial” is not distinguishable from possible synonyms, such as “innovative” or “creative,” then it would seem that critics who suggest that entrepreneurship does not constitute a distinct scholarly domain and is merely a context regarding new ventures would be justified in their argument. Although it took a number of qualifying phrases and clauses to derive our definition, it does appear that “entrepreneurial” is unique and conceptually distinguishable from rival adjectives. Moreover, we wish to point out that the “ship” in “entrepreneurship” suggests that entrepreneurship is an activity in much the same spirit that “leadership” is an activity. Therefore, the attempt to define the adjective “entrepreneurial” rather than the nouns, “entrepreneurship” or “entrepreneur,” is not only easier to do in the abstract, it is also logically advantageous for describing a behavior that is present to greater or lesser extents owing to its inherently transitory nature (Carroll & Mosakowski, 1987). 3. Note that these decisions involve a signal not an opportunity. Although we assume the objectivity of the signal, we do not make the same assumption regarding opportunity. The difference between the concepts, we argue, lies in the valuation process intrinsic to the decision to act. In other words, an opportunity can only be said to exist once the individual attributes a signal value. This happens as a result of imagining to a greater or lesser extent the payoff of each possible quadrant. Moreover, this valuation contains two components that may take only nanoseconds to occur: (1) some attempt at an objective calculation; and (2) a subjective evaluation of how motivating the first calculation is to the decision maker. Should this process produce a decision to act, then an opportunity is believed to exist. Whether this belief corresponds with a future reality is another matter – one of outcome. Therefore, judgment is something one exercises in the decision of whether to act, but judgment is also something classified as “good” or “bad,” depending upon the outcome of that decision. It is the first form of judgment with which this paper is primarily concerned. 4. Although the term judgment is frequently used in exclusive reference to the quality of the process used to derive an answer to the first question, i.e. whether a signal does or does not exist, we wish to point out that the concept is equally applicable to the second question, i.e. whether the signal is deemed worthy of action. If the concept of judgment refers to how closely the future reflects people’s present beliefs about it, then it would be equally applicable to questions of a motivational nature as well. For example, if one applies this definition of judgment, then to discriminate a signal accurately and to calculate that signal’s


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payoff accurately does not mean “good” judgment has been exercised unless that action also meets the individual’s belief regarding the subjective valuation criteria. Barnard (1938) makes a similar observation in his discussion of the difference between effectiveness and efficiency in which he argues that effectiveness refers to “what” to do, whereas efficiency refers to “why” one does it. If one acts and finds that “the why” is not satisfied, then a discrepancy between a future reality and present belief has occurred suggesting that “good” judgment has not been exercised. Therefore, we argue that, in reference to the decision of “whether to act,” judgment is as applicable to the motivational decision of “why to act” as it is to the cognitive decision of “what to do.” 5. To capture a complete description of how discriminable the signal is from no signal, the most widely used measure is called d-prime (d ). The following formula is provided to capture both the separation and the spread: d = separation/spread. This number, d , is an estimate of the strength of the signal. Its primary virtue, and the reason that it is so widely used, is that its value does not depend upon the criterion the subject is adopting. Instead, it is a true, objective measure of the internal response as captured by the situation. 6. Additionally, it is questionable whether the term “epistemology” is appropriate anymore. Because epistemology traditionally refers to a theory of knowledge grounded in the belief that an objective reality exists, some argue that, once this ontological assumption is replaced with hermeneutics or phenomenological philosophical paradigms, discussions of “epistemology” should cease (see Addleson, 1995 for an excellent articulation of such a position). 7. It should be noted that both Atkinson and McClelland developed models of Need for Achievement that resembled many of the arguments made by Schumpeter. In fact, the application of McClelland’s work to entrepreneurs was inspired by Schumpeter and constitutes one of the first rigorous psychological forays into entrepreneurship. Sharing a functionalist bent, these works are not contentious of the framework we have presented. Rather, they merely take a more prescriptive approach focused more upon encouraging people to fill the role identified by more descriptive Economic theories of the Entrepreneur. They can be incorporated into the motivational element of the SDT framework just as Schumpeter’s description of the entrepreneur as an innovator can be incorporated. Although they broach subjects such as how some people will find more opportunities than others, they are primarily concerned with how one can be encouraged to bear the uncertainty intrinsic to entrepreneurship.

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A TRANSACTION COGNITION THEORY OF GLOBAL ENTREPRENEURSHIP Ronald K. Mitchell ABSTRACT Global entrepreneurship may be defined to be the creation of new, value-adding transactions or transaction streams anywhere on the globe. The objective of this chapter is to present and examine a theory of global entrepreneurship. At the World Economic Forum held in Davos, Switzerland, in January 1999, UN Secretary General Kofi Annan called for global entrepreneurship to meet the needs of the disadvantaged and the requirements of future generations. This chapter first presents a transaction cognition theory of global entrepreneurship that is intended as a path for research that responds to this call. Second, this chapter examines the theory from three critical viewpoints: (1) capability for explanation; (2) theoretical and operational utility; and (3) verifiability through the logic of scientific inference, and presents likely propositions that are surfaced by the analysis. Finally in this chapter, some of the likely implications of this theory within the context of globalization are discussed.

INTRODUCTION It has been suggested that global entrepreneurship is: the creation of new, valueadding transactions or transaction streams anywhere on the globe (Mitchell, Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 181–229 Copyright © 2003 by Elsevier Science Ltd. All rights of reproduction in any form reserved ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06007-0

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Smith, Morse, Seawright, Peredo & McKenzie, 2002). Until it was understood within the scholarly community that a new global economy had emerged (Friedman, 2000), traditional entrepreneurship theory – especially in the West – focused on such definitions of entrepreneurship as “the creation of new ventures” (Low & MacMillan, 1988) and “the pursuit of opportunity without regard to resources currently controlled” (Stevenson & Jarillo, 1990). Questions have arisen within the global scholarly community about how such Western definitions apply in the global setting (Busenitz & Lau, 1996; Hofstede, 1994; McDougall & Oviatt, 1997). Entrepreneurship scholars throughout the world are reaching the inescapable conclusion that, with the globalization of the world’s economy, they also need to globalize entrepreneurship theory (McDougall & Oviatt, 2000). In this chapter I explain how global entrepreneurs use planning, promise and competition cognitions to organize exchange relationships that utilize the imperfections inherent in market systems to create new value. It appears to be probable that this process is a cross-border phenomenon, and that it occurs regardless of culture or version of the market system (see, e.g. Mitchell, Smith, Seawright and Morse, 2000; Mitchell et al., 2002). Accordingly, further development of these ideas might provide a foundation for the globalization of entrepreneurship theory. This two-section chapter presents a transaction cognition theory of global entrepreneurship that is intended to help to open a path for globalized entrepreneurship research. In the first section, I provide a brief summary of transaction cognition theory, which suggests a relationship between transaction cognitions – mental models guiding certain economic behaviors – and the success of transactions. In the second section, I explore the implications of this theory for an experimental science of global entrepreneurship, using concepts from scholars who have offered standards for assessing philosophy of science implications in theory development (Freeman, 1986; Kuhn, 1970; Popper, 1979; Stinchcombe, 1968). The purpose of this second section is, further, to establish a sound foundation for research, teaching, and practice in global entrepreneurship, and to present likely propositions that arise from the analysis.

SECTION 1: A THEORY OF GLOBAL ENTREPRENEURSHIP Transaction cognition theory (TCT) is derived from a fundamental model of transactions using principles from cognitive science and transaction cost economic (TCE) theory. In this section of the chapter, I explain how transaction cognition theory provides the foundation for a working definition of global entrepreneurship that itself “crosses borders” (McDougall & Oviatt, 1997, p. 293).

A Transaction Cognition Theory of Global Entrepreneurship

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Transaction cognition theory decomposes a transaction into its three basic elements: an individual, other persons, and the work. According to this theory, variability in human performance can be attributed to variability in cognitions related to these elements of a transaction. In particular, bounded rationality is a source of variability in cognitions related to the individual who is creating the work; opportunism has its impact due to cognitions that occur because of the presence in the transaction of other persons who are using the work; and work specificity has its impact on cognitions related to the work itself. The exact link of these cognitions to performance by and large follows the lines suggested by Williamson (1975, 1985, 1991, 1996; Schure, 2001 personal communication). As is described in more detail elsewhere (Mitchell, 2001b), the path that I have followed to develop a transaction-cognition-based foundation for studying global entrepreneurship follows a direction suggested by Arrow (1969) who drew attention to a parallel between physical systems and economic systems. Arrow (1969, p. 48) asserted that “transaction costs” are the economic equivalent of friction in physical systems. With this suggestion as a starting point for the development of a theoretical model of global entrepreneurship, I have asked two relevant questions: What model in physical systems is sufficiently basic that it crosses borders? and Is there a comparable socioeconomic structure? Through discussions in various meetings with colleagues from around the globe, I have identified a suitable exemplar in answer to the first question. I find common agreement that the planetary model of the atom is a physical system-model that is sufficiently basic that it crosses borders. This being the case, one can then ask: what is the equivalent, in economic systems, to the planetary model-atom in physical systems? Figure 1 illustrates an answer that provides a basis for a model of global entrepreneurship. It is a socioeconomic system-model that also appears to be basic enough to cross borders: both geographical and cultural. The transaction, as represented in this model, is the basic unit of analysis. However, in pursuing the physical/economic analogy further, one encounters a second standard that must also be satisfied: The economic model suggested must correspond to socioeconomic laws that also cross borders. Transaction cost economic theory suggests such principles. Arrow (1969, p. 48) defined transaction costs to be the costs of running an economic system. Notice that this definition does not appear to be limited by any particular borders, or to be confined to any particular economic system within such borders. The notion of transaction costs as defined by Arrow is useful to us then, because regardless of the base economic system considered, it enables us to specify the factors that cause socioeconomic costs – the (human nature-introduced) features of an economic environment that (due to the social

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Fig. 1. The Elements of a Basic Transaction.

aspect) are not perfect. Transaction costs in social systems are thus thought to be the equivalent of friction in physical systems (Williamson, 1985, p. 19). We can then reason that if one can similarly relate the manner in which transaction costs are used to achieve results in economic systems to the way that friction is used to achieve results in physical systems, principles and laws that cross borders can thereby be identified (Mitchell, 2001b). Williamson’s (1975, 1985) TCE approach to understanding the effects of transaction costs and the TCT approach differ fundamentally1 in at least one respect (Schure, 2001, personal communication). Williamson (1985) stressed that absences of bounded rationality, opportunism, and asset specificity have differing impacts on the contracting arrangements of agents. For example, when bounded rationality is absent (agents are perfectly rational), the contracting process greatly relies on planning (1985, p. 31). In this case, hierarchies (firms) are likely to govern transactions because planning is “cheap” and brings transaction costs down. Another example: when the asset to be traded in the transaction is not specific, low transaction costs are achieved by competition. A market transaction occurs with no need for the firm. Williamson therefore claimed that institutions (markets and hierarchies) arise to minimize transaction costs. By way of contrast, the assumption in TCT is that different individuals have different levels of planning, promise, and competition skills at a given time (though these skill levels can change over time). Bad planning skills lead to high transaction costs, especially in an environment that is characterized by opportunism and asset specificity. And bad competition skills imply high transaction costs if bounded rationality and opportunism are both present and assets


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Fig. 2. The Effects of Friction.

are not specific. In contrast, then, TCE derives social structure (markets versus hierarchies) from transaction costs, while TCT explains how social cognitions (Fiske & Taylor, 1984) change transaction costs by changing social structure. Williamson asserted that: “our understanding of complex economic organization awaits more concerted study of the sources and mitigation of friction” (1996, p. 87). Transaction cognition theory enables the beginning of such concerted study. A further examination of how physical system-friction is utilized assists with such study. For example, within the construction of automobiles, we find combinations of friction uses that demonstrate how friction is well employed in physical systems. Figure 2 illustrates four states of friction. To paraphrase Williamson (1981), in a well-working automobile, the bearings “glide,” the tires have “traction,” the gears do not “slip,” and there is low “drag” from wind resistance. This successful physical-system result is accomplished through the design of well-working physical interfaces that utilize friction where it is needed and minimize it where it is not. Elsewhere (Mitchell, 2001b), I have argued in more detail that high economic performance might also be designed into a system by creating and using effective levels of transaction cognitions, that – as in the physical systems case – minimize the effects of transaction costs.

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It is therefore useful to inquire what, in more detail, are transaction cognitions? And since the term “transaction” has already been specified, the explanation next requires an understanding of the concept of cognitions. Cognitions have been defined as all the processes by which sensory input is transformed, reduced, elaborated, stored, recovered, and used (Neisser, 1967). Transaction cognitions are the specialized mental models or scripts (Arthur, 1994; Read, 1987) that guide individuals’ economic responses to the three principal sources of variability in their economic behavior introduced by the fundamental nature of transactions (Fig. 1). Individuals/transaction creators introduce bounded rationality due to the cognitive limitations of individuals, the addition of “others” to the transaction introduces opportunism due to the lack of clarity about the extent of self-interest-seeking guile in the individual/other relationship, and the work introduces specificity (once time and effort have been expended in the creation of a particular work, that time cannot be recaptured and redeployed for the creation of some other work, Williamson, 1985). These three attributes of frequent transacting cause transaction costs under uncertainty and frequency of transacting (Williamson, 1985, p. 31). Bounded rationality produces the human cognitions that cause costs by converting intendedly rational behavior into limitedly rational behavior (Simon, 1979; Williamson, 1985, p. 30, 1996, pp. 326–327). Opportunism – a behavioral condition of selfinterest-seeking with guile (1985, p. 30) – creates the cognitions of social friction and increases transaction costs due to moral hazard and distrust. Asset specificity refers to nontrivial investment in transaction-specific assets (Williamson, 1985, 1991, p. 79); and such investment increases social friction through cognitions associated with commitment and nonreployability (Ghemawat, 1991; Williamson, 1985), which also increase transaction costs. Hence, the presence of bounded rationality, opportunism, and asset specificity creates particular cognitions that give rise to transaction costs (Williamson, 1996, pp. 326–327). It stands to reason that as a result, the parties to an exchange will think through (adopt cognitively based) social arrangements that take these marketimperfection-creating cognitions into account, to ensure that transactions can, in fact, be completed. Williamson (1985, p. 31) identified three special social structuring/contracting arrangements – planning, promise, and competition – that organize exchange relationships subject to transaction costs within imperfect markets. Planning is defined to mean a socioeconomic arrangement where all the relevant issues in a transaction are identified and settled by the parties, and that any dispute will be effectively resolved within a court system (1985, pp. 30–31). Promise is defined to be a socioeconomic understanding where the word of the transacting parties is as good as their bond (1985, p. 31). Competition is defined to mean a socioeconomic contracting situation where markets are efficacious,


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Table 1. Some Attributes of the Contracting Process (Williamson, 1985, p. 31). Behavioral Assumption Bounded Rationality 0 + + +

Opportunism + 0 + +

Asset Specificity + + 0 +

Implied Contracting Process Planning Promise Competition Governance∗

0 = absence; + = presence. Note: Williamson’s insight that “governance” results when all three conditions exist provides a foundation for further elaboration of transaction cognition theory that is beyond the scope of this chapter but is discussed thoroughly elsewhere (Mitchell, 2001b).

fully contestable, and where even natural monopolies are subject to bidding processes (1985, pp. 31–32). The transaction attributes of bounded rationality, opportunism, and asset specificity have implications for the social organization of the contracting process into planning-, promise-, and competition-based exchange relationships as suggested in Table 1. As illustrated in Table 1, in an imperfect economy, one in which behavioral assumptions and social organization are connected, the following three special cases arise: (1) in the absence of bounded rationality, planning will suffice to ensure the completion of transactions; (2) in the absence of opportunism, promise is sufficient; and (3) in the absence of specificity, competition enables transacting (1985, pp. 31–32). One can infer from this analysis, then, that this special set of cognitions – planning, promise, and competition – is also likely to impact the behaviors that give rise to market imperfections. In the real world one can observe and can therefore assume, that individual transaction creators introduce transaction costs due to bounded rationality, other persons introduce transaction costs due to opportunism, and the nature of the work itself introduces transaction costs due to specificity, into a given transaction. When these observation-based assumptions are mapped onto the basic transaction as illustrated in Fig. 3, the relationships denoted in Table 1 lead to derivation of the three cognition sets that are essential to a successful transaction: planning, promise, and competition cognitions. Table 2 presents definitions for planning, promise, and competition cognitions and suggests the relationship between these cognitions and bounded rationality, opportunism, and specificity, respectively. Thus, three types of cognitions – cognitions about planning, which are mental models that help individuals develop analytical structures for solving previously unstructured problems; promise, which are mental models that promote

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Fig. 3. The Transaction Cognition Model.

Table 2. Proposed Relationships between Planning, Promise, and Competition Cognitions and Transaction Costs due to Bounded Rationality, Opportunism, and Specificity, as Defined. Cognition Constructs

Relationship

Transaction Costs due to the Sources of Market Imperfection

Planning Cognitions: Mental models (Arthur, 1994) that assist in developing analytical structures and courses of action to solve previously unstructured market problems that relate to the production and delivery of the Work to Other Persons. Promise Cognitions: Mental models that help in identifying and prioritizing other parties to economic relationships, and in building the mutual trust in economic relationships needed to effect an agreement between the Individual transaction creator(s) and Other Persons. Competition Cognitions: Mental models that can create competitive bargaining positions (i.e. some Work to offer that can be created by Individual transaction creator(s)).

–

Bounded Rationality: Behavior that is intendedly rational, but limitedly so (Simon, 1979; Williamson, 1985).

–

Opportunism: Self-interest-seeking with guile (Williamson, 1985).

–

Specificity: The non-redeployability of assets (Williamson, 1985).


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trustworthiness in economic relationships (Mitchell et al., 1997); and competition, which are mental models that can create sustainable competitive advantage – are expected to impact transaction costs, the success of transacting, and therefore the amount of new value added by transactions that can now succeed which otherwise would have failed due to transaction costs. Entrepreneurial opportunity (Kirzner, 1982) may therefore be thought to occur when entrepreneurs use planning, promise, and competition cognitions to enact transactions that would otherwise fail owing to transaction costs. Entrepreneurship can in this respect be conceptualized as an essentially cognitive process (Mitchell et al., 2000). The Microsoft – IBM transaction, wherein Microsoft became the supplier of the operating system for all IBM personal computers, can illustrate each of the three cognitions and how it contributes to a successful transaction. As the model diagrammed in Fig. 3 suggests, a completed transaction between Microsoft and IBM required use of all three of these cognitions. A review of the actual circumstances illustrates the role of each type of cognition.2 First, for the product they envisioned to be competitive, it was necessary that Microsoft’s Bill Gates and colleagues acquire rights to use the early DOS (disk operating system) source code – not then owned by Microsoft – that would form the foundation of the product (Zone C: the Individual – Others link). Through the use of bargaining and competitive techniques (Fig. 3: Zone C), this key element of the product was acquired (transaction costs owing to specificity were reduced). Also necessary was the development of a relationship of trust between the IBM executives and Microsoft, which assured IBM that they could rely on the Microsoft team (Zone B: the Individual – Work link). Through the use of references and inperson meetings, the promise of reliable production and delivery (Fig. 3: Zone B) was communicated in such a way that the possibility of transaction costs from opportunism could be diminished to an acceptable point in the Microsoft – IBM deal, an action that made transaction completion more likely. Finally, before the transaction could occur, Bill Gates and his associates had to overcome their limited knowledge of the market for their services (Zone A: the Work – Others link). Gates and Co. reduced these knowledge limits through a series of events that can be labeled “the planning process” (Fig. 3: Zone A), while knowledge limits remained relatively higher for potential rivals. This permitted the fledgling Microsoft to minimize the effect of transaction costs – an action that, again, made a completed transaction more likely. Thus, three necessary preconditions for the occurrence of the Microsoft – IBM transaction, one of the signal high-performance economic events in computing history, were satisfied. The key point to note in this example is that without the presence of the requisite planning, promise, and competition cognitions, or mental scripts (Glaser, 1984, Mitchell, 2001b), the transaction would likely have failed owing to the transaction

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cost-based social frictions. With a sufficient level of these cognitions present, a completed transaction – despite, or perhaps because of the effective use of transaction costs/social friction3 – resulted. I therefore argue that in the cognitions of entrepreneurs as the designers of new transactions, one can identify certain fundamentals that one can expect to observe across borders. It then remains to elaborate how design (Simon, 1981) or effectuation (Sarasvathy, 2001) activity in the arrangement of socioeconomic systems (the creation of new transactions) ought to take place. This elaboration can be accomplished by our establishing a theoretical linkage between thinking (cognitions) and the reduction of social friction (transaction costs). Recall in the previous automobile example, the paraphrase of Williamson (1981, p. 552), which reads: With a well-working (socioeconomic) interface, as with a well-working machine, these (transactions) occur smoothly. In mechanical systems we look for frictions: do the gears mesh, are the parts lubricated, is there needless slippage or other loss of energy? The economic counterpart of friction is transaction cost: do the parties to the exchange operate harmoniously, or are there frequent misunderstandings and conflicts that lead to delays, breakdowns, and other malfunctions?

One might then ask: How is it that harmony can be increased, and malfunctions decreased in transacting systems? Psychologist William James wrote that the greatest discovery his age was the idea that, in essence, we become what we think about (James, 1890). This notion may provide a key to answering the question about increasing harmony and decreasing malfunctions in transacting systems. Recent entrepreneurship research suggests that common economic thinking patterns exist globally (Busenitz & Lau, 1996; McDougall & Oviatt, 2000; Mitchell et al., 2000, 2002). Transaction cognition theory implies that new transactions are more likely to succeed when an individual transaction creator possesses sufficient levels of planning, promise, and competition cognitions. Thus, I can offer these general and specific definitions of global entrepreneurship: General – Global entrepreneurship is defined to be: the creation of new (value-adding) transactions or transaction streams anywhere on the globe. (Global entrepreneurship therefore might be thought generally to occur because global entrepreneurs cause transactions to succeed that would have otherwise failed, or not occurred at all because of transaction costs/social frictions). Specific – Global entrepreneurship is defined to be: The use of transaction cognitions (planning, promise, and competition cognitions)


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to organize exchange relationships (among an individual, other persons, and the work) that reduce the transaction costs/social frictions caused by sources of market imperfections (bounded rationality, opportunism, and specificity) to create new value. Who, then, are the designers of new transactions anywhere on the globe? Transaction cognition theory suggests that these economic actors are, in fact, global entrepreneurs. Transaction cognition theory thus provides a basis for a definition of global entrepreneurship that is highly integrative and is useful for research, teaching, and for the development of practical technology for the creation of new (value-adding) transactions or transaction streams anywhere on the globe (Mitchell, 2001b). In my view, it is this border-spanning attribute that qualifies this theory as global. In the following section I examine the transaction cognition theory of global entrepreneurship from three critical viewpoints: (1) its capability for explanation; (2) its theoretical and operational utility; and (3) its verifiability through the logic of scientific inference, and present several propositions that surface in this analysis.

SECTION 2: AN EXAMINATION OF THE THEORY Previously in this chapter, drawing on the cross-level theories of transaction cost economics (Williamson, 1985) and social cognition (Fiske & Taylor, 1984), I have presented general and specific definitions of entrepreneurship that, I believe, are not only realistic – in that they correspond to actual economic behavior in the real (imperfect) economic world – but are also plausible bases for scholarly analysis. Such analysis relies on two key ideas: (1) the composition of a basic transaction that does not vary across borders; and (2) cross-border cognitions that explain the basic transaction’s occurrence in imperfect markets – which together suggest a transaction cognition theory of global entrepreneurship. In this section I hope to demonstrate that the general and specific definitions presented previously can provide the basis for further analysis in both this chapter and in future research. Also specified in Section 1, are the market imperfections basic to transacting, their impacts on exchange relationships, and the resulting cognitions, which are critical to successful transacting. These specifications, I hope, will help researchers to interpret prior work and to propose entrepreneurship theoretical models that flow from first principles, and contribute to the development of an entrepreneurship research paradigm.

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However, like most preparadigmatic research (Kuhn, 1970), global entrepreneurship research at present might be described to consist of mostly “random fact gathering” (Leahey, 1987, p. 16). Thus, in the search for better theory and measures in the field of entrepreneurship, an appeal to other disciplines for analogues has been suggested (MacMillan & Katz, 1992). I consider physics and genetics as both offering cases that illustrate the development of a composition theory4 (Rousseau, 1985) that provides a basic unit that applies across units of analysis. In physics, as noted earlier, Neils Bohr’s planetary model of the atom was a theoretical structure that could explain matter at the subatomic, atomic, and molecular levels while providing a basic “unit,” or integrating model. In genetics, Crick and Watson model of DNA provided a theoretical structure that could explain the development of living organisms at multiple levels of analysis based upon a basic “unit” in a way that is similar to the planetary atom model in the physical-system case. These analogues motivate the investigation and identification of an economic equivalent to physics’ planetary model and genetics’ double helix, which produces the basic transaction model as its result. But the need for a common basis upon which to successfully organize and interpret a set of random facts, poses commensurability problems – the need for a least common denominator. A problem of this nature prompts the following paradigm organizing “shared exemplar” – type challenge to the field (Kuhn, 1970): Produce a theoretically and empirically valid set of common terms for field of global entrepreneurship. Each expression in an analysis of global entrepreneurship needs to be representable using these common terms. Since entrepreneurial phenomena occur on at least two levels of analysis: the individual and the firm (Mitchell, 2001b), common terms must – like their arithmetic counterparts – enable cross level analysis. As such, they should represent theory that extends across levels of analysis but remains testable with “data at the lowest measurement level possible” (Rousseau, 1985, pp. 29 and 31). It is in the service of this objective that the global entrepreneurship assertions of transaction cognition theory will now be examined from several critical viewpoints: (1) their capability for explanation; (2) their theoretical and operational utility; and (3) their verifiability through the logic of scientific inference. The analysis in these three subsections proceeds as follows: in the first, I examine prior research to see if the theory can serve as a common term, explaining previously observed phenomena, even phenomena that prior theory has been unable to explain (Popper, 1979, p. 46). In the second subsection, I evaluate how useful the transaction cognition model might be for resolving some theoretical difficulties in entrepreneurship research, relating previously unconnected things, and predicting phenomena that have not so far been observed, as well as how testable the theory is (Popper, 1979, pp. 47–48). In addressing testability, I present


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analyses relating to the operational utility (susceptibility to operationalization) of the theory (Freeman, 1986; MacMillan & Katz, 1992; Mitchell, 1994). I conclude this section with an examination of verifiability, using the “logic of scientific inference” (Stinchcombe, 1968) to evaluate the theory’s external validity.

Capability for Explanation Somewhat fortuitously, the extant literature on entrepreneurship might be seen to fall quite easily into three groupings that respectively focus attention on individuals (entrepreneurs themselves), work (firms), and others (economies). This body of theory and findings, a foundation for thinking about entrepreneurship, chronicles both explained and unexplained phenomena. Table 3summarizes representative work from this foundation literature in three parts corresponding to: individual, work, and other, respectively. The phenomena observed in previous research are varied and extensive (column 1), although some observations have been contradictory (column 2), which understandably has created an obstacle to further theory building, especially to explaining global entrepreneurship phenomena. However, these observations appear to be coherent when examined using the lens of transaction cognition theory (column 3). The proposed transaction cognition theory explanations demonstrate the theory’s ability to serve as the necessary common term that might explain observed phenomena, both the previously explained and the previously unexplained. An examination of this assertion for each of the groupings in Table 3 follows. The Individual (Entrepreneur) As summarized in Table 3, at least eight major theoretical reasons for entrepreneurship among individuals were investigated in the period 1961–1986, the most recent active period of investigation at this level of analysis. Support was found for explanations based upon age, immigration, religion, and social learning; mixed support was found for gender; and findings have been contradictory in the case of locus of control, need for achievement, and risk-taking propensity. The equivocality of this research has lead many in the management sciences to view entrepreneurship theory at the individual level with distrust (MacMillan & Katz, 1992). However, as shown in the table and discussed below, transaction cognition theory suggests a common-term explanation that accounts well for each of these previously observed phenomena. At the individual level of analysis, the previously observed phenomenon in question is the regular, but not adequately explained, appearance of individual entrepreneurs. As the lead article in an issue

Theory

Findings

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Table 3. Transaction Cognition Theory Explanations of Some Observed Phenomena. Transaction Cognition Theory Explanation

Part 1: The Individual (Entrepreneur) Age. Self-employment is related to age (Evans & Leighton, 1986).

Education. Self-employment relates to education: strongly for women; weakly for men (Evans & Leighton, 1986). Gender. Gender affects likelihood of entrepreneurship (Hisrich & Brush, 1986).

Immigration. Immigrants are more likely to become entrepreneurs (Bonachich, 1973).

Need for Achievement. Men with high need for achievement are more likely to enter entrepreneurship (McClelland, 1961, 1965).

Mixed. Lower: due to fewer assets (Cromie & Birley, 1991) and less access (Brush, 1992); No effect: (Buttner & Rosen, 1989; Sexton & Bowman-Upton, 1990). Supported. Immigrants create social networks vs. rely on distant family (Aldrich & Zimmer, 1986); entrepreneurship substitutes for social mobility (Waldinger, Aldrich & Ward, 1990). Contradictory. Self-employed workers have higher locus of control; higher locus of control likely to prompt self-employment (Evans & Leighton, 1986); locus of control does NOT distinguish entrepreneurs (Brockhaus & Nord, 1979; Hull, Bosley & Udell, 1982). Contradictory. Supported, cross-sectionally and longitudinally (McClelland, 1961, 1965); but can’t distinguish from managers (Brockhaus & Horowitz, 1986).

Cognitive models can be created in young or old; mental models vs. age are the key variable (Ericsson & Charness, 1994; Gardner, 1983). Type of education matters (Chandler & Jansen, 1992; Vesper, 1996); knowledge gains can be accelerated (Glaser, 1984). Choice of entrepreneurship by men/women depends upon cognitive maps (Carter, Williams & Reynolds, 1997; Walsh & Fahey, 1986). Promise-based mental models build social networks, which decrease venturing transaction costs, as argued herein. Cognitions affect self-efficacy (belief in orchestration capacity) (Bandura, 1986; Gist & Mitchell, 1992), which affects perceptions of risk (Krueger & Dickson, 1993, 1994) and intention to venture (Krueger & Carsrud, 1993). Effective use of transaction cognitions satisfies achievement needs (Arthur, 1994).

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Locus of Control. Entrepreneurship is related to locus of control (Berlew, 1975).

Supported. The young are less likely to become entrepreneurs: time in labor force increases reputation, funding, and goodwill (Aronson, 1991). Supported. The educated are more likely to start businesses (Reynolds, 1991).

Table 3. (Continued ) Findings

Transaction Cognition Theory Explanation

Religion. The Protestant ethic encourages entrepreneurship (Weber, 1985 (1930)).

Supported. Protestants more likely to be self employed than non-Protestants (Carroll, 1965; Jeremy, 1984; Singh, 1985).

Risk-taking Propensity. Entrepreneurs are more risk taking than the general population (Hull et al., 1982).

Contradictory. High growth entrepreneurs less risk avoiding than managers (Miner, 1990); risk-taking propensity not distinguishing of entrepreneurs (Brockhaus, 1980). Supported. Heredity (Gardner, 1983), early experiences (Walters & Gardner, 1986), demographics (Csikszentmihalyi, 1988), and use of information processing strategies (Siegler & Shrager, 1984) affect traits.

Religion as social learning affects cognitions (VanLehn, 1989). Cognition variance explains outcome variance (Arthur, 1994; Gist & Mitchell, 1992). Level of cognitive competence (expertise) affects risk taking (Heath & Tversky, 1991), because uncertainty is reduced (Krueger, 1993).

Social Learning. Social learning and genetics lead to variance in traits, which leads to variance in venturing (McClelland, 1975).

Performance comes from cognitions created through deliberate practice (Ericsson, Krampe & Tesch-Romer, 1993), which depends upon individuals’ endowments (Ericsson & Charness, 1994; Gardner, 1983, 1993).

Part 2: The Work (Firm) Characteristics of the Venture. Venture characteristics affect performance (Stuart & Abetti, 1990). Environment. Environmental factors are associated with venture performance (Cooper, 1993; Gartner, 1985). Rate of Entrepreneurship. Low numbers of ventures created discourage subsequent organizational formation (Aldrich, 1990, and others). Venture Strategy. V-strategy affects performance (Sandberg, 1986).

Some support. The management team, stage of venture, type of product, etc. affect VC financing (Hall & Hofer, 1993). Supported. Industry structure, not personal characteristics affects venture performance (Kunkel, 1991; Sandberg, 1986). Supported (Shane, 1996).

Change. Entrepreneurship increases in times of technological change (Schumpeter, 1939).

Supported (Shane, 1996).

Supported (Kunkel, 1991; McDougall, 1987; McDougall, Robinson & DeNisi, 1992).

Pattern recognition cognitions affect performance (Arthur, 1994); venture patterns can be standardized (Mitchell, 1998b). Cognition-based skill and skill propensity (Herron, 1990), and venture expertise (Mitchell, 1994) related to performance. Domain experience improves cognitions through feedback (Ericsson et al., 1993); venture exposure affects feasibility perceptions (Krueger, 1993). Competition mental models affect venture success as argued herein.


Theory

Part 3: Other Persons (The Economy)

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Security seeking and thereby, security seeking cognitions increase during times of change (Durant, 1935).

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Table 3. (Continued ) Theory

Findings

Transaction Cognition Theory Explanation

Demand. Changes in demand influence rates of entrepreneurship (Stinchcombe, 1965).

Supported. Demand growth and self-employment are significantly and positively related (Aronson, 1991; Evans & Leighton, 1986). Contradictory. Failures create floating resources for ventures, but also signal trouble (Delacroix & Carroll, 1983).

The need for economic security (provisions in store) affects individual cognitions, which lead to need satisfaction behavior (Mitchell, 1998a).

Failure Rates. New business failure rates influence rates of entrepreneurship (Stinchcombe, 1965; Venkataraman, Van de Ven, Buckeye & Hudson, 1990). Interest Rates. The relationship between interest rates and rates of entrepreneurship over time will be negative and significant (Shane, 1996).

Supported (Shane, 1996).

Supported. Political turmoil enhances formation rates (Carroll & Hannan, 1989, and others).

Unemployment. People are pushed into self-employment by unemployment (Oxenfeldt, 1943; Phillips, 1962; Steinmetz & Wright, 1989). Wealth. Entrepreneurship is associated with societal (Stinchcombe, 1965) and personal (Evans & Leighton, 1986) wealth.

Supported. (Hamilton, 1989, and others).

Supported. Economic development is associated with entrepreneurship (Wilken, 1979) and entrepreneurship is associated with personal savings (Evans & Jovanovic, 1989).

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Political Change. Entrepreneurship is associated with political change (Aldrich, 1979; Stinchcombe, 1965).

Failure is a specialized experience that provides critical knowledge that increases expert cognitions (Malone, 1997); those with expertise perceive lower risks (Krueger & Dickson, 1993). Interest rates reflect risk – one way of conceptualizing the cost of failed transactions (Venkataraman et al., 1990) as it impacts upon cognitions in the economy. Cognition-based expertise affects risk taking (Heath & Tversky, 1991), because uncertainty is reduced (Krueger, 1993). As the need for economic security increases during times of turmoil, venturing cognitions are invoked and updated (Arthur, 1994) along with security seeking behaviors. The need for economic security creates a demand for cognitions to meet that need (Arthur, 1994), which are created according to the theory described later herein. Planning scripts lead to venturing arrangements (Leddo & Abelson, 1986) such as access to and assembly of resources, which enable the application of expertise (Mitchell, Smith, Seawright & Morse, 1998)


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of the Journal of Business Venturing dedicated to theory building in the field of entrepreneurship earlier stated, [In] over 200 years of the study of entrepreneurship . . . no theory of entrepreneurship has been developed that would explain or predict when an entrepreneur . . . might appear or engage in entrepreneurship (Bull & Willard, 1993, p. 183).

Further, in one of the most comprehensive studies of this phenomenon, Shane demonstrated that the rate of entrepreneurship in the U.S. economy has varied over time and that these variations have not been random (Shane, 1996, p. 761). He stated the need for a theory that could account for all of the findings he examined from earlier studies (Evans & Leighton, 1986; Steinmetz & Wright, 1989). Shane characterized research in the field as ad hoc hypotheses in need of new theory to “identify forces that change the propensity of Americans [individuals] to become entrepreneurs” (1996, p. 773). Of course, it would be even better if such theory also explained – in a reciprocal manner – why some individuals choose jobs instead of entrepreneurship. The transaction cognition model also sheds light on the entrepreneurship/job trade-off. As noted earlier in the chapter, transaction costs are the consequences of social friction in exchange behavior. At the organizational level of analysis, scholars have extensively used the concept of transaction costs to argue that hierarchies (firms) and markets are alternative systems for governing transactions that are based on transaction-cost-driven “substitutions at the margin” (Coase, 1937, p. 387; Williamson, 1975). But there appears to be no reason to suppose that the application of transaction-cost-driven substitution at the margin is limited solely to questions of how firms form when markets fail (Coase, 1937). Theoretically, transaction costs can explain a variety of alternative system choices at various levels of analysis, including the individual level. Thus, for example, scholars who have conducted research using prospect theory have found that that losses loom larger than gains to individuals in psychological “prospect” (Kahneman & Tversky, 1979, p. 288) and that actual utility tends to be less than expected utility – a difference that (although not suggested specifically by the authors) may, when viewed through the lens of transaction cognition theory, be suggested to likely occur as a consequence of the transaction costs that are generated within the situations studied.5 A person’s choice between a job and self-employment might therefore be explained by a transaction-cost-induced substitution at the margin (a decision to transact with a “boss” rather than with multiple customers in a marketplace), as perhaps could success or failure in a job (“in” or “out” of a particular economic governance system: e.g. “boss system” or “self-employed” system). Choosing whether venturing or job holding will be more reliable requires the use of specialized cognitions about creating social

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arrangements based upon promise. Promise cognitions help individuals assess the likelihood that those with a “stake” (Clarkson, 1995; Mitchell, Agle & Wood, 1997) in their economic well-being (or “stakeholders”) will, in fact, be reliable in exchange relationships. Under the assumptions of the transaction cognition model, the social commitments made by individuals – such as choosing a job – should be related to costs that attend the transactions associated with that social choice. Thus, where the cognitions of an individual might result in work-specificity (whether the preferred work is a job or self-employment) the costs of transacting in the alternative system become prohibitive. For example, if my exchange cognitions center on “work that I like and can do,” and if work that I like and can do involves using highly sophisticated equipment that is only available to people who take jobs in particular organizations, self-employment involves higher transaction costs, and I may see more “promise” in employment with a well-equipped organization. Alternatively, if I have been raised in a setting where mental models of self-employment have been readily available and I have internalized them along with a sense of positive self-efficacy (Gist & Mitchell, 1992; Krueger & Dickson, 1993, 1994), then I may view a job to have relatively higher transaction costs and see more promise in a venture. The transaction cognition model is therefore likely to account – through a logical extension of transaction cost economic theory – for the broad range of social commitment/promise decisions made in exchange relationships. Accordingly, it is expected that, regardless of geography or culture: Proposition 1. The effective level of the transaction cognitions (planning, promise, and competition cognitions, but especially promise cognitions) of individuals is associated with their venturing behavior vs. job holding (the substitution at the margin of one state of individual transacting, venturing behavior, for its alternative, a job). Table 3 demonstrates the idea of transaction cognitions explaining a wide variety of alternative system choices in the area of individual exchange relationships in an imperfect economy. As is shown in the table, both contradicted and supported findings are explainable using transaction cognition theory. Furthermore, it is noteworthy within this analysis, that the explanation logic appears to be unbounded by geography or economic system. This framework thus offers the possibility that the phenomenon of global entrepreneurship exists within a theoretically tractable domain, and it further suggests that specific planning, promise, and competition skills might be identified. Of course, one natural consequence of the identification of the specific skills within a domain is an increase in their teachability. I have explored the educational implications of the existence of a theory of global entrepreneurship in greater detail elsewhere (Mitchell, 2001b).


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But entrepreneurship is a cross-level (Rousseau, 1985) phenomenon. Thus, transaction cognition theory also suggests that individuals create firms using transaction cognitions. Four of the more common explanations of the phenomenon of firm creation are examined next. The Work (Firm/Venture) Between 1986 and 1993 – during a period of more intense focus on venture-based explanations for entrepreneurship – general support was found for theories that look to the characteristics of the venture, the environment, the number of ventures created, and venture strategy to explain entrepreneurial phenomena. As summarized in column 3 of Table 3: Part 2, transaction cognition theory accounts for each of these findings and, again, suggests a common-term explanation, that – at the firm level of analysis – accounts for both previously explained phenomena and for phenomena that prior theory has been unable to explain. At the venture level of analysis, the phenomenon in question is the formation and performance (success versus failure, Birch, 1988; Shapero & Giglierano, 1982) of ventures. Transaction cost theory suggests that an alternative governance system will be invoked when the costs of organizing an extra transaction within an existing governance system become equal to the costs of carrying out the same transaction through an exchange on the open market (Coase, 1937, p. 396). Thus, when exchange behavior by a firm is no longer effective, transaction costs will drive the transactions into the open market (i.e. a venture will fail). It follows that transaction failure and venture failure are closely related (Venkataraman, Van de Ven, Buckeye & Hudson, 1990). According to the transaction cognition model, ventures fail when plans fail, because planning scripts (cognitions that help individuals cope with bounded rationality) reduce the transaction costs that arise from bounded rationality. This simple but powerful idea appeals to the very essence of transaction cost economics, confirming the notion that economizing on transaction costs is the best plan (Williamson, 1991, pp. 76 and 90). Williamson suggests that such “first-order” economizing (e.g. waste elimination) can have many times more influence on results (e.g. “ten times”) than the ordinary cost and pricing decisions made in exchanges (1991, p. 79). It stands to reason, then – using the other half of this bi-directional argument – that lack of a plan for transaction cost economizing will have a great deal to do with the failure of exchange behaviors. For example, a plan to manage opportunism in a competitive marketplace can save a job or a customer (first-order economizing): a far more important result than the successful negotiation of wage rates or sale prices (“second-order” economizing). It is therefore likely that the success or failure of ventures will be correlated with effective planning for (or first-order economizing on) transaction costs – a very appealing

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public policy opportunity (e.g. cut waste, not wages; increase productivity, not prices). Most of the analyses cited above were conducted using the U.S. economy as a data source. Accordingly, it might be expected that the Western framing of the questions and the research (Hofstede, 1994) might limit the generalizability of the research into global theory. However, the reader is reminded of evidence that cognitive models (Busenitz & Lau, 1996), specifically, cross-cultural cognitive models of entrepreneurship (Mitchell et al., 2000, 2002) can explain venture creation decisions. Thus, it might be expected that, regardless of geography or culture, Proposition 2. The effective level of the transaction cognitions (especially the planning cognitions) of individuals is associated with the venture creation decision (the substitution at the margin of one state of hierarchical transacting, the decision to form a firm, for its alternative, the failure to form a firm). Other Persons (The Economy) The study of the effects of an economy on entrepreneurship levels has spanned most of the last 50 years. Included in Part 3 of Table 3 is a summary of seven representative and generally supported theories of entrepreneurship according to which changes in technology, demand for entrepreneurs, failure rates (contradictory), interest rates, political change, unemployment, and wealth are examined for their relationship to the size of the entrepreneurial group within an economy. Transaction cognition theory also accounts for these findings and provides a common-term explanation. At this level of analysis, the economy level, the previously observed phenomenon in question is the level of entrepreneurship within an economy. Transaction cognition theory suggests that the level of entrepreneurship within an economy will be affected by the level of competition scripts (specifically, cognitions that can create competitive advantage) because engagement in the exchange process is based upon decisions as to whether to bargain/exchange/transact, or not. The need for economic security has been defined as “the desire to have provisions in store for an uncertain future” (Durant, 1935, p. 2), and in modern society, “provisions” are mainly obtained through exchange relationships. Logically then, the reason why people in an economy may or may not enter into exchange relationships should relate primarily to the level of this need. By definition, a low level of the need for economic security could result from an absence of desire, from uncertainty, or both, and higher levels of this need – and the resulting competition cognitions – could explain why change, demand, and other factors (Table 3: Part 3) lead to variance in entrepreneurship levels within an economy.


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The propensity to “compete” may be higher or lower, given specific circumstances, but given the effect of provisions in store, desire, and uncertainty on the creation of competition scripts, the transaction cognition model is expected to account for levels of entrepreneurship. According to the model, those who do not seek to enter exchange relationships see the transaction costs of competing within them as just too high. For those who do enter into exchange relationships, the transaction costs of not doing so are unacceptable. Thus, there is reason to expect that, regardless of geography or culture, Proposition 3. The effective level of the transaction cognitions (especially the competition cognitions) of individuals is associated with the level of entrepreneurship within a society (the substitution at the margin of entry into exchange relationships for nonparticipation in exchange). The implications of this proposition are quite broad, and they illuminate the earlier-stated transaction cognition definition of global entrepreneurship. Whereas Schumpeter wrote that “everyone is an entrepreneur when he actually carries out new combinations, and loses that character as soon as he has built up his business when he settles down to running it as other people run their businesses” (1934, p. 78), the transaction-cognition-theory-based definition implies that entrepreneurial status occurs transaction by transaction, instead of business by business (unless, of course, each transaction constitutes a business). Thus, high economic performance, such as sustained growth, occurs when the obstacles to transacting are minimized (Williamson, 1996, p. 332). Under transaction cognition theory, it is entrepreneurship that accomplishes this objective, through transformations of socioeconomic “slippage and drag” into “glide and traction” (Fig. 2). (Please also see Mitchell, 2001b.)

Theoretical and Operational Utility The foregoing discussion provides reasons for the inclusion of transaction cognition theory within the body of mainstream entrepreneurship theory as a theory of global entrepreneurship, and accordingly, suggests the necessity for an evaluation of its theoretical utility: the capability of the transactioncognition model to contribute to that body of theory. Philosophers of science have repeatedly demonstrated that more than one theoretical construction can always be placed upon a given collection of data (Kuhn, 1970, p. 76). Thus, for new theory in a field to be taken seriously, it must be useful: (1) in resolving theoretical difficulties; (2) in simply relating previously unconnected things; (3) in predicting phenomena that have not yet been observed;

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and (4) in being more readily testable than other theory (Popper, 1979, pp. 47–48). Resolution of Some Present Theoretical Difficulties The field of entrepreneurship needs better theory (Low & MacMillan, 1988; MacMillan & Katz, 1992). Politicians (e.g. Newt Gingrich, former Speaker of the U.S. House of Representatives) have often called for the encouragement of “maximum entrepreneurial behavior” in the U.S. economy (Kimbro, 1995), and academicians have also called, for more and better teaching of entrepreneurship within universities (Porter, 1997; Porter & McKibbin, 1988). Yet weak theory leaves the field of entrepreneurship open, at best, to over dependence upon the unsystematic, such as the “war stories” of successful entrepreneurs (Katz, 1995), to provide guidance for scholars, policy makers, and practicing and aspiring entrepreneurs. And further, the lack of strong theory can, at worst, lead to the abuse of the entrepreneurship concept by a wide variety of individuals who are free to invoke entrepreneurship to support or explain virtually any means, end, or phenomenon (Harwood, 1982, p. 91; McMullan & Long, 1990, pp. 57–58). Existing entrepreneurship theory does explain some phenomena, such as the behavior of venture capitalists under various conditions (Hall & Hofer, 1993; Manigart, Wright, Robbie, Desbrieres & DeWale, 1997). It has been unable to explain others, such as (as previously noted) when an entrepreneur might appear or engage in entrepreneurship (Bull & Willard, 1993, p. 183). Further, the fields from which existing entrepreneurship theories have been drawn each impose domain-based limitations on theory development. For example, economics provides elegant theory, but it is difficult to operationalize in the case of individual entrepreneurs (Baumol, 1993). Psychology provides a rich analysis of individual characteristics, but psychology-based studies do not consistently relate individual characteristics to performance outcomes because these studies appear to be case-specific and not replicable (Brockhaus & Horowitz, 1986; Sexton & Bowman-Upton, 1991). Strategy research provides tools for explaining performance outcomes but heretofore has had limits for linking these to the behaviors of individual entrepreneurs (Cooper, Willard & Woo, 1986; Kunkel, 1991; MacMillan & Day, 1987; Sandberg, 1986). Transaction cognition theory resolves some of these theoretical difficulties (as demonstrated above) through its ability to explain previous findings at several levels of analysis. Further, when using transaction cognition theory, researchers are no longer constrained to view the economic, psychological, and strategic performance views as competing explanations; rather, they can view them as elements of an overall transaction cognition “composition” explanation (Rousseau, 1985). For example, transaction cognition theory reconciles strategy-based theories of


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entrepreneurship with those based on economics or personality by suggesting that individual cognitions influence venture strategy through competition cognitions. And further (if one takes the liberty of making a few substitutions in order to draw parallels), one can argue that some of the earliest entrepreneurship scholarship contains the outlines of transaction cognition theory. As an illustration, consider the writings of Nicholas Baudeau (parentheticals added), who provided part of the foundation for the study of the entrepreneurial function within economics. He argued as follows: “Nothing is more evident [than that] we need a numerous race of farmers or chief farmers endowed with the knowledge [cognitions] of their art . . . who are willing to translate that into [economic] action” (1910, p. 51). The outlines of planning, promise, and competition cognitions can also be inferred from the writings of a seminal psychologist, Jean Baptiste Say (parentheticals also added). He wrote this: “Those who are not possessed of a combination of these necessary qualities [cognitions] about the complex operations needed to surmount abundant obstacles [plans] the process of reducing anxiety and repairing misfortune [promise], and of devising expedients [competition] “are unsuccessful in their undertakings [transactions do not occur]; their concerns soon fall to the ground” (Say, 1847/1964, p. 331). A stretch? Perhaps; but perhaps not, if Say’s statement is viewed with the intention of evaluating whether transaction cognition theory can resolve theoretical difficulties in three previously separate streams in entrepreneurship theory. Simply Relating Previously Unconnected Things Prior to development of the relationships suggested in this chapter, the notions of planning, promise, and competition as implied contracting processes (Williamson, 1985) were theoretically unrelated to the organization of exchange relationships among the components of the basic transaction (the individual, the work, other persons). Further, these social processes were not explicitly suggested to be associated with types of cognitions that affect transaction success either locally or globally. In addition, none of these ideas had yet been associated with the notion that the use of the general market imperfection creators (bounded rationality, opportunism, and specificity) to advantage through the use of specific cognition sets might be the essence of global entrepreneurship (Section 1). It is beyond the scope of this chapter to develop more than a few of the implications of this new set of theoretical relationships for entrepreneurship research. Some of the most obvious are the need to investigate and specify geographically and/or culturally what cognitions are included within each set of effective planning, promise, and competition cognitions. Another line of research would be an attempt to link the notion of specific creators of market imperfections, such as isolating mechanisms (Rumelt, 1987), to the more general set of market

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imperfection creators (bounded rationality, etc.). Still another line of research might reexamine cross-level problems in prior research to ascertain whether applying transaction cognition theory provides new insight. Also, since much more theory and many more findings about entrepreneurship exist than I have excerpted in Table 3, a more complete evaluation of the capability of transaction cognition theory to explain prior literature should be undertaken. And, of course, the basic theoretical propositions that form the foundation of transaction cognition theory should be tested for external validity in new research at the individual, firm, industry, economy, and society levels of analysis. Further, an examination of the theoretical utility of transaction cognition theory should also explore the capability of the theory to help researchers frame new questions – to predict new phenomena. Predicting Phenomena That Have Not So Far Been Observed In the field of entrepreneurship – as a social science – “phenomena that have not so far been observed” may take at least three forms: (1) they may be manifest in new levels on existing relationships; (2) they may appear as new relationships; or (3) they may not yet be known to exist. The following paragraphs are a nonexhaustive discussion further examining transaction cognition theory as a theory of global entrepreneurship as to its capability to enable the prediction of phenomena that have not so far been observed. New levels of existing relationships. Above, I have developed the idea that existing relationships among entrepreneurship phenomena can be observed at least three levels of analysis: the individual, the firm, and the economy. At present, data show that roughly 90% of the individuals in the U.S. labor force at any given time are not involved in entrepreneurship (Evans & Leighton, 1986) and that approximately 80% of those individuals spend their entire careers in jobs (Steinmetz & Wright, 1989). Even research showing the doubling of the number of new businesses created per 1,000 individuals in the 1980s, from approximately 20 to approximately 40 (from 2 to 4%) (Gartner & Shane, 1995) does not indicate much movement toward an equally probably career choice between “jobs” and “entrepreneurship.” And, of the ventures created, a significant proportion fail – 50–80%, depending upon the analytic technique applied to the data (Cooper, Dunkelberg & Woo, 1988; Kanter, North, Bernstein & Williamson, 1990, p. 424; McMullan & Long, 1990; Shapero & Giglierano, 1982). Data also show that most of society participates in some exchange behavior through participation in the labor force (Levi, 1998). Transaction cognition theory suggests that entrepreneurship occurs at the transaction level. Under this new definition of entrepreneurship, it is likely that we might discover new proportions on levels of job holding and entrepreneurial employment. Under this construction, it is further possible that the percentage of individuals who are known to act entrepreneurially would be much


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Fig. 4. A Transaction Cognition Theory Model of Individual Economic Decision-Making Behavior.

higher than previously reported, thereby also suggesting new levels on existing relationships. Additionally, transaction cognition theory suggests a relationship between transaction cognitions and transaction success. One might therefore expect the revision of another type of level to be expected on existing relationships: that, as the level of transaction cognitions/scripts acquired by individuals increases, the levels of entrepreneurship at various levels of analysis should also increase. Also, as suggested in Fig. 4 (as described in note 3), it is logical to expect that a transaction-cognition-acquisition sequence begins with competition cognitions and continues with promise and planning cognitions, in that order. Further, it appears likely that a given population will have some proportion of individuals at each stage of this sequence of cognitions. However, every society contains a range of motivations to acquire and utilize transaction cognitions.6 We might therefore expect susceptibility to the acquisition and use of transaction cognitions to vary geographically and culturally depending

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upon the proportion of this group that exists as an initial condition at each stage, producing a further new set of levels on existing relationships. New relationships. One of the reasons that MacMillan and Katz (1992) give for suggesting an appeal to other disciplines for assistance in the development of entrepreneurship theory is that these somewhat more mature fields have encountered and solved problems that commonly occur in newer fields. As one example of new theoretical relationships that might be predicted in entrepreneurship theory, I wish to introduce into entrepreneurship and development theory and idea from another “milieu” (1992, p. 1): the field of electrical engineering. A problem that has been studied extensively in electrical engineering, and that is analogous to a similar problem in entrepreneurship, is the problem of inductance. Inductance, or reactivity, occurs in electromechanical situations such as electric motor acceleration or deceleration, where either sparks (from the application of more electricity to a motor than its inertial characteristics can transfer into motion) or shocks (from the energy remaining in an electric motor when motion is arrested: the generator effect) are created. In electrical engineering, the level of this reactivity is termed inductance (I) and can be computed as a function of a reactivity constant (C) that represents the inertial characteristics of the mechanism, multiplied by the rate of change (a derivative) as shown in the formula below. I=C

di dt

Transaction cognition theory suggests new inductance-based relationships. Transaction cognition-based inductance – the propensity for a transaction to fail (“sparks” or “shocks” in economic transacting) – might be thought of as a function of C, the level of planning, promise, and competition cognitions (the reactivity constant), multiplied by the rate of change in transaction flow. When conceptualized in this manner, new relationships in global entrepreneurship are suggested, especially in the area of value conservation. For example in the electric motor case, “sparks”-type (start-up) inductance has been managed through the creation of new motor designs that have lowered the level of inertia (represented in the above formula by the reactivity constant “C”) to result in the invention of the coreless motor in the late 1940s. “Shocks”-type (slow down) inductance is usually managed through the use of some type of capacitor (such as in the 2003 Honda Civic Hybrid Gasoline/Electric car which uses the braking process to recharge its batteries) to store excess energy. In the entrepreneurship case, one of the key implications of the theory proposed in this chapter is that the level of cognitive inertia in entrepreneurship (such as the capability to manage a startup without a lot of failure-generating waste) is susceptible to change (entrepreneurship as transaction cognitions can be taught),


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and therefore is susceptible to design. Also, the study of new methods to enhance the storage of previously wasted energy (to increase levels of created value that is retained/conserved), such as the study of learning from entrepreneurial failures, is only beginning (e.g. McGrath, 1999). Thus, one new relationship suggested by the theory might therefore be an association between the teaching of entrepreneurial cognitions (lowering levels of transaction reactivity/transaction costs: levels of “C ”) and increases in the capacity of venturers, organizations, industries, or economies to sustain: more rapid growth, accelerated change, or sustained environmental turbulence (i.e. in lowering transaction inductance: levels of “I ”). The further development of such newly suggested relationships within entrepreneurship and economic development domains, and the analysis of related measurement issues, is beyond the scope of this chapter. Doubtless other new formulations relating entrepreneurial phenomena can be derived using transaction cognition theory as well. But while space does not permit further development along these lines herein, it should be noted that “transaction inductance theory” holds promise as a partial explanation of global economic phenomena at the individual, firm, and economy levels, and provides some evidence that transaction cognition theory is useful in suggesting such new relationships. Phenomena not previously known to exist. One of the most exciting aspects of new theory development is that sound new theory also predicts phenomena not known to exist whose existence is subsequently confirmed by empirical investigation. Theory progresses no faster than its measures (Nunnally, 1978) because of the need for theoretical conceptualization to suggest what to look for next. What does transaction cognition theory suggest that might exist but has not yet been measured? The theoretical developments introduced here, suggest that researchers might expect to find the existence of stable planning, promise, and competition transaction scripts in a variety of contexts; these would include technical fields, industries, cultures, and jobs. So, for example, it should be possible to map phenomena not previously known to exist, such as a global culture of entrepreneurship, among all individuals who have created ventures, regardless of their country of origin (e.g. Mitchell et al., 2000), or to map the expert scripts one can use to rise to the tops of organizations, industries, or for that matter, economies (e.g. Yew, 2000). Also, like chess masters (Chase & Simon, 1972) and other superb performers (Ericsson, 1996), entrepreneurs should be susceptible to assessment as to level of expertise; such a rating scale would be a distinct advantage for those asked to finance their ventures. Another consequence of further development of transaction cognition theory, might be the advent of the professional entrepreneur (e.g. please see Mitchell et al., 2002) – evaluated for entrance into the profession much as

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are accountants, lawyers, and doctors. And should this prove to be possible, the creation of new firms might even become susceptible to management and assessment using the well-developed systems of quality assurance that have managed to eliminate all but a minute fraction of quality problems in other domains. Accordingly, should transaction cognition theory prove to be efficacious in these areas, one might also expect growing dissatisfaction with the 50–80% failure rate of new ventures (Cooper et al., 1988; Kanter et al., 1990, p. 424; McMullan & Long, 1990; Shapero & Giglierano, 1982), especially in non-first-tier economies, where failure is an unwelcome luxury. Hence, social policies would be explicitly framed to enhance planning, promise, and competition cognitions, and to thereby enhance overall economic welfare. Summary. The possibilities outlined above demonstrate the capability of transaction cognition entrepreneurship theory to predict phenomena that have not so far been observed. Additional possibilities can be expected as theory develops and as new studies are conducted. Next suggested, is the idea that improvement in the testability of entrepreneurship theory should also be possible through the introduction of a transaction cognition theory of global entrepreneurship. Be Better Testable Testability within the social sciences – at least as indicated by the structure of most empirical journal articles – revolves around data gathering, measurement, and data analysis. To be better testable, a theory should contribute to each of these activities, which together should enhance the theory’s operational utility. Data gathering. The creation of sampling frames has been problematic in the study of entrepreneurship, as it has been in most social science research (Freeman, 1986; McDougall & Oviatt, 1997, p. 303; Pedhazur & Schmelkin, 1991). One of the reasons for this difficulty is that the phenomena in question are idiosyncratic (MacMillan & Katz, 1992). However, when they are reduced to the transaction level, many of these idiosyncratic elements disappear, becoming part of the demographic or categorical aspects of a given sample. Whereas under prior theory it has been necessary to track entrepreneurs through venture entries and exits, it now becomes possible through the introduction of transaction cognition theory to identify entrepreneurs at the point of transacting. Entrepreneurship research will be well served by the creation of such a sampling frame, which will facilitate larger-sample studies that better capture the range of variance in independent variables (Freeman, 1986). Early results from studies drawing on transaction cognition theory suggest progress in the attainment of these standards; they demonstrate that although alternative explanations for differences in cognitions – such as age or country – may be significant, transaction cognitions still explain significant additional variance within and across countries (e.g. Mitchell, Smith,


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Morse, Seawright, Peredo & McKenzie, 2002). These studies thereby illustrate possible ways to ameliorate difficulties in the development of a sampling frame for venture formation research conducted at the individual unit of analysis (Freeman, 1986, p. 301). Entrepreneurship theory may thus advance through the easing effect that improvement in methods of measurement (Nunnally, 1978) has upon the generation of sampling frames. Measurement. I encourage scholars who wish to investigate cognition-based models of entrepreneurship but have been constrained by a lack of tested measures of cognitive constructs to explore use of the script cue recognition approach (Mitchell, 1994; Mitchell & Chesteen, 1995; Mitchell & Seawright, 1995; Mitchell et al., 2000; Morse, Mitchell, Smith & Seawright, 1999). Prior measurement operationalizations in cognitive psychology can be characterized as following a micro approach; for instance, color recognition studies depend on micro observations such as eye movements. A script cue recognition approach, that uses a formative indicators measurement logic (Howell, 1987, p. 121; Nunnally, 1978; Pedhazur & Schmelkin, 1991, p. 54), might alternatively be characterized as a macro approach that enables significant results through sampling (Nunnally, 1978) rather than through enumeration of script cues. Critics of transaction cost economics have long suggested that one of the critical flaws in the theory is its insusceptibility to measurement (Granovetter, 1985; Perrow, 1986). Linking cognitions to transaction cost theory to create a transaction cognition theory of global entrepreneurship represents a positive step toward the measurement of transaction costs. Just as cognitions (which are unobservable) can be measured by observing the behaviors they produce – such as eye movements (Posner, 1973) – transaction costs (which are also unobservable) can be measured by observing the transaction-cognition-based behaviors that transaction costs produce, such as the venturing behaviors indicated by venture creation script cue recognition (Mitchell et al., 2000). Further research should focus on the elaboration of this measurement method as a means to suggest more generalized measurement techniques in the field of transaction cost economics and in transaction cognition entrepreneurship theory. Data analysis. Early studies using transaction cognition theory to suggest sampling frames and measures have revealed no barriers to the use of advanced statistical analysis. Thus, where applicable, transaction cognition theory has produced theory and measures that have been used successfully in analysis of variance (ANOVA and MANOVA; Mitchell et al., 2000); exploratory, confirmatory factor, and multiple discriminant analysis (Mitchell, 1994; Mitchell & Seawright, 1995); regression analysis (Mitchell et al., 1999); and cluster analysis (Mitchell et al., 2002). In short, the concepts and measures of a transaction-cognition-based theory of global entrepreneurship appear to be susceptible to the creation of

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interval-based scales consistent with the assumptions of inferential statistics (Mitchell, 1994; Nunnally, 1978). Summary With the foregoing two subsections as a foundation (the examination of the theory as to its capabilities for explanation and utility), the evaluation of a theory of global entrepreneurship based upon transaction cognition theory can proceed to address its third objective. In the following subsection, then, the capacity of the theory to stand up to tests of external validity will be examined using the logic of scientific inference.

Application of the Logic of Scientific Inference In this section, I employ one of the fundamental approaches to evaluating the construction of social theory (Stinchcombe, 1968) to examine the credibility of a transaction cognition-based theory of global entrepreneurship. Some exploratory research that was conducted in the early stages of theory development is summarized here and is evaluated according to Stinchcombe’s criteria (1968, p. 20). These cited studies include primarily my own published empirical investigations between 1994 and 2002, which hopefully will serve as a template for replication and further evaluation of the external validity of the theory. The Logic of Scientific Inference Stinchcombe (1968) explained how, under the positivist, falsification logic that is a norm in the social sciences (Kuhn, 1970), theory that passes tougher tests is considered to be more credible than theory that passes only weak tests. Stinchcombe described four situations to illustrate this point; and Fig. 5 (where “⇒” signifies “implies”) presents these four situations. According to Stinchcombe, the relationships presented in Fig. 5 suggest “both that the more different things

Fig. 5. Credibility and Tests of Theory (Stinchcombe, 1968).


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we can derive (situation III), and the more different kinds of implications we can derive (situation IV), the stronger will be our test of the theory” (1968, p. 20). Further, “If the theory stands up under a tougher test, it becomes more credible than it is if it stands up when we have subjected it only to weak tests. If it fails any of the tests, it is false, either in the underlying statement or in the specification of the observations which the concepts of the theory refer to” (Stinchcombe, 1968, p. 20). To establish such a “guarantor of knowledge” (Mitroff & Turoff, 1973) at this point in the analysis, I depart somewhat from Hegelian skepticism as the primary means of proof and instead adopt a more Kantian integrative approach to address questions of external validity. When external validity is evaluated in light of Stinchcombe’s four exemplars (Fig. 5), it is, I hope, evident that any claim of substantial credibility for transaction cognition theory ought to be based more upon situations III and IV (the more integrative guarantors) and less upon situations I and II (the skeptical/falsification guarantors). This is not to say that the first two should be rejected, but rather – as I believe Stinchcombe does – should be treated as the foundation of an integrative ontology. As shown below, it is my assessment that the four cases of exploratory research cited as evidence constitute at least a “situation III” test. The research listed here has been underway for some time. In this case I consider these previous studies (that fall into the “B1 , B2 , B3 similar” category) to include: (1) B1 , my dissertation research, in which three similar outcomes, the composition, classification, and creation of new venture formation expertise, were studied quantitatively at the individual/firm level of analysis in a sample of entrepreneurs and business nonentrepreneurs from the western United States (Mitchell, 1994). In this study, the association of cognitive variables with new venture formation was tested, which resulted in an analysis of the composition, the capability to classify, and the capability for the creation of new venture formation expertise. B1 thus demonstrated “similarity in implications across types of tests” through an examination of the relationship between cognitive variables and new venture formation. (2) B2 , research that drew new samples from other countries, used the same or a similar research design to that of my dissertation (e.g. Mitchell & Seawright, 1995), and further explored the issues raised by the new sampling frames. Thus, in this study, with composition held constant, classification was tested in two countries beyond the U.S.: Mexico and Russia. B2 represents other similar implications across sampling frames. (3) B3 , qualitative research that explored in much more depth the nature and function of the expert scripts of entrepreneurs, while still utilizing exert information

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processing theory as the basic interpretive lens. In this study, the underlying concept that cognitive scripts are related to new venture formation was evaluated using qualitative methods with data from the same U.S. setting (Mitchell, 1996). B3 demonstrated further similar implications across data type. (4) B4 , quantitative research that expanded to utilize new types of analysis and additional sampling frames. In this study, 39 hypotheses based upon a finer-grained composition of new venture formation expertise scales were tested in seven Pacific Rim countries: Canada, the U.S., Mexico (North America), Chile (S. America), Australia, China, and Japan (Asia) (Morse et al., 1999). This list was further tested/expanded in Mitchell et al., 2000, 2002, respectively. B4 substantially expanded the list of similar implications across new types of tests and new sampling frames. Please note that in this research stream the implications of the theory (Situation III) exist in a variety of dimensions:

Across types of tests in B1 , Across sampling frames in B2 , Across data type in Study B3 , and Across new types of tests and new sampling frames in B4 .

Next Steps in the Inference Logic Categorized within the Stinchcombe framework previously presented (Fig. 5) are a set of suggestions developed and discussed with colleagues for further advancement of the credibility of transaction cognition theory itself. These suggestions have been divided into two lists: new implications of the Situation III list – research that can further render transaction cognition theory substantially more credible, and creation of a representative situation IV list: research that can lead to much more credibility. Additions to the Situation III List. The following list of additions includes possible research initiatives that, if successful, will further support the idea that transaction cognition theory is – based on these tests – substantially more credible. The suggestions for new initiatives include: (1) B5 , new quantitative research that, while still at the individual/firm level of analysis, develops new instruments from transaction cognition theory as introduced within this article, and collects data worldwide, perhaps utilizing the Web or another information technology to access respondents. (2) B6 , new qualitative research designed to enlarge understanding of the nature of cognitions that may cancel or limit the efficacy of planning, promise, and competition cognitions; fatalism, refusal, and dependency, for instance, may


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be negatively linked to the three transaction cognitions in the order listed (Gurnell, 2000; Mitchell, 2001b). (3) B7 , new quantitative research to calibrate such canceling cognitions (e.g. fatalism, refusal, and dependency cognitions) with the primary cognitions (planning, promise, and competition), and to develop a model for the use of resulting indexes in further research. Representative Situation IV List. These are research initiatives that have the potential to lead to much more credibility of a transaction cognition theory of global entrepreneurship. According to Stinchcombe (1968), to accomplish this task one needs to establish first that these implications as predicted by the theory are (in the Stinchcombe sense) “quite different” from one another, and second to establish the existence of these implications in the empirical world. At least seven possibilities are suggested: (1) B1 , new research that expands the transaction model to include multiple nodes in place of the standard structure. Such research might explore, for example, partnerships as transaction creators (e.g. individual 1, individual 2, . . . n), or specific, theoretically-driven additions to “others” or to “works” (e.g. please see Mitchell & Morse, 2002; Mitchell, Morse & Sharma, in press 2003 for a report on the first steps taken in this direction). (2) B2 , cross-level research in which the constructs and propositions proposed within this chapter are operationalized and tested as hypotheses. (3) B3 , new research that utilizes compatible theories (e.g. social exchange theory) to examine history and historical institutions for evidence of the interdependencies, processes, and relationships suggested by transaction cognition theory. (4) B4 , new research that addresses the some of the problems within neoclassical economics that yet remain to be explained. (5) B5 , new research that applies transaction cognition theory to issues in the management of currencies. (6) B6 , new research that designed to explain the transitions among transacting systems (e.g. barter to market ↔ market to barter). (7) B7 , new research that expands the transaction model to explain noneconomic phenomena, such as political transactions (e.g. Mitchell, 2001a) or religious transactions. But the foregoing are only a few ideas to “prime the pump” for additional transaction cognition theory research, and to help the reader to perhaps envision the likelihood of continuing increases in the credibility of the theory. I am therefore hopeful that colleagues in multiple disciplines will interpret the suggestion of

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this list of ideas to be an invitation to participate in a transaction cognition-based exploration of multiple topics that is truly just beginning.

Limitations To be useful, a theory must have boundaries: the specification of where and when its application is likely to be less valuable (Bacharach, 1989). Transaction cognition theory is no exception. In this section, I discuss the limitations of transaction cognition theory as they relate to the context, composition, classification, and creation of transaction cognitions. Context Explicit in the transaction cognition theory argument is the idea that transaction cognitions exist within a social world. Accordingly, a primary boundary of transaction cognition theory is that it is intended to apply to the analysis and explanation of socioeconomic phenomena.7 Also implicit is the idea that the social world does not exist in isolation but rather, exists within an environment. Thus, the veracity of the theoretical relationships suggested here might depend heavily upon both the short and long term environmental conditions under which they occur; for instance in the short term, the transaction cognitions that occur in the midst of a typhoon may not at all resemble those that occur under normal weather conditions. Longer-term environmental considerations consist of, for example, the natural resource endowments available to transaction-creating individuals. Thus, although higher levels of transaction cognitions may be related to higher levels of resource acquisition and use, physical limitations of climate, geography, geology, and so forth that could dramatically impact upon the relationships suggested here must be recognized. Further, however, one must recognize that the physical environment is only one part of an overall environment. Accordingly, it is important to acknowledge that individual transaction cognitions exist within a social web of institutions that will shape and constrain them. Thus, while it is possible to assert that transaction cognitions can have an impact on institutions through what is now becoming known as institutional entrepreneurship (Garud, Jain & Kumaraswamy, 2002), it should be recognized that the institutions existing at a point in time form the context within which transaction cognitions must operate. Institutions, and therefore contexts of operation, vary. Taking such variability into account in boundary setting is critical in the case of a theory such as transaction cognition theory, which has helping to explain global entrepreneurship as a goal.


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Composition I first investigated the composition of new venture formation expertise in my dissertation (Mitchell, 1994). The three-factor structure that emerged from that research has since been confirmed with multiple-country samples in several follow-on studies (e.g. Mitchell et al., 2000, 2002). Within this chapter, I have argued that there is a fundamental theoretical reason for the continued emergence of these three factors in empirical research: i.e. that the constructs/variables that result from empirical work in fact tap into an underlying cognitive map that is based in the three-element structure of the transaction itself, and thus arise from the actual existence of planning, promise, and competition cognitions in the empirical world. The propositions advanced here represent this thesis. However, it is important to remember, that to my knowledge only Proposition 2 has any empirically based validation (previously noted) at the time of this writing (Fall 2002). Thus, although transaction cognition theory can provide a likely argument for the levels of individual entrepreneurial employment, new venture formation, and entrepreneurship within a society, it still requires extensive further testing for the limits of its external validity to be established. Classification Underlying the assertion that the effective level of transaction cognitions is related to levels of individual employment (Proposition 1), venture creation (Proposition 2), and entrepreneurship within a society (Proposition 3) is the idea of a cognitively based classification that distinguishes between entrepreneurs and nonentrepreneurs on the basis of the notion that entrepreneurs are more “expert” than nonentrepreneurs (Mitchell, 1994). Fundamentally, this assertion involves making between-groups distinctions that are based upon individual possession of higher or lower levels of transaction cognitions. As Fig. 6 illustrates, however, within-group distinctions are also likely to exist. I have given extensive attention to the between-group theoretical case, but the theoretical development of reasoning for the within-group case is just beginning (e.g. Mitchell et al., 2002). Researchers should therefore take care to clearly specify the conditions under which transaction cognition theory is to be used in the withingroup case. It is key that the likely sources of variance be taken into account; these are unknown at this point but might include cultural values and cognitive biases (e.g. Busenitz & Lau, 1996) and might be of vast and material concern. It is obvious, I think, that this is a likely avenue for extensive future research. Creation The idea that transaction cognitions affect social structure, which in turn affects transaction costs and thereby economic opportunity, is a nontraditional use of

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Fig. 6. TCT-Based Classification Differences.

the principles of transaction cost economics (Schure, 2002, personal communication). However, as the theory is developed and elaborated, the reader will, I hope, see a theoretically sound justification for a nurture versus nature approach to entrepreneurship: a proactive effort to create entrepreneurs. But limits to the extension of this thesis should be noted. For example, while transaction cognition theory advances a global (universal) model of entrepreneurship – i.e. to explain after over 200 years of unsuccessful research . . . why an entrepreneur might appear and/or engage in entrepreneurship (Bull & Willard, 1993, p. 183) – it nevertheless does not explain global entrepreneurship – e.g. how to start or to build a global firm (Oviatt & McDougall, 1995). Further, as noted above, it is as yet unknown what impact canceling and other alternative socioeconomic cognitions (Gurnell, 2000) might have on transaction cognitions, and thus on transaction-cognition-theory-based explanations for global entrepreneurship. Thus, transaction cognition theory offers a cross-border model for the existence of entrepreneurship where it is found, but it does not purport to state that having transaction cognitions is a sufficient condition for the creation of entrepreneurs, ventures, or entrepreneurship within a given society. Further research might fruitfully explore the additional elements required in each of these cases.

CONCLUDING THOUGHTS The objective of this article has been to investigate and identify a theory of global entrepreneurship that crosses borders – an economic parallel to physics’ planetary


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model and genetics’ double helix – that uses composition theory, and produces basic concepts that can provide common denominators for understanding global entrepreneurship. In this chapter, I have defined global entrepreneurship as the creation of new (value-adding) transactions or transaction streams anywhere on the globe. This phenomenon has an ever-more important place in the world. At the World Economic Forum held in Davos, Switzerland in January 1999, UN Secretary General Kofi Annan focused the attention of the world on the possibilities for global entrepreneurship by stating: Let us choose to unite the power of markets with the authority of universal ideals. Let us choose to reconcile the creative forces of private entrepreneurship with the needs of the disadvantaged and the requirements of future generations.

This call, at this point in time, is important, because the second wave of globalization is now sweeping across the planet (Friedman, 2000). The first wave (from the mid 1800s to the late 1920s) was driven by the drop in the cost of transporting physical goods following the invention of steamships, railroads, and automobiles. The second wave, which began in the 1980s, is driven by the dramatic reduction in telecommunications costs – the ease of moving ideas from mind to mind via microchips, satellites, fiber optics, and the Internet (2000, p. xviii). The first wave of globalization created economic shifts that stimulated boom (1920s) and bust (the Great Depression). First wave-globalization also led to inequities in distribution of the new industrial-revolution-and-globalization-created wealth that polarized discussion predominantly around distribution issues (Marx & Engels, 1848) with scant attention to addressing production issues in tandem. Furthermore, first wave-globalization gave rise to class-struggle-based revolutions that effectively shut down Globalization 1 as a system, and replaced it with a Cold War System (Friedman, 2000, p. 7). But neither the first globalization system nor the Cold War system has produced satisfactory global economic results. In fact, the reverse has been true.8 The first wave of globalization entailed the creation of wealth from new methods for the production and distribution of industrial products, but the Cold War distorted the development of this new wealth production process early in its evolution, yielding a warped and misshapen economic world that has not fully, as yet, addressed problems of global wealth production and distribution. The legacy of the Cold War is a new planet wide patchwork of partitions, because during the Cold War, “both your threats and opportunities . . . tended to grow out of who you were divided from” (Friedman, 2000, p. 8). And thus the global community is left with unfair production and distribution of wealth worldwide, such that 5 billion people presently exist in second, third, and fourth economic tiers, with fewer than 1 billion people in the first tier producing and distributing a majority

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of the wealth (Mitchell, 2001b, pp. 346–348; Prahalad & Hart, 1999). This state of affairs raises serious questions about the wisdom of our approach – as a global community – to value creation and value sharing, both of which, I believe, are essential to truly achieving high-performance economic results. In his Ruffin Lecture on stakeholder value and the entrepreneurial process, Professor S. Venkataraman asserted that the foregoing two processes, value creation, and value sharing, are common ground for both the fields of business ethics and entrepreneurship (Venkataraman, 1999). This observation echoes the writings of Victor Hugo, who in the 19th century offered his opinion that the two main problems of society were: (1) the production of wealth (value creation); and (2) its distribution (value sharing) (Hugo, 1982/1862, p. 722, parentheticals added). The connections between transaction cognition theory and the stakeholder concept relate to both the production and the distribution of wealth in society (Mitchell, 2002). This is important, I think, because the second wave of globalization is now beginning to generate the capability to produce vast new reservoirs of wealth that is generated from information. I cannot help but wonder about the outcome of “globalization 2” if – as in the case of “globalization 1” – discussion becomes polarized around only the distribution of wealth. Can we expect a second wave of revolutions? A second Cold War? Or should we instead try to produce a better set of results? The evidence suggests that it is time to fully understand and engage global entrepreneurship, and the UN Secretary General has issued a call to do just that. But what might this in fact mean? First of all, because new wealth creation is based upon bringing “on line” the talents and capabilities of at least 3–5 billion presently under-engaged minds, functional “economic” literacy must be discussed and understood as a necessary condition. Seen through the transaction cognition theory lens, it might be viewed that the real enemy of economic development is ignorance – the LACK of transaction cognitions. In this chapter I have argued that the possession of three possibly universal subsets of knowledge liberates the creative forces that are at the foundation of functional economic literacy for everyone; these subsets of knowledge are (of course) planning, promise, and competition cognitions. Transaction cognition theory suggests that desired economic results can be achieved through accurate economic thought and thus, that those who possess effective levels of these three universal subsets of knowledge are “functionally” economically literate and therefore can enact successful new transactions anywhere on the globe, regardless of culture or political system. At present, functional literacy is defined as the ability of individuals to use reading, writing and computational skills in everyday life (Tharoor, 2002). Thus, to repair past economic damage and to establish a sound foundation for future


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economic development and entrepreneurship, I suggest that, to the present functional literacy list that normally includes: (1) reading; (2) writing; and (3) computational skills (Tharoor, 2002), should be added (4) economic thinking skills – in the form of transaction cognitions. Research, in as many countries around the world as have been so studied, has found that wealth creation, represented, for example, by the venture creation decision, is related to certain transaction cognitions (e.g. Mitchell et al., 2000, 2002). As of this printing, data have been collected and analyzed from Australia, Belarus, Canada, Chile, China, Czech Republic, France, Germany, Italy, Japan, Mexico, Russia, the United Kingdom, and the United States of America. I believe that as research continues, it will also be shown that poverty results at least in part, from the absence of key transaction cognitions (the confirmation of which is a likely extension of the foregoing research). Second, understanding and engaging global entrepreneurship might mean that our present new source of value creation (information-based value) provides an opportunity to revisit our conceptions of value creation and value sharing. I do not believe that the revenue model for the information age (i.e. who makes money from information, who should make money from information, and how can money be made from information) is yet fully understood. Thus, so-called “irrational exuberance” in the stock market of the late 1990s (Shiller, 2000) conjured trillions of dollars in e-stock market value, value that subsequently vanished for lack of a full understanding of how, for example, the information technology of the web would lead to investor returns (Will, 2001). And consequently, because the information age is still lacking a fully developed revenue model, there presently appears to be a significant opportunity to redefine the wealth distribution process, as linked through IT, to wealth creation. To further illustrate this point, I’ll develop this line of reasoning briefly in the following paragraphs. In both West and East, there is evidence that a myopic focus upon distribution only – through the creation of a variety of redistributive institutions – has been insufficient to create a high-performance economic world for the majority. Societies have experimented extensively with the idea of compulsory redistribution of wealth, and they continue to experiment. But after all of this trying, the idea of forced wealth redistribution has not yet succeeded in creating widespread prosperity within target groups, despite its egalitarian appeal. It seems that money can be redistributed, but not prosperity. So what if, instead of continuing down a problematic old road, the global community were to take the new opportunity offered by the emergence of the information-driven wealth creation possibilities9 of the second wave of globalization? What if we were to construct a global entrepreneurship model that is based upon both value creation and value sharing: production and distribution? The logic for one such argument follows.

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It is well known that to make money from information, one must be able to exclude others from it (Casson, 1982). But because information technology makes it virtually impossible over the long run to exclude people from information, the present pre-information age methods for excluding others (borders, locks, copyrights, etc.), are no longer very effective. It is possibly for this reason that the lack of a revenue model has been a problem for the valuation of dot.com companies in the stock market in recent years. However, what if we looked at this problem counterintuitively? What if we considered that the very absence of such a revenue model might be signaling an opportunity for more effectively producing and distributing wealth? What might be envisioned then, are new combinations that arise to reorganize socioeconomic relationships in the same way that Schumpeter (1934) envisioned new combinations reorganizing industry relationships to create new value. What might such new combinations look like? In the past (as noted), the separation of the production and the distribution of wealth was accepted as the natural state of affairs (Hugo, 1982/1862, p. 722). In the information age, this separation need no longer be the case, because – owing to the communications revolution – production and distribution are, or can be, much more closely connected. Thus we can speculate: What if every producer (individual who creates a work for other persons) could acquire functional economic literacy: a fundamental understanding of effective planning, promise, and competition cognitions as they apply within their industry and society? The information revolution would then offer new wealth creation/distribution opportunities for people to apply information to transform problems that are based on social friction and transaction costs (the problems that I have called “slippage” and “drag”), into the opportunities of “glide” and “traction,” (Mitchell, 2001b) which are also based on social friction and transaction costs. For example, why couldn’t a producer of IT-based intellectual property in Chengdu or Chittagong offer it for sale (an individual, produces a work, for other persons) in a global IP (intellectual property) “E-Bay”-type auction? And why couldn’t the created value – in a currency of choice – be credited to a bank account electronically immediately upon the completion of the transaction? Can we not therefore envision an IT-based production and distribution stream? And if we can, what would it take to make such a thing, and other such things, possible? These, and questions of like kind, motivate continuing research effort, with the transaction cognition approach offering possibilities. In conclusion, I should note that in addition to the specific limitations presented earlier, the foregoing presentation and analysis in this chapter is also limited by the typical disabilities of cross-disciplinary (Freeman, 1986) and cross-level (Rousseau, 1985) analysis. Further, the analysis presented in this chapter generates claims and in some instances propositions that have yet to be subjected to tests. However, I hope that this chapter offers sufficient evidence, argumentation, and


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perhaps imagination, that the additional work needed to elaborate the theory, and to refine it as needed, will be seen to be a worthy undertaking. It is to this task, and to the possibility that undertaking it will move the field of global entrepreneurship forward toward a complete entrepreneurship paradigm, that attention should now turn. I look forward to the dialogue that I hope these ideas will generate.

NOTES 1. Although in his 1996 book (Chapter 13, pp. 326–327), the conceptual distance is narrowed substantially, in that Williamson now suggests cognitive antecedents to institutional organization. 2. Interestingly, most events in the transaction creation sequence seem to follow steps that successively answer the questions: (1) what do I have to offer? (2) can I make a deal? and (3) can I produce and deliver it? This suggests that the order of cognition use may not, in practice, be planning, promise, competition, but rather, competition, promise, planning. Bounded rationality would not, then, be the first transaction attribute that transaction creators would address. Instead, the sequence appears to be first specificity, then opportunism, and then bounded rationality. Planning is thus made practical because bounded rationality has itself been “bounded” in the enactment of the transacting sequence. 3. For more information, please see the more detailed discussion of social frictions in a related research monograph (Mitchell, 2001b). 4. Composition theory contains constructs that are functionally similar across levels. A properly specified compositional model is a prerequisite for the specification of multi-level models (Rousseau, 1985, p. 29). 5. Kahneman and Tversky provide one of the clearest illustrations of the transaction costs that arise from bounded rationality. Essentially, they found that the actual value of economic choices made by individuals (actual utility) was less than the possible value (expected utility) because the individuals ignored or overweighted highly unlikely events or neglected or exaggerated highly likely events. These errors stemmed from reflection effects – risk aversion in the positive domain and risk seeking in the negative domain (1979, p. 268) – and isolation effects: disregarding the shared attributes of decisions to focus on the distinguishing ones (1979, p. 271). According to prospect theory, these effects arise from cognitive errors that occur in individuals’ coding, combination, and/or cancellation (1979, p. 274) of relevant information, which taken together limit, or bound, rationality. 6. For example, in every society there are individuals who lack the desire to exchange. This desire may be absent for many reasons; a nonexhaustive list includes the following: a value choice (for instance, self-denial for a spiritual purpose); age (for instance, individuals being too young or old to care for themselves); a disability (for instance, no awareness of the need owing to developmental difficulties); or an individual judgment that provisions in store are sufficient, given the perceived level of uncertainty (for instance, being rich, or rich enough – a perception that, of course, also varies by case). Further, some locations on Earth are so congenial, and the societal norms so structured, that economic uncertainty, and thus exchange behavior, is virtually irrelevant. 7. One exception is the exploratory application of transaction cognition theory in the political realm by my son Rob, in his integrated studies thesis (Mitchell, 2001a).

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8. Some authors interpret the increase in global GNP from $1.3 trillion in 1960 to almost $30 trillion in the late 1990s, the doubling of world trade between 1987 and 1997, and the fact that the number of overweight people on the planet today has caught up with the number of underweight people to mean that “the last half of the 20th century has brought unequalled prosperity and a better standard of living to most of the world’s population” (LaChance, 2000, pp. 82 and 85). To some? Perhaps. To more people than ever? Certainly, due to population growth. An accomplishment? Definitely. Enough? In my view, not even close. 9. This argument does not diminish the value of “such cutting edge industries as brick, carpet, insulation, and paint” (Buffet, 2001) or other basic businesses, which arguably work better with improved information. Rather, it suggests that a possible information age revenue model should more closely align the value creation and value distribution.

ACKNOWLEDGMENTS The author gratefully acknowledges the contributions of colleagues to his endeavor: the editors and reviewers; several colleagues who have commented in detail on the research monograph from which this chapter is drawn: Jim Chrisman, Paul Godfrey, Norris Krueger, Patricia P. McDougall, Eric Morse, Craig Pinder, Paul Schure, Brock Smith; my 2001 UVic post-doctoral seminar colleagues: Po-Chi (Paul) Chen, Chun-Hung (Brendon) Lai, Shaw-Chang (Roy) Maa, Ana Maria Peredo, Chenting (Eric) Su, and Wen Ching (Peter) Yu; and, the participants in the 2002 Minneapolis Doctoral Workshop on International Entrepreneurship sponsored by Georgia State University and the University of Minnesota. The refinements and improvements I credit to them, and I retain as author, full responsibility for any remaining deficiencies. I also wish to acknowledge the invaluable contributions of Persephone Doliner, Fritz Faulhaber, Wendy Farwell, and Charmaine Stack, and to thank my family for their unfailing support.

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THE IMPACT OF ENTREPRENEURIAL EXPERIENCE ON OPPORTUNITY IDENTIFICATION AND EXPLOITATION: HABITUAL AND NOVICE ENTREPRENEURS Deniz Ucbasaran, Mike Wright, Paul Westhead and Lowell W. Busenitz ABSTRACT Evidence suggests habitual entrepreneurs (i.e. those with prior business ownership experience) are a widespread phenomenon. Appreciation of the existence of multiple entrepreneurial acts gives rise to the need to examine differences between habitual and novice entrepreneurs (i.e. those with no prior business experience as a founder, inheritor or purchaser of a business). This paper synthesizes human capital and cognitive perspectives to highlight behavioral differences between habitual and novice entrepreneurs. Issues relating to opportunity identification and information search, opportunity exploitation and learning are discussed. Avenues for future research are highlighted.


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INTRODUCTION Empirical studies in entrepreneurship have generally ignored the heterogeneity of entrepreneurs in their samples. In this chapter, we argue that there is a need to distinguish between entrepreneurs who have had no prior experience in entrepreneurship (novice entrepreneurs) and those who have been involved in entrepreneurship prior to their current venture (habitual entrepreneurs). MacMillan (1986) clearly distinguishes novice entrepreneurs from habitual entrepreneurs. He argues that novice entrepreneurs do not develop an experience curve with respect to the problems and processes involved in starting a new business. In contrast, habitual entrepreneurs have established many businesses, analyzed these efforts, and after several attempts have recognized their mistakes and at least partially corrected them in subsequent ventures. Ignoring the heterogeneity of entrepreneurs in this respect has led to an unduly static view of the entrepreneurial process, since evidence suggests that a significant proportion of businesses are owned by habitual entrepreneurs (Westhead & Wright, 1998). Furthermore, entrepreneurship is not restricted to the creation of new businesses. The exploitation of wealth creating opportunities may take the form of the purchase or the inheritance of a business (Cooper & Dunkelberg, 1986; Davidsson, Low & Wright, 2001; Shane & Venkataraman, 2000; Wright et al., 2000)1 . Habitual entrepreneurs may therefore be defined to include individuals who have started, purchased or inherited more than one venture and novice entrepreneurs as those who have started, purchased or inherited one venture. The magnitude of habitual entrepreneurship is evident in the U.S. with figures ranging from 51% (Schollhammer, 1991) to 63% (Ronstadt, 1986). Outside the U.S., Kolvereid and Bullv˚ag (1993) found that 34% of surveyed entrepreneurs in their Norwegian sample were habitual entrepreneurs. Westhead and Wright (1998) reviewing existing studies reported figures ranging from 11.5 to 45.5% for the United Kingdom. Despite the prevalence and significance of the phenomenon and a plea over a decade ago that to really understand entrepreneurship there was a need to research entrepreneurs who had undertaken more than one venture (MacMillan, 1986), there has been limited theoretical development, and systematic empirical examination of the habitual entrepreneurship phenomenon. In particular, there is a need to understand the impact of entrepreneurial experience on the critical entrepreneurial step of opportunity recognition (Hills, 1995; Shane & Venkataraman, 2000; Venkataraman, 1997). To help fill this gap, we synthesise two complementary bodies of research, human capital and (entrepreneurial) cognition, and build a model that provides greater insights into the value and contribution of entrepreneurial experience to the opportunity identification and exploitation process. Traditional perspectives

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on human capital suggest that experienced entrepreneurs would have higher levels of human capital endowments, which in turn will affect behavior in a positive way. Entrepreneurial cognition theory, which attempts to explain how entrepreneurs think, provides a tool for analyzing how human capital may be used in the entrepreneurial process (Baron, 1998; Busenitz & Barney, 1997; Busenitz & Lau, 1996; Manimala, 1992; Palich & Bagby, 1995; Smith et al., 1988). Integrating this body of research, this paper attempts to explain how novice and habitual entrepreneurs may differ in the way they identify opportunities. Further, building on attribution theories, the paper goes to explain why certain entrepreneurs will remain novices and why some will continue to exploit entrepreneurial opportunities and become habitual entrepreneurs. This paper is structured as follows. The following section outlines the human capital and cognitive approaches to entrepreneurs. This is followed by the development of a model of opportunity identification and exploitation. The final section discusses the implications of the analysis, draws some conclusions and identifies areas for further research.

HUMAN CAPITAL AND THE ENTREPRENEUR Human capital resources consist of achieved attributes, which are linked to increased levels of productivity (Becker, 1975). More recently, the term human capital has been broadened to include the cognitive abilities of entrepreneurs (Alvarez & Busenitz, 2001) as well as accumulated work and habits that may have a positive or negative effect on productivity, both in market and non-market sectors (Becker, 1993). An entrepreneur’s human capital can impact the extent to which other resources, such as social and financial, can be accessed and leveraged. We now address the links between human capital, experience and cognition. First, we examine the role of entrepreneurial experience in building human capital. Second, we examine cognition as a component of an entrepreneur’s human capital.

The Role of Entrepreneurial Experience in Building Human Capital Education and work experience are the characteristics most often thought of in reference to human capital (Greene & Brown, 1997). Following Becker (1975) and Brüderl, Preisendorfer and Zeigler (1992), Cooper, Gimeno-Gascon and Woo (1994) focused on four categories of an entrepreneur’s human capital: general human capital, management know-how; industry specific know-how and the ability

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to acquire financial capital. Prior research highlights that human capital comprises a broad range of aspects: the owner-founder’s achieved attributes (Becker, 1975), family background characteristics (Greene & Brown, 1997), reputation (Dollinger, 1998), attitudes and motivations (Birley & Westhead, 1990), gender, ethnic origin, industry specific know-how (Cooper, Gimeno-Gascon & Woo, 1994), competencies (Chandler & Jansen, 1992), age (Cressy & Storey, 1995), education and management experience (Cooper, 1981, 1985; Greene & Brown, 1997; Westhead, 1995). Entrepreneurs can develop their human capital over time which can then be utilized to gain access to a predictable uninterrupted supply of critical resources (Cooper, Gimeno-Gascon & Woo, 1994; Dahlqvist, Davidsson & Wiklund, 2000; Greene & Brown, 1997; Hart, Greene & Brush, 1997). The experiences, skills and competencies associated with the human capital resources of entrepreneurs are widely regarded as influencing organizational survival and development (Bates, 1998; Chandler & Hanks, 1994; Gimeno, Folta, Cooper & Woo, 1997; Mosakowski, 1993; Storey, 1994; Westhead, 1995). In general, studies reveal that successful businesses are associated with owner-founders who possess greater amounts of human capital. Entrepreneurs with more diverse levels of human capital are purported to have the ability to develop relevant skills and contacts, and are able to tap into dense information and resource networks. Once an initial opportunity has been exploited, an entrepreneur may choose to engage in a subsequent venture. Managerial work experience is seen as a key empirical indicator of managerial human capital (Castanias & Helfat, 2001). Following a similar logic, entrepreneurial experience may be viewed as a significant contributor to an entrepreneur’s human capital (Chandler & Hanks, 1998; Gimeno, Folta, Cooper & Woo, 1997; Stuart & Abetti, 1990). Previous business ownership experience may provide entrepreneurs with a variety of resources or assets that can be utilized in identifying and exploiting subsequent ventures, such as real entrepreneurial experience; additional managerial experience; an enhanced reputation; better access to finance institutions; and broader social and business networks. Indeed, the view that individuals accumulate resources over time has a long standing tradition in vocational and career theory.2 The development of subsequent businesses owned by habitual entrepreneurs can therefore be enhanced by overcoming the liabilities of newness (Aldrich & Auster, 1986; Stinchcombe, 1965) and attaining developmental milestones quicker (Starr & Bygrave, 1991). Prior entrepreneurial experience can be utilized to enhance entrepreneurial skills and reputations that help to influence the reallocation of resources in subsequent ventures established, purchased or inherited (Shane & Khurana, 2003). Wright, Robbie and Ennew (1997b) showed that venture capitalists perceived certain assets of serial entrepreneurs (i.e. those habitual entrepreneurs who choose to


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exit from their previous venture(s) before embarking on another one) that gave them greater credibility and leverage in obtaining financial resources for their subsequent ventures. Entrepreneurs with successful track records are generally perceived as being more credible. Most notably, some habitual entrepreneurs may leverage this experience to obtain financial resources for their subsequent ventures from banks, venture capitalists and informal investors. Entrepreneurs with successful track records in business are more credible and have more experience in dealing with the technical requirements generally requested by investors. Habitual entrepreneurs can lever this experience and obtain financial resources for their subsequent ventures from banks, venture capitalists and informal investors. Habitual entrepreneurs who learn from their experiences can enrich their entrepreneurial skills. Getting through the ambiguity of one or more entrepreneurial situation gives them the confidence to find their way through another entrepreneurial experience. Hart, Greene and Brush (1997) found that both the depth (i.e. measured in years) and breadth (i.e. measured in number of ventures founded) of entrepreneurial experience were important contributors to success in garnering and maintaining access to resources. Conversely, Chandler and Jansen (1992) found that the number of ventures previously initiated, and the years spent as an owner-manager were not significantly related to the performance of the surveyed venture. Similarly, neither Kolvereid and Bullvag (1993), Birley and Westhead (1993) nor Westhead and Wright (1998, 1999) were able to identify performance differences between businesses founded by novice entrepreneurs and those founded by habitual entrepreneurs. This evidence supports the view that prior entrepreneurial experience is associated with assets (e.g. attaining developmental milestones quicker) and liabilities (e.g. hubris and denial). Nonetheless, prior entrepreneurial experience is likely to have a significant impact on subsequent ventures owned by the entrepreneur. The assets and liabilities approach to experience, while useful, provides a somewhat static view of the contribution of entrepreneurial experience to an entrepreneur’s human capital and subsequent behavior. This literature highlights the role of entrepreneurial experience ex post, that is as a product rather than a process. Further, it provides limited insight into why certain entrepreneurs will choose to engage in subsequent entrepreneurial activity while others will choose to stick with a single venture or exit the business. The next section extends this traditional view of human capital to incorporate entrepreneurial cognition. The cognitive perspective on human capital suggests that entrepreneurial behavior (i.e. the opportunity identification process) is significantly influenced by the way entrepreneurs think, perceive, and evaluate their environment and experiences.

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Cognition as a Component of an Entrepreneur’s Human Capital An individual’s human capital will impact their subsequent activities. Similarly, individual cognition will influence decision making and actions (Schwenk, 1986). Cognition and human capital are linked to the extent that an individual’s mindset (i.e. cognition) is an important contributor to the development and deployment of human capital (Castanias & Helfat, 2001). There is a case therefore for taking a broader view of human capital to encompass both traditional components (e.g. skills and education) and cognitive components which in turn will determine how such skills and education are utilized. Many researchers in the 1960s and 1970s attempted to better understand the entrepreneurial difference by examining a host of traits such as risk-taking and need for achievement, but unfortunately, those findings were largely disappointing (see Low & MacMillan, 1988 for a review). Though the adoption of a cognitive approach to explore the entrepreneurial difference is well-established3 (cf. Jaques, 1976), recently an increasing amount of attention has be channelled into understanding how entrepreneurs think and make strategic decisions from a cognitive perspective (Baron, 1998; Busenitz & Barney, 1997; Forbes, 1999). If entrepreneurs do indeed have a unique mindset or orientation (Lumpkin & Dess, 1996), then given the strengths and weaknesses of this mindset in various competitive environments, it may be a potential source of competitive advantage (Barney, 1991). Cognitive theory is concerned with how incoming sensory stimulation is “transformed, reduced, elaborated, recovered, and used” (Neisser, 1966, p. 4). Cognitive theorists view stimuli largely as information. Processed information is integrated into a “belief” that gives “meaning” to the external environment (Weiner, 1980). The essence of a cognitive model of behavior can be illustrated as follows: Stimuli (i.e. information or event) → Mediating Cognitive Event → Behavior The “mediating cognitive event” (Weiner, 1980) involves a set of cognitive processes such as information scanning and selection, information combination and, perception of causality. If cognitive processes are not carefully considered, an incorrect understanding of entrepreneurial behavior will be presented (Hitt & Tyler, 1991). In this study, we focus on two types of such processes. First, we examine heuristics which are central to the processing of information. Second, we examine attribution theory which explains individual perceptions of causality. A discussion of these two cognitive processes and associated biases is presented before moving on to examine how they can impact entrepreneurial behavior (i.e. opportunity identification and exploitation).


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Cognitive Processes I: Heuristics The cognition literature suggests there are two broad types of cognitive orientation. These are a systematic cognitive orientation (also referred to as conscious cognitive processing or rational information processing) and heuristic-based cognitive orientation (also referred to as automatic cognitive processing or limited capacity processing). Systematic processing (rational) models assume that people thoroughly process all relevant information in order to maximize a relevant outcome (Lord & Maher, 1990). Unfortunately, while this type of processing is optimal (i.e. accurate), it is slow (requiring time) and requires effort (requiring cognitive resources) (Kullik & Perry, 1994). Heuristic-based processing models focus on how individuals simplify information processing while still generating adequate but not optimal behaviors (Lord & Maher, 1990). The latter type of processing is easier (requiring less cognitive effort) and is more efficient (requiring less time) than systematic models. Individual heuristics (and associated biases) can influence the strategic decisions made by individuals. An understanding of strategic decision making is incomplete without attention to these heuristics (Schwenk, 1986). Limited mental processing capacity requires people to indulge in strain-reducing activities (i.e. heuristics) when making strategic decisions, especially in complex situations where less complete or uncertain information is available. This has particular implications for entrepreneurs because they regularly find themselves in situations that tend to maximize the potential impact of various heuristics (Baron, 1998). Such heuristics and biases may include “anchoring and adjustment,” “availability,” and “overconfidence” which may result from “representativeness” (Bazerman, 1990; Hogarth, 1980; Powell, 1987; Tversky & Kahneman, 1974). Indeed, Katz (1992) demonstrates how the heuristics of availability, anchoring and adjustment and, representativeness can be used to model an individual’s choice to become self-employed (as opposed to being a salaried employee). The relevance of these heuristics (and biases) may be particularly strong in the context of entrepreneurship, as these cognitive processes can be an effective and efficient guide to strategic decision-making especially in complex situations where less complete or uncertain information is available. Entrepreneurial cognition is associated with the more extensive use of heuristics and individual beliefs that impact decision-making (Wright, Hoskisson, Busenitz & Dial, 2000). A more systematic decision-making style is typically associated with accountability, compensation schemes, the structural coordination of business activities across various units, and future developments are justified with quantifiable budgets. New insights are usually not obtained from existing data. Rather, they are identified from experience and interpreting new combinations of information via unique heuristic-based logic (Wright, Hoskisson, Busenitz & Dial, 2000).

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In probing these cognitive processes, it is important to first understand the utility of such decision-making. Individuals engaging in entrepreneurship typically operate under the conditions of decision uncertainty and decision complexity (Hambrick & Crozier, 1985). Given the level of uncertainty they face, entrepreneurs frequently use heuristics to piece together limited information to make convincing decisions in the face of much turbulence (Busenitz & Barney, 1997). Without heuristic-based logic, the pursuit of new opportunities becomes too overwhelming and costly for those decision-makers who seek a more factual base. Without the elaborate policies, procedural routines and structural mechanisms common to established organizations, heuristics may have a great deal of utility in enabling entrepreneurs to make decisions that exploit brief windows of opportunity (Tversky & Kahneman, 1974). Cognition is not homogeneous across individuals and a variety of cognitive styles, strategies and processes exist. The following bipolar continuum indexes, for example, have been presented to categorize individuals in terms of their cognitive style (where cognitive styles are enduring differences in cognitive structure and processes across individuals (Schneider & Angelmar, 1993): Kirton’s (1976) adaptation-innovation inventory (KAI); Riding’s (1991) wholist-analytical dimension; Allinson and Hayes’s (1996) analytical-intuitive cognitive style index; Gavetti and Levinthal’s (2000) looking forward-looking backward approach and; Gaglio and Katz (2001) non-alert and alert continuum. Groups of individuals at extremes of these continuums tend to be distinguished on the basis of the extent to which they thoroughly process all relevant information. Recent research on entrepreneurial cognition indicates that entrepreneurs are more significantly influenced by heuristics in their decision-making than managers (Baron, 1998; Busenitz & Lau, 1996; Forbes, 1999). Entrepreneurial cognition studies (see Forbes, 1999, for a review) have tended to focus on entrepreneurs as a single group. However, a number of studies suggest that entrepreneurs are heterogeneous (Westhead & Wright, 1998; Woo, Cooper & Dunkelberg, 1991). It is possible, therefore, that entrepreneurs will differ with regard to their cognitive processes. As intimated above, cognition can be viewed as lying along a continuum. In this study, we distinguish between entrepreneurs who exhibit a strong reliance on entrepreneurial cognitive processes (i.e. heuristics and beliefs), and those that exhibit a weak to more moderate reliance on entrepreneurial cognitive processes. We refer to these two extremes as strong entrepreneurial cognition and weak entrepreneurial cognition respectively. The two polar extremes relating to the entrepreneurial processes continuum describe “different” rather than “better” thinking processes, though particular cognitive processes may be more appropriate in certain situations.


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Cognitive processes can be utilized to differentiate novice entrepreneurs from habitual entrepreneurs. Both novice and habitual entrepreneurs will identify a business opportunity that is facilitated by their entrepreneurial cognitive processes. From the outset, habitual entrepreneurs are characterized as relying heavily on heuristics. Novice entrepreneurs exhibit an entrepreneurial orientation (i.e. tendency to use heuristics), but it is generally not as pronounced as the orientation exhibited by habitual entrepreneurs. Novice entrepreneurs embarking on a venture, possibly based on some innovation may be comfortable with seeing the business mature over time, as is consistent with their weaker entrepreneurial cognition4 . On the other hand, a habitual entrepreneur will generally become very restless with an individual business, as it grows into the more mature phases, and the ambiguity diminishes. Indeed, arousal (activation) theory (Hebb, 1955) posits that individuals prefer and seek out “optimal levels” of stimulation, with the “optimal level” varying from individual to individual. A habitual entrepreneur’s strong entrepreneurial cognition draws them towards more ambiguous and complex environments and information, in turn facilitating the identification of additional ventures. The extent to which these two groups of entrepreneurs rely on heuristic-based cognitive processes may be crucial to the identification of opportunities and the nature of these opportunities. While an entrepreneurial cognitive orientation may be important in distinguishing habitual entrepreneurs from novice entrepreneurs, these two groups may also differ with regard to the way they attribute causality to events and outcomes. Attribution theory is a useful tool for understanding why certain entrepreneurs will move on to becoming habitual entrepreneurs while others will remain novice entrepreneurs. Cognitive Processes II: Causal Attribution The way previous entrepreneurial experiences are evaluated and interpreted may impact whether an individual becomes a habitual entrepreneur. If entrepreneurial experiences are seen as experiments, entrepreneurs should evaluate carefully and objectively the feedback from these experiences (Nystrom & Starbuck, 1984). Attribution theories, first developed by Heider (1958), suggest there may be a number of cognitive processes that influence how individuals evaluate and learn from their experiences. These theories assume that people are motivated to seek meaning in their own behavior and surrounding environment. Heider’s (1958) model suggested that people attempt to enhance or protect their self-esteem by taking credit for success (internal attribution) and denying responsibility for failure by attributing this to external factors (external attribution). Weiner (1985) extended Heider’s single internal-external dimension of causal attribution. He presented three dimensions of attribution: causal locus (internal or external cause); stability

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(whether the cause is transient or not); and controllability (whether the cause can be controlled or influenced by the individual or not). Individuals who reflexively make stable (the cause is going to last for a long time), global (it is going to undermine many areas of my life), and internal (it is my fault) explanations for unfavorable outcomes are more likely to give up and suffer learned helplessness (Seligman, 1975). The “learned helplessness” paradigm, which derives its origins from attribution theories states that individuals often possess the requisite skills and abilities to perform a particular task. However, some individuals may exhibit sub-optimal performance because they attribute prior failures to causes which they cannot change, even though success is possible (Abrahamson, Seligman & Teasdale, 1978; Martinko & Gardner, 1982). In contrast, those who seek external attribution view the cause of the problem as being transitory and narrow in its effects. As a result, such individuals will be more likely to see adversity as a challenge, transform problems into opportunities, endeavour to adapt/develop skills, maintain confidence, rebound quickly from setbacks and persist (Schulman, 1999). The term “learned optimism” has been used to describe this cognitive orientation. The concept of learned optimism is similar to the principle of reactance theory, which states that if one loses control, attempts are made by the individual to restore control (Brehm, 1966). Applying the principles of attribution theory, Gatewood et al. (1995) have explored how the cognitive orientation of potential entrepreneurs will have a significant influence on their willingness to persist in entrepreneurial activity in the face of difficulties. They found that the attributional measures used in their study were effective in predicting both persistence in activities and persistence for success in business creation. Both types of cognitive processes discussed above (i.e. heuristics and causal attribution) may be useful in understanding how entrepreneurs behave (the relationship between these cognitive processes and entrepreneurial behavior will be discussed later). There may, however, be appropriate and inappropriate uses of these cognitive processes (Nisbett & Ross, 1980). The extent to which these processes induce effective behavior, however, will be determined by learning. Cognitive Processes, Bias and Learning Experience provides a framework for processing information and allows informed and experienced entrepreneurs with diverse skills and competencies (i.e. networks, knowledge, etc.) to foresee and to take advantage of disequilibrium profit opportunities that they proactively or reactively identify (Kaish & Gilad, 1991). Based on an earlier learning experience, entrepreneurs can use their acquired


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skills and knowledge to identify a business opportunity or to leverage resources. For example, they can utilize experience gained from structuring deals to secure finance from a venture capitalist (Wright et al., 1997a, b). The value of resources and skills acquired through prior business ownership experience is, in part, dependent on the ability of experienced entrepreneurs to learn from their previous experience. Cognitive processes may influence the extent and nature of learning. Central to most models of learning is achieving new understanding and interpretations (Daft & Weick, 1984). Higher-level learning involves the formation and use of heuristics to generate new insights into solving ambiguous problems (Lei, Hitt & Betis, 1996). Conversely, lower-level learning tends to be associated with repetitious observations and routinized learning. Consistent with the notion of single-loop learning, lower-level learning is associated with few changes in underlying policies or values (Argyris & Schön, 1978). Gavetti and Levinthal (2000) characterize cognitive processes (“off-line” thinking) as being forward-looking and based on individual beliefs and mental models of how the world works. These mental models often provide linkages between choices and the potential impact of those choices. Entrepreneurs who rely extensively on heuristics may therefore be more likely to generate new insights as a result of making such linkages. This in turn can induce and reinforce the use of higher-level learning. Cognitive processes are difficult to change, especially if an entrepreneur has been previously successful (Busenitz & Barney, 1997). Whether or not the initial venture can be interpreted as a success or a failure may impact on the learning and subsequent behavior of entrepreneurs. Success is frequently sought, while failure is avoided (McGrath, 1999). Individuals may be forced to evaluate their thinking and behavior when faced with failure (Sitkin, 1992). In contrast, there may be minimal incentive to evaluate or reconsider thinking patterns or behaviors if success is the outcome (irrespective of the causes of that success). Indeed, attribution theories (Heider, 1958) suggest that individuals have a tendency to attribute their successes to themselves (internal attribution) and failure to external factors (external attribution). The ability of entrepreneurs to objectively reflect on and evaluate their experiences (whether they are successes or failures) may be critical in determining their future performance. Hence, while cognitive processes may be a source of sustained competitive advantage they may limit the ability of some entrepreneurs to adapt in response to changing/different market and technological conditions. In some cases, however, habitual entrepreneurs may effectively reflect and evaluate their experiences and develop expertise. Habitual entrepreneurs may develop expertise in various stages of the entrepreneurial process, such as opportunity recognition or resource acquisition. On the other hand, heuristics and biases

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may influence ones perception of uncertainty and complexity. As intimated earlier, there is an obvious danger that habitual entrepreneurs operating in the same sector as their previous venture may attempt to replicate actions that were previously successful (i.e. hubris). If experienced entrepreneurs are not aware of (or fail to respond to) changing external environmental conditions, there is a risk that habitual entrepreneurs may make serious mistakes when operating their subsequent ventures. Individuals generally adjust their judgment by learning from feedback about past decisions (Bazerman, 1990). Due to delays or bias in this feedback, individuals may be prone to errors in their learning. Even experienced decision-makers do not always know why and how they do what they do. Due to this problem, some entrepreneurs may exhibit basic judgmental biases that are unlikely to be corrected in the real world (Tversky & Kahneman, 1986). Nisbett and Ross (1980) argue that an indiscriminate use of heuristics can lead people into serious judgmental errors. However, they insist that in many contexts, heuristics can be helpful and efficient tools of inference. They argue that the normative status of using heuristics, and the pragmatic utility of using them, will depend on the judgmental domain and context. Louis and Sutton (1991) argue that individual effectiveness is not determined by how well an individual functions in a particular cognitive mode. Rather, individuals who are able to “switch cognitive gears” are likely to be more effective in a given domain. Northcraft and Neale (1987) found that even experienced decision-makers tended to be very biased. Further, while most “effective decision-makers” are effective in a specific domain, experience without expertise can be quite dangerous when it is transferred to a different context or when the environment changes (Dawes, 1988). Neale and Northcraft (1989) have argued that the development of expertise could eliminate or mitigate biased decision making. They view experience simply as repeated feedback, while expertise requires that the decision-makers have a conceptual understanding of what constitutes a rational decision-making process. Developing expertise requires constant monitoring and awareness of the decision-making processes. Consequently, individuals who know what they are doing and why, and those who have learnt from previous events (i.e. failures and successes) generally avoid decision-making biases (e.g. over-confidence and insufficient adjustments resulting in the repetition of past errors). In order to understand the impact cognitive processes have on entrepreneurial behavior, the following section uses the above dynamic human capital perspective to developed a model of how opportunities are identified by novice and habitual entrepreneurs and why certain novice entrepreneurs go on to become habitual entrepreneurs.


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THE USE OF HUMAN CAPITAL IN THE ENTREPRENEURIAL PROCESS: OPPORTUNITY IDENTIFICATION AND EXPLOITATION One of the fundamental reasons for the fascination with entrepreneurs and the inventions that they develop seems to center around why and how they see new opportunities. An entrepreneurial opportunity invariably involves the development of some new idea that most others overlook. In the context of environmental change, those with an entrepreneurial cognitive orientation (i.e. extensive use of heuristics) often see new opportunities where most others are concerned with protecting themselves from emerging threats and changes. Very few studies have focused upon opportunity recognition and information search processes exhibited by different types of entrepreneurs (Alsos & Kolvereid, 1999). While stocks of information create mental schemas providing a framework for recognizing new information, opportunity recognition and information search by entrepreneurs may be a function of an individual’s capacity to handle complex information (Venkataraman, 1997). Venkataraman (1997) draws attention to three main areas of difference between individuals that may help understanding of why certain individuals recognize opportunities while others do not: knowledge (and information) differences; cognitive differences; and behavioral differences. “Why,” “when” and “how” certain individuals exploit opportunities appears to be a function of the joint characteristics of the opportunity and the nature of the individual (Shane & Venkataraman, 2000). This section explains differences between novice and habitual entrepreneurs with respect to two stages of the entrepreneurial process: opportunity identification and the exploitation of opportunities. Opportunity Identification The possession of idiosyncratic information allows people to see particular opportunities that others cannot, even if they are not actively searching for such opportunities (Shane, 2000). However, simply being in possession of valuable information is insufficient for entrepreneurship. The ability to make the connection between specific knowledge and a commercial opportunity requires a set of skills, aptitudes, insights, and circumstances that are neither uniformly nor widely distributed (Venkataraman, 1997). It follows, therefore, that the extent to which individuals recognize opportunities and search for relevant information to evaluate the opportunity will depend on the make-up of the various dimensions/aspects of an individual’s human capital.

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Two broad perspectives relating to opportunity and search behavior have been identified (Kaish & Gilad, 1991; Woo, Folta & Cooper, 1992). The first perspective, based on neo-classical economic theory, takes a “conscious search perspective” in which information search is a means of optimizing performance (Caplan, 1999; Stigler, 1961). Entrepreneurs are assumed to know a priori where the invention/innovation needs to be made and can accurately weight the cost and benefits of acquiring new information. Entrepreneurs may invest in specific information surrounding a targeted invention enabling them to be in a better position to discover the new opportunities (Fiet, 1996). The second perspective, based on Kirzner’s (1973) work relating to “entrepreneurial alertness,” suggests that the opportunities cannot be accurately modelled as a rational search process, since opportunities are unknown until discovered (Kaish & Gilad, 1991). The focus therefore should be on the process side of discovery. Entrepreneurial alertness refers to “flashes of superior insight” that enable one to recognize an opportunity when it presents itself (Kirzner, 1997). Entrepreneurial alertness involves a distinctive set of perceptual and cognitive processing skills that direct the opportunity identification process (Gaglio & Katz, 2001). In assuming that both the search for information and the process involved are important, we argue here that an entrepreneurial cognition perspective enables us to extend models of opportunity identification. Entrepreneurial cognition enables one to build on specific information to make new leaps in the development of new discoveries and inventions. It is apparent that, although all information cannot be codified and explicitly stated, entrepreneurs have a deep sense of the relevant inter-relationships between what appears to be superficial and unnecessary information. Building on these deeper understandings, entrepreneurs often quickly develop new hunches about how a new variable such as another technological breakthrough or an environmental change will impact a specific project long before it can be methodically and rationally explained. These hunches can be viewed in terms of mental schemas (the cumulative experience, learning and meanings an individual has encountered and constructed about a specific domain) that provide a framework for recognizing and evaluating information relevant to an opportunity (Gaglio, 1997). The introduction of several new signals and bits of information may indicate that a change is starting to occur. Combining these bits of information with a heuristic-based logic may prompt an entrepreneur to conclude that an important opportunity resides here. To invest in more complete information will require cost and further delay the development of the discovery process for two reasons. First, given the very limited resources that entrepreneurs typically possess, investing in substantial amounts of information is rarely possible. Second, obtaining the


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critical information will probably require considerable amounts of time, further delaying the discovery process. If new opportunities are not pursued until relatively complete information is obtained, the window of opportunity for the new invention is likely to be closed. Heuristic-based logic also has relevance for entrepreneurial discovery that goes beyond the practical economics of investments in substantial amounts of information. The decision-making literature has typically approached heuristic issues as a phenomenon that needs to be corrected (e.g. Schwenk, 1986; Zajac & Bazerman, 1991). However, as already indicated, there is an emerging thought that there may be some positive implications to their use (Krabuanrat & Phelps, 1998; Wright et al., 2000). As intimated in the previous section, habitual entrepreneurs can be distinguished from novice entrepreneurs with respect to their more extensive reliance on cognitive processes (i.e. heuristics). This cognitive approach allows individuals to make inferences and envisage cause-effect relationships even though they may not be individually experienced (Gavetti & Levinthal, 2000). Applied to the entrepreneurial context, we may expect the strength of entrepreneurial cognition (i.e. reliance on heuristics) to influence the opportunity identification process. We examine several aspects of opportunity identification: the search for information, the quantity of opportunities identified in a given period and the nature and value of the opportunities identified. The Search for Information The amount and nature of information sought (Kaish & Gilad, 1991) can be influenced by the extent to which an entrepreneur relies on entrepreneurial cognitive processes (i.e. heuristics). A strong reliance on heuristics can enable an individual to build on specific information to make new leaps in the identification and development of opportunities. A strong entrepreneurial cognitive orientation may result in the individual feeling less need to collect relevant information. This is because cognitive approaches (Gavetti & Levinthal, 2000) allow the individual to envisage the outcome of a particular opportunity without actually having to exploit it and hence bear the associated risks and costs. Habitual entrepreneurs, who we have argued display a strong reliance on entrepreneurial cognitive processes, might therefore require less information to identify an opportunity than their novice counterparts. Indeed, both empirical and conceptual work suggests that this may be the case. Cooper et al. (1995) found that on average, habitual entrepreneurs sought less information than novice entrepreneurs. McGrath and MacMillan (2000) suggest that habitual entrepreneurs avoid “analyzing ideas to death” and may therefore avoid deliberate, time-consuming and analytically correct models. Fiet et al. (2000) suggest that habitual entrepreneurs may search for less information because they may be more likely to concentrate on searching within a more

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specific domain of venture ideas based on routines that worked well in the past. Ronstadt’s (1988) “corridor principle” suggests that the best new venture opportunities may only be revealed when the entrepreneur is involved in a venture since greater information becomes available about relevant networks, resources, markets and products. Finally, evidence from other fields suggests that when faced with an ill-structured problem, individuals with high levels of knowledge will attempt to add structure by making inferences and drawing on existing knowledge (Simon, 1973). On the basis on this discussion, the following proposition can be derived: P1. Habitual entrepreneurs will search for less information in the opportunity identification process than their novice counterparts. The Quantity of Opportunities Identified While habitual entrepreneurs may search for less information relative to their novice counterparts, this does not necessarily mean that they identify fewer opportunities. Indeed, due to their extensive use of heuristics, habitual entrepreneurs may be able to make more efficient use of information. Gaglio and Katz (2001) propose the possibility of entrepreneurial alertness falling along a continuum. Due to their cognitive orientation and experientially acquired human capital, habitual entrepreneurs may be at the alert end of the spectrum. Gaglio (1997) has argued that some people habitually activate a schema5 regardless of its appropriateness to the moment. Such “chronic activators” have an added sensitivity to the features stored in their schema such that they can notice it in unambiguous situations and notice it in the midst of an otherwise overwhelming amount of stimuli. Hence, entrepreneurs (or certain groups of entrepreneurs) may be characterized by their “habitual” schema activation, which would explain how alertness might be uncontrollable as Kirzner (1973) claims. We would expect habitual entrepreneurs to display a tendency towards habitual schema activation. Further supporting this tendency is the possibility of some individuals having a higher “optimal level” of stimulation as posited by activation theory (Hebb, 1955). In addition, some habitual entrepreneurs may have accumulated financial resources which they may want to channel into a subsequent venture. The availability of these funds may make them more “alert” to opportunities and increase their tendency to unify and connect what otherwise appear to be superficially disparate information. This discussion suggests the following proposition: P2. In a given period, habitual entrepreneurs will identify a greater number of opportunities than their novice counterparts.


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Quality of Opportunities Identified When an individual broadens the domain of their search, s/he may increase the likelihood of identifying a valuable business opportunity. Habitual entrepreneurs who have a stronger reliance on entrepreneurial cognitive processes, may be able to use their heuristics to identify opportunities in domain they have no prior experience. Wright et al. (1997a, b) noted, however, that some habitual entrepreneurs move into domains in which they have limited knowledge while trying to replicate successful practices used in a familiar domain. It may be beneficial, therefore, if the entrepreneur’s previous investments and repertoire of routines (i.e. history) constrain his/her future behavior. Indeed, Shane (2000) argues that knowledge in a particular market is crucial in identifying opportunities in that area. Gavetti and Levinthal (2000) posit that intelligent action is driven both by cognitive and experiential influences. Cognitive search is broad in that it considers numerous alternatives simultaneously whereas experiential search is narrow since it is limited by the number of experiences one can have in a given period. On the other hand, cognitive search can be misspecified while experiential search tests the alternatives on the basis of the actual environment rather than a mere representation of the environment (Gavetti & Levinthal, 2000). Previous entrepreneurial experience may reduce the likelihood of an entrepreneur moving into unfamiliar territory where they may be at a relative disadvantage to incumbents. Habitual entrepreneurs may have a unique advantage in that they can combine experiential search with cognitive search. For habitual entrepreneurs, their human capital comprising of experientially acquired knowledge and their cognitive orientation may be critical in identifying and taking advantage of valuable dis-equilibrium profit opportunities (Kaish & Gilad, 1991). Novice entrepreneurs who have a relatively weak reliance on entrepreneurial cognitive processes and who have had no prior experience in entrepreneurship, may therefore be at a disadvantage when it comes to evaluating the quality of an opportunity. We therefore propose the following: P3. Habitual entrepreneurs will be more effective in identifying valuable opportunities than their novice counterparts.

Opportunity Exploitation While existing research on entrepreneurial cognition may explain how entrepreneurs identify opportunities and how there may be differences between habitual and novice entrepreneurs in this respect, minimal attention has been paid to why opportunities are exploited once they are identified. The use of

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heuristic-based thinking may also have implications for the exploitation of opportunities. Heuristic-based thinking may allow individuals to overcome barriers more effectively. For example, the representativeness heuristic can enable decisions to be made without having complete information. Since the execution of an entrepreneurial idea often takes place in an uncertain environment, the representativeness heuristic may be critical to enable the entrepreneur to move forward. Similarly, the over-confidence heuristic may also encourage the entrepreneur to make the transition from opportunity identification to opportunity exploitation. The heuristic dimension of entrepreneurial cognition does not, however, explicitly explain why certain entrepreneurs may choose to become habitual entrepreneurs. Building on these theories of attribution and learned helplessness, we develop a simplified model that theoretically predicts whether or not an entrepreneur will remain as a novice entrepreneur (or indeed exit from the entrepreneurial career) or continue entrepreneurial activity to become a habitual entrepreneur. Once the entrepreneur has exploited his/her initial entrepreneurial opportunity, he/she will at some point evaluate the venture with respect to its performance. Based on the entrepreneur’s mode of causal attribution we may anticipate the effects on the individual’s entrepreneurial career. Figure 1 provides an overview

Fig. 1. The Impact of Attribution on the Decision to Become a Habitual Entrepreneur.


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of the model. The model highlights several stages involved in the decision whether or not to become a habitual entrepreneur. The first stage involves identifying the perceived outcome of the entrepreneurial venture and determining whether it is a success or a failure. The second stage involves interpretation of the outcome and identification of the causes of the success or failure (causal attribution). The third stage relates to the behavioural response and outcome. Individuals will persist in an activity if they attribute the reasons for their success to internal, stable, and intentional factors while attributing their failures to external, variable, or accidental factors (Gatewood et al., 1995). Having attributed the outcome to a particular set of causes, however, the entrepreneur may respond proactively (through further evaluation and learning) or reactively (subject to bias). Finally, the entrepreneur will make a decision as to whether he/she will continue entrepreneurial activity to become a habitual entrepreneur or remain a one-time (novice) entrepreneur. These various stages are discussed below. Stage 1: Perceived Outcome of Entrepreneurial Act Following exploitation of an initial entrepreneurial opportunity, the entrepreneurs will at some point evaluate the venture with respect to its performance. This performance may be evaluated in numerous ways. For those who initiated the venture primarily motivated by financial reward for example, the venture’s success may be valued in terms of financial performance indicators. Some entrepreneurs may be motivated by other criteria and hence may, for example, evaluate the venture in terms of personal satisfaction. McGrath (1999) defines failure as the termination of an initiative that has fallen short of its goals. Gimeno, Folta, Cooper and Woo (1997) presented a model in which the decision to terminate a venture depends on an entrepreneur’s own threshold of performance which is determined by human capital characteristics such as alternative employment opportunities, psychic income from entrepreneurship and the switching costs involved in moving to alternative occupations. Irrespective of how it is measured, however, the entrepreneur will decide whether the venture is to be deemed a success or a failure. The entrepreneur will then attribute this success or failure to various internal or external factors. Stage 2: Causal Attribution As intimated above, individuals may attribute different causes to a particular outcome, which may in turn influence their subsequent behavior. The success or failure of the venture may be attributed to internal causes (e.g. skills and intelligence) or external causes (e.g. market conditions, regulatory changes) (Zacharakis et al., 1999). Internal attributions may be associated with individual

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ability or effort, while external attributions are associated with task difficulty or luck (Heider, 1958). Once a causal attribution has been sought and identified, the entrepreneur may consciously or subconsciously respond in a number of ways. Indeed, the term explanatory style has been used to explain how individuals will explain success and in particular failure (Abrahamson, Seligman & Teasdale, 1978). These explanatory styles and their impact on subsequent behavior are discussed below. Stages 3: Behavioral Responses and Outcomes Attribution of Success: If the entrepreneur attributes their success to internal causes, we propose that there may be two behavioral responses. Firstly, the entrepreneur may not truly evaluate the causes of the success and may do so due to self-serving bias or overconfidence. In turn, the entrepreneur may not objectively evaluate the experience and identify lessons to be learned from that experience. The perception of success may have positive and negative elements. Sitkin (1992) argues that success may be helpful in a number of ways – the rewards of success may stimulate confidence and persistence, increase the coordinated pursuit of common goals and enhance efficiency. Success is thought to stimulate persistence not simply because individuals are rewarded for success, but also because it provides a secure and stable basis for launching future activity (Weick, 1984). Sitkin (1992) also argues, however, that a number of liabilities may be associated with success, which may take the form of complacency, restricted search and attention, riskaversion and homogeneity. If an entrepreneur does not objectively evaluate their success therefore, he/she may be prone to these liabilities. While they may wish to replicate their success, they may find themselves sticking with their winning “formula” even though the circumstances may have changed (what Sitkin, 1992, refers to as “homogeneity”). These liabilities may be particularly apparent when the entrepreneur relies on and is unable to switch out of heuristic-based thinking. On the other hand, if the entrepreneur is objective about the experience and attempts to learn from it, he/she may further evaluate the cause of success. As discussed earlier, attribution theorists suggest that decisions subsequent to the causal attribution may be influenced by additional characteristics of the cause – such as stability and controllability. If the entrepreneur identified the cause of success as being unstable, he/she may be reluctant to repeat entrepreneurial activity. Alternatively, if the entrepreneur feels he/she has significant control over the cause, they may choose to continue entrepreneurship. In contrast to the concept of learned helplessness, individuals susceptible to learned optimism (Schulman, 1999) may be more likely to view causes as controllable and unstable (this is in contrast to learned helplessness when causes, particularly negative ones, are seen as stable and uncontrollable).


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Where causes of success are attributed to external factors, we may expect differing behavioral responses depending on the extent to which the entrepreneur objectively evaluates the experience. If the success is considered to be due to an external factor and the entrepreneur displays patterns of behavior resembling learned helplessness, as highlighted above, they may feel they have no control over the cause. Faced with the belief of lack of control, the entrepreneur may be reluctant to repeat entrepreneurial activity in fear that the external causes may not be present a second time round. In cases where the entrepreneur displays learned optimism and conscious learning, however, the response may be different. If the entrepreneur views the cause as being unstable but controllable, the entrepreneur may choose to continue entrepreneurial activity. On the other hand, if the entrepreneur identifies the cause of success as being due to unstable and uncontrollable factors, then he/she may choose not to pursue a second venture. The differing causal attributions of success lead to an array of behavioral outcomes. Therefore, the following propositions are derived: P4. Those novice entrepreneurs who attribute their success to internal factors will become habitual entrepreneurs. P5a. Those novice entrepreneurs who attribute their success to external factors and who are susceptible to learned helplessness will remain novice entrepreneurs. P5b. Those novice entrepreneurs who attribute their success to external factors and who are able to clearly evaluate and learn from their experience will either; (i) remain a novice entrepreneur if they perceive to have no control over the cause or; (ii) become a habitual entrepreneur if they perceive to have control of the external cause. Attribution of Failure: Attribution theories have been most commonly applied to negative outcome situations. Faced with a particular negative outcome, once again we may expect entrepreneurs to vary in terms of their explanatory styles. If the entrepreneur attributes the cause of failure to internal factors and is susceptible to learned helplessness they are unlikely to engage in a subsequent venture and in most cases will choose to exit from the initial venture. We may anticipate a similar response even if the locus of the cause is external. If, however, the entrepreneur is not subject to learned helplessness and is able to objectively evaluate the venture, he/she may choose one of two options. In the event that the entrepreneur identifies the cause as being unstable and controllable (such as lack of skills), he/she may choose to do something about this cause (e.g. attend training courses, bring in a

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partner). Indeed, Sitkin (1992) argues that failure represents a “clear signal” that facilitates the recognition and interpretation of otherwise ambiguous outcomes. Further, with failure, old ways of thinking and acting may be shaken and new ways may be developed (Louis & Sutton, 1991). In contrast, however, the cause of failure may be viewed as being stable and uncontrollable, in which case the objective entrepreneur is unlikely to engage in subsequent entrepreneurial activity. We may expect a similar response if the cause is attributed to external factors. Entrepreneurs susceptible to learned optimism (Schulman, 1999) and hence persistence that attribute the cause of failure to external factors may hold the view that a change in the external environment could allow them to succeed the second time round. The above discussion suggests that: P6a. Those novice entrepreneurs who attribute their failure to internal causes and who are susceptible to learned helplessness will remain novice entrepreneurs. P6b. Those novice entrepreneurs who attribute their failure to internal factors and who are able to clearly evaluate and learn from their experience will either; (i) remain a novice entrepreneur if they perceive to have no control over the internal cause or; (ii) become a habitual entrepreneur if they perceive to have control over the internal cause. P7a. Those novice entrepreneurs who attribute their failure to external causes and who are susceptible to learned helplessness will remain novice entrepreneurs. P7b. Those novice entrepreneurs who attribute their failure to external factors and who are able to clearly evaluate and learn from their experience will either; (i) remain a novice entrepreneur if they perceive to have no control over the external cause or; (ii) become a habitual entrepreneur is they perceive to have control of the external cause. The above discussion suggests that entrepreneurs, faced with a particular outcome will behave differently in their decision to pursue an entrepreneurial career. These differences can be explained by the attribution and learning styles adopted by the entrepreneur. Those entrepreneurs who decide to subsequently become habitual entrepreneurs will do so by identifying further entrepreneurial opportunities. To do so, the entrepreneur may rely on heuristics and their mental schemas discussed in the previous section. Experience may have a significant contributory influence


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on the identification of subsequent venture ideas. This may take various forms such as broadening the entrepreneur’s mental schema (since this is influenced by experience and accumulated knowledge). Further, there is evidence to suggest that due to their track record, habitual entrepreneurs may also find themselves in a situation where an opportunity is brought to them (Wright et al., 1997a, b).

DISCUSSION AND CONCLUSIONS In this study, we have synthesized a human capital and cognition perspective to explain the emergence of different types of entrepreneurs. The study defines human capital in a broad sense to incorporate the cognitive styles utilized by entrepreneurs. Entrepreneurial experience is often considered an important component of an entrepreneur’s human capital and hence subsequent activities. The extent to which entrepreneurs can translate previous ownership experience into higher subsequent entrepreneurial (and organizational) performance is likely to depend on a number of intangible considerations such as cognition and learning. It is suggested that entrepreneurs may adopt different cognitive approaches when interpreting events and making decisions. Two broad categories of cognition have been highlighted: heuristic-based (i.e. automatic) thinking and systematic (i.e. rational) thinking. Entrepreneurial cognition is often associated with heuristic-based thinking. While heuristic based thinking has its merits, particularly under conditions of uncertainty it may lead to a number of errors and biases in decision-making, such as over-confidence and representativeness. Systematic thinking can overcome some of these biases. It can, however, often be timely and costly. Further, heuristic-based thinking can facilitate the identification and exploitation of entrepreneurial opportunities. Habitual entrepreneurs, as a result of their strong entrepreneurial cognition may be particularly effective in the identification of entrepreneurial opportunities. Based on their entrepreneurial cognition, we proposed that habitual entrepreneurs would search for less information but would identify a greater number of opportunities in a given period. Further, it was argued that habitual entrepreneurs would be more likely to identify opportunities of superior quality. While entrepreneurial cognition may explain an entrepreneur’s tendency to identify opportunities, it does not explicitly explain why certain entrepreneurs embark on subsequent ventures while others do not. The cognitive and learning strategies utilized to evaluate and learn from experiences may influence the decision of an entrepreneur to become a habitual entrepreneur. Drawing on attribution theory, we explain why certain entrepreneurs will select continued entrepreneurship (i.e. habitual entrepreneurship) while others will choose to remain one-time

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entrepreneurs. Attribution theory was used to explain the tendency to exploit opportunities. Our analysis highlights that causal attribution itself is subject to potential bias. For example, an entrepreneurs who was successful the first time round may attribute this success to his/her own ability, when in fact external factors may have had a crucial role in the success. Failure to acknowledge this may have a negative effect on subsequent ventures, if the entrepreneur is susceptible to overconfidence. The impact of experience per se on overcoming the problems associated with biases and heuristics is debatable. Bazerman (1990) suggested that experienced decision-makers may not explicitly know why and how they do what they do. If experience is not truly evaluated, it becomes simple feedback that is interpreted with limited awareness. Some entrepreneurs may reflect and consciously evaluate their previous business ownership experiences whilst others do not. If experience is translated into expertise, decision-makers have a conceptual understanding of what constitutes a rational decision-making process. Most notably, it can be used to avoid biases. Further, expertise may facilitate the switching of cognitive gears (Louis & Sutton, 1991) from heuristic-based thinking to systematic thinking where appropriate. Our analysis suggests several areas for future research. Since entrepreneurs may be seen as “idiosyncratic” and “path-dependent” units under the human capital perspective, there is scope for understanding this heterogeneity. Exploring entrepreneurs as a complex set of resources and capabilities is likely to aid our understanding of entrepreneurship. Most notably, the approach is likely to be of great use in understanding which path entrepreneurs take (i.e. strategies) and how this will affect their performance. This chapter has attempted to highlight that while human capital is crucial in determining the viability and nature of the entrepreneurial act, it may serve as a barrier if the individual experience biases his/her thinking and learning. Furthermore, depending on the environmental conditions faced by entrepreneurs, human capital may erode over time or with changing circumstances. The entrepreneur must, therefore, develop the necessary skills, resources and capabilities to renew their human capital base in order to maintain/obtain a sustained competitive advantage. While this may be relevant for entrepreneurs generally, in terms of venture survival, it may also be important in the context of habitual entrepreneurship where the entrepreneur may be carrying out a subsequent entrepreneurial act. Additional entrepreneur-level as well as firm-level studies are required to explore the relationship between entrepreneurial human capital (and its development and deployment) and the competitive strategies pursued by different types of entrepreneurs and organizations. Additional research is also warranted focusing on how entrepreneurs learn and how they use their experience-based knowledge. In order to take advantage of


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the efficiency benefits that heuristic-based thinking can offer, it may be important to understand how entrepreneurs can (and should) switch from one mode of thinking to the other. An appreciation of these issues is likely to require in-depth exploratory qualitative research. Also, there is a need to explore the extent to which entrepreneurs adopt heuristic based information processing or systematic information processing with regard to the entrepreneurial process. There is, therefore, a need to examine the link between the extent to which entrepreneurs adapt (or learn) from their previous entrepreneurial experience. Studies suggest that even in the learning process, entrepreneurs may be prone to biases (e.g. attribution bias). There is evidence to suggest that individuals can be taught to overcome various decision-making biases with the potential for improving subsequent performance (Koriat & Goldsmith, 1996; Strack & Hannover, 1996). High levels of deliberate practice, associated with informative feedback, opportunities for repetition and opportunities for correction of errors, may increase an individual’s awareness, and may induce non-biased learning (Ericsson, Krampe & Tesch-Romer, 1993). Further, there may be broader benefits to society as a result of developing expertise. Knowledge may be easier to transfer, whereas “mindless” learning from experience is difficult to communicate (Bazerman, 1990). Where heuristic-based thinking is used, such “mindless” learning may be commonplace. An understanding of habitual entrepreneurs compared with novice entrepreneurs has implications for the investment behavior of financial institutions and for policymakers and practitioners providing support for entrepreneurship and economic development. From a policy perspective therefore, there may be scope for assisting entrepreneurs in overcoming detrimental biases and barriers to subsequent success. To encourage best practice, the resources (such as skills, competencies, networks, etc.) accumulated and leveraged by successful habitual entrepreneurs need to be identified and disseminated. As intimated earlier, unless it is understood how and why entrepreneurs behave the way they do, the transfer of knowledge is prohibited. In order to address this research gap, studies conducted in a variety of industrial, locational, national and cultural settings need to carefully examine the human capital and cognitive processes of habitual entrepreneurs compared with novice entrepreneurs and their implications for opportunity recognition, information search, opportunity exploitation and ultimately entrepreneurial performance. In order to examine, in particular the cognitive dimension of human capital, researchers can draw upon existing studies from psychology, management and entrepreneurship. Measures are already available that operationalize various aspects of entrepreneurial cognition, in particular in the area of biases and heuristics (Forbes, 1999). Further, a number of learning inventories have been developed to distinguish individuals on the basis of their learning preferences and styles (e.g. Kolb, 1984). Entrepreneurial experience should be captured not

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just in terms of whether the entrepreneur has prior entrepreneurial experience but in terms of the magnitude and nature of the experience. Stuart and Abetti (1990) detected that their composite measure of entrepreneurial experience (measured in terms of the number of previous ventures and the role played in them) was the only factor that significantly explained variations in the selected performance indicators. In distinguishing experience from expertise, expert information processing theory can provide insight into how experts think and behave. In addition, an examination of the literature on meta-cognitive knowledge (i.e. knowledge about an individual’s cognitive processing, including awareness of thinking resources and capabilities, culminating in the ability to direct the learning process (Metcalfe & Shimamura, 1994; Nelson, 1992)) may prove useful in distinguishing expertise from simple feedback experience provides. A variety of techniques can be used to measure the dimensions of opportunity identification and exploitation discussed in this study. Numerous measures have been used to examine the sources and intensity of information search (Cooper, Folta & Woo, 1995; Kaish & Gilad, 1991). In evaluating the quality of an opportunity, a selection of measures may need to be used. Fiet and Migliore (2001) use a panel to rank ideas using four criteria from the resource and competence literatures that reflect the capacity necessary to generate a sustainable competitive advantage and above average earnings. Using a panel of experts ranging from academics, expert entrepreneurs and financiers, it may be possible to identify a list of attributes associated with a valuable opportunity. Chandler and Hanks (1994) use a six-item scale to measure the quality of an opportunity. The nature and amount of resources utilized to initiate the venture may also be an indicator of its potential value, particularly in terms of financial and human capital. The amount of initial finance invested in the business may at least give some indication of the initial scale of the venture (Cooper, Folta & Woo, 1995). The willingness of external financiers may also be an indicator of potential value. In this paper we have focused on the simple dichotomy between novice and habitual entrepreneurs. Research has suggested that there may be important within-group differences regarding novice and habitual entrepreneurs. Some habitual entrepreneurs may exhibit serial behavior, exiting one venture before entering subsequent ones, while others may develop a portfolio of contemporaneous ventures (Westhead & Wright, 1998). Similarly, while some novice entrepreneurs may only ever exploit one venture, others may go on to become habitual entrepreneurs. Further research might also usefully examine the extent to which these within group differences are associated with different types of cognitive processes and learning. Further, while we have assumed that entrepreneurship may involve the start-up of new ventures, or the purchase or inheritance of an existing business, empirical


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evidence suggests that both novice and habitual entrepreneurs may cite different motivations for business ownership (Westhead & Wright, 1998). These motivations have been found to range from wealth creation to autonomy and ensuring family security. Not all business owners will be wealth creators. Additional research is required to examine the extent to which motivations for business ownership are influenced by the entrepreneur’s cognition and how these motivations affect entrepreneurial behavior. As becoming a habitual entrepreneur takes place over time, there is a need for research in this area to be conducted on the basis of longitudinal analyses. Longitudinal samples are subject, in particular, to major problems regarding sample attrition. However, this may be less of a problem for theory building purposes. Longitudinal analysis may be especially important for analyzing those novice entrepreneurs who may or may not go on to become habitual entrepreneurs.

NOTES 1. We acknowledge that not all business ownership will involve wealth creation. This is evident from the motivational diversity highlighted in a number of empirical studies (Birley & Westhead, 1990; Westhead & Wright, 1998). In this study, however, we assume that business ownership involves wealth creation (whether this be in the form of startup, purchase or inheritance of a business). This is consistent with Hawley’s (1927) work where he argued that ownership rights are crucial for undertaking entrepreneurship, since they allow the entrepreneur to make decisions about the coordination of resources to gain entrepreneurial rents, in return for absorbing the uncertainty of owning those resources. We return to this issue in the conclusion. 2. Osipow (1973) introduced an open systems model of careers in the 1960s, while Ronstadt (1988) independently developed his work on the corridor principle in the 1970s. Katz (1992), integrating the two, developed a psychosocial model of employment status choice. 3. Eliot Jacques developed a model of bureaucracy which incorporated small firm creation. This model was based on the cognitive differentiation approach of psychoanalyst Melanie Klein. His work has been argued to have influenced other scholars exploring entrepreneurship through a cognitive lens such as H. Levinson and A. Zalenznik and more recently Kets de Vries and Danny Miller. 4. Some novice entrepreneurs become habitual entrepreneurs. We can reasonably speculate that these “transient” novice entrepreneurs will exhibit a reliance on entrepreneurial cognitive processes exhibited by habitual rather than one-time novice entrepreneurs (i.e. individuals who will only ever own one independent business). Over time, “transient” novice entrepreneurs who benefit from learning will develop a knowledge base similar to a habitual. Their cognitive processes will, therefore, enable them to have ownership stakes in more than one venture. 5. Where schema are defined as “dynamic mental models that represent an individual’s knowledge and beliefs about how physical and social worlds work” (Gaglio & Katz, 2001, p. 97).

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OPPORTUNITY DEVELOPMENT: A SOCIO-COGNITIVE PERSPECTIVE Alice de Koning INTRODUCTION Over the last ten years, researchers have increasingly focused on the pursuit of opportunity as one of the central acts of entrepreneurship. This chapter proposes a model of opportunity recognition which emphasizes the process through which entrepreneurs interact with their social contexts to develop opportunities, that is, to develop and shape ideas into attractive opportunities. The central research question is “how does an individual use his or her social context to recognize opportunity?” The question can be re-phrased in two parts, highlighting the two sides of the influence process. First, how do the people around the individual affect both the entrepreneurial thinking process and the opportunity ideas? And second, how does the individual structure his or her social context and use the people surrounding him or her for recognizing and pursuing opportunities? Granovetter (1985) argued that economic activity is embedded in a social context. The implication of being embedded may be both limiting and enabling. Studies of entrepreneurial networks have shown that entrepreneurs’ networks may play a significant role in giving advice, providing resources, access to expertise, and numerous other aspects (e.g. Aldrich, 1999; Hansen, 1999; Ostgaard & Birley, 1996; Starr & Macmillan, 1990). Social context also defines the rewards, and therefore may shape the personal incentives of the people who recognize opportunities (Davidsson, 1995). Thus, an individual’s social network should have a direct or indirect impact on opportunity recognition. With the diversity of


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network studies now available as a basis for further investigation, the theoretical link between opportunity recognition and social context is now being explored. In this study, I specifically link insights into social context to the cognitive process at an individual level, to contribute to our understanding of the process of opportunity development. By bringing social context to the question of opportunity development, I try to enhance the perspective on the entrepreneur as an actor in a process leading to the emergence of a firm, thus linking the recognition and pursuit of opportunities into an interdependent process. This approach helps connect research on opportunity recognition and firm emergence. I also hope to create a model that may be applied to the ongoing development and growth of a firm. By focusing on the entrepreneurial actor in a social context, we may be able to generate insights common to opportunity orientation in both corporate and individual entrepreneurship. In this chapter I present a framework of opportunity development as a sociocognitive process. The framework is based on insights generated through two phases of exploratory field research with mostly successful multiple entrepreneurs (serial and portfolio, cf. Westhead & Wright, 1998). These entrepreneurs were selected because their many successful new ventures suggested some expertise in opportunity recognition. The field work was used to ground the development of a tentative descriptive theory of opportunity development (Eisenhardt, 1989), rich in detail and scope. The goal of the study was to propose a perspective on opportunity recognition which places the entrepreneur in his social context and takes into account the time required to develop ideas.

Opportunity Recognition Opportunity recognition is used in two ways in this chapter. First, opportunity recognition may be used to describe the general area of research into how, when, and why opportunities are recognized. Second, as noted below, the term opportunity recognition may refer specifically to the cognitive experience of noticing an opportunity in the market. In this paper, unless otherwise specified, I use opportunity recognition to refer to the general area of research. Opportunity recognition has been investigated under a number of different terms, often reflecting underlying differences in research question and concept. For example, Gaglio (1997) uses the term opportunity identification, following Long and McMullan’s concept of creative insight, and proposes the framework of cognitive schema to integrate findings on information search, biases, alertness and other influencing factors to push forward the research agenda on opportunity recognition. Shane and Venkataraman (2000) prefer the term opportunity

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discovery, following Schumpeter’s usage (see also Shane, 2001). Using Hayek’s conceptualization of markets (1988), they argue that opportunities exist in the market due to localized information distribution, and that an entrepreneur moving through this information landscape may discover specific opportunities. Hills, Lumpkin and Singh (1997) and Hills and Shrader (1998), working more clearly in a Kirzner paradigm, use the term opportunity recognition, and emphasize the moment of insight into the commercial value of an idea. Their research uncovers a number of characteristics and antecedents of opportunity recognition. Not only are there various terms for opportunity recognition used within entrepreneurship research, there are also important differences underlying the semantics. For example, one perspective suggests that opportunities exist in the public market and are recognizable to any one with eyes to see, as is implied in the concept of alertness (e.g. Hills, Lumpkin & Singh, 1997). At the other extreme, opportunities are so rooted in the prior knowledge, local information, skills or resources of the individual that the opportunity does not exist for more than a few people who are able to effectuate the opportunity into a venture (e.g. Sarasvathy, 2001; Shane, 2000).1 Despite the differences in terminology and possible underlying conceptualizations, the empirical research findings are complementary, suggesting that the nature of opportunities exists on a continuum from market opportunities for any interested party to recognize to highly personalized opportunities rooted in an individual’s location or prior knowledge (cf. Dew, Sarasvathy, Velamuri & Venkataraman, 2002). In this chapter, the data gathering and analysis tend toward the second view of opportunity and opportunity recognition, using a more personalized concept of opportunity. In the context of research on opportunity recognition, Bhave (1994) used the term opportunity recognition to describe both a process of turning an idea into a business concept, and as a triggering event that identified the initial idea. O’Connor and Rice (2000) illustrate the complementary perspectives of event and process in their study of breakthrough innovations in large companies, which they argue requires a series of opportunity recognition events by project leaders as part of a broader development process, before an innovation can reach the market. Inspired by Bhave (1994), I use the term opportunity development to describe the process of developing initial opportunity ideas towards a business concept. By initial opportunity idea, I mean the first idea or observation that is perceived as a potential opportunity by the actor. This idea could be a focus on a customer problem, a technology or technique with potential for developing applications, or the desire to leverage a resource. A business concept defines how a new product or service creates value added for targeted customers, and how the business will capture some of that value added as profits. The process of opportunity development may be incomplete at the time of launching a venture, if the ideas are

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not shaped fully into a business concept. While recognizing that these definitions include a number of relatively problematic terms and assumptions, the primary emphasis in this chapter is not on the specific type or timing of the conceptual milestones achieved, but rather on the iterative evolution of the opportunity.

THE RESEARCH METHODOLOGY: TWO PHASES OF FIELD WORK AND ANALYSIS The field study was designed to explore the process of identifying and developing new business opportunities. Exploratory research is appropriate given the relative lack of previous research in the field of opportunity recognition at the time the study was conducted, and I choose to analyze extreme cases, for which qualitative methods are deemed more appropriate (Birley, Muzyka, Dove & Rossell, 1995). For this study, the extreme case of successful serial entrepreneurs was chosen as the basis of the sample. The other key exploratory aspect of the field study was the decision to collect data through open-ended questioning designed to encourage the entrepreneurs to generate detailed narratives that could include types of data not anticipated by my theoretical training. The primary objective for the field research was to collect narratives of pre-venture processes from entrepreneurs, including both pursued and abandoned opportunity ideas. Through an iterative process of data analysis and literature review, beginning early in field study process, the opportunity development model was developed. The field study provides a basis for theoretical generalizability beyond the unique characteristics of the study participants. Although extensive examples and anecdotes are drawn from the data to build the model of opportunity development, these citations cannot be construed as evidence supporting the empirical generalizability of the model. Without the insight gained from the entrepreneurs’ narratives, however, the combination of concepts drawn from research in opportunity recognition and social context would have been unlikely. The focus of the field study was successful serial entrepreneurs, because they seemed to have a well-honed ability to recognize opportunities (Macmillan, 1986; Starr & Bygrave, 1991). Each one would also have several opportunity experiences. The data gathering was collected in two phases, which followed guidelines for bounding data gathering, and thus also facilitating analysis (Miles & Huberman, 1994). In the first phase, successful serial entrepreneurs with two or more successful ventures were included; in phase two greater emphasis the screening characteristics were refined to include entrepreneurs with at least three successful ventures, more successes than failures, and perceived significant increase in personal wealth. Thus, an entrepreneur with marginal wealth gain or who chose to conceal his financial


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Fig. 1. Sample Structure: Participants of Phase Two.

success for whatever reason would not be included in this study. The comparative sample was intended to contrast success and failure, and many businesses versus one business (see Fig. 1). The goal of these screening procedures (and eliminating a few cases after interviews) was to avoid ambiguous cases, allowing the analysis of theoretical extremes (Birley et al., 1995; Eisenhardt, 1989; Yin, 1984).

Study Participants For both phases, the participants were identified largely through people with some connection with INSEAD or its faculty. These ranged from MBA alumni, to occasional guest lecturers, to participants in the owner director program. In all cases, the individuals were screened for whether they “fit” the defined case. In some cases, interviews were organized before a true evaluation could be completed. In these cases, the interviews were conducted, but the interview data was ignored (e.g. in the case of entrepreneurs who were under 30 years of age). In addition to INSEAD contacts, potential participants were suggested by colleagues in other institutions and by study participants. The rather international sample should not confound data, as entrepreneurs tend to be quite similar despite nationality (McGrath & MacMillan, 1992). In phase one, nine successful serial entrepreneurs and one successful private investor in a wide range of service and manufacturing industries were interviewed.

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The participants interviewed were males between the ages of 40 and 65. In phase one, the nationalities were quite mixed: one each lives in Britain, the U.S., Belgium, Canada, and the Netherlands, and five in France. Of the French residents, one was an American, and another French-American. I chose to continue using data from the interview with a private investor because he was instrumental in the opportunity development process at science-based start-ups that were still defined products and markets (cf. surrogate entrepreneurs, Franklin, Wright & Lockett, 2001). Data gathering in phase one was conducted in 1996 and no further participants were interviewed when conceptual saturation was reached, and more in-depth analysis and conceptual development was needed to motivate the next phase of field research. Phase two included male entrepreneurs of the same range of ages focused on low and medium technology manufacturing, to avoid possibly confounding elements such as degree of “novelty” of the opportunities (Bhave, 1994). The phase two participants were interviewed in 1997 and 1998. Of the serial entrepreneurs, one each lived in Mauritius, Sweden, U.S.A., Switzerland, two in England, and four in France. The five single business entrepreneurs were located in Nigeria, Austria, and France. The comparative analysis provides insight into differences in frequency of opportunity development that leads to venture creation. Only one failed serial entrepreneur was interviewed – more were identified, but their contacts preferred to protect them from direct enquiry. This limits the ability to tease out distinctions between high and low quality opportunity development, separate from the issue of frequency. Some indicators of the participants of the two phases of the field research are summarized in Tables 1 and 2. The “ignored” participants are noted in the Table 1. Participants of Phase One. Code A B C D E F G H I J

No. of Business

No. of Success

No. of Industry

23 15 10 3 8 9 8 7 8 7

20 8 8 3 6 7 7 7 8 7

2 5 3 2 3 2 2 1 2 4

Country England France (U.S.) France U.S.A. France (U.S.) France France Belgium Netherlands Canada

Approx. Age 45 45 55 65 55 45 40 55 60 65

Number of Industries: This is a strictly subjective evaluation, essentially reflecting the entrepreneurs’ own characterisation of the differences between their businesses. Entrepreneur C is the private investor; all others are successful serial entrepreneurs.


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Table 2. Participants in Phase Two. Code

K L M N O P Q R S T U V W X Y Z AA

No. of Business

No. of Success

No. of Industry

3 2 2 1 4 6 3 1 4 1 4 15 2 1 8 53 3

2 2 2 1 3 6 3 1 3 1 3 15 2 1 8 52 2

2 1 1 1 2 1 1 1 2 1 1 3 2 1 1 6 2

Country

Switzerland Nigeria France Austria France Mauritius England England France France France Sweden Norway France Gulf States U.S.A. England

Approx. Age 45 40 45 55 40 45 65 30 55 60 35 50 30 60 40 60 40

Included in Analysis?

Category

Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes

SSE OBE OBE OBE SSE SSE SSE SSE OBE SSE SSE OBE SSE SSE FSE

SSE: successful serial entrepreneur; OBE: one business entrepreneur; FSE: failed serial entrepreneur. Number of Industries: This is a strictly subjective evaluation, essentially reflecting the entrepreneurs’ own characterisation of the differences between their businesses. Number of companies: In the case of Entrepreneur Z, several companies operated essential the same business in different geographical areas. Entrepreneurs L & M are counted as a successful one business entrepreneur, because the first venture is an inherited family business and shared with other family members.

summary charts below, to allow the reader to evaluate the overall mix. Brief case summaries are in the appendices. Where specific cases are cited in this paper, the entrepreneurs are identified by letter codes, reflecting the concern of some entrepreneurs for confidentiality.

Data Gathering In both phases, the primary objective for the field research was to collect narratives of pre-venture processes. Building a conceptual framework during and after the first phase of interviews resulted in more focused interview protocols, including some directed lines of questioning to collect types of contextual data that did not naturally emerge while the participants told the narratives of specific opportunities. Each interview began with an explanation and discussion of the research

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questions. After summarizing the entrepreneurs’ career path, the participant and I jointly selected some a few opportunity development processes (typically 2–4 situations), both those pursued and those abandoned and with some concern for timing within the entrepreneurs’ career and for variance in types of opportunities. The interviews concluded by reviewing the interview protocol, to ensure that all the major issues would be covered. In fact, the discussion of major failures and successes, and how those ideas emerged, usually covered the questions quite naturally. Most interviews lasted 1–2 hours, and on-site interviews sometimes included plant tours. The interviews were most often recorded by hand, although in phase two, a tape recorder was also occasionally used, when appropriate and if the participant felt comfortable with recording. Field notes of the interviews were typed up promptly, within 24 hours for phase one, and within one or two days for phase two, except during a particularly productive week when several interviews were conducted and the volume of typing required extra time. In phase two, the field notes were sent to the entrepreneurs for immediate feedback. This process was designed to allow for efficient and timely verification of the details. The oral histories of the entrepreneurs allowed me to reconstruct longitudinal data on specific opportunity development processes and careers. Retrospective narrative does have inherent biases of recall which must be frankly recognized. Holstein and Gubrium (1995) suggest shifting the emotional frame and redirecting attention to different content can lead to participants recalling more details. Also, Hansen (1995) and Curran, Jarvis, Blackburn and Black (1993) found that event-based questions elicited detailed and seemingly more accurate information. Thus, even given the biases of retrospective narrative, these factors can give some confidence that the data is sufficiently rich and reflective of entrepreneurial processes to allow insights and model building.

Analyzing the Oral Histories The analysis of the interview data was conducted through iterative comparisons of the case histories, allowing similarities and themes to emerge from the data. Initially broad categories were used to organize the data (cf. Glaser & Strauss, 1967). These themes pointed to a broad range of published research, which was used to explain and structure further analysis. The iterative process of data analysis and literature review helped eliminate some issues where extensive existing research obviated the value of exploratory research. It also indicated new themes and deeper understanding of certain factors (such as the role of networks), which led to a fresh analysis of the narratives of phase one. As the analysis of phase one drew to a close, a model of opportunity development was constructed


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of two levels of analysis, three cognitive activities and four clusters or roles in the social context. Data analysis for phase two followed the process discussed by Eisenhardt (1989) in her synthesis of earlier guidelines for comparative case research. The focus of the first order analysis was to validate the proposed opportunity development model and related propositions through checking whether the cases replicated the proposed relationships. The process began by looking within the successful serial entrepreneur cases, looking for similarities in practices and context. The tentative conclusions generated by these comparisons then were checked against the comparative groups. The process was necessarily iterative, given the inherent challenges in analyzing qualitative data of large samples. The second stage of analysis was oriented to looking more closely at the dynamics and the interactions between cognitive and network activities implied by the opportunity development model. The final model now includes two levels of analysis, four cognitive activities, and four clusters or roles in the social context, with one role excluded and another added after analyzing the full data set. Some comments on these shifts in the model are included in the concluding discussion. The process of gathering and analyzing the data was conducted by the author, and is therefore somewhat idiosyncratic in both coding the data and in the choice of related research literatures. The interim results were regularly “tested” on colleagues, and their suggestions contributed to the overall process. For example, colleagues pointed to several recent papers in network analysis linking together earlier work on source of relationships, nature and stability of strong ties, and its impact of venture growth related to resource access, that were especially valuable new sources for informing analysis (e.g. Ostgaard & Birley, 1996; Young, 1998). This suggests a possible problem with replicating the results, again emphasizing the exploratory nature of the study, and that the primary goal of the study was to generate theoretical insight rather than empirical generalizability. The integrative approach used to develop the model drew inspiration from the empirical data, but relies on existing research to develop the arguments. One shortcoming of this approach is that the underlying data which inspired this perspective is less evident; an advantage is that the discussion shows linkages between research streams and suggests possible new interpretations throughout.

A SOCIO-COGNITIVE PERSPECTIVE OF OPPORTUNITY DEVELOPMENT The opportunity development model is presented in three steps. First, the four cognitive activities are defined and explored. Second, four roles of the social context

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Fig. 2. Cognitive Activities and Roles in the Social Context.

are identified. In the third step, the interaction of the cognitive activities and roles in the social context are explored. The interactions in the opportunity development process are summarized in Fig. 2. From the perspective of the entrepreneurial career, information scanning occurs largely within the network of weak ties provides opportunities for opportunity identification and for solving specific problems in the opportunity development process. Concept creation occurs within the network of strong ties as the entrepreneur uses thinking-through-talking with his or her inner circle. From the perspective of the specific opportunity or venture, the entrepreneur seeks needed information from a network of entrepreneurs and experts whose knowledge makes them useful. These relationships are usually weak ties also. Concept creation is deepened through a process of accessing resources and building an action set.

Cognitive Activities of Opportunity Development As opportunities are developed, the entrepreneur engages in two categories of cognitive activity, information gathering and concept creation. The distinction between information gathering and concept creation (or information processing) is somewhat artificial, but is in fact a common simplifying assumption made in artificial intelligence, and applied to human intelligence (see Simon on creativity, 1984). In this framework, information gathering is oriented to collecting bits of


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information, which are processed (sorted, combined, etc.) to create new concepts or ideas, a specific combination of the blocks. The two elements of the opportunity development may also be described as a process of collecting, combining and configuring a specific set of information bits into a unique business concept. Information gathering includes information scanning and seeking. Information scanning refers to generalized information gathering which may be spontaneous or planned. Information seeking is a more targeted search of information. Concept creation is the cognitive process which creates the (re)combination of information or convergence of ideas. Two specific activities are highlighted in concept creation: thinking-through-talking and assessing resources. Both of these emphasize cognition in relation to other people; strictly speaking, many more cognitive activities could be indicated, but the opportunity development framework limits its focus on the two activities most clearly identifiable in the narrative analysis. Information Scanning Information scanning is a constant human activity. We notice our environment, and pick out the bits that seem most relevant and interesting. Scanning can be planned or spontaneous, and is driven by conscious and unconscious priorities. Hamrefors (1998) argues that spontaneous scanning activity is driven by cognitive filters, which reflect the biases and interests of the scanner and may include strictly personal interests or potential for profitability, depending on the individual. The scanning may seem somewhat random, if the entrepreneur has broad interests. In the case of entrepreneurs with many existing activities in one particular industry, they may notice new opportunities “in the line of duty” (corridor effect, Ronstadt, 1988). The general orientation to that industry, leads to a relatively predictable spontaneous scanning pattern. In fact, many of the entrepreneurs in this study had more than one business in the same industry, with related concepts or customers or technology. In the case of successful entrepreneurs, opportunity-oriented information scanning seems part of their “nature” (Hills, 1995). The entrepreneurs’ cognitive filters or schema are more opportunity or business oriented than most people. Thus, successful entrepreneurs seem to pick out entrepreneurially relevant information from their environment. The research on cognitive biases in entrepreneurs could be partly reinterpreted as descriptions of scanning filters. For example, Sarasvathy, Simon and Lave (1998) found that entrepreneurs are more oriented to emphasizing low risk than high return in making business choices. Gaglio and Taub (1992) found that entrepreneurs had a very strong tendency to personalize business problems, using themselves and their own abilities as the framework for evaluating business situations, rather than the analytic models of management studies. Both

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these studies suggest that entrepreneurs have different filters than managers in organizations, affecting what information is noticed, how it is valued, and how analyzed. Planned information scanning may be described as a deliberate attempt on the entrepreneurs’ part to change their filters. Sometimes the entrepreneurs in the study made a conscious decision to start a new business, even though no opportunity had been identified. (Cf. Zeitsma (1999) finds motivation precedes opportunity recognition.) In this case, the narratives show that information scanning was launched as a deliberate strategy. For example, Entrepreneur O described how he and his brother agreed that their current (inherited) business had too low margins, and that they would never be able to be more than a commodity broker (they also produced a commodity product which supplemented their trading business, but not with significantly better margins). The brothers discussed what sorts of businesses might be interesting, and began scanning every avenue for possible opportunities (both start-up concepts and takeover targets were considered). Survival motivated their scanning. Likewise, as young entrepreneurs, Entrepreneurs U and P wanted to avoid working for someone else yet had few resources. They “aggressively” scanned their environments for possibilities. The initiating opportunity recognition event described by Bhave (1994) or opportunity identification (Gaglio, 1997) comes out of the information scanning. In other words, a convergence of data is “suddenly” recognized as an opportunity, something worth considering. Entrepreneur Q’s partner, who ran the building supply shop, read an article about increasing asthma problems related to dust mites, and realized there was an opportunity in easy-to-install floor coverings to replace carpeting. Entrepreneur U returned to his home in southern France after a long trip in Japan, and noticed that the golf courses and charming chateaux could be combined into affordable golf holidays for Japanese tourists. (He never pursued the concept – later research showed that a new entrant into tours had high upfront costs.) These cases strongly suggest spontaneous information scanning. In the case of Entrepreneur O and his brother, the planned scanning strategy turned into opportunity development when they recognized a takeover target which was the right size (small), complexity (quite simple) and situation for them. They still had to develop an opportunity out of this target, but the process started with recognizing these elements and an attraction to the product (chocolate truffles). Judged retrospectively, some opportunity development processes do not begin with a specific event of opportunity identification. Entrepreneur Q describes his entry in to cement post manufacturing as the result of wanting to extend the product range in his London lumber store. This time, though, the product extension issue got out of hand as a result of his search for suppliers. (A summary of his narrative runs like this: I want to have cement posts in the store; supply


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is uncertain and the price is too high for many good uses of cement posts, yet the idea is great; are there other ways of making cement posts cheaper? Other suppliers? This beginning led to a fact-finding tour in France and Belgium.) If we see the decision to extend the product line as the beginning of the opportunity development process, then it began with a fairly normal purchasing event. In any case, an initiating vision or opportunity identification event is not essential to the process, but effective information scanning is. As the idea evolves in the early stages of opportunity development, the need for firmer information to develop the original opportunity ideas become clear. One effect of realizing that more information is required, is that the entrepreneur’s cognitive filters are changed by the new interests generated by the developing opportunity, thus changing the scanning. This change in cognitive filters often leads to the serendipitous discovery of relevant information. To use a metaphor, the entrepreneurs’ antennae are tuned to receive the right broadcasts. In some cases, the information discovered can lead to a dramatic shift in focus. For example, Entrepreneur U discovered an opportunity for developing karaoke in France, because he was attuned to information that would help create attractive package holidays for Japanese. Also, at this point, the entrepreneur also begins seeking information in earnest. Information Seeking Information seeking is directed at answering specific questions or enhancing contextual knowledge. Without the information, the opportunity either never progresses or is poorly conceived. It is more proactive than scanning and is driven by a specific and conscious agenda. The seeking activity may be more or less precise, in the sense that the entrepreneur may have a relatively broad question (Entrepreneur Z asked an engineering consultant how the sewage treatment industry worked) or a very specific question (later, he asked the consultant about the utility of a specific type of low cost piping). Whatever the question, the entrepreneur does not wait for the answer to appear from casual and unplanned encounters, but actively seeks out the necessary information. For the entrepreneurs interviewed, even the least successful of them, information seeking usually meant contacting people, and did not include a trip to the library or other data search. Any business opportunity concept has many gaps and more information can always be deemed necessary. The data in this study echoed Vesper’s early research on patterns in questioning by entrepreneurs is an unusual study that tried to create a systematic understanding of the process of information seeking (1991). Vesper developed a list and typology of questions which entrepreneurs used to get information, emphasizing the early stage or “filtering” questions. As Vesper noted, the entrepreneurs clearly prioritize their information needs, shifting from one area to

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another. Thus, the entrepreneurs note only not the gaps in their ideas, but also prioritize their information seeking to fill the most important gaps. For example, once Entrepreneur Z had identified a customer (his heuristic for deciding to launch), his next priority was to identify his downside risks and use information seeking to find ways to minimize those risks. Many times, the entrepreneurs said that they acted while still unsure about many things, but had confidence in their ability to respond to those issues because they had good information on the critical elements. Information gathering is a necessary part of developing opportunities, providing the fodder for creating the details of the business concept. Parallel to gathering information, the entrepreneurs worked on concept creation in using conversation (thinking-through-talking) and assessment of potential resources for the business, as discussed below. The concept creation activities would in turn identify specific problematic or interesting elements, which would return attention to information seeking. Thinking-Through-Talking, Or Using Conversation as a Tool for Concept Creation Thinking-through-talking is a core part of concept creation. In opportunity development, the initial opportunity idea needs to be clarified and fleshed out. Talking about opportunity ideas to others, entrepreneurs are forced to flesh out the vision and to describe details. Conversation makes the entrepreneurs think through building details and arguments. Writing to an imaginary audience (e.g. a business plan) may perform the some of the same function, but none of the entrepreneurs in the sample made use of writing to think. Thinking-through-talking suggests that the very process of articulating ideas often leads to actually forming the ideas. Shotter suggests, “even in the sphere of business our ways of talking work to produce rather than simply to reflect the objects of which we talk” (1993, p. 101). In his book, Shotter argues that managers should be understood as authors of “conversations for action,” who through their conversation with others create a reality in which they and others act. This goes beyond Weick’s perspective, “how can I know what I think until I see what I say” (1989), which suggests that the articulation of ideas makes us conscious of our thoughts. Once consciousness is achieved, we may evaluate the concepts and move forward in shaping these ideas. Shotter goes further, suggesting that the rules of conversation impose a logic on our messy thoughts, and thus talking is thinking in a very real sense (1993). In this study, the perspective of Weick dominates, but Shotter’s argument that conversation is the means through managers produce history (action) has relevance, both for thinking-through-talking and assessing resources. The process of articulation, and of trying to make the listener understand, forces the entrepreneurs to become more precise and explicit in their thoughts. As


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the conversation proceeds, it may not be possible to fully articulate the new idea, and thus the entrepreneur realizes more work on the idea is needed. One way of characterizing the opportunity development dialogue is that the entrepreneur is forced by the listener to follow the rules of conversation (a language game, in philosophical terms). The rules of conversation enforce a kind of logic on the creative thought. Part of the listener’s job is to force the entrepreneur to follow the logic of conversation, thus challenging the entrepreneur to think more clearly. The listener’s other role is challenge the ideas themselves, for example challenging market assumptions, and questioning whether customers would pay for the product. Weick does not emphasize the role of the listener in this process, but simply argues that it is hearing one’s own talking that helps thinking. Given research in psychology which suggests that writing down one’s thoughts can be as effective as talking to a therapist (Persaud, 1998), Weick’s perspective may be sufficient. Thinking-through-talking is most obvious, and possibly most important, in the cases of long-term partnerships. Entrepreneur O, for example, has always worked with his brother, sharing an office, overhearing all phone conversations and running their companies together. For them, talking is essential to thinking if only because their thinking is as much team work as any other aspect of the business. Entrepreneur G, an inventor, is also tied to a “career” partner, a sales and marketing expert with no understanding of science. Entrepreneur G relies on conversation with his partner to extend his thinking, precisely because his different expertise forces G to find new ways of expressing his ideas so that his partner understands. Often this understanding takes months of dialogue, said Entrepreneur G, but he had learned through past mistakes the value of taking all the time necessary because successful communication usually signaled successful invention. Three of the successful serial entrepreneurs in phase two would frequently downplay the importance of these conversations, because they do not seem to contribute much to the opportunity development, but the narratives have details suggesting otherwise. Entrepreneur Z denied speaking about business matters with his wife, for example, but then described several ventures by saying “my wife and I decided . . .” In contrast, Entrepreneur J seemed much more conscious of the role and value of these early conversations, noting several people that acted as his confidants. None of the one-business entrepreneurs seemed to engage in thinking-through-talking. This cognitive activity, judging by the analysis of the case stories, is the most ambiguous of all four. As noted above, three of the successful serial entrepreneurs seemed to discount the value of talking things through, to take these conversations for granted. Perhaps we all do not sufficiently appreciate the role of dialogue in thought. Only two serial entrepreneurs in the two phases of field research had a self-conscious attitude to this cognitive activity, though not necessarily seeing it as opportunity development or thinking but more part of a close personal

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relationship. Nonetheless, the opportunity narratives showed such processes at work. Six serial entrepreneurs, for example, talked with their parents or siblings often in the early phases of their career, yet these were most often viewed as advice sessions (e.g. Entrepreneurs P and U). In some cases, the very negative attitudes of these listeners led an entrepreneur to discount the positive cognitive value of the debate. Entrepreneur J took a contrary position, in that he specifically mentioned his old accountant and wife, pessimists both, as essential partners in the opportunity development process. Assessing Resources Assessing resources plays a critical part in concept creation and contributes to information seeking. Assessing resources begins when the details of how to exploit the business opportunity are not clear, when no business strategy and organization has been fixed. When asking questions and discussing things with colleagues is not enough to advance the opportunity development process, identifying, co-opting and assessing resources may be the next step or alternative (cf. Starr & Macmillan, 1990; Stevenson & Jarillo, 1990). The entrepreneurs put detail into the business opportunity through assessing resources, moving the organic big-picture vision towards a more detailed plan for the business. By searching for resources, assessing both what they want and what they think they can get on what terms, entrepreneurs explore the possibilities of the opportunity idea. In this context, the term “resources” refers to the broad range of necessary resources for starting a business, from expertise and customer contacts, to plant and equipment and funding. Important examples for assessing resources include identifying and meeting with a potential customer, or identifying a critical asset (e.g. plant site). Entrepreneur S actually secured a large packaging order with a three-month delivery date, before looking for a plant or funding. He believed he could serve a high margin niche with innovative packaging design and manufacture, and first secured the technical expertise of an engineer and the revenue in a large contract. Securing these two critical assets was important to his thinking process, helping him define more precisely his target market and product strategy. Assessing resources also may play an important role in information seeking. The successes and failures in response to the entrepreneurs’ attempts to gain resources become very valuable information, as strong signals that supplement information that gained through direct questioning. For example, the search for funds to start an R&D project, may provide better information on how “sellable” the concept is, partly because the request for a commitment of funds induces greater honesty and less social niceness than simple questions. Also, identifying customers by name may be a better estimate of potential market acceptance, than


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an analysis of macro market demographics. Thus, the process of identifying and assessing the resources required and available also contributes to the information gathering aspect of opportunity development. Entrepreneur P experienced the importance of assessing resources as he moved from acting as an agent to building his own manufacturing business. The process of assessing resources not only confirmed the potential of the business, but also helped focus on the product line strategy. Entrepreneur Z used the identification of customers as his key market research – if there were customers, he was sure he could develop a profitable business concept. Equally important, however, is the impact of assessing resources on concept creation. Assessing resources is essential to the creative process, and is more than getting better data to use in creating the concept. The resources available are assessed in terms of the opportunity, and directly impact the development of the opportunity. The business concept is shaped by and perhaps even changes dramatically, according to how much and what is available. Assessing resources provides an important bridge or transition between opportunity development and venture launch. One factor in assessing resources on opportunity development is resource parsimony. Starr and Macmillan (1990) noted that entrepreneurs responded to constrained resource availability by creatively figuring out how to do more with less. One example of this parsimony was Entrepreneur Z’s use of a signed lease to secure a loan to start his sewage treatment business. Bankrupt only two years earlier, he otherwise would have obtained no credit. The credit constraint led to re-conceiving his business concept, and rewriting the terms of his customer contracts to allow him to use the contracts as collateral. Likewise, the credit constraint forced him to become very creative in minimizing his plant costs – this was achieved by asking “stupid” questions about industry practice during a meeting with an expert. The net result was a business that was much more profitable and required less capital than his earlier concept. Resource parsimony is not the only aspect of assessing resources that directly impacts the refining process. Other possible factors may include resource bounty. For example, the willingness of a business contact to give away rights to a patent gave Entrepreneur V a windfall opportunity to exploit an invention for checking rubber quality that his company developed for a client. Entrepreneur Z’s lease contract mentioned above was granted on the condition that he also provided servicing. Although initially he hesitated, the “condition” quickly became an important profit-generating part of the business following a little research (How much work is involved? How hard is it to manage? Can the plant be redesigned to further simplify servicing?), and again rethinking the business concept. In the contrasting cases of one-business entrepreneurs seemed to abandon the process

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of developing their ideas if the immediately available resources did not fit their initial assessment of risks and what they needed. For example, Entrepreneur T briefly investigated the possibility of acquiring a small related business, but quickly abandoned the project when he decided that the political constraints would inhibit his ability to actually run the plant efficiently. This judgment may have been accurate – the previous owner had gone bankrupt – but he did not seem to consider possible solutions or resources to overcome those constraints. The Interdependence of Cognitive Activities As noted in the beginning of this section, the distinction between information gathering and concept creation is somewhat stylized. The four cognitive activities do not happen independently of one another, in a logical and serial fashion. Rather, the process is typically initiated by identifying an opportunity idea in through scanning, and the other activities begin when appropriate, ending with rather intense work to “finish” assessing resources. The activities overlap, intensify and become more interdependent as the entrepreneurs become more and more committed to the opportunity development process and move towards refining the business concept. While the focus of the process becomes more narrow and precise in the sense of knowing what product-market is being targeted, the cognitive activities generate a broader concept with greater detail. This is particularly evident in the similarities between information seeking and assessing resources. Seeking information may begin by asking about the potential market size is, and move to identifying specific customers. At this point, the search for more information about the potential market and the assessment of resources such as customers overlap dynamically. Similarly, the process of co-opting and assessing resources is an important part of thinking, because it allows the entrepreneur to test some key assumptions or critical variables that are identified as the business concept is being developed. Nonetheless, it is useful to distinguish these four cognitive activities, to tease out the differences between gathering more building blocks versus constructing the building (to use metaphoric terms).

Opportunity Development Roles in the Social Context Having described the opportunity development process in terms of the cognitive activities, we now turn to the social context. In Fig. 3, the social context is pictured as concentric circles around the entrepreneur: the inner circle, action set, and network of weak ties and experts. In this section the four roles or clusters in the social context are defined. After discussing the social context at a general level, we return to how social context is linked to the cognitive activities.


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Fig. 3. Social Context and Cognitive Activities.

The literature on entrepreneurial networks typically uses the typology of networks of strong ties and weak ties developed in sociology. Strong ties are defined as relationships which have frequent contact and close personal ties to the entrepreneur. Most studies on entrepreneur’s networks do not make the distinction between the inner circle and action set that is proposed here. One reason may be that cross-sectional surveys cannot easily distinguish between the entrepreneur and his or her firm (cf. Dubini & Aldrich, 1991). In fact, entrepreneurs of smaller firms seem to identify their personal networks with the firms’ network as they choose not to delegate networking type tasks (e.g. Birley et al., 1991; Ostgaard & Birley, 1996). The result is that two levels or units of analysis are collapsed, with a resulting fuzziness in some of the results. It is when researchers look at serial entrepreneurs that these problems become most obvious. Following phase one, this study identified the two separate levels of analysis; Westhead and Wright came to the same conclusion following their study of serial and portfolio entrepreneurs (1998). Without making the distinction between entrepreneur and venture, all the strong ties may be treated equally, or else some personal ties may be ignored although they are significant for the entrepreneur’s career, because they are not directly involved in the current venture. The Inner Circle The inner circle describes the people close to the entrepreneur personally. The narratives suggest that the entrepreneurs have a small circle of people with whom they discuss things regularly. These people are not necessarily entrepreneurs, and in fact may have no entrepreneurial orientation. On the contrary, in some

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cases they are valued for the counter or negative perspective, as for example Entrepreneur J valued his wife and pessimistic accountant. Where the phrase “inner circle” is misleading perhaps, is that it is too suggestive of a formal council, or a group of people who recognize themselves and the others as being influential. The inner circles described by the successful serial entrepreneurs were not a group, but rather a collection of individuals. As individuals, they act as intelligent and trustworthy listeners who are willing to engage in tough discussions with the entrepreneur. To the extent that the case interviews were able to identify specific inner circle members, the number of people seemed to range from one to four people. The inner circle often included one or two family members or old friends; it often also included professionals with whom there was a long-standing relationship. For example, Entrepreneur O related to two brothers, one a partner and the other a neighbor, plus an accountant and a valued executive in his company. The inner circle seemed to be stable over time, even when the entrepreneur moved from one enterprise to the next. Although the relationships of the inner circle are stable, they are not static. As with all people, close relationships can evolve over time. Some changes in the inner circle were observed and seemed logical. For example Entrepreneur F’s most valued confident in his younger years was his father, but he was later supplanted by a partner. Relative to other people in the entrepreneurs’ network of strong ties, however, the inner circle seemed very stable over longer time periods. As the cases suggest, the relationships in the inner circle often were rooted in social relationships, but over time evolved to include relationships originating in the business task environment. This finding echoes Johannisson’s rare longitudinal network analysis of a sample of Swedish entrepreneurs’ top five contacts (1996). Johannisson found that the more successful entrepreneurs, over a seven year time period, were more likely to have the same people as part of their top five contacts. He also found that over time the successful entrepreneurs had an increasing number of close friendships that began as task relationships. The Action Set The action set describes a network of strong ties, like the inner circle, but is built by the entrepreneur to pursue a specific venture opportunity. The term was used in this context by Hansen (1995), who defined the action set as the resource providers for a venture who form a strong network around the entrepreneur as the business opportunity is shaped and moves towards start-up. The action set, Hansen found, may include people providing funding, professionals with critical expertise, customers, and other types of resource providers. Building the action set is clearly closely related to assessing resources, and this connection and its implications will be discussed in the next section. As the venture moves closer to


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start-up, the members of the action set build stronger ties with the entrepreneur and with each other in the sense of high frequency of interaction with the entrepreneur and each other. The entrepreneur recruits the members of the action set, in response to the evolving shape of the business opportunity, as it is being developed, from existing strong or weak ties, or even strangers. This study found the action sets were typically recruited from the weak ties, and thus within the opportunity development process the roles of certain people shifted. The entrepreneurs relied somewhat on existing relationships to build the action set, for example recruiting people who had worked with them in previous ventures. In comparison to the inner circle though, the action sets of the entrepreneurs seemed much more transient group of people, and in all cases were chosen for their appropriateness to the specific venture concept. At its best, the action set reflects (and helps create) the entrepreneur’s best possible business concept. Within this group of people, the entrepreneur is very much the visionary leader, who enacts his vision and creates a high level of commitment from participants (Filion, 1991). Gartner, Bird and Starr (1992) suggest that entrepreneurs “act as if,” and thus induce the emergence of new ventures. In recruiting the action set, the entrepreneurs created a team as if the business concept was viable, and in that process built a viable concept. Recruiting and building the action set seemed to be an important task for the entrepreneurs within the process of opportunity development. The most striking example of the venture-oriented action set in the field study was Entrepreneur A. Most of his businesses involved purchasing an existing business and turning around the operations. Before making a decision on whether to acquire an interesting target, he made sure that critical parts of the action set were organized. In one situation, for example, this included a customer who historically had purchased 30% of the plant’s products and a production manager with an interest in part ownership and skill in running daily operations. Similarly, Entrepreneur Y had a strong focus on identifying the people to help run a new venture before making a commitment to start, particularly when locating in a new country. Entrepreneur Z always had at least one partner identified and at least one customer. In all, only four successful serial entrepreneurs seemed to have a weaker emphasis on building an action set, and these all had fewer than five ventures. Of the one-business entrepreneurs, only one seemed to recognize the value of building a strong action set. The Network of Weak Ties From the perspective of opportunity development, the network of weak ties is important for two purposes. First, the network of weak ties is a major source

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of information. Second, the network of weak ties is an important source of potential resource providers, and therefore also a source of potential action set members. Entrepreneurship research investigating networks often emphasizes the role of the entrepreneur as an information broker in a large network of weak ties (e.g. Krackhardt, 1995). A weak tie is an acquaintance or contact with whom one meets or speaks rarely, and from whom useful or fresh information may be obtained (Granovetter, 1973, 1975). An alternative theoretical explanation for the information value of weak ties is Burt’s structural holes argument (1992). Burt argues that access to fresh information is less dependent on the frequency of contact, and more on the density of ties between networks of contacts. He defines an entrepreneur as an information broker with ties into many unconnected networks, holding an informational advantage of others in his or her network. The surveys on entrepreneurs’ networks have tried to capture the number of useful weak ties (how many people have you talked to about your business in the last six months, besides the top five contacts) as well as the networking activity (e.g. how many hours a week do you spend networking, and are any networking activities delegated to employees in the company) (e.g. Drakopoulou Dodd & Patra, 2002; Johannisson, 1996; Ostgaard & Birley, 1996, among many others). The results show that entrepreneurs initiate most of these contacts, suggesting a general pro-active approach to finding useful weak ties. The narratives of phase two, suggested that constant networking activity is required to create new ties and keep tenuous links to others. Dubini and Aldrich (1991) point out that over time, the weak ties in an entrepreneurs’ network are more likely to get to know each other, so that the information value of these ties decreases over time. In fact, the Aldrich, Reese and Dubini (1989) study found a correlation between increasing density in personal networks, and the amount of time spent on maintaining contacts versus initiating new contacts. In considering the network of weak ties, it is useful to consider the structure of the ties and the networking activities used to create new ties. The weak ties described in the interviews came from an number of sources. First, many ties were created through the “task environment,” that is through the normal operations of their business. In some cases, 5–15 years of corporate careers helped build a network of industry and professional contacts. Entrepreneurs P and H made particular use of these networks in their early years, while the fact that B, T and Z started their entrepreneurial careers around age 40, gave them quite powerful networks. In the narratives, a wide range of sources for the networks of weak ties were evident. Looking specifically at information “incidents” that impacted an opportunity development process, in addition to ties originating in the task environment, the major sources of useful weak ties seemed to be college


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alumni networks, military links and occasionally hobbies. College ties were a very common and important source of information, both undergraduate and graduate, but especially MBA. Military links figured in some cases, usually related to compulsory military service. Hobbies were more rarely a source of useful network contacts, though entrepreneur S was a skiing fanatic, and would meet useful people on the slopes. Over time, new ties tended to come from the task environment. In particular, all the entrepreneurs built strong industry networks, sometimes through dominating a relatively small market, sometimes through using the more formal means of trade associations and fairs. Networking activities ranged from trade fairs, to local business associations, to small informal events and parties. In the cases where trade fairs were a critical part of the entrepreneurs’ networking activity, it is not clear whether this bias reflects an industry imperative or the entrepreneur’s choice in networking activity. For example, Entrepreneurs I, M, and P specifically mentioned that they never missed trade fairs (respectively, in turbine engines, automotive and knitwear industries). Others also attended trade fairs, but did not emphasize their role. In addition, industry ties would be built through friendships developed in normal trade activity, or being active in boards. Many of the ties would be initiated by the entrepreneur, based on a specific and defined interest in a person’s expertise – chance plays a role in every person’s life, but was not relied on unduly (cf. Birley, Cromie & Myers, 1991, who found most contacts were initiated by the entrepreneurs, regardless of their overall success). The college alumni network showed surprising resilience: the contacts between alumni were infrequent, but regular enough to maintain links. Many of the entrepreneurs named former colleagues who were successful in a various industries who had proved a rich source of information and other new contacts. These contacts were often based on an MBA network, but also included les grands e´ coles (France’s elite undergraduate professional schools) or other undergraduate programs. These ties were maintained by attending alumni functions and arranging small informal parties or events. For example, Entrepreneur K organized a small annual golf tournament with three fellow alumni. Entrepreneur S lunched, dined and skied with many fellow alumni, both from his MBA and undergraduate years. In many cases, for example Entrepreneurs V and Z, new contacts were built through taking teaching positions at business schools. In comparing single business and serial entrepreneurs in phase two, a significant difference showed in the level of effort spent in maintaining these weak ties. Interestingly, phase two showed that the single business and failed serial entrepreneurs initiated distinctly less networking activity with their college network, a more dramatic difference than in networking with industry based networks. The

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single business entrepreneurs seemed to maintain relations with only a few old colleagues, and never attended alumni events. The failed serial entrepreneur in phase two, Entrepreneur AA, had annual contact with two undergraduate friends, and none with previous MBA colleagues except his one-time partner. Also, his industry networking was limited to occasional phone calls to a number of old contacts at related and customer companies. Network of Entrepreneurs and Experts A subset of the network of weak ties deserves special attention. Many of the entrepreneurs spoke of networking with other entrepreneurs and with recognized experts to advance their opportunity development process. Many of the entrepreneurs had relatively more frequent contact with other entrepreneurs, though not necessarily in the same industry. The data suggests that within a more general network of weak ties, the successful serial entrepreneurs also have a network of information brokers like themselves. In other words, they are adept at maintaining ties to others who also enjoy the role of an information broker with a network full of structural holes. To the extent that entrepreneurs are willing to reciprocate in sharing contacts and information, this more specialized network could lead to highly efficient access to an extremely broad network of weak ties. Note that the ties are still weak ties; that is they are not tightly linked in the sense of frequent contacts and overlapping friendships. This phenomenon was most evident in Entrepreneurs K, A and S. In fact, Entrepreneur S had a very high level of confidence that he could get any information or resource he needed through these contacts. In addition to other entrepreneurs, various experts also played a special role for opportunity development. Lawyers and accountants acted as weak ties who were information brokers. These contacts were less likely to be interested in pursuing opportunities themselves, but often could identify resources or takeover targets. Industry experts, for example specialized consultants, were another significant source of information. Many of these ties were initiated during the opportunity development process, in order to address a specific issue. Several examples from the narratives are cited in the next section. In some cases, these experts would eventually become part of the action set. Summary Comment on Social Context This section has built a terminology for discussing roles in the social context in terms of opportunity development: inner circle, action set, network of weak ties and network of entrepreneurs and experts. Many of the links between the cognitive activities and social context are implicit in the descriptions of the terms, but will be developed in greater detail in the following section.


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The definite distinction between the two types of strong ties became evident because the successful serial entrepreneurs seemed to feel free to seek the resources they wanted from their extended network of weak ties, and did not feel tied down to what is available “close to home.” Thus, the structure of social context proposed here may reflect a pattern unique to successful serial entrepreneurs. As a counter example, a more restricted use of social context was observed in African-Americans (Young, 1998), and Greek entrepreneurs (Drakopoulou Dodd & Patra, 2002), who tended to stick with family and close friends for advice and resources. In this study, I am found that at the individual and career level of analysis, the emphasis of the strong ties may be on the advice network (i.e. inner circle), while at the business or venture level, the strong ties may be more focused on resources. This particular aspect of successful serial entrepreneurs’ networking behavior would be worth investigating in a future empirical study.

Opportunity Development Interactions of Cognitive Activities and Social Context This section describes the relationship between the cognitive activities and social context, with a stronger focus on what seems to be the best practice of successful serial entrepreneurs. Note that these assertions should be viewed not as normative suggestions for practitioners, but as propositions for future research. The major emphasis in the discussion is not the linear order of the opportunity development process, but rather on the link between a specific activity and types of relationships in the social context. Briefly, the linkages between activity and social cluster are as follows. Information gathering, both scanning and seeking, was linked to the network of weak ties. Information scanning seemed much broader, at times almost random; while information seeking was more likely to target other entrepreneurs or experts. Thinking-through-talking, which begins early in the process, occurs most frequently and iteratively within the inner circle. In some cases, though, an entrepreneur may make unexpected thinking progress in a conversation with a relative stranger. Assessing resources is part of the process of building the action set. The action set by definition does not exist at the beginning of an opportunity development process, but is built during the process. Within each of the interactions of cognitive activity and social context, the narratives of the entrepreneurs gave further detail and more specific insights. Although the larger picture gives a strong framework for opportunity development, the details are also illuminating.

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Information Gathering in the Network of Weak Ties Effective information scanning seems to depend on a number of factors. According to cognition researchers, everyone engages in information scanning. The issue for opportunity development is how much new and useful information is gathered. A high level of networking activity is necessary, both to maintain and initiate new contacts. These two themes have been developed by previous entrepreneurship research (for review see Aldrich, 1999), within a broader context of entrepreneurial activity. With information scanning, cognitive filters must be oriented towards seeing market gaps, technological possibilities, etc. Because the cognitive filters may affect the contacts initiated or “scanned,” the filters not only affect the information noticed, but also the networking activity. In information seeking, successful serial entrepreneurs seemed inclined to emphasize personal sources of information, rather than other sources. The personal sources were almost invariably experts or other information brokers – their efficiency in finding information was evident. Scanning and Networking for “Fresh” Weak Ties Networking activity seems to be a critical factor in effective information scanning. It is clear from the case data that scanning by the successful serial entrepreneurs depended on regular contacts with people, more than printed sources. The range of activities cited was broad – from lunching regularly with former colleagues and new contacts, attending trade fairs with the express intention of meeting people for the first time (meetings may be planned or not), to participating in civic functions or donating time as board members. What was striking, comparing the most successful to the comparative sample, was the overall frequency and range of these contacts. Entrepreneur AA, the failed serial entrepreneur said he kept in touch with the industry by calling a few old contacts working for customers, and annual dinners with three old college buddies. Because he handled sales for his company, that task involved some travel and meeting strangers, but otherwise his scanning activities were quite passive and his social activities restricted to a small group of people. This pattern contrasted with many of the successful serial entrepreneurs, such as Entrepreneur S who maintained contacts with dozens of colleagues from previous jobs and schools, and was always meeting new people. The difference between efforts to find customers and networking for build useful contacts for building the business or finding information, was clear in the cases. As Birley et al. (1991) found, building a customer base may involve the same pro-active attitude and types of activities as building a useful network of weak ties, but the entrepreneur who did not balance efforts in both directions seem to lack growth potential.


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Success, both in performance and opportunity development, seemed related to the level of networking activity. From the perspective of network theory, the association is logical. If we accept that opportunity development depends on finding useful new information, then an effective network of weak ties is necessary. Over time, people who have a mutual acquaintance are more likely to know each other directly (cf. Aldrich, Reese & Dubini, 1989). Thus, to maintain an information advantage over the long term, the entrepreneur must engage in continual networking activity to refresh the network by building new weak ties. In the case of college alumni networks, the problem of staleness may decrease because the alumni often do not live in the same geographic area nor work in the same industries as their peers. The lack of proximity may ensure that these “old” weak ties maintain a high level of information novelty. Having a large network of weak ties is not a sufficient condition, however, for opportunity development. It is logically possible to have a large circle of contacts, and never notice or develop a good business idea. Porac, Baden-Fuller and Thomas (1989) found that many companies in the Scottish knitwear industry confirmed their beliefs about the market by restricting their information scanning to certain sources. Most striking was the tendency to use existing customers to analyze the market for their products, which clearly restricted any reinterpretation of their customer profile or evolution of their product lines. Their work suggests that cognitive schema or filters can become self-fulfilling prophesies, because of their impact on who is contacted. To take the entrepreneurs’ perspective, their cognitive filters have an impact not only on information scanning, but also on networking activity. One danger suggested by Porac et al. is that the network of weak ties may be too small or not diverse enough to provide new information or perspectives. Entrepreneurs in the sample differed in their networks of weak ties. Both the failed serial entrepreneurs and successful single-business entrepreneurs seemed to have significantly more restricted networks of weak ties than the successful serial entrepreneurs. Although his perspective had changed in the last two years, Entrepreneur N clearly relied on a few local industry contacts and his employees as his informants. Entrepreneur AA, as noted, relied on a small and very stable set of industry contacts for information, although for survival reasons he was actively searching for customers. Entrepreneur X became nostalgic when remembering his old MBA pals, but did not have any contact with any one of them in the last several years. Entrepreneur T was similar, with his contacts restricted to a few local entrepreneurs, his customer network (which required extensive travel), and other horse racing fanatics. None of these entrepreneurs seemed strongly oriented to moving outside their existing circle. These entrepreneurs contrasted strongly with the successful serial entrepreneurs who would comment that they had just spoken with a former colleague recently, or attended some business-oriented function,

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or other obvious places or means of networking. Their patterns suggested that a greater diversity was sought and maintained, although perhaps not consciously. The narratives seemed to suggest that real diversity must exist in the network of weak ties to actually challenge the entrepreneurs’ tendency to be stuck in restrictive cognitive filters. Information Seeking from Entrepreneurs and Expert The successful serial entrepreneurs seemed to use specialized experts and other entrepreneurs for information seeking, depending on the situation. The most striking example was Entrepreneur Z, who hired a consultant to tell him everything he knew about the sewage treatment industry. This incident was early in his entrepreneurial career, and soon after becoming totally bankrupt – the expense must have been significant. Yet interviewing an expert allows for a higher level of interactivity with the information. Because Entrepreneur Z felt free to ask “stupid questions” (his words), which sometimes surprised his informant, the answers he received tapped the person’s expertise (and even tacit knowledge) in a way that a database search could never do. For example, he asked if the plant had to be installed underground? The consultant was very surprised – at the time, no one had ever built above ground – but he was also expert enough to be able to say confidently it was only a problem of aesthetics. The expert’s answers gave Entrepreneur Z ideas for refining the business concept of leasing sewage treatment, in ways that dramatically increased his profitability (by simplifying construction and later deconstruction of the plants). The narratives did not include other examples of actually hiring experts for information seeking, but in many cases an expert was relied on. For example, Entrepreneur V used a consultant while developing process equipment business, although he was able to “pay” for the time and expertise with goodwill. The successful serial entrepreneurs emphasized their direct approach to information seeking – they find the right person, and ask their questions. Entrepreneur Z, for example, would even hire a consultant at an hourly rate, in order to learn about an industry that he was interested in. Entrepreneur K would invariably call a friend or use friends to make the necessary contacts. He observed that, unlike his manager friends, he would never waste a whole evening having dinner to get a few bits of information. A direct approach was preferred. The entrepreneurs seemed to ask for information without hesitation – and a few noted that they themselves would never hesitate to answer other people’s questions either. The contacts used for information seeking were rarely casual meetings. Entrepreneur I used trade fairs to meet people, but made sure to arrange meetings before leaving home. Entrepreneur S used his alumni network extensively, but initiated the contacts when and where he needed them, not relying on special alumni events to supply contacts. This may seem


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obvious, but the single business successful entrepreneurs seemed to initiate far fewer contacts with experts and relied more on their existing networks or on chance. Information seeking is much more targeted than scanning, because the entrepreneur has specific questions to answer and problems to resolve. The value of the network of weak ties for information seeking is both for identifying and/or gaining access to people with right expertise, and for having direct contacts that can provide the necessary answers. All the entrepreneurs almost invariably used people to answer their questions – databases and library resources were rarely used. Even industry data, which probably was published somewhere, was sought through people and through conversations. This bias to personal information sources may reflect the low-tech nature of most of the entrepreneurs’ businesses in this study. Alternative explanations for using experts are that interviews are often more time efficient than library searches in new topic areas, and no public source can be as up-to-date nor have the tacit knowledge of an expert active in their field. Entrepreneur K observed that he often got information from “lone wolves” like himself. In talking to other entrepreneurs, which all the successful serial entrepreneurs seemed to do, they often were interested in checking information of a more generic nature. For example, locations for plants or hints about specific people, would be freely traded information. Many of the successful serial entrepreneurs had a network of powerful entrepreneurs and professionals from whom they got information readily; some of the contacts overlapped with their alumni network. These contacts most likely were part of the entrepreneurs’ more specialized network of information brokers, each with their own strong network of weak ties. In addition to their existing contacts, they did not seem to hesitate to call some one, if they wanted their views. Entrepreneur A’s narrative, for example, is sprinkled with comments like “I learned from . . .” and “I called . . .” As suggested in the studies of networking activities (e.g. Johannisson, 1996; Ostgaard & Birley, 1996), the entrepreneurs were much more likely to initiate the majority of their business ties, rather than to rely on people approaching them. This proactive stance suggests a strong task orientation, perhaps with specific information seeking questions in hand, influences how successful entrepreneurs build their networks. Unlike Birley et al.’s (1991) Northern Ireland entrepreneurs, however, there seemed to be little hesitation among the successful serial entrepreneurs to maintain contacts with other entrepreneurs. To summarize, the successful serial entrepreneur’s information seeking, in using experts and entrepreneurs, seemed to have a greater potential to collect better information. First, a person who works in a field is more likely to have current and up-to-date information on trends, than a database which often has delayed availability. Second, by interviewing an expert, the entrepreneur is using an more efficient information retrieval tool. And finally, by learning through conversation,

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the entrepreneur is more likely to access tacit knowledge in addition to explicit information. The value of targeted, prioritized information seeking is directly related to the entrepreneurs’ process of concept creation. By balancing information seeking with thinking-through-talking and later assessing resources, the entrepreneurs focus on the business opportunity and building a concept, and not on broader and vaguer questions of industry and market. Thus, without a strong process of concept creation (both thinking-through-talking and assessing resources), information seeking could become an endless journey.

Concept Creation and the Network of Strong Ties Concept creation is linked to an entrepreneur’s strong ties, in contrast to information seeking. Taking a longer-term perspective than many entrepreneurship studies, however, we can discern a pattern that distinguishes between the inner circle and action set. The entrepreneur is far more likely to engage in repeated, open-ended discussions about opportunities with longer-term strong ties. The implication is that these people know more and see more of the entrepreneur than any one else. In contrast, the action set is a more instrumental group of strong ties. Members of the action set may be old friends or colleagues, but their role is based on their potential contribution or commitment to the specific business. The distinction between these types of strong ties appears to be robust, based on an informal meta-analysis of existing network analysis studies. In this sub-section, I develop observations on the interaction between the activities of concept creation and the strong ties, with arguments and details that suggest why the successful serial entrepreneurs seem to enjoy a more effective opportunity development process. Several factors are discussed which may explain “best practice” in concept creation. Themes relate to the inner circle, include persistence in the relationship, character of the interaction, trust, and expertise of the individual members. Themes on the action set include the dynamic process of building the action set, the source of the members (i.e. largely weak ties), the strong task orientation in building the action set, and the relationship of assessing resources and building the action set to refining the business concept. The sub-section ends with some comments on the entrepreneurs’ credibility and its impact on recruiting an action set. Thinking-Through-Talking and the Inner Circle When thinking-through-talking about an opportunity helps develop the concept, the character of the inner circle is often important. Most significantly, many of the


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successful serial entrepreneurs rely on the willingness of the listeners to ask tough questions and honestly report what they do or don’t understand. The members of the inner circle are protective in the sense that they are willing to spend time and energy to listen, and to take the ideas seriously, however roughly articulated. They are challenging in the sense that they force the entrepreneur to formulate her ideas more clearly, more logically. The critical aspect of the inner circle is the persistence of the relationships and the persistence of the dialogue. These two aspects allow opportunities to develop into business concepts, both in sense of increasing detail and in the sense of developing a holistic vision (as used by Filion, 1991). For Entrepreneur F, the process of building new opportunities based on innovation in process technology was a process of repeated dialogues between Entrepreneur F and his partner until complete clarity and understanding had been achieved. Entrepreneur F also relied on his father, who was also scientifically trained, and thus his perspective was valued for different reasons. Entrepreneur P observed that he chatted with his father frequently – in the context of the narrative it is clear that his father helped him get started as an entrepreneur, and also remains critical as a frequent and trusted dialogue partner. Entrepreneur J valued his accountant both for his pessimism and his loyalty. Entrepreneur V and his two brothers institutionalized the concept of an inner circle and thinking-through-talking in their week long “white smoke” council meetings. The meetings included any one with relevant expertise for the developing business opportunity, often the inventor and one or two managers with relevant manufacturing or marketing experience in related businesses, as well as the brothers. The meetings are designed to talk through all and any major issues of the business opportunity, with particular focus on the technology, market, and efficiency competitive advantages for the invention and the business. The meetings are held behind closed doors, and do not conclude until consensus is reached and decisions made. Yet the idea that entrepreneurs pursue thinking-through-talking with members of their inner circle, though intuitively appealing, is not unambiguously supported by the data. Many people, including the entrepreneurs in the study, have had the experience of talking about some problem or idea to a total stranger, and suddenly realizing the new formulation is a good way of articulating the ideas and have new insight in the business concept. The narratives suggest that several of the successful serial entrepreneurs perhaps did not have partners or other confidants. In fact, several entrepreneurs denied any need to talk to people – in Entrepreneur K’s words, he sees entrepreneurs as “lone wolves.” However, in most of these cases, a little later in the interviews one or more discussion partners is revealed. Most often, these inner circle members have no equity stake in the business, and no operating responsibility, which may explains why the entrepreneurs discount

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their significance for the opportunity development process. Lacking the unusual self-awareness of Entrepreneur J (who recognized the role of his wife for his own thinking, even when she was an at-home mother and housewife), the entrepreneurs perhaps tended to ignore their inner circle while telling their “business” story. In reviewing the field notes, it is difficult to discern significantly different patterns in inner circles between the different groups of entrepreneurs. Rather, the narratives of the comparative sample of entrepreneurs suggested a mix between having no apparent inner circle, and not taking the time to think or talk. In the case of Entrepreneur AA, the pattern seems to be more a lack of thinking-through-talking in general, even though over time he owned several businesses with different partners, most recently with an MBA colleague. His narratives often included the comment that he was too busy to notice something, or too busy to talk about issues. Only two of the single business entrepreneurs had partners, and they were both in family businesses. Thus, one suggestion of the data is that successful single business entrepreneurs tend to keep to themselves, not using the benefits of a partner or an inner circle for developing their ideas at all. Curran et al. (1993) noted that their field work using critical incidents to investigate network behavior, showed that half the entrepreneurs in fact talk to no one at all, even in the face of serious crises. Thus, the difference between successful serial entrepreneurship and one-business or failed serial entrepreneurs may be talking about issues versus not talking. Thinking-through-talking seems to have more impact on success and serial ventures, than the existence of the type of relationship that would allow such intense conversation. The ambiguity of the entrepreneurs themselves in giving their narratives, however, suggests that some caution in interpretation is necessary. Where interpretation of the narratives is clear, the members of the inner circle were either partners or else not involved in the businesses at all. In the case of the partners, they were usually very tight dialogue partners, and thus it often seemed the entrepreneurs restricted their inner circle to their partners. Otherwise, the entrepreneurs often seemed interested in inner circle members who offered some strong perspective or expertise. The professional qualifications or business expertise of the inner circle would help directly in the listening skills, providing a high level of intelligence and/or knowledge to the listening and questioning. In addition, however, their expertise would also allow them to provide timely and appropriate advice. It seemed that the inner circle is not only a circle of listeners, but also valued for offering timely advice, which re-directed the thinking process or provided needed encouragement. Among the inner circle members identified in the data, the most common professions are accountants or lawyers. Less common was engineers. The professionals often form a key part of the inner circle, perhaps because despite their expertise they are unlikely to become competitors. Another


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possibility is that these professionals are associated with the business(es), and thus have a solid background knowledge to enable discussion, yet because their role is neither as entrepreneur nor manager provide a more distanced perspective. These professionals often become a minor part of the action set also, and provide access to useful secondary networks. Even when the entrepreneurs’ businesses grow, they often continue to retain the same professionals, not for their services (which often are supplemented or even replaced to better serve the businesses’ needs), but for their character and relationship with the entrepreneur. For example, Entrepreneur Z and J used their accountant in this way, and Entrepreneur O had this type of tie with a lawyer. A few entrepreneurs did rely on employees in the firm as an inner circle. Entrepreneur Y was particularly remarkable in this respect: he has created a learning organization in which many people have their own inner circles. Discussion of any new idea is welcomed, and even if he personally hates an idea, his employees feel free to persist in discussing and exploring new opportunities. He himself has built a core of senior staff and line executives, who function as his inner circle. Entrepreneur N was very interesting as a successful single-business entrepreneur, in that he had recently discovered the value of delegation and of treating his top employees more as equals. Assessing Resources and Recruiting the Action Set Assessing resources and creating the action set are dynamically intertwined. More than any of the other roles in the entrepreneurs’ social context, the action set is created to respond to the opportunity being developed. As the business concept evolves, the process of recruiting the action set becomes less and less tentative, moving from networking to extracting commitments. The networking activity of recruiting an action set is a key part of the cognitive process assessing the resources. Recruiting the action set and assessing resources are concurrent activities, because both move the vision or idea, closer to the realm of reality. The action set seemed to be usually recruited from the network of weak ties in most cases. There were some cases, where the entrepreneurs relied on some repeated relationships. For example, some members of an action set may have cooperated in previous ventures (typically as investors), or they were also members of the inner circle, especially in the case of repeated partnerships. Entrepreneur E, for example, had started three of his businesses with the same partner. Although most of their business activities were separate and their major venture together failed, they were close friends and seemed to act as inner circle members for each other. In the sample as a whole, the proportion of overlap between the pre-existing strong ties and the action set seemed low, and in any one action set the majority of members seemed to be recruited from the network of weak ties. Because so

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much of the action set depends on the network of weak ties, clearly an extensive and well maintained network is an important aspect of building strong action sets. The lack of a strong network of weak ties may also partly explain why so many successful single business entrepreneurs did not pursue new business opportunities – even if the information gathering was sufficient to identify an opportunity, they were excessively limited in building an appropriate action set to manage risks and investments. The opportunity development narratives suggest that the process of assessing resources and recruiting the action set had a very strong instrumental or task orientation among the most successful entrepreneurs. The entrepreneurs emphasized the concerns or needs of the business opportunity in their drive to assess the resources, rather than allow the social relationships of their strong ties drive the range of resources recruited. In his longitudinal study, Johannisson (1996) observed that growth entrepreneurs built task-based relationships effectively, and often turned them into friends, while non-growth ones stayed “stuck” or embedded in their pre-existing social ties. The reason assessing resources and recruiting the action set are so critical to refining the business opportunity, is partly because surprise resource discoveries can open up new possibilities for exploiting the opportunity, and also because not getting an apparently essential resource forces the entrepreneur to think up new strategies and even new opportunities. A very important aspect of these resources is the customer – identifying and recruiting the support of critical customers can lead to a very powerful action set. Numerous opportunity development narratives seemed to gain steam as a customer was identified – Entrepreneur P’s buying agency, Entrepreneur V’s quality process robot venture, Entrepreneur Z’s sewage treatment business, Entrepreneur U’s French karaoke video production – the examples are throughout the narratives. The interaction of recruiting an action set and assessing resources is well illustrated by Entrepreneur S. Following his MBA graduation, he managed to get a large order for a unique style plastics container from a major cosmetics company. This order was obtained through a slightly misleading networking strategy (he pretended to interview for a job, until he was set up with the relevant Vice-President, and then switched to selling mode). During the following series of meetings, Entrepreneur S had to prove he could make the product, which he did with the help of an engineer he recruited. Once he had the order confirmed, he had two months to find a plant and equipment, to hire staff, and finalize the moulds. As he says, this was in the days before venture capital existed in France, when getting funding was very difficult – much more so than now, in his opinion. Through effective networking activity, and careful assessment of his options to ensure a profitable solution, he was able to find a plant to “borrow” and equipment to lease.


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The moulds he purchased with the pre-payment on the order. The whole process involved intense yet effective assessment of resources: depending on what funding and other resources he could find, he had to consider alternative strategies of taking an existing firm as a partner, or subcontracting the manufacturing. Neither of these strategies were ideal, because he might have lost his knowledge-based advantage in creating the unique containers and thus would have had a weaker business concept. The narratives contain many similar stories. Often, the entrepreneurs themselves seemed most intrigued by the process they went through in scrounging for resources, and adapting the projects accordingly. Obviously, the problems of too few resources were experienced by the successful serial entrepreneurs more often earlier in their careers. Finding the “right” resources continued to be an interesting challenge to the successful serial entrepreneurs. Nonetheless, this process of adapting to the circumstances was a consistent theme. Entrepreneur A, for example, mentioned that people specifically asked him to include him in the next project, which eased some aspects of assessing resources, but he did not accept their offer if they did not improve the business concept. In contrast, Entrepreneur T, who manufactured collapsible kayaks, seemed to reject business opportunities because they weren’t feasible, rather than considering ways to make changes in the concept. He apparently considered several small businesses in his region, but each one had problems or else he didn’t have the time or money right at the moment the opportunity was available. To a certain extent, these explanations are very reasonable, particularly since Entrepreneur T enjoyed a wonderful lifestyle that he would not want to jeopardize. But he also seemed to put very little effort into assessing the resources and considering other ways to shape the concept. The other single business entrepreneurs seemed similar, often lacking the patience and persistence to assess resources beyond their immediate circle and to adapt the business concept. From the perspective of analyzing sources of greater success in opportunity development, the successful serial entrepreneurs seemed to explore and assess a greater range of resources, and showed a greater willingness to refine the business concept appropriately through building an action set from a diverse network of ties.

DISCUSSION OF FINDINGS The exploration of interactions between social context and cognitive activity make clear that two different levels of analysis are relevant to opportunity development, the entrepreneurs’ careers and their ventures. Although the focus of the study is primarily on the process of developing opportunities, there clearly are aspects to

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managing or acting within the social context which must be considered in terms of the entrepreneurial career and not just the immediate situation of developing the opportunity. Interestingly, the distinction between an opportunity and the entrepreneurial career has not often been evident in entrepreneurship research. One result of studying serial entrepreneurs, with their many ventures, is that the different levels of analysis become patently clear (cf. de Koning & Muzyka, 1996; Westhead & Wright, 1998). In looking at the social context, the discussion so far suggests many ways in which the entrepreneur is affected by and affects the social context, which clearly would impact the opportunity development process. Many aspects of the social context, for example the network of weak ties, appear as constants or even constraints on the opportunity development process. These factors influence the quality of what occurs within opportunity development. The broader or longer-term perspective of the entrepreneurial career is necessary to explore how the entrepreneur affects the social context and moves beyond the limits of their context. Before turning to these issues, I briefly discuss the role of the two phases of field study and the iteration of analysis on the evolution of the model of opportunity development.

Two Phase Field Study and the Evolution of the Model The model of opportunity development presented in this paper represents the concluding arguments of an iterative and two-phase exploratory process. To clarify the impact of the process on the model, I will review some specific changes that were introduced into the model. First, information gathering was initially treated as a single construct. After the data was collected, I was read a series of studies on the concept of spontaneous scanning, which lead to further review of the interviews with the intent of tying my information gathering category to information scanning. In the process, I realized that scanning represented only a subset of information gathering references, and the idea of information seeking, as distinct from scanning, emerged. This tied in well with research on entrepreneurial questioning by Vesper (1991). This distinction in information gathering then led to re-evaluating the category of the network of weak ties. I first noted that the information seeking items seemed tied to experts, and then realized the fellow entrepreneurs also were asked frequently for information. Thus, the two new distinctions were added to the model. Second, the model on opportunity development no longer gives a distinct role to entrepreneurs’ partners. Analysis of the partner data, using the combined sample of phase one and two, showed that generalizations about partners were


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impossible. Even when equity was shared 50-50 over a series of different ventures, the relationship between the partners varied. For example, Entrepreneurs O, V and H were partnership of equals in terms of decision making, initiating strategic decisions and developing opportunities. These relationships suggested that the single entrepreneur was not relevant unit of analysis, but rather the dyad functioned as a unit. Other long-term partnerships were more like a visionary leader matched with a strong operating or marketing person, such as Entrepreneurs F and Q. These types of entrepreneurial partnerships suggested inner circle roles. Many entrepreneurs relied on different partners for each venture, according to the needs of the business, such as with Entrepreneurs A and Z. These partnerships suggested members of the action set. This finding suggests interesting research questions for future research, but more immediately, I eliminated the category of partnerships from the model of opportunity development.

The Career and Sources of Ties Within research on entrepreneurial networks, considerable attention has been paid to the issue of the source of relationships. One reason for this concern seems to be the implication that entrepreneurs are restricted by their embedded networks (Dubini & Aldrich, 1991, for comment on this point). The implication of the research is that over time, the entrepreneurs should have more strong ties which do not originate in the social ties, as already discussed in the third section. The source of contacts in fact has implications for all aspects of the social context. Over time, we find more and more contacts and even close friends of the entrepreneur (strong ties) come from relationships that began as task relationships. For example, the career partner and close friend of Entrepreneur F, began as a sales manager in his first venture. Also, there were numerous examples of the dangers of relying too closely on social contacts, even from the successful serial entrepreneurs. Entrepreneur P, for example, regretted the fact that his family convinced him to start a business with his uncle as a partner, and was currently thinking about how to remove him from the business without doing damage to the family dynamics. Entrepreneur U went through a horrible year, after taking a very close friend as a partner in his business – a decision apparently based entirely on sentiment, and not a good idea. The social context seems to shift naturally from ties sourced from largely social sources to more task or instrumental sources. Over time, the entrepreneurs invested more and more time in relationships that birthed in the task environment. Hite (2001) argues theoretically, and Johannisson (1996) shows in a longitudinal study, that this shift is necessary for the growth of the businesses.

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Liability of Experience Starr and Bygrave point out that some entrepreneurs are handicapped by their experience (1991). These entrepreneurs are successful at a certain stage of the product life cycle, and their skills are well adapted to that stage, yet they seem to overestimate their skills. As they lead ventures in other parts of the product cycle or industry evolution, their over-confidence may result in failure, because they don’t adapt to different market and industry conditions. The problem, say Starr and Bygrave, is that they have learned or extrapolated from too few incidents, a well documented cognitive bias. For the successful serial entrepreneurs, this does not seem to be a problem, most obviously because the sample selection would have eliminated those who started to experience failure. Entrepreneur F did mention that he took nearly four years to re-conceptualize a venture which nearly failed. He argued that the near-failure was due to the fact he did not think through the concept enough before starting, and that carelessness was due to his own over-confidence. Taking responsibility for the near-failure, he was determined to be more careful in the future. Entrepreneur AA, the failed serial entrepreneur, may be partly explained by this phenomenon, although his most recent failure was in an industry he never had worked in before, so this suggests whatever overconfidence he may have had, was not in skills related to the product life cycle, but rather his self-efficacy beliefs in general. Interestingly, Entrepreneur B has experienced much more failure recently, and this is probably due to the learning liabilities suggested by Starr and Bygrave. After a career in finance, he became an entrepreneur in the shrinking low-technology sector of old-fashioned household and cleaning products, where his skills in buying, selling and restructuring companies was critical to survive and succeed in the consolidating industry. Cash rich and confident of his entrepreneurial skills, he later started several companies in unrelated industries. His skills did not fit these ventures, and the result has been mediocre success or outright failure. His continuing survival is due to his ability to exit these weak companies, again due to his ability to buy and sell effectively. Most of the other successful serial entrepreneurs seemed to be more modest in their ventures, sticking more carefully to a range of activities that fit their skills and their success continues.

When Success Attracts Resources The growing reputation of the successful serial entrepreneurs attracts resources, of a type and amount not normally available to nascent entrepreneurs. This “embarrassment of riches” can be a problem, if the result is less careful and less


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creative opportunity development. The impact on assessing resources is most obvious, as discussed briefly in the third section. Many types of resources may become accessible, from technology to capital to management staff. For example, Entrepreneur H described a young inventor who approached him with a new technology, who needed his credibility and his cash to create the new company. Many of the successful entrepreneurs were approached by potential investors, who wanted to participate in future ventures. The biggest danger is that the excess of resources means that asset parsimony (Starr & Macmillan, 1990) does not enforce the discipline the entrepreneur needs to continually improve the business concept and therefore the opportunity development process may become weak in its final stages. Even if the business starts, it may fail to achieve strong margins, and thus be vulnerable to competitors. Entrepreneur F’s near failure, noted above, was also partly due to the fact all the necessary resources fell into place quickly. With more constrained resources, Entrepreneur B may have avoided several mistakes also. Many of the successful serial entrepreneurs invested in ventures of younger entrepreneurs. The private investment activities were not researched in detail, but the evidence that most successful serial entrepreneurs had at least one or two private investments provides strong evidence that financial resources were no longer a constraining factor. If the successful serial entrepreneurs were able to avoid the mistake of skipping critical parts of the concept creation, the increasing access to resources should result in more companies, starting more frequently and running more companies concurrently. This acceleration depends partly on the preferences of the entrepreneurs. Entrepreneur S, for example, clearly preferred to use his resources to grow his current venture and did not seem interested in having more than one company at a time. (He was very passionate about skiing, and preferred to spend his “free” time on the slopes.) In contrast, Entrepreneurs A, V and Z enjoyed the challenge of starting up and turning around businesses, and their narratives show an increasing rate of new businesses over time. Thus, of the successful serial entrepreneurs with the most ventures, there seems to be a disproportionate rate of business foundings which can be best explained by the increased access to resources.

CONCLUSION This study extends current research on opportunity recognition by proposing a model of cognitive activities within a social context that emphasizes the process of developing initial opportunity ideas into business concepts. To summarize, the opportunity development model begins with the initial process perspective

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of Bhave (1994), and explores issues of cognition and social context to better understand the process. In this perspective, opportunity development includes both information gathering within the context of the network of weak ties, and concept creation in the context of strong ties. But introducing two levels analysis, the entrepreneurial career and the venture, we can further distinguish between information scanning in the network of weak ties, and information search related to the specific venture among entrepreneurs and experts, also linked by weak ties. We can also distinguish between thinking-through-talking in the inner circle, a stable group of people around the individual entrepreneurs, and assessing resources while building an action set appropriate to the specific venture. The ideas presented in the model are grounded in an extensive and emerging research literature on the broad areas of opportunity recognition and the social context of entrepreneurs. I believe opportunity development provides a valuable addition to the concepts of opportunity identification, alertness, and opportunity evaluation in the general field of opportunity recognition research. The opportunity development model adds new perspectives to both current opportunity recognition research and entrepreneurial networks, both independently and in the interaction between the two areas. In many cases, such as use of information, information search and questioning, and the role of the network of weak ties, the linkage between these research streams has been apparent already; the opportunity development model discusses in detail further useful and potentially valuable distinctions. Also, I believe the exploration of the distinction between the entrepreneurial career and the venture have helped unpack the ideas related to the strong ties network. In addition, the concept of thinking-throughtalking provides an interesting addition to cognition studies in opportunity recognition. The model is grounded in oral histories of opportunity development and in entrepreneurship research literature. The approach provides a rich basis for theoretical development, but the results must be submitted to empirical testing. The structured comparative sample for this study uses performance in terms of opportunities recognition and wealth creation as the basis for selecting participants; this implies the model provides guidelines for improving opportunity development. To test such a model, then, a longitudinal study is needed which would allow for evaluation of performance outcomes after collecting data on the opportunity development process. Specific issues, such as the role of partners and the inner circle, or the role of thinking-through-talking in the development of opportunities, could be studied for further insight without making any performance connections. Research techniques from experiments to longitudinal network analysis provide possibilities to further develop the model and provide empirical support.


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As a first step, this study provides a basis for further understanding the process of opportunity development. The most obvious limitation in this study, of course, is that the data used to generate the model of opportunity development cannot be said to validate or prove the accuracy of the conclusions. Through evaluation of the results in published studies, some supporting data can be found for parts of the model. Nonetheless, further empirical tests should be pursued as a next step. Three specific issues must be addressed. First, the emphasis on successful serial entrepreneurs has been an interesting field research process, but this sample may be too unique to offer any generalizability. Second, the sample emphasizes low and medium technology manufacturing, which many of the serial entrepreneurs supplemented with a number of service companies. This focus means that the model may have less relevance for high-technology, innovation-driven entrepreneurship. Third, the situation of well-established entrepreneurs may be so different than that experienced by novice or nascent entrepreneurs, that the model may have limited generalizability or practical value to a broad range of entrepreneurs. Finally, the retrospective narratives provide a rich set of data for analysis and reflection, but the natural fading of memories and other recall biases probably distort the data. These issues are key concerns to be addressed in future extensions of the research. Stevenson and Jarillo (1990) suggested that entrepreneurship is “the pursuit of opportunity without regard to the resources currently controlled.” The entrepreneurs’ concern with both opportunities and resources comes through in the opportunity development process, because the need to achieve a good business concept implies a clear vision of how the business will operate and profit from the opportunity. If we understand the pursuit of opportunity as the dynamic of creating a business concept, the phrase could be restated “development of opportunities with regard to resources being brought under direct or indirect control.” The issue of resources raises the question of how entrepreneurs gain knowledge of and access to resources – which turns our attention back to the entrepreneurial context. The general research question “How are opportunities developed?” is too broad a question to be answered satisfactorily in a single research project. The specific research question of the thesis “What is the impact of the social context on opportunity development?” emerged from the first phase of exploratory field work and was addressed more directly as the research developed. Combining the elements of process, time and social context has been challenging, but I believe the research has resulted in a useful and important model of opportunity development which moves away from an individualistic perspective, and introduces a process of dynamic interaction between the entrepreneurs and their social context.

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NOTE 1. For more detailed exploration of this and other issues see Gaglio’s (1997) excellent review of opportunity identification.

ACKNOWLEDGMENTS I gratefully acknowledge all the feedback and support I have received over the years from conference participants at Babson Kauffman Research Conference, the Academy of Management meetings, Global Entrepreneurship Research Conference and the Marketing/Entrepreneurship Interface; from colleagues at INSEAD, Stockholm School of Economics, Jönköping International Business School and Georgia State University. Your suggestions have helped improve the paper in every way; the failings that remain are all mine. I particularly acknowledge Daniel F. Muzyka, whose financial, moral and intellectual support made this research both feasible and exciting.

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Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage Publications. O’Connor, G. C., & Rice, M. P. (2000). Opportunity recognition and breakthrough innovation in large established firms. California Management Review, 43, 95–116. Ostgaard, T. A., & Birley, S. (1996). New venture growth and personal networks. Journal of Business Venturing, 36, 37–50. Persaud, S. (1998). Staying sane. London: Metro Books. Porac, J. F., Thomas, H., & Baden-Fuller, C. (1989). Competitive groups as cognitive communities: The case of Scottish knitwear manufacturers. Journal of Management Studies, 26, 397–416. Ronstadt, R. (1988). The corridor principle. Journal of Business Venturing (Winter). Sarasvathy, S. D. (2001). Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial contingency. Academy of Management Review, 26, 243–263. Sarasvathy, D. K., Simon, H. A., & Lave, L. (1998). Perceiving and managing business risks: Differences between entrepreneurs and bankers. Journal of Economic Behavior & Organization, 33, 207–225. Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science, 11, 448–469. Shane, S. (2001). Technological opportunities and new firm creation. Management Science, 47, 205–220. Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25, 217–226. Shotter, J. (1993). Conversational realities: Constructing life through language. London: Sage Publications. Simon, H. A. (1984). What we know about the creative process. Carnegie-Mellon University. Starr, J. A., & Bygrave, W. D. (1991). The assets and liability of prior start-up experience: An exploratory study of multiple venture entrepreneurs. In: Frontiers of Entrepreneurship Research (pp. 213–227). Babson Park, MA: Center for Entrepreneurial Studies, Babson College. Starr, J. A., & Macmillan, I. C. (1990). Resource cooptation via social contracting: Resource acquisition strategies for new ventures. Strategic Management Journal, 11, 79–92. Stevenson, H. H., & Jarillo, J. C. (1990). A paradigm of entrepreneurship: Entrepreneurial management. Strategic Management Journal, 11, 17–27. Vesper, K. H. (1991). Venture idea discovery mental sequences. In: Frontiers of Entrepreneurship Research (pp. 148–173). Babson Park, MA: Center for Entrepreneurial Studies, Babson College. Weick, K. E. (1989). Theory construction as disciplined imagination. Academy of Management Review, 14, 516–531. Westhead, P., & Wright, M. (1998). Novice, portfolio, and serial founders: Are they different? Journal of Business Venturing, 13, 173–204. Yin, R. K. (1984). Case study research: Design and methods. Newbury Park, CA: Sage. Young, N. (1998). The structure and substance of African American entrepreneurial networks: Some preliminary findings. Babson Entrepreneurship Conference, Gent. Zeitsma, C. (1999). Opportunity knocks – Or does it hide? An examination of the role of opportunity recognition in entrepreneurship. In: Frontiers of Entrepreneurship Research (pp. 242–256). Babson Park, MA: Arthur M. Blank Center for Entrepreneurship.


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APPENDIX A Short Biographical Sketches of Phase One Participants Entrepreneur A specialises in manufacturing industries that are dominated by “poor management.” He buys factories, always with at least one partner, turns around the operation, and eventually sells out. In some cases, he consolidates several operations before selling. His primary interest is in food processing, but he has also invested in other similar basic manufacturing industries (e.g. paint brushes). In addition to his entrepreneurial activities, he also invests as a silent partner in new ventures. Entrepreneur B likewise specialises in turning around operations, although he has also started up several ventures. He has worked with several different partners. He is less focussed than entrepreneur A, however, and has interests in commercial and manufacturing operations, and sells services and products. For example, one start-up operation works in the executive out-placement industry, helping redundant executives create their own businesses rather than finding another job. Another involved a successful turnaround operation of a bankrupt factory that achieved break-even within a year. Entrepreneur B was an investment banker, and became an entrepreneur so he could continue to live in Paris. One of his self-reported skills as an entrepreneur is structuring creative financial deals. Since the completion of phase one, Entrepreneur B has experienced a number of failures, although retaining a few successful ventures and high level of personal wealth. Entrepreneur C is a self-described “business angel” who “actively” invests in new ventures. However, we included him in the study because he is very involved in the ventures he invests in and acts as a de facto team member. The line between “angels” and entrepreneurs, as we noted above, is sometimes very hazy. He provided an interesting perspective both on inventor-entrepreneurs (he invests in very high technology companies, using his Ph.D. in nuclear physics to inform his evaluation and to influence the evolution of the companies), and on how the actual business opportunities evolved from pure technical invention. In the case of his investments, the opportunity formation process occurred largely after the venture had started. Entrepreneur D described himself as an analytic entrepreneur, one who decided to start a business, and very analytically invested in markets to find the right opportunity. In practice, most of his activity has been in the communications industry, starting as a journalist and lobbyist, and eventually running a company which designed, manufactured and sold communications hardware (see Churchill, 1988). He is now semi-retired, and keeps busy as a board member and “angel.” Entrepreneur E has invested primarily in retailing or marketing operations, including a few franchises. He usually focuses on consumer products or services,

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where he sees a big gap in the market. He always has been involved in start-ups – even his franchise investments were always made when the business concept was just entering the French market. He has a long-term partner, with whom many of his ventures were (and continue to be) initiated. Entrepreneur F and G have been working together for most of the last 20 years, starting up new ventures in a number of industries. Their most consistent, though not exclusive, commitment has been in a natural resources/processing industry, providing innovative lower-cost solutions to basic processing problems. Entrepreneur F is a creative engineer, while Entrepreneur G takes the business management and market orientation. These entrepreneurs often have sold new ventures before they have proven successful, in order to concentrate on another business. Entrepreneur H left consulting to become an entrepreneur with his partner (an ex-investment banker). They have built several international specialty retailing chains. Starting with a distribution license to bring a carpet retailer to Benelux, they eventually created a number of joint ventures with successful but small family-owned specialty retailers, complementing the original owners’ knowledge of the products with their experience in rolling out nation-wide chain stores. In other ventures, their involvement is usually restricted to board membership. Entrepreneur I has created and purchased companies in die-casting, component maintenance, heat treatment, and other related industries for several decades. His current focus is on jet engine and turbine components. He continuously searches for new applications and new customers for his product lines and specialities, and has gone through several cycles of diversification and focussing. He started decades ago, he says, by bringing U.S. process technology to Europe. Today, his companies serve international customers, with plants located in several countries – although he still describes himself as a relatively small player. Entrepreneur J has developed a large family business, with two major product groups, media and advertising. He is well-known within Canada for introducing TV Guide into the market (despite the dominance of large publishing houses) and cellular phones. He continues to expand, and is now looking for a new major product line. He described most of his major successes as beginning with casual comments by people in his business network.

APPENDIX B Short Biographical Sketches of Phase Two Participants Entrepreneur K left INSEAD with the intention of starting his own company as soon as possible. After working as a personal assistant to an entrepreneur, he took


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over a candle factory. Despite widespread bankruptcies in the industry, he has remained profitable. From making candles, he moved into consignment sales in retails shops, primarily convenience stores. His latest venture has been a chain of discount perfume shops, which grew very rapidly. He has recently exited this business. Entrepreneur L is an engineer, with a bent for solving technical problem. His closely related businesses involve an innovation for oil fields. His biggest challenge is pricing high enough, so that his company is credible against “Western” competitors – even with generous salaries, Nigerian employees are relatively cheap. He also struggles with Western perception that good technology cannot originate in countries like Nigeria. Within the country, he says he is unique as an entrepreneur with a very long time horizon for investment, unlike the more short-term horizon of the numerous trader entrepreneurs. Entrepreneur M had a successful 15-year career in the automotive industry, before joining the family firm. This business supplies automotive components. In reaction to pressure from the automotive companies, who wanted to devolve component design to the suppliers, he initiated a new design company. Investors and partners included other component suppliers. The design company is doing well, despite early challenges. Entrepreneur O, with his brother, took over the family hide trading firm after their father nearly bankrupted the firm in an ill-conceived joint venture. They have managed to save the company, and added a tanning factory to the operations. Hides have become a commodity product, however, so the brothers are aggressively pursuing a diversification strategy. They have sold a hide-related business they started earlier, and recently purchased a small chocolate truffles factory (against a rival bid from Nestlé). This factory is now profitable, and they continue to search for similar small troubled firms. Entrepreneur P began his first firm, a buying agency for knitwear, when he and his wife returned to Mauritius for the birth of their first child. The agency began with contacts in Germany and France, and became quite successful. Over the last 15 years, he has extended his interests in knitwear with a knitwear machine manufacturing and sales agency, label manufacturing, and other specialized agencies. He recently has invested in manufacturing in South Africa also. Entrepreneur Q began his career as an entrepreneur when he took over the family lumber supply store. In serving the contractors of North London, he soon identified other opportunities. A major success was a cement post factory, using a new technique he discovered and imported from France. After the drama of discovering his long-time accountant and partner was not trustworthy, he has had to rebuild and reinvest in his businesses. Over the last few years, his son has joined him as a partner in the business.

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Entrepreneur R was not included in the thesis. He has created a thriving business in computer services, supporting installation and ongoing training and support for major financial and inventory management programs. His customers include Glaxo. The company began as a small software programming consultancy, with two partners and one employee. After struggling for three years, Entrepreneur R took a three month sabbatical to rethink the company. The result has been a dramatic shift in the product and market, with resulting high growth. One interesting aspect of his managerial style, is an emphasis on hiring long-term unemployed people and providing the necessary training. In one case, a 55-year-old former executive provided the credibility he needed to sell to older executives of large companies. Enthusiasm and commitment is understandably very high. Entrepreneur S began dabbling in entrepreneurship while working as an acquisitions analyst in New York, a job he took after enjoying a year as a ski bum (in his own words). His early venture was a charter tour company, specializing in Alps ski trips, which began as a group of friends who wanted to ski together. After moving to London to turn around a failing acquisition related to his full-time employer, he discontinued the charter business, quit his job, and went to Zurich where he eventually founded an agency selling plastics packaging machinery. While earning an MBA at INSEAD, he began a packaging company with a contract from L’Oreal. About 10 years later and after growing to 10 factories across Europe, he sold out to a larger company. He tried becoming a private investor, but experienced failures and frustration with bad management. He then bought a company based in Provence, with manufacturing and a related retail chain. Products are a mixture of perfumes, soaps, and bathroom decorations, around the traditional scents and themes of Provence. After fixing the operations so that they became profitable, he now is focused on developing a luxury brand and global chain of retail shops. At present there are over 200 stores world wide. Entrepreneur T decided to leave his corporate career in his late 30s. After much research, he bought a company manufacturing collapsible kayaks for military and civilian markets. The founder was over 70, and no longer able to manage profitably. After cutting a sympathetic deal, Entrepreneur T moved the company into Brittany, took advantage of some government subsidies, and managed to quickly become profitable. Despite a long-term problem with French tax authorities, his efforts support a comfortable lifestyle which includes a stable of racehorses and a home in a restored abbey. The company has 17 employees, sells its products globally, and competes against about 15 rivals in this niche market. Entrepreneur U returned from his youthful global wanderings, to a very tight labor market in the early 1990s. He spent several months trying to develop ideas for businesses, and eventually backed into an opportunity to supply karaoke machines to the French market. Since that initial business started, he has expanded


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his activities almost every year. Two major businesses are corporate event management (including karaoke), and French music video production, which he started to supply to the French karaoke market. Entrepreneur V began his first venture in large, custom-designed doors, for example for. He is the youngest of three brothers, who have worked together on at least 15 ventures. Another early venture was the development of accounting software, which they regret they sold out too soon. The brothers have started many businesses related to construction engineering. Eventually they decided they excelled at business start-ups, and decided to try to formalize their knowledge through setting up a business incubator. They are now working through their second incubator. They are confident they understand the necessary processes and problems of opportunity formation and start-up, but struggle to formalize how to “pick” good entrepreneurs. Their businesses now also include CAD/Cam and lasers technology expertise. Two recent companies include a machine that provides low-cost and simple analysis of the quality of rubber before it is manufactured into products, and the technology to make customized ceramic dental implants. Entrepreneur W was not included in the thesis. He has recently become an entrepreneur, after spending several years in Hong Kong working in sales. He recently purchased a floor covering factory from a larger company, located in his home town in Norway. The factory produces both coverings, and the machinery required to make the coverings. He successfully purchased the company through a two-stage, and well-timed negotiation which minimized his personal costs. He also refocused the product line and sales staff, to emphasize high margin products. The company is now profitable. He is currently investigating an opportunity to start a floor covering factory in southern Africa, and expects to become a serial entrepreneur. Entrepreneur X is the third generation in the family sugar business. Because of the massive consolidations in this industry, the family business survives as a relatively minor shareholding in a large company. He continues to represent the family as an active director of the company. Current plans include acquisitions outside of France. His own entrepreneurial experience was limited to a few years, when he ran a concession selling and servicing cars. Entrepreneur Y is the youngest of three brothers, who was sent by his father from India to manage the family operations in Saudi Arabia. He eventually developed a group of companies, all related to the bakery industry, throughout the Gulf states. As his children are growing older, and the business is developing very independently of the Indian concerns, he recently bought out his Gulf operations and withdrew all claims from the family business. He has a very interesting managerial style, which emphasizes experimentation and learning throughout the organization.

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Entrepreneur Z started his entrepreneurial career by investing all his money – $2 million – in a ship which was being converted to a hotel in Florida. He lost every penny when the ship had a fire because of a legal technicality in the contracts (hotel and ship liens are very different apparently). In the process of rebuilding, he started a series of companies, to a total of 52 today. Many of the companies are separate entities but similar businesses. The range of activities includes group homes, juvenile delinquent homes, rendering plants, denture manufacturing, sewage treatment installations, leases and service, and golf/cross-country properties. The companies are run by people he hires, to whom he eventually gives an equity stake. He has direct contact with most companies only twice a year, but is always active looking for ways to improve or extend the businesses. He also has directed the entrepreneurship program and taught at university business schools. His scope of activity is impressive, considering he started his first major venture at 39 years. His only early ventures were small activities to earn money as a college student. Entrepreneur AA started his first business, extremely detailed maps of London city showing each building and listing occupants, in response to his frustration in finding clients’ offices. He kept his full-time job at an investment bank, and developed a profitable little business which he eventually sold. His next ventures were a partnership to sell Italian wine in England, and a peripheral involvement in an agency to sell English automotive components in France. The wine agency was sold; he withdrew from the other business because of lack of commitment. After completing his MBA, Entrepreneur AA and a fellow alumnus bought out a Vickers factory, to produce small engines. Within a few years, most of their British customers were bankrupt, and they began looking globally for customers. Larger Japanese companies seriously challenged their survival. The company was sold (and soon after closed) to a downstream English company. Entrepreneur AA is now trying to establish a viable small business, using some of the plant and equipment that was excluded from the company sale.

THE DOMAIN OF ENTREPRENEURSHIP RESEARCH: SOME SUGGESTIONS Per Davidsson DEVELOPMENT – AND LACK THEREOF – IN ENTREPRENEURSHIP RESEARCH There is progress in entrepreneurship research. Important works in entrepreneurship increasingly appear in highly respected, mainstream journals (see Busenitz et al., 2003; Davidsson, Low & Wright, 2001). There is conceptual development that attracts attention (e.g. Shane & Venkataraman, 2000) and handbooks are compiled, providing the field with more of a common body of knowledge (Acs & Audretsch, 2003a; Shane, 2000a; Westhead & Wright, 2000). Further, there is evidence of methodological improvements (Chandler & Lyon, 2001) and accumulation of meaningful findings on various levels of analysis (Davidsson & Wiklund, 2001). Moreover, due to time lags in publication the reported improvements are likely to be underestimated. This author’s experience as organizer, reviewer and participant in core entrepreneurship conferences on both sides of the Atlantic (e.g. Babson; RENT) suggests that much of the lower end of the quality distribution has either disappeared from the submissions or is screened out in the review process. Much more than used to be the case a few years back we find among the presented papers research that is truly theory-driven; research on the earliest stages of business development, and research that employs methods suitable for causal inference, i.e. experiments and longitudinal designs. Cognitive Approaches to Entrepreneurship Research Advances in Entrepreneurship, Firm Emergence and Growth, Volume 6, 315–372 Copyright © 2003 by Elsevier Science Ltd. All rights of reproduction in any form reserved ISSN: 1074-7540/doi:10.1016/S1074-7540(03)06010-0

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This is not to deny that there is confusion, signs of identity crisis, or widespread frustration among entrepreneurship researchers because of a sense that the field of entrepreneurship research has not come “far enough, fast enough” (Low, 2001) or that we are “getting more pieces of the puzzle, but no picture is emerging” (Koppl & Minniti, 2003). The literature is full of definitions of entrepreneurship, which differ along a number of dimensions, e.g. whether entrepreneurship should be defined in terms of dispositions, behavior, or outcomes,1 whether it belongs in the economic-commercial domain or can be exercised also in not-for-profit contexts; whether it belongs only in small and/or owner-managed firms or in any organizational context, and whether purpose, growth, risk, innovation or success are necessary criteria for something to qualify as entrepreneurship (Gartner, 1990; Hébert & Link, 1982; Kirzner, 1983). There is, no doubt, disagreement on conceptual issues and a perceived need to try to sort these out (Bruyat & Julien, 2000; Gartner, 2001; Low, 2001; Shane & Venkataraman, 2000, 2001; Singh, 2001; Zahra & Dess, 2001). There are also numerous empirical attempts to understand the field or assess it progress (Aldrich & Baker, 1997; Busenitz et al., 2003; Cooper, 2003; Davidsson & Wiklund, 2001; Grégoire, Déry & Béchard, 2001; Landström, 2001; Low, 2001; Meeks, Neck & Meyer, 2001; Meyer, Neck & Meeks, 2002; Reader & Watkins, 2001). Of these, Low (2001, p. 20) and Meeks et al. (2001) find almost no order at all in empirical work published under the entrepreneurship label. The others find meaningful patterns but also reason for frustration, or even for very pessimistic views on the future and potential contribution of the field. I personally think that on the contrary, we now finally have the intellectual building blocks in place that are necessary for the creation of a strong paradigm in entrepreneurship, which can lead to academic credibility and respect as well as a stream of scholarly and practically meaningful research contributions. The purpose of this manuscript is to facilitate further progress in entrepreneurship through elaboration on several such intellectual building blocks. Drawing predominantly on ideas developed by Kirzner (1973), Venkataraman (1997; Shane & Venkataraman, 2000, 2001) and Gartner (1988, 2001), I strive to achieve three things. Firstly, I want to make a clearer distinction between the definition of entrepreneurship as a societal phenomenon, and the delineation or entrepreneurship as a scholarly domain. These are not identical. The former describes the function of entrepreneurship in society, while the latter suggests what entrepreneurship researchers should study in order to generate maximum knowledge about this societal phenomenon. Arguably, the distinction should make it easier both to agree upon and communicate what entrepreneurship is, on the one hand, and what entrepreneurship research should study on the other. In addition, it may be useful to regard the teaching subject “entrepreneurship” as a – in part – separate

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issue. Second, I want to achieve a domain delineation that is more complete than its predecessors; one which makes room for both Venkataraman’s and Gartner’s views on entrepreneurship, and which tries to find an agreeable middle ground on important issues where entrepreneurship scholars seemingly disagree. This may seem an insurmountable task given the apparent conflict and confusion reported above. However, I believe that a lot of the apparent conflict is superficial and can be reconciled. Third, I want to go further than the predecessors in pointing out what the suggested domain delineation implies for the design and analysis of empirical research on entrepreneurship. In the next section, I will discuss entrepreneurship as a societal phenomenon, arguing that from this perspective Kirzner’s (1973) notion that entrepreneurship consists of the competitive behaviors that drive the market process is highly useful. I will then turn to entrepreneurship as a scholarly domain, which also includes a discussion of the central concept “opportunity.” After reviews of Venkataraman’s (1997; cf. Shane & Venkataraman, 2000) and Gartner’s (1988) viewpoints I will propose that when talking about the scholarly domain, we would benefit from a delineation that does not presuppose the outcome, and focus on the behaviors undertaken in the processes of discovery and exploitation of ideas for new business ventures. The scholarly domain, then, should study these processes as well as their antecedents and effects. I will further discuss how entrepreneurship relates to other scholarly domains, essentially agreeing with Low (2001) that “entrepreneurship as distinct domain” and “entrepreneurship belongs in the disciplines” are, in fact, mutually dependent strategies for the development of the field. Before concluding I will also discuss some of the many methodological challenges that arise for entrepreneurship research because of issues related to emergence, process, heterogeneity, and level of analysis.

ENTREPRENEURSHIP AS SOCIETAL PHENOMENON Many scholars include in their understanding of the concept “entrepreneurship” the criterion that the outcome is somehow successful or influential. Others hold that entrepreneurs act under genuine uncertainty and that therefore one should base the definition on the behavior itself and not the outcome, which is more or less contingent on luck (cf. Gartner, 1990). This is a strong indication that we need to separate entrepreneurship as a societal phenomenon – its role in societal organization and/or the economic system – from entrepreneurship as a scholarly domain, i.e. what entrepreneurship research should study. When we think of entrepreneurship as a societal phenomenon it is a distinctive advantage to include

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an outcome criterion and make clear, for example, that mere contemplation over radically new ideas or vain introduction of fatally flawed ones do not amount to “entrepreneurship.” It is along with this type of view on entrepreneurship, then, that criteria like “wealth creation” or “value creation” rightfully belong (Drucker, 1985; Morris, 1998). A discussion of entrepreneurship as a societal phenomenon, including an outcome criterion, benefits from the work of economic theorists. The major intellectual building block I will use in this section is the notion in Austrian economics that entrepreneurship consists of the competitive behaviors that drive the market process (Kirzner, 1973, pp. 19–20).2 This definition is based jointly on behavior and outcomes. I choose this definition because it gives a satisfactorily clear delineation of the role of entrepreneurship in society. It puts entrepreneurship squarely in a market context and makes clear that it is the suppliers who exercise entrepreneurship – not customers, legislators, or natural forces that also affect outcomes in the market. The “drive the market process” part is about the outcome: entrepreneurship makes a difference. If it does not, it is not entrepreneurship. That is, sellers who introduce new, improved or competing offerings in an emerging or pre-existing market give presumptive buyers new choice alternatives to consider, attract additional new entrants as followers, and/or give incumbent firms in existing markets reason to, in turn, improve their market offerings. As a result, resources are put to more effective and/or efficient use. This is what driving the market process means, and this is what entrepreneurship does. Importantly, driving the market process does not require that the first mover makes a profit but refers to the suppliers as a collective. Even if it eventually loses out the first mover contributes to driving the market process if subsequently someone gets it right, which leads to a lasting change in the market. Put in slightly different words, entrepreneurship as a societal phenomenon is the introduction of new economic activity that leads to change in the marketplace (cf. Herbert Simon in Sarasvathy, 1999b, pp. 2 and 11). This is illustrated in Fig. 1. Note that “new” along the market axis means either that an entirely new market emerges, or that an activity is new to an existing market. Likewise along the firm axis “new” means that the new activity is an independent start-up, i.e. a new firm emerges as a result, or it is an internal new venture, i.e. the activity is new to the firm. Under the suggested definition the left hand side of the figure – quadrants I and IV – exemplify entrepreneurship, whereas quadrants II and III do not. This conjures also with the argument developed at some length by Baumol (1993) in that imitative entry and internationalization are included in the concept, whereas, e.g. take-over is excluded.


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Fig. 1. Firm and Market Newness of Economic Activities.

New Offer as Entrepreneurship Starting with quadrant I the first entry reads “New offer.” This refers to the situation where something so new is introduced that a new market is created (Bhave, 1994, p. 231; Sarasvathy, 1999a) or at least no supplier has previously made the same offer in the same market. There is hardly any disagreement among scholars that this should be included in the concept of entrepreneurship, although some might want to restrict the inclusion to situations where a new and/or independent firm is behind the new offer. The first category, new product or service, corresponds to Schumpeter’s (1934) “new product” and Bhave’s (1994) notion of “product novelty,” respectively, and requires no further explanation. The second category, new bundle, refers to any combination of product and service components that – as a package deal – is unique relative to what has previously been offered on the market, although no individual component may be strictly new. This overlaps with Schumpeter’s (1934) general idea about “new combinations,” with Bhave’s (1994) notion of “new business concept,” and with Amit and Zott’s (2000) “new business model” – as long as the new combination, concept or model includes newness as perceived by buyers and competitors. In some cases it amounts to Schumpeter’s (1934) category “re-organization of an entire industry.” An illustrative case is IKEA, where the

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newness was not in the piece of furniture in use, but in the division of labor among different actors, including the consumer, in the production and distribution of the end product. IKEA would also qualify under the third category included in “new offer,” new price/value relation. This does not create a new market but drives the market process because it changes consumer choices and give other competitors reason to change their offerings. Consequently, Kirzner (1973, pp. 23–24) explicitly discusses offering the same product at a lower price as one form of entrepreneurship. A new price/value relation may be contingent upon organizational change (quadrant II), but this is not necessarily the case. It may also represent a strategic change that relies on expected scale economies in production or a switch from low volume/high margin to high volume/low margin strategy.

New Competitor as Entrepreneurship The second main entry in quadrant I is “New competitor.” This is when a new, start-up firm enters the market, or an existing firm launches a new product line in a situation where other firms already supply the market with essentially the same product. That is, I suggest that not only innovative but also imitative entry be included in the entrepreneurship concept (cf. Aldrich, 1999; Aldrich & Martinez, 2001). The reason for imitative entry to be included in the entrepreneurship concept is that such entry drives the market process in the sense that consumers get additional choices and incumbent firms get reason to change their behavior to meet this new competition. Moreover, it has been observed that entry with complete lack of novelty tends not to appear empirically (Bhave, 1994, p. 230; Davidsson, 1986). No entrant is a perfect clone of an existing actor. Therefore, trying to include an innovativeness criterion in the definition of entrepreneurship would create problems. Rather than drawing the line at zero innovation (which would exclude no cases) one would be forced to define an arbitrary limit across different industries and types of novelty. This problem is aggravated by the fact that what appears new in one market may be a blueprint copy of what already runs successfully in a different market (Gratzer, 1996). All in all, then, there are several good reasons to include imitative market entry in the concept of entrepreneurship as a societal phenomenon. While both aspects of the entrepreneurship, it may be advantageous to model the antecedents and effects of “innovation” and “imitative” new ventures differently in theories and empirical analyses (cf. Samuelsson, forthcoming).


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Geographical Market Expansion as Entrepreneurship Defining entrepreneurship the way we have done makes it logical to include also quadrant IV – geographical market expansion – in the concept of entrepreneurship. Although by now the activities are (largely) no longer new from the firm’s perspective their introduction in new markets – if not totally unsuccessful – drives the market process in these new places. This may to some look like over-extending the entrepreneurship concept. However, when IKEA enters its nth country market it may well be as revolutionary for the consumers and competitors in that market as it was for Swedish consumers and furniture retailers when IKEA first developed its concept. If IKEA’s entry is successful it reflects Schumpeter’s (1934) “new market” category of economic development. The alternative to require newness to the firm as a criterion would lead to less desirable consequences. For example, had Southwest Airlines successfully introduced their concept in the European market it would not constitute entrepreneurship. If instead a new actor (e.g. Ryan Air) copied the concept and took it to the European market it would count as entrepreneurship. This is less than satisfactory from any perspective, and from a market perspective it is unacceptable.

Organizational and Ownership Changes are not Entrepreneurship By contrast, according to our conceptualization the organizational and ownership changes listed in quadrant II do not by themselves constitute entrepreneurship. It is certainly conceivable (and likely) that reorganization facilitates the creation of new economic activity by the organization. However, it is also conceivable that organizational units that are transferred to new ownership and/or undergo internal reorganization experience changes in job satisfaction and/or financial performance without at all changing the consumers’ choice options or influencing the behavior of competitors. Actually, there are at least four cases: (a) an organizational or ownership change is intended to lead to more new market offerings by the firm, and does so; (b) same as (a) but the intended increase in new market offerings does not happen; (c) the change is undertaken for other reasons and has no effect on the firm’s market offerings; and (d) the change is undertaken for other reasons but has the unintended effect of also making the firm more entrepreneurial. I think it is valuable to conceptually separate the organizational or ownership change from its effects. Therefore, it is the (successful or influential) launching of new business activities that might follow from it, and not the organizational change itself, that constitutes entrepreneurship.

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The argument is perhaps easier to accept if we move to the level of societal organization. Politicians can decide on changes in how society is organized and introduce, e.g. de-regulation or other institutional changes which create opportunity in market x and therefore an increase in competitive behaviors that drive the market process in that market, i.e. entrepreneurship. According to my argument, it is not the politician who exercises entrepreneurship in market x, but the micro-level actors in that market. The political decision facilitates entrepreneurship. In the same way, a manager may facilitate entrepreneurship through organizational change, but it is the market related activities that may result, and not the organizational change per se, that constitute entrepreneurship. This conceptual distinction is also the reason why I refrain from including Schumpeter’s (1934) “new production method” and “new source of supply,” as well as Bhave’s (1994) “novelty in production technology,” in the definition of entrepreneurship as societal phenomenon (cf. Davidsson, Delmar & Wiklund, 2002; Kirzner, 1983, p. 288). As we shall see, the study of how organizational change relates to discovery and exploitation of new venture ideas remain an important question for entrepreneurship as a scholarly domain.

Business as Usual and Non-Entrepreneurial Growth Turning now to quadrant IV, “Business as usual” here is at first glance as easy to exclude from the notion of entrepreneurship, as was “New offer” in quadrant I easy to include. But not even here does there seem to exist full agreement. First, we have von Mises’ denial of the existence of such a thing as “business as usual” when saying that “In any real and living economy every actor is always an entrepreneur” (Mises, 1949, p. 253). One can argue that no market action is completely void of novelty. For example, when a daily newspaper carries out the totally expected and routine actions of producing a new issue and distributing it to its subscribers and usual sales outlets, it is a new issue, and not yesterday’s paper, that is being distributed. Competitors will equally routinely read it, and it cannot be ruled out that some part of the contents may have a twist that inspires the competitor to do something in a future issue, which it would otherwise not have done. In other words, we find an element of “competitive behavior that drives the market process” in these routine actions. Although this seems to lead to a delimitation problem similar to the arbitrary innovation criterion discussed above, my conclusion in this case goes in the other direction. That is, there is a lot of “known products for known buyers” activity going on that is so clearly predominantly of a “business as usual” character that it is not very difficult to classify it as such both conceptually and empirically, and thus exclude it from entrepreneurship as a societal phenomenon.


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More problematic, perhaps, is the fact that there exist explicit and implicit definitions of entrepreneurship, which do not clearly require that “business as usual” be excluded. For example, Cole (1949) defined entrepreneurship as “a purposeful activity to initiate, maintain and aggrandize a profit-oriented business.” This means that he included mere “maintenance” while stressing “freedom of decision” (p. 88), making entrepreneurship equal to “starting and/or running and/or expanding one’s own firm.”3 Although explicit reference to Cole is infrequent, this is a recurrent implicit definition in research published under the “entrepreneurship” label. While I hold that many differences in views on entrepreneurship can be reconciled or are of marginal importance, this is not one of them. When entrepreneurship is defined as the competitive behaviors that drive the market process, “business as usual” can never be included. The issue of non-entrepreneurial growth is tricky for slightly different reasons (see Davidsson, 2002, for an elaborate discussion). When an economic actor exploits a venture idea, there will be no well-defined moment at which “entry” ends and “continued, routine exploitation” begins. Schumpeter (1934) held that mere volume expansion was not entrepreneurial, while he included the opening of new markets. It is a similar distinction I have in mind here. By “non-entrepreneurial growth” I mean passively or re-actively letting existing activities grow with the market. This would not provide much cause for alert among competitors nor give customers new choices.

Outcomes on Different Levels It was pointed out in the beginning of this section that while we have included an outcome criterion in the definition of entrepreneurship, it is not necessary that each and every individual venture that drives the market process is successful in itself. This is illustrated in Fig. 2. “Venture” could here mean the sole activity of a new firm or a new, additional activity by an established firm. Thus, “venture” should not be interpreted (necessarily) as new firm or company, but as a new-to-the-market activity as discussed above. Na¨ıve conceptions of venture outcomes typically classify them as successes or failures. Figure 2 complicates the picture by considering outcomes on two levels, venture and society. If we turn first to quadrant I we find ventures that are successful in themselves and which produce net utility to society as well. These ventures are analytically unproblematic. Their successful entries into the market no doubt “drive the market process” and hence they exercise entrepreneurship under the definition we have chosen. Likewise, the failed ventures in quadrant III are analytically unproblematic. These represent launching efforts that do not take

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Fig. 2. Outcomes on Different Levels for New Ventures (New Economic Activities).

off financially, and neither do they inspire followers or incumbent firms so that the eventual net effect becomes positive on the societal level. The catalyst ventures in quadrant IV are an interesting category, and probably make up a large share of all new ventures (internal or independent) in any real economy. Although not successful on the micro-level – perhaps because they are outsmarted by followers or retaliating incumbents – they do “drive the market process” precisely because they bring forth such behavior on the part of other actors. An unsuccessful venture that inspires more profitable successors does not complete the entrepreneurial process but still contributes to entrepreneurship as a societal phenomenon. As the total effect on the economy is not necessarily smaller than for “success ventures” the catalysts are a very important category from a societal point of view (cf. Low & MacMillan, 1988; McGrath, 1999). This should serve as a warning against too simplistic a view on micro-level failure. The ventures in quadrants I and IV, then, represent entrepreneurship while the failed ventures in quadrant III do not. What about the “Re-distributive” ventures in quadrant II? These are ventures that yield a surplus on the micro-level while at the same time the societal outcome is negative. Examples could be trafficking with heavy drugs or – as in an actual case in Sweden – a graffiti removal operation whose owners used nighttime to generate demand for their business. Thus, those involved in the venture enrich themselves at the expense of collective wealth.4 Does this represent entrepreneurship? It has been pointed out that re-distribution of wealth is an important function of entrepreneurship in capitalist economies


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(Kirchhoff, 1994). However, what has here been labeled “success ventures” also re-distribute wealth, in addition to creating new wealth. The theoretical status of “re-distributive” ventures is determined, I would argue, by the answer to “towards what?” entrepreneurship drives the market process. Schumpeter (1934) and Kirzner (1973, p. 73) give seemingly contradictory answers to that question, but in actual fact the movement from Schumpeter’s (local) equilibrium and the movement towards Kirzner’s (global) equilibrium are in full agreement insofar as that entrepreneurship drives the market process towards more effective and/or efficient use of resources. Therefore, I would on theoretical grounds suggest that “re-distributive” ventures do not represent entrepreneurship.5 Entrepreneurship as a societal phenomenon leads to improved use of resources in the economic system as a whole. The portrayal of possible outcomes in Fig. 2 is, of course, still a radical simplification. Outcomes are described as dichotomous and no explicit time horizon was introduced. Only two out of many possible levels of outcomes (e.g. venture, firm, industry, region, nation, world) were discussed. In practice, assessing exactly where individual ventures fit into this framework would in many cases be very difficult, and contingent on the time perspective. Nonetheless, I think it is useful to highlight the distinctions made here and to note that as theoretical categories not only “success ventures” but also “catalyst ventures” carry out the entrepreneurial function in the economy, whereas neither “failed ventures” nor “re-distributive ventures” fulfill this role.

Degrees of Entrepreneurship? The inclusion of imitative entry, as well as the admittedly vague borderline between the end of the entrepreneurial exploitation process and the beginning of non-entrepreneurial growth, call for a discussion of “degrees” of entrepreneurship (cf. Davidsson, 1989; Schafer, 1990; Tay, 1998). It seems natural to treat entrepreneurship not as a dichotomous variable, but to say that some ventures show more entrepreneurship than others. But what should be the criterion by which we judge the degree of entrepreneurship? There are at least three possibilities: The degree of (direct and indirect) impact on the economic system. This is a criterion that is consistent with defining entrepreneurship as the competitive behaviors that drive the market process. In a theoretical discussion of entrepreneurship as a societal phenomenon, then, this should be the preferred criterion, i.e. the most correct one. For research practice the criterion has severe shortcomings because impact can only be assessed after the fact and not in real

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time, and because even then it can be very difficult to obtain even roughly correct estimates of total impact of direct and indirect effects on a complex economic system. A variation (or an indicator) of this criterion is “how much net wealth is created,” but this suffers from similar assessment problems. The degree of novelty to the market. This is intuitively appealing in the sense that what is more creative is seen as a higher degree of entrepreneurship. Although the above-discussed problem of comparing very different kinds of novelty pertains to this criterion it has the advantage that it can be reasonably well assessed in real time. The main problem is that while successfully introduced innovative new activities are likely to have larger market impact on average, there is no guarantee that a high degree of novelty ascertains market effect. History is full of weirdo inventions that nobody wanted or cared about. Some seemingly relatively marginal innovations revolutionize markets and create great private and societal wealth while some radical innovations have marginal impact or fail altogether. Therefore, when market effect is part of the definition of entrepreneurship the degree of novelty is at best a rough proxy for degree of entrepreneurship. The degree of novelty to the actor. Sometimes expressions like “That was very entrepreneurial of you (or of that firm)” are heard, meaning that the action was radically different from what that actor has done before (but not necessarily very novel or valuable as the market sees it). Relating the degree of entrepreneurship to the history of the actor rather than to the market in this way has highly undesirable consequences. With this type of criterion previous inactivity or conservatism increases an actor’s potential for showing a high degree of entrepreneurship. Moreover, it is a criterion that regards it more entrepreneurial to do something totally unrelated to one’s prior experience. Theories as well as empirical findings suggest this may not be a wise move (Barney, 1991; Sarasvathy, 2001; Shane, 2000b). I would therefore discourage its use in any academic context. In all, while there is a conceptual need for discussing “degrees of entrepreneurship” there is no easy or straightforward way to actually assess such variation. Of the available alternatives, the degree of impact on the economic system is the criterion that matches the definition of entrepreneurship (as a societal phenomenon) that I have proposed. One might conceive of entrepreneurship itself as a graded phenomenon or hold that empirical instances that qualitatively are instances of entrepreneurship have quantitatively different impact on the economic system. I do not believe it to be a hugely important distinction whether it is entrepreneurship itself or its impact that is a matter of degree. However, degree of novelty either to the market or to the actor is better regarded as a possible cause of variations in the degree of entrepreneurship (or impact of entrepreneurship) than being a direct measure of such variation.


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Conclusions on Entrepreneurship as a Societal Phenomenon I have suggested here that entrepreneurship as a societal phenomenon consists of the competitive behaviors that drive the market process (towards more effective and efficient use of resources). In contexts where less precision is required the even easier and roughly equivalent entrepreneurship is the creation of new economic activity can be used. Relative to many other alternatives I would argue that the suggested definitions have advantages in terms of being clearly delimited, logically coherent, and easy to communicate. They are clearly and fully explained as “when a supplier introduces something on a market so that buyers get a new alternative to choose from (potentially increasing the value they get for their money); this action may also make incumbent suppliers change their market offerings and/or attract additional suppliers to the market.” Further, despite being clearly delimited the definition is permissive in that it does not take a restrictive stand on purposefulness, innovation, organizational context, or ownership and personal risk-taking. Hence, while some would like to include more restriction in the definition they should in these regards at least find room for their favorite notions of entrepreneurship within the definition suggested here. Importantly, the view of entrepreneurship I propose is consistent with the views expressed by professional users of the concept. In Gartner’s empirical analysis, out of 90 attributes the most agreed upon central features of “entrepreneurship” were: (1) the creation of a new business; (2) new venture development; and (3) the creation of a new business that adds value. That is, new activity and successful outcome are emphasized (note that items 1 and 2 mention new “business” or “venture” – not “firm” or “organization” – and that item 3 says “adds value” which may or may not mean micro-level success). By contrast, few regard, e.g. buy-out as an important entrepreneurship attribute (Gartner, 1990, p. 20). The view I suggest is also consistent with Lumpkin and Dess’ (1996) definition of entrepreneurship as “new entry,” which they separate from the concept of “entrepreneurial orientation.” In some respects the suggested definition of entrepreneurship as a societal phenomenon is restrictive, and this may cause some controversy. First, a successful outcome – at least indirectly in the form of lasting market impact – is required. As described above this is necessary in order to exclude fundamentally flawed attempts to launch inferior novelty on the market. The outcome criterion will be relaxed when we turn to entrepreneurship as a scholarly domain. Second, the exclusion of organizational change from the definition may arouse opposition. However, the exclusion concerns organizational change per se. Hence, the study of how organizational change affects entrepreneurial action remains a valid and important question for entrepreneurship research. For these reasons the exclusions of

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failed ventures and organizational change from the definition are not as restrictive as it might first seem. The remaining aspect most likely to be a source of disagreement, I believe, is the restriction to market situations, to new economic activities. However, “economic” should not necessarily be interpreted as restricting the term for the “commercial” domain. Markets or market-like situations exist outside of industry and commerce. For example, politicians try to appeal to voters and journalists, and when they find novel ways to do so rival politicians may try to copy or improve upon winning recipes. In various forms of arts and sports there exist everything from a fully commercial industry to human action that is governed by entirely different principles than the market logic. As long as there are close equivalents to both customers and competitors, it may be meaningful in such domains to talk about “entrepreneurship” as defined here. Admitting that similar processes of creative re-combination of resources occur in other domains as well I believe it is useful to restrict the use of the entrepreneurship concept at least to the extended domain of market-like situations. One reason for this is, simply, that it is valuable to make the concept as distinct and well defined as possible. Moreover, those who want to include novelty through “new combinations” (Schumpeter, 1934) in any domain of human behavior in the concept of “entrepreneurship” have reason to contemplate the full implications of this choice. For example, when this view is applied the events of September 11, 2001, must be considered an entrepreneurship masterpiece. To conceive of a fully fueled passenger jet as a missile and to combine the idea of hi-jacking with that of kamikaze attacks is certainly innovative, and in terms of impact – economic and otherwise – it has few parallels. However, regarding these attacks as driving market processes is far-fetched, and this author would therefore suggest they be not regarded an instance of entrepreneurship.6

ENTREPRENEURSHIP AS A SCHOLARLY DOMAIN Entrepreneurship as a scholarly domain7 aims at better understanding of the societal phenomenon we call “entrepreneurship.” Paradoxically, however, delimiting research only to empirical cases known to qualify under the definition we discussed above would not lead to maximized knowledge accumulation, and therefore it does not work adequately as a delineation of this scholarly domain. Most importantly, while including an outcome criterion is desirable when we discuss entrepreneurship as a societal phenomenon, it becomes a burden when we think of entrepreneurship as a scholarly domain. This is because we have to be able to study entrepreneurship as it happens, before the outcome is known. It would


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be awkward indeed not to know until afterwards whether one was studying “entrepreneurship” or not. To study the processes as they happen is important also in order to avoid selection and hindsight biases. In order to understand the successful cases we need to study also those that fail. Further, it is not reasonable to ask of every empirical study of “entrepreneurship” that the outcome on every relevant level be awaited and assessed. Researchers must be allowed to go deeply into aspects of the process without following up on the outcomes – and still be acknowledged for doing “entrepreneurship research.” That is, attempts to offer buyers new choices should suffice. Moreover, it is not a given that previous and current entrepreneurship practice has all the answers needed to develop normative theory about entrepreneurship, or that finding real cases of “best practice” is the only or most accessible road towards developing such knowledge (Davidsson, 2002). Empirical entrepreneurship research may be well advised to study induced entrepreneurial situations as well, such as experiments or simulations (cf. Baron & Brush, 1998; Fiet, 2002; Sarasvathy, 1999a). While helpful for clarifying the role of entrepreneurship as a societal phenomenon, Kirzner’s (1973) theorizing – like that of many other economists – only provides limited guidance for what empirical studies should be conducted in order to understand and facilitate entrepreneurship. There is little process perspective on individual entrepreneurial events in Kirzner’s analysis. Discovery is conceived of as instantaneous and ascribed to “alertness” – an ability that is costless and thus has to be inborn, or – as critics have pointed out – equivalent to luck (Demsetz, 1983; Fiet, 2002). Neither does Kirzner consider exploitation to be part of entrepreneurship.8 Kirzner’s interest is to distill the theoretical kernel of the function of entrepreneurship in the economic system and not to guide empirical research.9 To seek guidance for entrepreneurship as scholarly domain – including empirical work – we will have to look elsewhere. Acknowledging that others have also made important contributions to giving direction to entrepreneurship research (e.g. Aldrich, 1999; Fiet, 2002; Low, 2001; Low & MacMillan, 1988; Sexton, 1997; Stevenson & Jarillo, 1990) I will concentrate on the contributions of the two probably most persistent and cited proponents of entrepreneurship as a distinct domain of research, Bill Gartner and Sankaran Venkataraman. The reasons why I focus on those two perspectives are the following. First, as I see it, each represents a major step forward towards making entrepreneurship a coherent, productive and respected scholarly domain. However, each also contains elements that may make it difficult for some prospective followers to fully embrace them. Secondly, there are emerging signs of a divide between those two perspectives. This is an unfortunate and unnecessary development. As I see it,

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with some clarification, elaboration and slight modification, the two perspectives can be combined and extended into a delineation of the scholarly domain of entrepreneurship that current “entrepreneurship researchers” as well as outside observers can appreciate. Venkataraman’s View Venkataraman’s suggested delineation of the field was first presented to a broader audience in Venkataraman (1997). It has subsequently been refined and elaborated by Shane and Venkataraman (2000). The latter state as their point of departure (2000, p. 217) that “For a field of social science to have usefulness it must have a conceptual framework that explains and predicts a set of empirical phenomena not explained or predicted by conceptual frameworks already in existence in other fields.” They go on to define the field of entrepreneurship as: [T]he scholarly examination of how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated, and exploited (Venkataraman, 1997). Consequently the field involves the study of sources of opportunities; the processes of discovery, evaluation, and exploitation of opportunities; and the set of individuals who discover, evaluate, and exploit them (p. 218).

They further point out the following three sets of research questions as especially central: (1) why, when and how opportunities for the creation of goods and services come into existence; (2) why, when and how some people and not others discover and exploit these opportunities; and (3) why, when and how different modes of action are used to exploit entrepreneurial opportunities. In the subsequent dialogue they agree with Zahra and Dess (2001) that the outcomes of the exploitation process represent a fourth important set of research questions, adding that outcomes on the level of industry and society should be considered as well (cf. Venkataraman, 1996, 1997). As regards antecedents of the process and its outcomes they emphasize the characteristics of individuals and opportunities as the first-order forces explaining entrepreneurship and hold that environmental forces are second order (Shane & Venkataraman, 2001). They describe their approach as a disequilibrium approach (cf. Eckhardt & Shane, 2003). They highlight variations in the nature of opportunities as well as variations across individuals. Further, they point out that entrepreneurship does not require, but can include, the creation of new organizations (cf. Simon in Sarasvathy, 1999b, pp. 11, 41–42; Van de Ven, 1996). In short, they depict the economy as fundamentally characterized by heterogeneity. One reason to show particular interest in this delineation of the field is, simply, that it has stimulated considerable discussion, debate and commentary, as well


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as some following (Busenitz et al., 2003; Davidsson & Wiklund, 2001; Erikson, 2001; Gartner, 2001; Low, 2001; Meyer et al., 2002; Shepherd & DeTienne, 2001; Singh, 2001; Zahra & Dess, 2001). Behind this great interest lies, I believe, the fact that the focus is clearer and in important ways different from that of some other explicit or implicit definitions of entrepreneurship. At the same time it is open-ended on issues where others may have been overly restrictive. In my view, the combination of focus and openness that Shane and Venkataraman (2000) show solves many of the problems associated with earlier definitions and research streams in entrepreneurship. Some important merits of their contribution are listed below. They try to delineate the scholarly domain rather than suggesting yet another definition of the societal phenomenon. Making this distinction is in itself a contribution. Focusing on the creation of future goods and services, their delineation directs attention to the problem of emergence (cf. Gartner, 1993). This adds a distinctive feature to entrepreneurship research; an element that is missing in established theories in economics and management. They put the main focus on goods and services rather than including organizational change per se (cf. Sharma & Chrisman, 1999) or creative behavior in any context. They thereby carve out a domain that has a manageable size and relatively clear boundaries, and which is consistent with Kirzner’s (1973) notion that entrepreneurship is what drives the market process. While retaining an interest in individuals they emphasize their actions (entrepreneurship) and fit with the specific “opportunity” rather than general characteristics of entrepreneurs. They thereby avoid the dead end of “trait research.”10 As to openness, their domain delineation includes two partly overlapping processes, discovery and exploitation.11 In line with empirical evidence (Bhave, 1994; de Koning, 1999b; Van de Ven, 1996) this refutes the view that discovery is instantaneous and that entrepreneurship consists solely of discovery (cf. Fiet, 2002; Kirzner, 1973). No mention is made of the age, size or ownership of the organizations in which “opportunities” are pursued. Shane and Venkataraman (2000) even point out the existence of alternative modes of exploitation for given “opportunities” as an important research question. Hence, the stated domain includes corporate entrepreneurship as well (Stevenson & Jarillo, 1990; Zahra, Karutko & Jennings, 1999). By implication, small business research is included only when it deals explicitly with discovery and exploitation of “opportunities” to create future goods and services (cf. Hornaday, 1990). They do not include purposefulness (cf. Bull & Willard, 1993; Cole, 1949) in their domain delineation. They thereby avoid an overly rationalistic view and

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make room for the possibility of luck (Demsetz, 1983) and serendipity (Bhave, 1994; Gartner, 1993; Sarasvathy, 2001) in entrepreneurial processes. Finally, if we disregard for the moment their definition of opportunity, Shane and Venkataraman’s (2000) wording “. . . with what effects” makes the field open to different types of direct and indirect outcomes of processes of discovery and exploitation, e.g. satisfaction, learning, imitation and retaliation in addition to financial success or failure. Importantly, the perspective suggests that in line with Fig. 2 above, an important task for entrepreneurship research is to assess not only outcomes on the micro-level, but on other levels (e.g. societal wealth creation) as well (Shane & Venkataraman, 2001; Venkataraman, 1996; Venkataraman, 1997, cf. Low & MacMillan, 1988). These many positives arguably make Shane and Venkataraman’s framework the best effort to date to delineate entrepreneurship as a distinct scholarly domain. However, in order for it to gain more widespread acceptance there are some aspects that need further elaboration, clarification or even modification. Firstly, as observed also by Singh (2001), their central concept “opportunity” is problematic. They hold that, among other things, we should study with what effects “opportunities” are exploited. They then adopt Casson’s (1982) definition of opportunity as “those situations in which goods, services raw materials and organizing methods can be introduced and sold at greater than their cost of production.” This makes entrepreneurship become characterized by certainty rather than uncertainty regarding one important aspect of the effects of the pursuit of opportunity: it is profitable. As I see it, Casson’s definition is compatible with the view of entrepreneurship as a societal phenomenon that we have developed above, but largely unhelpful for entrepreneurship as a scholarly domain as it is inconsistent with having the outcomes of entrepreneurship as an unrestricted research question. This apparent weakness of Shane and Venkataraman’s exposition points at a more general problem in the entrepreneurship literature, namely that “opportunity” is becoming a central concept but one which often is ill-conceptualized or applied in an inconsistent manner. Secondly, the phrase “discovery, evaluation and exploitation” contains words with objectivist and abusive connotations, and may leave the impression of a rationalistic, linear process. Such interpretations misrepresent Shane and Venkataraman’s intended meanings and positions, but some clarifications on how to interpret the terms and the process may be needed before others are willing to subscribe to this vocabulary. Third, Shane and Venkataraman (2000) position themselves away from Gartner (cf. below), emphasizing that they address a different set of issues than the creation of new organizations (cf. also Eckhardt & Shane, 2003). They have good


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reason for doing so, as they want to highlight the possibility of different “modes of exploitation” for a given “opportunity.” However, this may also create an unnecessary divide or make it wider than it needs to be. After discussing Gartner’s view of the field of entrepreneurship and trying to combine it with Venkataraman’s I will return to each of these three issues and try to offer solutions to the identified problems.

Gartner’s View Gartner’s (1988) view – which he is careful to present as a suggestion for re-direction rather than a formal “definition” – is that entrepreneurship is the creation of new organizations. This choice of focus has two origins. One was a perceived lack of treatment of organizational emergence in organization theory. Somehow organizations were assumed to exist; theories started with existing organizations (cf. Katz & Gartner, 1988). The other was a frustration with the pre-occupation that early entrepreneurship research had with personal characteristics of entrepreneurs. For these reasons, Gartner (1988) suggested that entrepreneurship research ought to focus on the behaviors in the process of organizational emergence. This view certainly has a lot to commend it: It has a clearly defined focus, thereby avoiding the risk of over-extending the field. It inspired a fruitful re-direction of the field from a dispositional to a behavioral view on entrepreneurship. It has a strong process orientation. It addresses an ecological void that has been given only cursory treatment in economics and management studies.12 It is offered as a minimalist definition. Gartner does not exclude other aspects of entrepreneurship, but argues that organization creation is a situation where we should “all” be able to agree that entrepreneurship is taking place. Accordingly, Gartner has no problem welcoming Shane and Venkataraman’s (2000) article as “a significant theoretical contribution” and “a courageous step in the right direction” (Gartner, 2001, pp. 29, 35). The main problem with Gartner’s (1988) approach is that it does not emphasize the discovery process. Further, his approach directs no or only cursory attention to the possibility of alternative modes of exploitation for given “opportunities” (Shane & Venkataraman, 2000; Van de Ven, Angle & Poole, 1989). If interpreted as a delineation of the (entire) scholarly domain his take on entrepreneurship appears overly narrow in these regards.

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On the other hand his perspective may seem overly permissive in that he does not explicitly restrict what kind of emerging organizations qualify. Taking the argument to extremes, a new stamp collectors’ club and even a new anthill or school of fish is a “new organization.” Many would be reluctant to accept these as instances of “entrepreneurship.” I have not found in Gartner’s writings a clear statement regarding whether an attempt to create a new organization has to be successful in order to constitute entrepreneurship. It is possible to read into his argument that regarded as a societal phenomenon entrepreneurship consists of the actual emergence of new organization, i.e. that success is required. His emphasis on behavior (Gartner, 1988) and his involvement in real-time study of start-up processes (Carter, Gartner & Reynolds, 1996; Gartner & Carter, 2003) clearly suggest that start-up attempts regardless of outcome qualify as the object of study for the scholarly domain. The creation of a new organization is a special case of organizational change. I have argued above that organizational change does not in itself constitute entrepreneurship. My argument may thus seem decidedly anti-Gartnerian. This is not my intention. I regard Bill Gartner as one of the greatest intellectual contributors to the field of entrepreneurship research, in particular for re-directing interest from characteristics of small business owners to behavior in the entrepreneurial process. Importantly, while I would challenge that “the fundamental outcome of entrepreneurial behavior is the organization itself” (Gartner & Carter, 2003) it is important to note that his “creation of new organization” should not necessarily be read as “creation of new, owner-managed firms.” Gartner (1988, p. 28) explicitly discusses internal venturing. Although he – arguably with good reason – regards the emerging new firm as a particularly promising arena for studying it, his interest is in “organizing” in the Weickian sense (Gartner, 2001, p. 30; cf. Gartner & Carter, 2003), not necessarily the creation of formal and legally defined organizations. Organizing is an important aspect of the exploitation process for all new activity regardless of the formal or legal organizational context. In conclusion, I see Gartner’s focus on organizing as an incomplete domain delineation (of entrepreneurship) because it disregards the discovery process. The focus he suggests is, I believe, the natural task for an organization theorist to take on within a somewhat broader domain.

Combining Gartner’s and Venkataraman’s Perspectives As I see it, Shane and Venkataraman’s and Gartner’s views on entrepreneurship are not opposing but compatible and complementary. While highlighting different aspects of the entrepreneurial process as being the most fundamental, the two


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perspectives are far from clashing heads on. In order to further develop the scholarly domain of entrepreneurship I believe we should try to combine and extend their respective contributions. In his various writings, Gartner has established at least three very important foundations for entrepreneurship as a scholarly domain: Entrepreneurship is about behavior (rather than dispositions/characteristics). Entrepreneurship is a process. Entrepreneurship is about emergence. Shane and Venkataraman (2000) have adopted these three aspects of Gartner’s reasoning. In addition, they offer important broadening of Gartner’s domain: The entrepreneurial process consists of two sub-processes, discovery and exploitation. Entrepreneurship leads to the emergence not only or primarily of new (independent) organizations, but to the emergence of new goods or services. While their emergence has to be organized (an important part of the exploitation process) this can occur within new or established organizations, i.e. through different modes of exploitation. Entrepreneurship can have a range of interesting and important outcomes on different levels. Emphasizing a disequilibrium perspective, Shane and Venkataraman (2000; cf. Eckhardt & Shane, 2003) also suggest a particular perspective on the economic system. Although some pressure towards conformity should be admitted (Aldrich, 1999; Raffa, Zollo & Caponi, 1996) I believe it sound to regard this as fundamental to this scholarly domain: The economy is characterized by heterogeneity; this remains a permanent and fundamental feature of economic actors and environments.13 In a powerful manner, this combination of the two perspectives offers clear direction for the field. The scholarly domain of entrepreneurship should study the processes of discovery and exploitation from a behavioral perspective under the assumption of heterogeneity, taking an interest in different types of outcomes on different levels of analysis. Standing on the shoulders of solid predecessors also allows us to see – and hopefully solve to some extent – additional conceptual issues that have to be dealt with before we have achieved a strong paradigm for entrepreneurship research. Some of the issues I believe need further elaboration are the concept of “opportunity” and the role of uncertainty, as well as the meaning of “discovery”

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and “exploitation” and the interrelatedness of these two processes. This is what I will turn to in the immediately following sections.

Uncertainty and the Concept of Opportunity In addition to the seven points derived from Gartner’s and Venkataraman’s perspectives, let me suggest as the eighth fundamental point for the scholarly domain of entrepreneurship that: The economy is also characterized by uncertainty; this remains a fundamental feature of most economic actions and environments in the context of discovery and exploitation of ideas for new goods and services. What I refer to here is genuine, Knightian uncertainty (Knight, 1921), i.e. a situation where the future is not only unknown, but also unknowable (Sarasvathy, Dew, Velamuri & Venkataraman, 2003). I do not argue that all decisions for all actors are non-calculable. However, the situations in which behaviors aimed at creating new economic activity are undertaken often have this characteristic. That is, information collection and processing, careful planning and calculation cannot give a conclusive and reliable answer as to whether something will be successful or not; only (trial) implementation will tell. Very rarely are entrepreneurial situations certain in the way Kirzner (1973) portrays them. Kirzner likens entrepreneurial opportunity with realizing that a free ten-dollar bill is resting in one’s hand, ready to be grasped. If we should use the ten-dollar bill metaphor at all, I would suggest the true situation is more like spotting the bill from your balcony. From that distance one would face the (calculable) risk that the bill was for anything from one to a hundred dollars. But moreover, while you dash down the stairs it may blow away, or someone else may get it before you, or it may turn out upon closer look that it was not a real money note, after all, but some kind of toy money. There is no way the finder can tell before she takes the decision to run down the stairs. In order to understand behaviors in such situations it is important to start from a theoretical perspective that acknowledges or even emphasizes uncertainty. This brings us to the concept of opportunity. The increased use of this concept in entrepreneurship has been accompanied with increased attention to the earliest phases of the entrepreneurial process, which is sound development. However, despite its recent popularity and apparent centrality to entrepreneurship (de Koning, 1999b; Eckhardt & Shane, 2003; Gaglio, 1997; Hills & Shrader, 1998; Sarasvathy et al., 2003; Shane & Venkataraman, 2000), there is reason to question whether “opportunity” really is a very useful concept for entrepreneurship research. By


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Casson’s (1982) definition, which Shane and Venkataraman (2000) adopt, an opportunity is known to be profitable. By almost any definition, an opportunity is known to be a favorable situation. For example, the Oxford English Dictionary (cited from Sarasvathy et al., 2003) defines opportunity as “A time, juncture, or condition of things favorable to an end or purpose, or admitting of something being done or effected.” Therefore, the term “opportunity” is fundamentally opposed to acknowledging uncertainty as an inescapable aspect of the environment of the emerging activity and/or organization that the entrepreneurship scholar tries to study and understand. At the time, the actors cannot know whether or not what they pursue is an “opportunity.” If we take Shane and Venkataraman’s (2000) delineation of the field and their definition of opportunity at face value, we have a suggested scholarly domain that should only study successful cases, and which is – in practice – restricted to doing so retrospectively (cf. Baumol, 1983; Singh, 2001). If only profitable “opportunities” are studied, Shane and Venkataraman’s (2000) second and third sets of research questions (about individual differences and different modes of action) would not address why some people pursue unprofitable venture ideas or why in some cases a particular mode of exploitation leads to an unprofitable result. These appear to me to be highly interesting and relevant questions for entrepreneurship research, which relate to the uncertainty emerging activities operate in.14 This is the first major problem with the opportunity concept. Entrepreneurship as a scholarly domain needs to regard on-going emergence as an instance of entrepreneurship, and acknowledge the uncertainty that typically surrounds such activity. The positively laden concept “opportunity” as normally understood and defined is therefore an unsuitable label for on-going pursuits. One way to solve this problem is to consistently talk about perceived opportunity as long as a situation’s profitable or favorable nature is unproven. However, resorting to “perceived opportunity” is not an ideal solution because of its inherent ambiguity. It could mean either “objectively existing opportunity, and also perceived” or “perceived to be an opportunity, but not (necessarily) objectively being one.” This brings us to the second major problem with the opportunity concept. This is the question whether “opportunities” objectively exist or if the actor creates them. That is, do “opportunities” exist “out there,” independently of a person identifying and acting upon the opportunity, or do entrepreneurs create “opportunities” where none existed before they conceived of them? This is a hotly debated issue where scholars tend to take strong positions, which points to a risk for a major divide among entrepreneurship researchers. This problem is aggravated by the fact that leading proponents of the perspectives that I try to merge and extend tend to take different sides on this issue (cf. Eckhardt & Shane, 2003; Gartner & Carter, 2003; Shane & Venkataraman, 2000; Venkataraman, 1997). However, rather

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than being deeply ontological I believe those differences to be based largely on semantic issues. Scholars may take different positions in part because they, simply, mean different things when they use the concept. With a more refined view of “opportunity” and its “components” (cf. Moran & Ghoshal, 1999) more agreement is possible to achieve. As I see it, there are at least three possibilities: Objectivist: Opportunities exist “out there” as individual, “ready-to-use” entities. They are like mushroom in the forest. Some are bigger and some are smaller; some grow early and some grow late. Although they are not necessarily easy to find, they are out there, and they are equally big and equally accessible to anyone who goes to or happens to be in the forest. Objectivist-Subjectivist: Opportunities exist “out there” as individual, “ready-touse” entities. However, because of individual differences in perception, knowledge and skills, all actors do not have access to exactly the same opportunities.15 It is like mushroom-picking for the chosen few. The mushroom still exist “out there” at the same times and sizes for all actors, but some of us have developed better perceptual abilities regarding well-camouflaged mushroom, and can therefore find opportunities hidden for others. Alternatively, some only know about a few types of edible mushroom whereas others are experts and find edible species all over the place. Further, some are good chefs and can convert the mushroom into a delicacy, whereas others mess up in the kitchen due to lacking cooking skills. That is, after successful discovery they fail in the exploitation process. Subjectivist-Creative: Opportunity is not about anything existing “out there” at all. Opportunities are created in the entrepreneur’s mind and it is not meaningful to talk about these opportunities separate from their creators. If it has anything at all to do with mushroom, it is because the actor chose to paint or sculpture a mushroom (which could be two-dimensional, blue in color, five feet high, make funny noises – and be started as an attempt to create an apple rather than a mushroom).16 The objectivist position clashes with our heterogeneity assumption and seems to have few if any followers among entrepreneurship scholars who ever gave the issue a thought. Shane and Venkataraman (2000; cf. Shane, 2000a, b; Eckhardt & Shane, 2003) seem to favor the objectivist-subjectivist position, whereas writers like de Koning (1999b), Gartner and Carter (2003) and Sarasvathy (2001) are more supportive of the subjective-creative perspective. Now, who is right? Both, I believe. As I see it, the following three points should be easy for most scholars to agree upon. (a) Opportunity exists out there, independently of particular actors. However, opportunities do not exist as complete, individual entities. Rather, opportunity exists as an uncountable in the form of technological possibilities, knowledge, and unfulfilled human needs backed with purchasing power.


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(b) Venture ideas are the creations of individuals’ minds. They are specific (but changeable and more or less elaborate) entities that are acted upon. Whether these reflect opportunity or not can only be known afterwards and – paradoxically – only when the outcome was successful (because failure may be due either to poor exploitation or to lack of opportunity; cf. Eckhardt & Shane, 2003). (c) Because of differences in knowledge, skills, motivations and other dispositions, individuals (and firms) differ from one another as regards what venture ideas they can and will pursue and as regards what external opportunity they can profitably exploit, and how. Let us take the Ice Hotel – an unlikely but highly successful international tourist attraction in the far north of Sweden – as our example. Dismissing entirely the idea that opportunity exists “out there” in this case means denying that its success and viability has anything to do with its location in a dark, cold and remote (i.e. exotic) location, albeit within reasonable reach for international air travel. Dismissing the notion that venture ideas are the creations of individuals’ minds would mean arguing that the specific Ice Hotel concept – this particular response to the co-existence of coldness, darkness and remoteness in one place and wealthy potential tourists hungry for new experiences in other places – somehow existed before an entrepreneur (Yngwe Bergkvist) conceived of it. Dismissing the third point means holding that you or I would be equally likely as Mr. Bergkvist to come up with the Ice Hotel idea and/or succeeding with it. Apart from those who subscribe to extreme ontological positions at any cost, I think it should be much easier for scholars to agree with all of the above three points than to refute one or more of them. It is not fruitful for entrepreneurship as a scholarly domain that a central concept like “opportunity” is used for: (i) a set of external conditions known in retrospect to be favorable (to some people) for the successful discovery and exploitation of new business activities; (ii) a set of external conditions thought (by some people) but not proven to be existing and favorable for the successful discovery and exploitation of new business activities; (iii) specific new venture initiatives known in retrospect to be viable; and (iv) specific new venture initiatives that are currently being pursued but whose viability is not yet proven. The term “opportunity” is particularly misleading for the last category, which at the same time arguably is the most central unit of interest for the scholarly domain of entrepreneurship. I suggest this entity be referred to as Venture Idea in order to underline that its viability is not yet proven and to disconnect it from any argument as regards to which extent it is externally or internally based. Venture ideas are internally generated (i.e. created in individuals’ minds) based on more

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or less explicit and more or less “correct” perceptions of external conditions. Over time, they can change and become more and more elaborate. This leads us to: The entrepreneurial discovery process starts with the conception of a venture idea. This venture idea, including the activities and structures that evolve around it, is the focal unit of interest in entrepreneurship research.

The Meaning and Interrelatedness of “Discovery” and “Exploitation” The term discovery may be suspected to reflect an objectivist view on venture ideas, i.e. that they somehow exist “out there,” ready to be discovered. It should be clear from the above that this is not the view I recommend. Rather, like Eckhardt and Shane (2003) I use the term “discovery” to maintain consistency with prior literature, despite its potentially misleading connotations. Discovery refers to the conceptual side of venture development, from an initial idea to a fully worked out business concept where many specific aspects of the operation is worked out in great detail, especially as regards how value is created for the customer and how the business will appropriate some the value (de Koning, 1999b, p. 121). Importantly, discovery is a process – the venture idea is not formed as a complete and unchangeable entity at a sudden flash of insight. Thus, it includes not only what is elsewhere called “idea generation,” “opportunity identification” and “opportunity detection,” but also “opportunity formation” and “opportunity refinement” (Bhave, 1994; de Koning, 1999a, b; Gaglio, 1997). Also importantly, discovery refers to the initial conception and further development of a venture idea, not a proven opportunity. In some cases, then, the discovery process ends with the realization that the venture idea did not reflect favorable external conditions in the way the involved actors initially thought it did. The term exploitation may evoke negative associations from its use in other contexts. In the present context I would suggest it is a neutral term referring to the decision to act upon a venture idea, and the behaviors that are undertaken to achieve its realization. The exploitation process deals primarily with resource acquisition and co-ordination, as well as market making (see Eckhardt & Shane, 2003; cf. also Sarasvathy, 1999a; Van de Ven, 1996). Exploitation thus has to do with the attempted realization of ideas, and should in this context carry none of the negative connotations associated with the word “exploitation” is certain other contexts. Like discovery, exploitation is a process that may or may not lead to the attainment of profit or other goals. Further, the sequential feel of the phrase “discovery, evaluation and exploitation” may give the impression of a linear, orderly process. In line with empirical


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evidence (Bhave, 1994; de Koning, 1999b; Sarasvathy, 1999a) I think discovery and exploitation are best conceived of as overlapping processes. For example, an entrepreneurial process may start with an individual perceiving what she thinks is an opportunity for a profitable business [discovery]. In the efforts to make this business happen, contacts with resource providers and prospective customers [exploitation] make it clear that the business as initially conceived will not be viable [feedback to discovery]. The individual changes the business concept accordingly [discovery] and continues her efforts to marshal and coordinate the resources needed for the realization of the revised business concept [exploitation]. Although the above process starts with an element of discovery, this is not necessarily always the case. Empirical research suggests that venture creation processes can follow almost any sequence (Carter et al., 1996; Gartner & Carter, 2003).17 With those clarifications, I hope that a broader set of scholars are prepared to accept the notions of discovery and exploitation processes as useful conceptual tools for the scholarly domain of entrepreneurship.

Core Research Questions for the Scholarly Domain of Entrepreneurship Returning to Fig. 1, according to the perspective developed here entrepreneurship research is research that ask questions about real or manipulated instances of “new offer,” “new competitor” or “geographical market dispersion” (quadrants I and IV). Relating to the heterogeneity issue, a seriously under-research area here concerns the characteristics of new venture ideas and how these characteristics relate to antecedents, behavior and outcomes. Samuelsson (2001) represents one of the few entrepreneurship studies that have explored the nature and effects of characteristics of venture ideas, and followers are needed. While an abundance of studies have tried to assess the characteristics of entrepreneurs, very few have focused on the characteristics of the venture ideas they pursue (cf. Shane & Venkataraman, 2000, p. 218).18 With regard to quadrant II, while organizational changes do not in themselves represent entrepreneurship they remain important possible antecedents in entrepreneurship research. Therefore, studies referred to by Ucbasaran, Westhead and Wright (2001, p. 64) showing that management buy-outs are associated with increased development of new products, are examples of entrepreneurship research. Empirical tests of Stevenson’s argument that certain organizational changes would facilitate entrepreneurship in established organizations (Stevenson, 1984; Stevenson & Jarillo, 1986) would clearly be instances of entrepreneurship research, as would empirical tests of the relationship between “entrepreneurial orientation” on the one hand, and actual discovery and exploitation behaviors on

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the other (cf. Lumpkin & Dess, 1996). Creation of new organizations (Gartner, 1988) remains a very central aspect of the exploitation process in entrepreneurship research, at least as long as these new organizations aim at “new offer” or becoming a “new competitor.” Finally, quadrant III (business as usual, and non-entrepreneurial growth) does not exemplify entrepreneurship but can be included in entrepreneurship research for comparative purposes. In relation to Fig. 2, I suggested above that only success ventures and catalyst ventures exercise entrepreneurship as we defined the societal phenomenon. However, entrepreneurship as a scholarly domain should not delimit its empirical study to these two categories but include also re-distributive ventures and failed ventures. Indeed, I would suggest that in showing a genuine interest in outcomes on different levels, and in providing a more refined and empirically informed view on “failure,” entrepreneurship can distinguish itself from other fields and make strong contributions to social science at large (cf. Low, 2001; Venkataraman, 1997). The question of when successful venture level outcomes are and are not associated with successful outcomes on the societal level, and vice versa, is highly relevant but seldom asked. It is conceivable that under certain circumstances the successful pursuit of ideas for new ventures does not benefit society (cf. Baumol, 1990). It is also possible to conceive of a situation where entrepreneurial efforts on the whole benefit society while at the same time the most likely outcome on the micro-level is a loss – and that therefore the rational decision is to refrain from entrepreneurship (cf. Olson in Sarasvathy, 1999b, p. 35). Both of these situations represent important problems that entrepreneurship research can help societies to solve or avoid. The question of differential outcomes on different levels can also be asked from the perspective of the corporate manager: when and why does and does not new venturing – successful or not at the venture level – contribute to company performance? Again, because of potential learning and cannibalization the answer is not a simple one to one relationship between venture- and organizational level outcomes. The issue of catalyst ventures, then, is of particular interest. Too narrow or simplistic a view on “failure” may lead to gross misrepresentation of the benefits of attempts to create new business activity, on micro – as well as aggregate levels. What in a narrow perspective appears to be a “failure” may instead be a beneficial “catalyst” either because those directly involved in the failure learn for the future or because others imitate. Kogut and Zander (1992) discuss the first possibility while Van de Ven (1996) casts some doubt on the extent to which learning really takes place. Aldrich (1999) and McGrath (1999) discuss both possibilities. A possible outcome of deeper and more refined research into apparent “failure” is that pure failure as defined in Fig. 2 is far less usual than previously thought (cf. Gimeno, Folta, Cooper & Woo, 1997, pp. 69 and 72). I think one of the first things entrepreneurship scholars should try to get rid of is the bias against failure. In


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addition to the “catalyst” potential, both theory and empirical evidence actually suggest that experimentation that may end in failure as well as the demise of less effective actors are necessary parts of a well-functioning market economy (Davidsson, Lindmark & Olofsson, 1995; Eliasson, 1991; Reynolds, 1999; Schumpeter, 1934). An alternative point of departure for a discussion of core research questions are the four sets of research questions that Shane and Venkataraman (2000, 2001) suggested for the scholarly domain of entrepreneurship research. Adapted to our above reasoning these questions read as follows: (1) Why, when, where, how and for whom does opportunity for the creation of new goods and services come into existence? (2) Why, when and how do individuals, organizations, regions, industries, cultures, nations (or other units of analysis) differ in their propensity for discovery and exploitation of new venture ideas? (3) Why, when and how are different modes of action used to exploit venture ideas? (4) What are the outcomes on different levels (e.g. individual, organization, industry, society) of efforts to exploit venture ideas?19 The first question is about the existence of entrepreneurial opportunity. It requests empirical study of when and why (as well as “where” and “for whom”) “real” opportunity has come into existence, e.g. as a result of technological or institutional changes. As depicted in Fig. 3, it is a question that can be asked at different types of entities or levels of analysis, e.g. for nations, regions or other spatial units over time or across space, as well as for organizations, industries or population sub-groups. Asking this question is a prerequisite for building strong theory about where opportunity will emerge in the future. Building such theory is a challenging but important aspect of scholarship in entrepreneurship, which feeds directly into entrepreneurship education (cf. Davidsson, 2002) where learning where to look for opportunity should be one of the most central features (cf. Drucker, 1985; Vesper, 1991). As noted earlier, proven “opportunity” can only be studied in retrospect. That, however, is not the only problem. As remarked in an earlier note it is impossible to know the universe of not-yet-discovered, but potentially viable, venture ideas. Therefore, not even the number of venture ideas that are both acted upon and proven successful is a direct measure of opportunity-density.

Fig. 3. Graphical Representation of RQ 1.

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Fig. 4. Graphical Representation of RQ 2–4.

It is inescapable that whatever measure is used for opportunity density, it will be a proxy measure. Research questions 2–4 are graphically represented in Fig. 4. As I see it, the middle box is a necessary part of the research design for these questions to be “entrepreneurship research,” whereas the outer boxes may or may not be included in the research design. In other words, an explicit focus on behaviors in the discovery and/or exploitation processes, or on the existence and/or characteristics of venture ideas as discussed above, or on or both, is required (cf. Davidsson & Wiklund, 2001). As discussed earlier, I think that questions 2–4 have to be addressed for venture ideas rather than only for “opportunities” proven to be profitable. Otherwise, many aspects of question 4 would not be very meaningful. With regard to question 2, I agree with Shane and Venkataraman (2000) that individual(s) should remain a core interest for entrepreneurship research, at least when the interest in personality is replaced by an interest in knowledge and cognition (Busenitz & Barney, 1997; Shane, 2000b). However, when the individual is used as the level of analysis it is often advisable not to have a single event (e.g. being or not being in the process of starting a firm; acting or not acting upon a particular venture idea) as the dependent variable. In order to be able to single out what was truly attributable to the individual from the idiosyncrasies of the particular venture idea a more appropriate analysis might be to relate individual characteristics to patterns of repeated entrepreneurial behavior (cf. Davidsson & Wiklund, 2001; Ucbasaran et al., 2001; Venkataraman, 1997). I have generalized question 2 also to other levels of analysis. That is, it should include the questions about why, when and how, e.g. organizations, networks, competence clusters, regions, industries, cultures or nations differ as regards discovery and exploitation propensity, in addition to differences in opportunity density (cf. Reynolds, Storey & Westhead, 1994; Shane, 1992). For example, due to the distinction between “opportunity-based” and “necessity-based” entrepreneurship, nations and regions may have similar firm start-up rates for very different reasons, and representing very different levels of real, profitable opportunity. Therefore, also the quality of the enacted ideas has to be considered (Davidsson, 1995; Reynolds, Camp, Bygrave, Autio & Hay, 2001). The same problem is likely to occur on the organizational level. A firm desperately struggling


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for its survival may take more new initiatives than a firm that is doing well, even if more objective opportunity is available for the latter (March & Sevón, 1988). The third question concerns modes of action. This can be understood as including all aspects of behavior in the process. This very central set of questions – what individuals and other economic entities actually do in order to come up with ideas for businesses, how they refine those ideas, and make them happen – needs much more investigation. Researchers have only just begun to address them seriously (e.g. Bhave, 1994; Carter, Gartner & Reynolds, 1996; Chandler, Dahlqvist & Davidsson, 2002; Delmar & Shane, 2002; Fiet & Migliore, 2001; McGrath, 1996; Samuelsson, 2001; Sarasvathy, 1999a).20 One important aspect that this question highlights is the need for studies that apply the “venture idea” itself as the unit of analysis (Davidsson & Wiklund, 2001). This is a possibility rarely used or even considered by researchers in other fields. Applying this level of analysis is highly relevant for entrepreneurship research, and a possibility for entrepreneurship researchers to make unique contributions. Such studies would follow samples neither of individuals nor of organizations, but precisely new, emerging activities – i.e. venture ideas and what evolves around them – from their conception and through whatever changes in human champions and organizational contexts might occur along the way. With this approach the mode of exploitation would not be locked in by the design. In some cases what originated as a de novo start-up is transferred to an existing firm; in other cases what originates within a firm may be spun out at an early stage. Case studies describing the process in detail (Van de Ven et al., 1999) as well as survey studies designed for the purpose (Chandler et al., 2002) can apply this level of analysis. Concerning the fourth question entrepreneurship researchers can make a contribution by employing a more complex and less narrow-sighted view on outcomes, as discussed above in relation to Fig. 2. As indicated in Fig. 4 this entails also following up on outcomes on more than one level. Put differently, both direct and indirect outcomes are of interest. Not only venture and societal levels are of interest (and the latter may be very hard to assess). For example, in a venture-level study, as discussed above, outcomes can be assessed for its host organization (if any) and for the industry, in addition to assessing the venture-level outcome. Different types of outcomes are also of interest. Entrepreneurial processes do not only have financial outcomes, and affect not only those directly involved in the project. Supplementary outcome assessment may concern, e.g. satisfaction, learning, imitation and retaliation. Related to the issue of heterogeneity, Venkataraman (1997) raises the important issue that relative (financial) performance may often not be an adequate outcome measure for entrepreneurship research. Venkataraman focused on firm performance but the problem is the same for other levels of analysis as well. If

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heterogeneity is taken seriously the following situation is conceivable. Individual A chooses to try to exploit a venture idea (x) that actually has less potential than the best possible idea she could have pursued with success. At the same time, individual B manages to find and act upon the very best venture idea available to her (y). Further, individual A’s exploitation of x is substandard relative to what would have been possible for her, whereas individual B’s exploitation of y reaches the theoretical maximum. And yet, while y performs to B’s satisfaction and even exceeds her expectations, x performs better than y when we compare across them. To whom and for what purpose is this relevant to know? And if it is relevant to know, would not in this case other yardsticks be more relevant to A and B as well as to outside observers? It is not easy to give clever and general advice on what should be done instead of assessing relative performance. However, Venkataraman’s (1997) observation should caution against habitual and mindless application of relative performance as the (sole) outcome measure in entrepreneurship research. Another set of “questions within the questions” that are seriously underresearched – and perhaps under-emphasized above – concerns the several issues of fit that arise from heterogeneity along several dimensions. This concerns fit between individuals’ prior knowledge and (information about) the new opportunity (e.g. Cooper, Folta & Woo, 1995; Shane, 2000a, b); relatedness between organizations’ prior knowledge, resources or capabilities and (information about) the new potential venture (e.g. Cohen & Levinthal, 1990; Teece, Pisano & Shuen, 1997; Van de Ven, 1996); relatedness of the knowledge and characteristics of key individuals involved in the processes, i.e. the homogeneity or heterogeneity of the team (e.g. Nahapiet & Ghosal, 1998; Zahra & Wiklund, 2000); fit between existing resources and what strategies can lead to the venture’s success (e.g. Chandler & Hanks, 1994) and fit between characteristics of the new potential venture and current user practices (e.g. Raffa, Zollo & Caponi, 1996). Empirically based knowledge on these issues is limited, which means abundant opportunity for research contributions.

Conclusions on Entrepreneurship as a Scholarly Domain Summing up the above, when we think of the scholarly domain a suitable definition of entrepreneurship could be the behaviors undertaken in the processes of discovery and exploitation of ideas for new business ventures. This definition connects well to Venkataraman’s and Gartner’s perspectives without presupposing the outcome. Not even this definition, however, fully describes the scholarly domain. Therefore, I would propose the following delineation of entrepreneurship as a scholarly domain:


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Starting from assumptions of uncertainty and heterogeneity, the scholarly domain of entrepreneurship encompasses the processes of (real or induced, and completed as well as terminated) emergence of new business ventures, across organizational contexts. This entails the study of the origin and characteristics of venture ideas as well as their contextual fit; of behaviors in the interrelated processes of discovery and exploitation of such ideas, and of how the ideas and behaviors link to different types of direct and indirect antecedents and outcomes on different levels of analysis. As I see it, this delineation is broad enough to encompass core issues highlighted by Venkataraman’s and Gartner’s respective perspectives. At the same time, it is distinct enough to be the basis of a community and coherent theory building (Gartner, 2001; Low, 2001). As will be discussed in the next main section I would argue that this scholarly domain holds promise of explaining and predicting “a set of empirical phenomena not explained or predicted by conceptual frameworks already in existence in other fields” (Shane & Venkataraman, 2000) although considerable overlaps exist, which entrepreneurship researchers should take advantage of.

What Has Been Left Out? I have excluded from the suggested domain delineation criteria like “value creation,” “wealth creation,” or other indicators of “success,” arguing that such criteria belong in a definition of entrepreneurship as a societal phenomenon (cf. Section 2 above). I have further excluded criteria like purpose and motivation, skill or expertise, and expectations of gain for self (cf. Bull & Willard, 1993; Cole, 1949; Fiet, 2002; Gartner, 1990; Hisrisch & Peters, 1989) from the definition of the phenomenon as well as from the domain delineation. This is because these are, as I see it, not necessary ingredients of entrepreneurship as a societal phenomenon or scholarly domain. They are of central interest, however, from a third perspective: entrepreneurship as a teaching subject. Entrepreneurship students can be assumed to expect to learn what it takes to succeed in entrepreneurial endeavors, and it is therefore understandable that scholars who have the teaching subject in mind want to include criteria of this kind. For entrepreneurship as a scholarly domain or societal phenomenon I would argue, however, that they are unnecessary and potentially misleading restrictions. The American Academy of Management Entrepreneurship Division includes in its domain statement also issues like self-employment, small and family business management, and management succession (cf. Gartner, 2001). Such topics may also make sense from the perspective of entrepreneurship as a teaching subject. Teaching is directed at individuals; in this case often entrepreneurs

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or would-be-entrepreneurs, meaning “business founders” (and sometimes also “venture champions”). From this perspective, entrepreneurship easily becomes “anything that is of great concern to entrepreneurs, i.e. business owner-managers.” Business founders become – and often stay – self-employed. If they stay with the business they have founded they become managers of a small business, which often engages other family members as well. Eventually, the issue of succession becomes a major concern. Therefore, these issues become natural parts of courses or programs in “entrepreneurship.” However, unless explicitly related to competitive behaviors that drive the market process the issues pertaining to self-employment, or smallness, or family, or succession do not reflect the societal phenomenon “entrepreneurship.” A scholarly domain could be delimited to the concerns of business owner-managers over their lifetime. However, this would be a domain far removed from our definition of the societal phenomenon, because small and independently owned businesses do not necessarily have a larger role in driving the market process than have other types of suppliers. Further, given the diversity of issues such a domain would encompass, it would also be a poor prospect for coherent theory development (Gartner, 2001). Many scholars who are interested in entrepreneurship as defined above also have interests in – and do research on – issues like firm growth or small business management. We should not give up those other interests. However, we should be careful not to inappropriately put the “entrepreneurship” label on them. Careless application of that label gives the entrepreneurship domain a “hodgepodge” or “pot-pourri” appearance, which hinders theory development and academic legitimacy (Gartner, 2001; Low, 2001; Shane & Venkataraman, 2000). When small- or family business research explicitly addresses characteristics of venture ideas and/or behaviors in the processes of discovery and exploitation, it qualifies as entrepreneurship research. When these issues are not explicitly treated in the research, I strongly suggest the entrepreneurship label not be used.

THE DISTINCTIVENESS OF THE ENTREPRENEURSHIP DOMAIN It appears that for Venkataraman (1997; Shane & Venkataraman, 2000) and for Gartner (1988, 2001) establishing entrepreneurship as a distinct (and respected) scholarly domain, is an important goal. One could counter-argue that entrepreneurship research is best pursued within established disciplines like economics, psychology and sociology as well as within the various branches of management studies. A first strong reason for this is that despite Shane and Venkataraman’s (2000) alleged focus on explaining and predicting phenomena


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not explained or predicted in other fields, there are few contingencies of interest to entrepreneurship scholars that are not the topic of theory in at least some discipline in the social sciences (cf. Acs & Audretsch, 2003a; Delmar, 2000; Thornton, 1999). Not making full use of the tools available within the disciplines would appear to be a wasteful practice. Second, disciplinary research is required to meet the quality criteria of the respective discipline. Thus, the pursuit of entrepreneurship questions by disciplinary researchers should be a way for entrepreneurship to attain academic respectability. Therefore, I agree with Low (2001, p. 23) that entrepreneurship as a distinctive domain is desirable but not viable in isolation, i.e. without theoretical input and quality standards from other fields of research. It is not so easy, however, that all the theory entrepreneurship researchers need already exists in the disciplines. No matter how sophisticated the tools, they may not always be adequate for the task at hand (cf. Davidsson & Wiklund, 2000). Under the perspective on entrepreneurship research that I have developed some of the questions one should ask before applying existing theory “as is” are the following: (1) Does the theory acknowledge uncertainty and heterogeneity? (2) Can it be applied to the problem of emergence, or does it presuppose the existence of markets, products or organizations in a way that clashes with the research questions? (3) Does the theory allow a process perspective? (4) Does it apply to the preferred unit of analysis (e.g. “venture idea” or “emerging venture” rather than “firm” or “individual”)? (5) Is it compatible with an interest in the types of outcomes that are most relevant from an entrepreneurship point of view? Theories exist, and whenever possible, entrepreneurship research should deductively test theory from psychology, sociology and economics as well as from various branches of business research. However, as a scrutiny of some existing theories in relation to the five questions above would show, they are not always optimal for research questions addressing the processes and analysis levels of most relevance to entrepreneurship research. Therefore, the domain must allow also for filling gaps and asking new questions through inductive, theory-building approaches. Thus, the existence of disciplinary theories that relate to all or most entrepreneurship research questions does not prove there is no need for entrepreneurship as a distinct scholarly domain. In addition, if it is clear that most core research questions in entrepreneurship would fit in some discipline, it is equally clear that entrepreneurship is not in its entirety a sub-division of any one established discipline (Shane & Venkataraman, 2001). As pointed out by Low (2001) the obvious

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problem with leaving entrepreneurship research to the disciplines is the lack of community. The existing community of long-time entrepreneurship scholars has a portfolio of hard-earned, close-up knowledge about entrepreneurship, e.g. that it is not primarily about individual heroes; that most start-ups are imitative and not very growth-oriented; that only a tiny minority ever use venture capital; that available data sources typically do not cover the early stages of emergence of new ventures; that “start-up” is not one event but a series of behaviors that may be undertaken in different order and over very different durations of time; about the extent of heterogeneity of entrepreneurial efforts along a number of dimensions, and so forth. Temporary visitors would no doubt be na¨ıve about many of these things, as were entrepreneurship researchers in the formative stages of the field. Therefore, without the trading of such knowledge through a research community, strong theoretical and methodological schooling from disciplines may not suffice for making maximally fruitful contributions to the understanding of entrepreneurial phenomena. The issue of lack of community is a very real problem. In an interesting and comprehensive citation analysis, Landström (2001) has shown that the recent tremendous growth of entrepreneurship research has been accompanied by an increasing share of “transitory” contributors, i.e. scholars with their main home in some mainstream discipline and just one publication in entrepreneurship. In addition, his analysis shows that despite their disciplinary training these scholars are largely non-influential. This supports the idea that disciplinary knowledge has to be combined with deep familiarity with the phenomenon in order to make really valuable contributions. The strongest argument for entrepreneurship (also) as distinct domain, however, is the following. If left to the disciplines, there is no guarantee that a lot of research would be conducted on the most central questions of entrepreneurship, as we have here outlined that scholarly domain. Many of these questions may be peripheral to every discipline (cf. Acs & Audretsch, 2003b). Therefore, a failure to collectively cover the entrepreneurship agenda is neither a problem nor a shortcoming on the part of the disciplines. When an interest in maximum knowledge development about entrepreneurship is the vantage point, however, it becomes a problem. Concluding the discussion of the domain vs. the disciplines I would argue that knowledge about entrepreneurship is best developed if deep familiarity with the phenomenon is combined with disciplinary knowledge and standards. This can be achieved in three ways: (a) researchers who focus their research more or less exclusively on entrepreneurship learn more theory and method from the disciplines; (b) disciplinary researchers who occasionally apply their knowledge to entrepreneurship read a lot of entrepreneurship research before conducting and publishing their studies; and (c) direct collaboration between topical and disciplinary experts.


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I would argue that all three of these are more likely to happen in the presence of a distinct, coherent and acknowledged domain of entrepreneurship research.

SOME FURTHER SUGGESTIONS FOR EMPIRICAL RESEARCH IN ENTREPRENEURSHIP Although there has been considerable progress in empirical entrepreneurship research, much more is needed in order to realize the potential of entrepreneurship as a scholarly domain. At the time of this writing it is still the case that a lot of the research presented in “entrepreneurship” outlets address research questions that are not clearly about entrepreneurship as defined here – or anywhere. Neither are they always clearly different from research questions addressed in other fields. Most studies are cross-sectional rather than process-oriented, and work with samples of existing firms or established business owner-managers rather than emerging new ventures and people in the process of becoming entrepreneurs (Aldrich & Baker, 1997; Chandler & Lyon, 2001; Davidsson & Wiklund, 2001). But there are hopeful signs. For example, in the Frontiers of Entrepreneurship Research 2001 (Bygrave et al., 2001) the first 172 pages were devoted to Nascent and Start-up Entrepreneurs, Opportunity Recognition, and New Venture Creation Process. As recently as in the 1999 edition most of the first 200 pages were devoted to “characteristics of entrepreneurs” (Reynolds et al., 1999). Below I will offer some guidance that I hope can help strengthen this positive trend.

General Design Issues Before going into any detail, let us just point out some of the most important and obvious implications for empirical work that the argument so far has given. First, theories exist. Whenever possible, entrepreneurship research should apply existing theory, after ascertaining that the theory is conceptually adequate for the task. However, the domain must allow also for filling gaps and asking new questions through inductive, theory-building approaches. This will likely require both in-depth and broadly based investigations. I would argue that a systematic combination of qualitative and quantitative approaches within focused research programs has the highest probability of attaining a high yield. Second, entrepreneurship is about emergence. This means that the objects under study have to be captured at – or traced back to – a very early stage. Studying samples of established small firms or business owner-managers does not automatically capture aspects of emergence. Third, we have discussed

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entrepreneurship as behaviors in the processes of discovery and exploitation. This calls for longitudinal research. Cross-sectional designs do not capture processes very well. Fourth, we have accepted heterogeneity and uncertainty as fundamental and permanent features of the economy. This has a series of implications for selection of theories, samples, analysis methods and interpretations. Clearly, doing good empirical work on entrepreneurship is going to be difficult and requiring some creativity and ingenuity. It is not for the lazy or the faint-hearted. For those who appreciate challenges it can be all the more rewarding.

Sampling and Data Collection In my treatment of sampling and data collection issues I will put the heaviest emphasis on a kind of study that I find to be very short in supply in spite of its potential for addressing very central research questions in entrepreneurship. This is the longitudinal, real time study of samples of emerging business activity, using the venture itself as the level of analysis. I will only provide occasional commentary on other types of study. I have argued that entrepreneurship research should study the behaviors undertaken in the processes of discovery and exploitation of ideas for new business ventures. Behaviors in such processes can be studied on various levels of analysis, which entails the problem of measuring but not sampling them (cf. Fig. 4). However, as suggested above and unlike the previous preference for samples of individuals or firms (Chandler & Lyon, 2001; Davidsson & Wiklund, 2001), entrepreneurship researchers should consider using the emerging new venture itself as the level of analysis. Doing so involves several tough but interesting sampling challenges related to (non-)existence, frequency, and heterogeneity. The essence of the problem of existence is that it will be difficult to sample directly from available business registers. In many countries most new, independent start-ups remain so small that they never enter official business registers (cf. Aldrich, Kalleberg, Marsden & Cassell, 1989). When and where they do, they typically do so at a late stage. Internal ventures are even more invisible in business registers. Archival data can be of some use for aggregate level or historical studies, and advice on how emerging firms and populations can be located in archives at early stages is provided by Aldrich et al. (1989), Aldrich and Martinez (2001) and Katz and Gartner (1988). However, even with the most ingenious approach, success (selection) bias is almost certain to hamper the analysis. Because only efforts that have survived to a certain stage are included, risk-taking behaviors that increase outcome variance will be interpreted as success factors. Therefore, primary data collection techniques for capturing discovery and exploitation


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processes at early stages are needed for the study of entrepreneurial processes as they happen, without selection and hindsight biases. This leads to the problem of frequency. In the absence of a sampling frame that lists the population of emerging ventures, the sampling process has to start with something else. In the Panel Study of Entrepreneurial Dynamics (PSED) the solution was to start from a sampling frame of households to arrive at a representative sample of the adult population of individuals (Reynolds, 2000; cf. Reynolds et al., 2001). With this approach, all contacted individuals are asked a series of nested screening questions, the most important being whether they are at present trying to start a new business. Because only a small fraction is involved in entrepreneurial processes at any given time a very large sample has to be screened in order to arrive at a sizable number of cases eligible for continued study. Although there may exist ways to make the sampling more efficient (cf. Reynolds & Miller, 1992) the frequency problem will always mean that sampling for entrepreneurship studies will be expensive. So-called “snowball sampling” (Douglas & Craig, 1983, p. 213) could reduce the monetary cost but only at the cost of introducing bias. Because more and more people know about an emerging venture the longer it has been active, and because well-networked nascent entrepreneurs appear to be more successful (Davidsson & Honig, 2003), snowball sampling is likely to yield a sample of emerging ventures that is farther into the process, and more successful, than average.21 It may be possible to further refine the PSED method of capturing processes in early stages, but a two-stage sampling process of this kind is likely to remain an important tool for entrepreneurship research. For example, Chandler et al. (2002) recently extended it to internal ventures by starting from a large cohort of firms and screening them for emerging internal ventures. In their case the firms were young and small. If extended to larger firms the additional complication is added that no single individual can be assumed to know early on about all new initiatives that are taken within the firm. Therefore, procedures for locating relevant informants have to be developed. Another aspect of the frequency problem concerns the earliest stages of the discovery process, i.e. when new ideas are initially conceived of. This is a particularly infrequent phenomenon, which creates particular challenges for the researcher (cf. Simon in Sarasvathy, 1999b, p. 52). For example, field studies of “entrepreneurs” mimicking Mintzberg’s (1974) study of managers are unlikely to capture initial discovery. The early stages of discovery may be better researched through laboratory methods (Fiet & Migliore, 2001; Sarasvathy, 1999a). This also attracts attention to another important sampling issue, namely that the behaviors of practicing entrepreneurs do not necessarily give all the answers needed for the development of normative entrepreneurship theory. Therefore, entrepreneurship

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research – especially when addressing discovery – can work also with samples composed of individuals other than (nascent) entrepreneurs (Davidsson, 2002; Fiet, 2002). The frequency problem is further aggravated by the problem of heterogeneity. After investing in the expensive screening procedure needed for obtaining a sample of ongoing entrepreneurial processes the research may end up with a sample that is too diverse for any strong relationships to emerge. This is one of the problems with PSED and its sister projects in other countries. A random sample of ongoing independent start-ups will be heterogeneous along many dimensions. In addition, it will be dominated by relatively modest and imitative efforts (Aldrich, 1999; Delmar & Davidsson, 1999; Samuelsson, 2001). Pre-stratification of the underlying screening sample may be a way to get more homogeneous samples, or samples with a higher yield of high-potential ventures. Individuals may be stratified by, e.g. education or occupation. When firms are used for screening of samples of emerging internal ventures traditional stratification variables like firm age, size and industry can be used. However, it is not a given that these pre-stratifications can deal with the most relevant aspects of heterogeneity. For example, Bhave (1994) points out that type of novelty (in product, business concept, or production technology) may be a better indication of similarity than is industry classification. For many purposes post-stratification may be the only way to obtain more homogeneous samples. When this is the case the only alternative is to increase the size of the study, so as to make possible analysis of subgroups not identifiable a priori. This, of course, further increases the cost of sampling for good empirical research on entrepreneurship. With a qualitative approach it may be easier to distil cases that are at the same time less heterogeneous and more relevant for the research questions. However, the very heterogeneity that would motivate such an approach in the first place makes the applicability of theory generated from a small number of cases even more narrow and uncertain than when the cases are drawn from a more homogeneous population. Heterogeneity is not only a problem that should be designed away. Aspects of heterogeneity may just as well be the essence of research questions in entrepreneurship (cf. above). The conventional way of doing this is to carefully measure the aspects of heterogeneity that are of interest and to include control variables and interaction effects in the analysis. This helps, but can never simultaneously address all aspects of heterogeneity in a satisfactory manner. Less conventional studies combine homogeneity and heterogeneity in fruitful ways. Gratzer’s (1996, 1999) complete reconstruction of the rise and fall for the automated restaurant industry in Sweden, and Shane’s (2000b) study of all individuals and business initiatives associated with a particular technological innovation are


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examples of retrospective studies where focus on a narrow empirical context (i.e. homogeneity) allows interesting insights about heterogeneity.

The Problem of Process The process character of entrepreneurship creates additional challenges. We have noted already that this calls for longitudinal studies, which are still short in supply (Aldrich & Baker, 1997; Chandler & Lyon, 2001). The first problem that comes to mind when discussing longitudinal data collection is attrition, i.e. the tendency for the sample to get smaller and smaller over time because cases cannot be located or refuse to continue to participate in the study. Experiences from PSED and related studies have been, however, that attrition in this regard is not the big problem. On the contrary, once nascent entrepreneurs have been identified and taken through the initial interview they have been very willing or even enthusiastic about further participation.22 Instead, there are other but less obvious problems that have to be dealt with. First, firm start-up processes have different duration. In some cases the time between the first concrete step towards a new business, and an up and running firm, is a matter of weeks or months. In other cases it takes several years, or the process may never be completed nor terminated. As longer processes are eligible for sampling over longer periods of time, a sample of on-going initiatives identified at a given point in time will in a sense have an over-representation of long start-up processes relative to short processes. This may require some kind of correction either in the sampling procedure or in the analysis (cf. Delmar & Shane, 2002). Second, when sampling emerging business ventures some minimum criterion is needed in order to determine whether a case qualifies, e.g. that some concrete start-up activity like “talking to the bank,” “writing a business plan” or “renting premises” has been undertaken. Likewise, a maximum criterion is needed beyond which the case is no longer an emerging venture but an established one (cf. Shaver et al., 2001). With these criteria in place, however, the problem remains that when sampled at a particular point in time the sampled venture efforts will be captured at different stages of development. Some will be caught at the very earliest stages while others may be close to “up and running.” In the PSED research, the use of questions that give “time stamps” for different gestation activities helps address this question. Cases that appear to be “eternal start-ups” that will never be completed may be eliminated from the analysis, and the data can be re-organized using the reported time of a certain activity as the anchor rather the time of the interview (Delmar & Shane, 2002). Alternatively, either the number of start-up behaviors or the time elapsed since the first behavior can be used as a control

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variable in the analysis (Honig & Davidsson, 2000). However, it is inevitable that samples of real emerging processes will have some heterogeneity of this kind on the time dimension. Third, when re-contacted over time it will happen in each wave that some of the cases no longer are “emerging ventures” but either abandoned efforts or established business operations. Conceptualizations, analysis strategies and methods have to be applied that ensure that these differential outcomes do not cause biased results. Fourth, among those cases that still are “emerging ventures” when re-contacted, one possibility is that the initial respondent is still pursuing the same venture idea. This is an unproblematic case, as is the case when the case when the original respondents and all other team members have abandoned the project. It is also possible, however, that: (a) the initial respondent is still trying to start a business, but based on a completely different idea; or (b) the initial respondent is no longer active in the process, but other team members continue to pursue the original venture idea. Because of these unstable relationships between individuals and ventures it has to be decided what is the level of analysis, i.e. what it is, that should be followed over time. This is not a decision that should be taken lightly. A data set that follows individuals may be appropriate for some theories and research questions whereas a venture-based data set may be more appropriate for other theories and research questions. Therefore, one attractive alternative is to create, within the same study, different versions of the data set, where the different versions use the individual(s), the emerging venture, or the juxtaposition of the two (cf. Shane & Venkataraman, 2000) as the basic unit. Finally, when the emerging venture is the entity being followed situations will arise when it has to be asked whether the studied entity is in a meaningful way still the “same” unit, or if it has changed so much that it is now a different emerging venture than the original one. This, too, is a tricky issue to settle. For some purposes keeping such chameleons in the sample may create disturbing noise. In other cases the changes in the business concept that occur over time may be the researchers’ main interest. While perhaps particularly pronounced when studying early stages and dynamic aspects of the economy, this problem is in no way unique to entrepreneurship research. For example, in a study that followed business firms over a ten-year period we found that a majority of these firms underwent such changes that it could be questioned whether they could meaningfully be considered “the same” units at the end of the period (Davidsson & Wiklund, 2000).

Measurement and Data Analysis Apart from the challenges of sampling and following the sample over time, there are additional challenges associated with measurement and data analysis. There is


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a lack of validated measures of central concepts in entrepreneurship (Chandler & Lyon, 2001). This is particularly true for concepts that are central in entrepreneurship research but not in the disciplines or in other fields of research, such as “venture idea,” “discovery behaviors” and “exploitation behaviors” (Davidsson, 2000). Although some work in this direction has been done in PSED and related studies (Chandler et al., 2002; Davidsson & Honig, 2003; Reynolds, 2000; Samuelsson, 2001) much more remains to be done. Likewise, non-traditional assessment of outcomes on different levels needs to be developed. Another aspect of improved measurement is the combination of data sources for triangulation purposes, which has been infrequent in entrepreneurship research (Chandler & Lyon, 2001). As noted above, archival data are likely not to exist for emerging business activities. However, teams rather than single individuals run a large share of all independent start-ups, and probably an even larger share of all internal ventures. This makes it possible reduce common method variance through the use of multiple respondents. I have emphasized repeatedly that entrepreneurship is characterized by heterogeneity and that it is a process, which should be studied over time. One aspect of heterogeneity is that the most interesting cases are likely to be found at the outskirts of distributions. Another is that a sample of emerging businesses is going to consist of entities that were initiated at different points in time, and likewise will “graduate” into established new businesses at different points in time. I have also remarked, with Venkataraman (1997), that relative performance may not be the most relevant outcome variable. As a consequence, the standard package of statistical analysis methods will not be the most appropriate tools for analyzing this phenomenon. These methods are often developed for cross-sectional analysis and focus around central tendencies and variance – preferably normally distributed – around them. Outliers are a problem, as are incomplete data. This means that the researcher who wants to do really good empirical work will have to find and learn methods that better match the research questions and data characteristics at hand. This, too, is a development that has only just begun. To name a few examples that probably point out the right direction we have Gimeno et al.’s (1997) careful adaptation of analysis tools to the analysis problem, Delmar and Shane’s (2002) use of event history analysis and Samuelsson’s (2001) introduction of latent growth modeling to the domain of entrepreneurship research.

CONCLUSION I argued in the introduction to this chapter that rather than being a confused research community heading for disaster we now have the intellectual building

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blocks in place to build a strong paradigm for entrepreneurship research. My purpose with this manuscript has been to bring together and elaborate on insights that others have provided, in the hope that doing so could help researchers conduct entrepreneurship research that constitutes genuine and valuable contributions to academia and practice. The field of entrepreneurship can achieve greater coherence, I argued, if we realize that different views on what entrepreneurship is to a great extent are due to differences in emphasis on entrepreneurship as societal phenomenon, as scholarly domain, or as a teaching subject. I have tried to clarify these distinctions and to point out where various criteria do and do not belong, in the hope to show that what appears to be opposing views are in fact rather easy to reconcile in many cases. Reaching a reasonable level of agreement on what entrepreneurship research should study may be much easier than it might first seem. More specifically, I have suggested that it is adequate to include some kind of “success” in the definition when we have entrepreneurship as a societal phenomenon – but not the scholarly domain – in mind. Leaning on Kirzner (1973) I suggested that the societal phenomenon is well captured by the notion that entrepreneurship consists of the competitive behaviors that drive the market process. I argued that criteria like purposefulness, skill and expectation of gain for self come naturally when we think of entrepreneurship as a teaching subject, but may be overly restrictive from the other perspectives. I also argued that while topics like self-employment, small business management, and family business succession might fit naturally in an entrepreneurship teaching context, they represent a diverse set of phenomena that are not necessarily related to “entrepreneurship” as we have defined the societal phenomenon. With regard to the scholarly domain I have tried to develop a perspective that makes use of and room for earlier contributions by Gartner (1988, 2001) and by Shane and Venkataraman (2000, 2001). I have also proposed what I hope to be an agreeable middle ground position on the issue of opportunity as created or existing independently of the actor. I have further proposed – along with Low (2001) – that neither “entrepreneurship as separate domain” nor “entrepreneurship belongs in the disciplines” is the right strategy for maximizing knowledge development about entrepreneurship. Entrepreneurship research requires input from the disciplines but it also needs the community created by or in a distinct domain. The domain delineation I suggested reads: Starting from assumptions of uncertainty and heterogeneity, the scholarly domain of entrepreneurship encompasses the processes of (real or induced, and completed as well as terminated) emergence of new business ventures, across organizational contexts. This entails the study of the origin and characteristics of venture ideas as well as their contextual fit; of behaviors in the interrelated


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processes of discovery and exploitation of such ideas, and of how the ideas and behaviors link to different types of direct and indirect antecedents and outcomes on different levels of analysis. Finally, I discussed a number of method challenges in entrepreneurship research. In particular, I argued for more studies that use the venture idea and the activity that evolves around it as the unit of analysis. Such studies would capture new business initiatives at an early stage and follow them over time, through whatever changes in human champions and organizational contexts that might occur. Entrepreneurship as a scholarly domain has the potential to generate unique insights about phenomena of very high societal relevance. In order to realize that potential, the field needs to continue to improve. In the role as researchers this is a task we can take on along two routes. First, we can be more careful with how we use the word “entrepreneur” and its derivatives. Second, we can conduct better research, following some of the suggestions outlined in this chapter. That is, we can make our research more theory-driven, have it address research questions closer to the heartland of the scholarly domain, and apply more adequate methodology. Doing such research requires ingenuity and attention to many new challenges in sampling, measurement and analysis. The problems may seem prohibitive, and one should not expect every single study to have a perfect solution to every possible problem. That would be asking too much. From senior researchers and research foundations one can reasonable demand that more large-scale, longitudinal studies be conducted. Doctoral students and junior scholars under time and tenure constraints could then “tap into” these pre-existing studies. Alternatively, they could focus on research questions that do not demand process data, such as development and validation of better measures of concepts, or “laboratory” research on discovery. Researchers do not have the only key role. Reviewers, conference organizers and journal editors are very important for the field’s future development. As I see it, they should give priority to research on emergence of new business activities. They should also continue to welcome research on, e.g. self-employment, small business, family business, organizational change, regional development, or strategy and firm performance – but only when these issues are explicitly linked to the existence and characteristics of venture ideas, to behaviors in the processes leading to their discovery and exploitation, and to the outcomes of such efforts. It is when holders of such roles become tougher in asking “is this really about entrepreneurship?” that the scholarly domain of entrepreneurship can become a logically distinct and coherent field of research. Achieving this is necessary for entrepreneurship research to make real progress, to earn and deserve respect, and provide a better basis for community.

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EPILOGUE Writing a manuscript of the present kind is an idea one gets or a kind of assignment one accepts only in moments of outrageous hubris. It is, of course, beyond the capacity of most scholars, and certainly beyond the capacity of the current author, to have the overview that would be needed in order to really manage such a task. So the punishment for the hubris, I guess, is to realize that every reader will be able to spot many omissions, misrepresentations, or even pure errors. However, I rationalize my overly pretentious effort on the grounds that: (a) I have admired others work of the same kind and found it very rewarding to read it, even if I did not find every line they wrote well-informed or logically convincing; and (b) if we were allowed to speak only when in possession of complete knowledge we would not say much at all. A conference reviewer of the extended abstract of a (much different) early draft of this manuscript opened her “comments to author(s)” by pointing out that “Paradigm development seldom takes place through normative claims . . . .” This is a critique well deserved – and well taken. In a similar vein, Aldrich and Baker (1997, p. 398) point out: What lesson can be learned from history? Influence comes from exemplary research, not from propagation of rules or admonition. The field will be shaped by those who produce research that interests and attracts others to build on their work (. . .). Those who believe they know the path forward need to do such work themselves and (. . .) provide exemplars that attract others to follow.

This is an idea that I have tried to take seriously. Therefore, I have referred repeatedly in this manuscript to other researcher’s work that I find exemplary and worth following, in the hope that some curious readers may check the sources. I have further had the privilege to lead the Program on Entrepreneurship and Growth (PEG) at the Jönköping International Business, where we have tried to apply some of the ideas outlined above. That is, we run longitudinal, real time projects using the “emerging new venture” as the level of analysis; we do study behaviors that shape the discovery and exploitation processes, and we do research on the characteristics of venture ideas, and their effects. Whether or not any of our research will be regarded “exemplary” and worth following is, however, for others to judge.

NOTES 1. The disposition-based view sees (the degree of) entrepreneurship as an inherent characteristic of, e.g. individuals, regions, or cultures. While it is not impossible to


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gain valuable insights from a dispositional view (e.g. Baumol, 1990) I would generally discourage its use, and instead use behavior- and outcome-based criteria. 2. This choice should not be interpreted as a general preference by the author for Kirzner’s theorizing over, e.g. Schumpeter’s or Baumol’s. As will become evident, while I find Kirzner’s way to express the role of entrepreneurship very useful and clarifying there are many aspects of Kirzner’s theory that I find debatable or less useful. 3. Alternatively, Cole’s definition can be interpreted as requiring initiation and maintenance and aggrandizement. While much tougher than the “and/or” interpretation this is still fundamentally different from the market-based view of the societal phenomenon of entrepreneurship that I suggest be used. 4. Starting from an ideal situation where existing regulatory frameworks were optimally designed for the functioning of the economy, “re-distributive” ventures would coincide with ventures that break the law in order to achieve their goals. In a real economy regulatory frameworks are unlikely to be optimally designed and “legal yet re-distributive” and “illegal yet socially beneficial” ventures are both possible, making it very difficult to classify with certainty in which category (quadrant) each individual venture belongs. The conceptual distinctions between the categories in Fig. 2 may nevertheless be valuable. 5. Kirzner (1973, p. 94) asserts that entrepreneurial activity is always competitive and competitive activity is always entrepreneurial. In combination with the assertion that entrepreneurship moves the economy towards equilibrium, i.e. towards more efficient resource use, this does not seem to leave room for the existence of “re-distributive” ventures. However, Kirzner points out that his assertion is made for a (hypothetical) market economy free of government limitation on individual economic action. In real economies, I would argue, “re-distributive” ventures undoubtedly exist. Moreover, one might wonder whether Kirzner’s reciprocal identity between competitive and entrepreneurial behaviors would hold in an economy free of government intervention. In such an economy a producer may well try to win the market by killing his competitors and/or burning their premises. Either “competitive” must be defined in way that such behaviors for some reason do not qualify, or their existence is inconsistent with Kirzner’s assertion that all competitive behavior is entrepreneurial. 6. With a strained argumentation one can say, of course, that al-Quaida operates in the “market” for recruiting future terrorists, and that by demonstrating the “power” and “success” of the September 11 attacks it drives the market process in that market, presumably making it harder for “competing” terrorist organizations to attract the same recruits. 7. I have chosen to follow Shane and Venkataraman’s (2000) terminology. Alternatively, what I discuss could have been called entrepreneurship as “research domain” or “field of research.” 8. According to Kirzner (1973) “Entrepreneurship does not consist of grasping a free ten-dollar bill which one has already discovered to be resting in one’s hand; it consists of realizing that it is in one’s hand and that it is available for the grasping.” 9. Reportedly when asked at a seminar whether entrepreneurs could be studied empirically, Kirzner was not able to give an answer (Beckman, 1990, p. 100). 10. In fact, one of Schumpeter’s (1934) few weaknesses was that despite first defining the entrepreneur as a function in the economy and not as a flesh-and-blood individual, he could not resist the temptation to speculate about the goals and characteristics of the “entrepreneur,” thereby probably inspiring a lot of not very productive research (Gartner, 1988; Kilby, 1971). However, the “trait approach” in early entrepreneurship research did not come out with a complete lack of findings (cf. Johnson, 1990). Personality has

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also “bounced back” to some extent both in psychology proper and in entrepreneurship research, showing that with better conceptualizations, sampling and measurement stronger results can be obtained (Church & Burke, 1994; Gasse, 1996; Miner, 1996). However, innate characteristics of individuals will no doubt remain a minor issue in explaining entrepreneurial behavior and outcomes. Researchers who find it difficult to give up the idea of attributing entrepreneurial processes to the entrepreneur have a tendency to end up in circular reasoning (Ensley, Carland & Carland, 2000) or very strained definitions of “individual” (Bruyat & Julien, 2000) when faced with the fact that between the original identification of a “new to the world” business idea, and the successful exploitation of that idea in a particular geographic market, we may find a series of different individuals who assume various initiating, supporting, implementing and imitating roles, either concurrently or sequentially (Gratzer, 1996). Shane and Venkataraman (2000) retain a strong interest in the role of individuals – so much so that Venkataraman (1997) has been criticized for precisely that reason (Schoonhoven & Romanelli, 2001). Cole (1969, p. 17) admitted that the Harvard center he led for many years devoted considerable effort to defining the “entrepreneur” – but without success. However, Shane and Venkataraman’s (2000) interest in individuals concerns primarily the matching of individuals and venture ideas (Shane, 2000b) and with a well chosen “by whom” – which could mean one or more people who assume different roles in the discovery and exploitation processes; concurrently or in a relay – they avoid most of the problems associated with such an interest. 11. The observant reader may note that Shane and Venkataraman (2000) actually distinguish between three processes, as the quote reads, “. . . discovered, evaluated, and exploited . . . .” Venkataraman’s (1997) original reads, “. . . discovered, created, and exploited . . . .” In Shane and Venkataraman (2000) separate sub-sections are devoted to elaboration on discovery and exploitation, but none to evaluation. The same is true for Eckhardt and Shane (2003). On this basis I think it makes sense to say that “discovery” and “exploitation” are the two main processes, and that the possibility of “opportunity creation” as well as the process of “opportunity evaluation” are captured within these two main processes. 12. This has also led other scholars to adopt Gartner’s definition (Aldrich, 1999; Thornton, 1999) although some would exchange “creation” for “emergence” thus de-emphasizing behavioral and strategic aspects. While keeping behavior as the main interest Gartner (1993) himself has later preferred “emergence” in order to de-emphasize the planning and rationalistic connotations of “creation.” 13. For example, individuals are heterogeneous with respect to experience, skills and cognitive capacity (Cohen & Levinthal, 1990; Conner & Prahalad, 1996; Shane, 2000a, b) and also have heterogeneous motivations (Birley & Westhead, 1994). Two important aspects of organizational heterogeneity are governance structure (Coase, 1937; Foss, 1993; Williamson, 1999) and resources (Barney, 1991; Cohen & Levinthal, 1990; Collins & Montgomery, 1995; Foss, 1993; Galunic & Rodan, 1998; Greene, Brush & Hart, 1999; Penrose, 1959; Teece, Pisano, & Shuen, 1997). Whether or not a new venture evolves within an existing organization the external environment in a broader sense will also be heterogeneous (Baumol, 1990; Chandler & Hanks, 1994) and the characteristics of the external environment may have profound effects on what venture ideas are attractive and likely to succeed (Zahra & Dess, 2001). Heterogeneity also occurs over time. Individuals and organizations learn and change over time and whether or not they choose to remain


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in the “same” environment, the characteristics of the environment are not stable, either (Aldrich, 1999; Aldrich & Martinez, 2001; Miner & Mezias, 1996). It follows from all this heterogeneity that the universe of perceptible and profitable opportunity is not the same for all individuals or organizations, and that therefore they will come up with different venture ideas and different exploitation strategies. Importantly, they will also have different views on what constitutes a successful or acceptable outcome (Gimeno, Folta, Cooper & Woo, 1997; Venkataraman, 1997). 14. Judging from other parts of their writings I think it is safe to say that Shane and Venkataraman (2000) did not intend to suggest that entrepreneurship can only be studied retrospectively. Disappointingly, though, they did not take the chance to sort this out in the debate following upon the publication of their article (Shane & Venkataraman, 2001). Both Shane and Venkataraman have subsequently been involved in manuscripts portraying a more refined view of “opportunity” (Eckhardt & Shane, 2003; Sarasvathy et al., 2003) but none that completely solves the problems with the “opportunity” concept discussed here. At the root of the problem, I believe, lies that Shane and Venkataraman (2000) first set out to delineate the scholarly domain of entrepreneurship, but then fail to uphold the distinction between the scholarly domain and the societal phenomenon. I would argue that it is entrepreneurship as a societal phenomenon in the sense discussed above that Shane and Venkataraman (2000) have in mind when they adopt Casson’s (1982) definition of entrepreneurial opportunity. This definition fits with their first research question, about why, when and how “opportunities” come into existence, and with their assertions that “To have entrepreneurship, you must first have entrepreneurial opportunities” (p. 220) and “Although the discovery of an opportunity is a necessary condition for entrepreneurship, it is not sufficient” (p. 222). When they argue (Shane & Venkataraman, 2000, 2001) that ventures fail because opportunities were poorly exploited one might wonder by what criterion we can determine that they were “opportunities” at all in Casson’s sense, and thus that they belong in the scholarly domain of entrepreneurship? How difficult Casson’s opportunity concept is to apply consistently is illustrated also by Shane and Venkataraman’s assertion that “many people exploit opportunities that are unlikely to be successful” (Shane & Venkataraman, 2001, p. 15), which is not congruent with defining opportunity as profitable. 15. According to this perspective, although opportunities objectively exist “out there,” it is impossible to know the universe of not-yet-discovered, viable venture ideas that are within reach for a particular actor (cf. Sarasvathy et al., 2003). It is therefore reasonable to think of each individual’s universe of viable venture ideas as infinite. Nonetheless, because of perceptual and knowledge differences some individuals have easier access to more viable ideas than have others. This statement may seem paradoxical but is no more so than the fact that the universe of all positive integers and the universe of all positive even integers are both infinite, and nonetheless the latter is “smaller” than the former. 16. We may still require, however, that it is an opportunity only if the entrepreneur (or possibly an imitator) successfully convinces the world that this creation has value. 17. Eckhardt and Shane (2003) suggest there is a sequence from existence of opportunities, to discovery of opportunities, and further to exploitation. They hold that “While this process may have feedback loops and certainly is not linear, we theorize that it is directional. Opportunities exist prior to their discovery and opportunities are discovered before they are exploited. The opposite direction is not possible because opportunities cannot be exploited before they exist.” I do not think that even a directional hypothesis should be a basic

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assumption for entrepreneurship as a scholarly domain. The problems inherent in starting with the “existence of opportunity” have been dealt with above. Moreover, consider the following examples (note that Eckhardt and Shane admit that “discovery” does not necessarily reflect “real” opportunity). Discovery without existence: any process that turns out a failure because the actor was wrong about the perceived external opportunity; it did not exist. Discovery before existence: an entrepreneur develops a business concepts that becomes viable only because of an external chock that happens after the idea was developed, and which was unknowable until it occurred. Exploitation before discovery: Bhave’s (1994) result suggest it is rather common that while trying to solve a problem for themselves, individuals engage in what would be classified as exploitation behaviors and only afterwards do they come to see their solution also as an idea for a business. Exploitation without discovery: a venture may become successful “by mistake,” i.e. generate revenue by other means and from other buyers than the intended ones. That is, the “discovered” opportunity did not exist, but the attempt to exploit it successfully exploited another, existing but non-discovered, set of external conditions (“opportunity”). Having said this, empirical results support the notion that the process is – on average – directional, involving first an intention, which over behaviors related to resource acquisition and boundary-creation lead to exchange (Samuelsson, forthcoming). 18. Interestingly, this disproportionate interest in the individual is shared by diffusion research, where only about one percent of the close to 4,000 studies have focused on the characteristics of the innovation, whereas more than half of them focus on the individuals who adopted them (Rogers, 1995). The categorization of innovations in diffusion research along the dimensions relative advantage, complexity, compatibility, trialability and observability is nevertheless a source of inspiration for assessing venture ideas. The distinctions imitation, competence-enhancing innovation, and competence-destroying innovation are also likely to be useful (cf. Aldrich, 1999; Anderson & Tushman, 1990), as are Bhave’s (1994) distinctions between different types of novelty: in product, in business concept or in production technology. 19. Relative to the original, I have added “where” and “for whom” in question 1, and changed “opportunities” to “opportunity.” In questions 2–4 I have substituted “venture ideas” for “opportunities.” I have also generalized question 2 to any unit of analysis rather than restricting it to the individual level. Note that the fourth question is not explicitly stated by Shane and Venkataraman (2000) but derived from their domain definition as well as from Shane and Venkataraman (2001) and Venkataraman (1997). 20. Shane and Venkataraman (2000) have a more narrow view on question 3, showing a particular interest in why and with what consequences some venture ideas are commercialized as de novo start-ups, whereas for others an existing organization is used for the launching. Acknowledging that this is a highly interesting and previously much neglected issue in entrepreneurship research I have little to add to Shane and Venkataraman’s (2000) treatment. 21. Note that the suggested two-stage sampling procedure will result in over-sampling of team ventures. This can be corrected by post-weighing given that information exists on the true proportions of team and solo start-ups. 22. For the Swedish study I have this information in the capacity of principal investigator (cf. also Delmar & Shane, 2002). As regards the US effort I have the information from being a member of its executive committee and from personal communication with the initiator and co-ordinator of the project, professor Paul D. Reynolds.


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ACKNOWLEDGMENTS The ideas presented in this chapter are an outgrowth of conceptual and empirical work conducted within the “Program on Entrepreneurship and Growth in SMEs” (PEG), which was funded mainly by the Knut & Alice Wallenberg Foundation, and which involved a large number of Swedish and international scholars. Forerunners to – and early drafts of – this manuscript have been presented at several seminars and doctoral consortia. Colleagues have been generously sharing their views on earlier versions, thus helping to shape my thinking, sharpening my arguments, and clarifying the exposition. With the risk of forgetting someone who has been really important I would like especially to thank PEG collaborators Candida Brush, Gaylen Chandler, Jonas Dahlqvist, James O. Fiet, Veronica Gustavsson, Scott Shane and Johan Wiklund, as well as Jerry Katz, Pramodita Sharma, Ivo Zander and an anonymous reviewer for the RENT 2001 conference, for their comments and suggestions. While their help has been invaluable and certainly increased the quality of the end product, the responsibility for the views put forward in this manuscript, and the remaining flaws, remains with the author.

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