Sources of Knowledge and Entrepreneurial Behavior 9781487512538

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SOURCES OF KNOWLEDGE AND ENTREPRENEURIAL BEHAVIOR

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Sources of Knowledge and Entrepreneurial Behavior

DAVID B. AUDRETSCH AND ALBERT N. LINK

UNIVERSITY OF TORONTO PRESS Toronto Buffalo London

© University of Toronto Press 2019 Toronto Buffalo London utorontopress.com Printed in the U.S.A. ISBN 978-1-4875-0112-9 Printed on acid-free, 100% post-consumer recycled paper with vegetable-based inks.

Library and Archives Canada Cataloguing in Publication Audretsch, David B., author Sources of knowledge and entrepreneurial behavior / David B. Audretsch, Albert N. Link. Includes bibliographical references and index. ISBN 978-1-4875-0112-9 (hardcover) 1. Entrepreneurship. 2. Intellectual capital. 3. Organizational learning. 4. Technological innovations. I. Link, Albert N., author II. Title. HB615.A93 2018

338′.04

C2018-904342-3

University of Toronto Press acknowledges the financial assistance to its publishing program of the Canada Council for the Arts and the Ontario Arts Council, an agency of the Government of Ontario.

Funded by the Financé par le Government gouvernement du Canada of Canada

Contents

List of Figures and Tables vii Foreword xiii 1 Introduction 3 2 The Knowledge Spillover Theory of Entrepreneurship 3 The AEGIS Database

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4 The Experience Base of Firms 5 Sources of Knowledge

63

87

6 Sources of Knowledge and Entrepreneurial Behavior 7 Lessons Learned Notes 153 References 157 Index

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16

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Figures and Tables

Figures 1.1 1.2 1.3

3.1 3.2 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5.1

Antecedents of Entrepreneurial Behavior from an Epistemological Perspective Antecedents of Entrepreneurial Behavior from a Human Capital Perspective Antecedents of Entrepreneurial Behavior from Synthesized Human Capital and Epistemological Perspectives Antecedents of Entrepreneurial Behavior Cross-Country Relationships between Innovation-based Firm Activities and Innovation Outputs Mean Firm Age Mean Number of Firm Employees Mean Number of Firm Founders Mean Educational Level of Firm’s First-listed Founder Mean Years of Experience in Current Sector of Firms’ First-listed Founder Mean Percentage of Experienced-based Nascent Firms Mean Percentage of Occupational-based Nascent Firms Mean Importance of Design Knowledge as a Factor in the Formation of the Firm

9 11

12 57 58 66 68 69 71 72 75 77 90

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5.2 5.3 5.4

Figures and Tables

Mean Importance of Knowledge of the Market as a Factor in the Formation of the Firm Mean Importance of Customers as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Suppliers as a Source of Knowledge for Exploring New Firm Opportunities

Mean Importance of Competitors as a Source of Knowledge for Exploring New Firm Opportunities 5.6 Mean Importance of Public Research Institutes as a Source of Knowledge for Exploring New Firm Opportunities 5.7 Mean Importance of Universities as a Source of Knowledge for Exploring New Firm Opportunities 5.8 Mean Importance of External Commercial Labs/R&D Firms/Technical Institutes as a Source of Knowledge for Exploring New Firm Opportunities 5.9 Mean Importance of In-House R&D Laboratories as a Source of Knowledge for Exploring New Firm Opportunities 5.10 Mean Importance of Trade Fairs, Conferences, and Exhibitions as a Source of Knowledge for Exploring New Firm Opportunities 5.11 Mean Importance of Scientific Journals and Other Trade or Technical Publications as a Source of Knowledge for Exploring New Firm Opportunities

91 92 94

5.5

5.12 Mean Importance of Participation in Nationally Funded Research Programs as a Source of Knowledge for Exploring New Firm Opportunities 5.13 Mean Importance of Participation in EU Framework Programs as a Source of Knowledge for Exploring New Firm Opportunities 5.14 Mean Level of Participation in Strategic Alliances as a General Source of Knowledge 5.15 Mean Level of Participation in R&D Agreements as a General Source of Knowledge

95

96 97

98

99

100

101

102

103 105 106

Figures and Tables

5.16 Mean Level of Participation in Technical Cooperation Agreements as a General Source of Knowledge 5.17 Antecedents of Entrepreneurial Behavior from an Epistemological Perspective 6.1 Mean Percentage Change in Firm Sales, 2007–09 6.2 6.3 6.4

Antecedents of Entrepreneurial Behavior from a Human Capital Perspective Mean Percentage of Firms with a Female First-Listed Founder Mean Percentage Change in Firm Sales, 2007–09

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107 116 121 123 130 131

Tables 3.1 3.2 3.3 3.4 3.5 3.6 3.A.1 3.A.2

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Definitions of Knowledge-Intensive Entrepreneurship Distribution of AEGIS Firms Innovation Enablers Innovation-based Firm Activities Innovation Outputs Innovation Policy Instruments, Selected EU Countries Segmentation of Industries Impact of a Firm’s Capacity to Adapt Products/Services to Different Customers or Market Niches on Its Ability to Create and Sustain a Competitive Advantage Mean Firm Age Mean Number of Firm Employees Mean Number of Firm Founders Mean Educational Level of Firm’s First-listed Founder Mean Years of Experience in Current Sector of Firm’s First-listed Founder Distribution of Last Occupation of Firm’s First-listed Founder Mean Percentage of Experienced-based Nascent Firms Mean Percentage of Occupational-based Nascent Firms

51 55 55 56 56 59 61

62 66 68 69 70 72 74 75 76

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Figures and Tables

4.9 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

5.9

5.10

5.11

5.12

5.13

5.14

Correlation Coefficients among Measures of Experience Mean Importance of Design Knowledge as a Factor in the Formation of the Firm Mean Importance of Knowledge of the Market as a Factor in the Formation of the Firm Mean Importance of Customers as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Suppliers as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Competitors as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Public Research Institutes as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Universities as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of External Commercial Labs/ R&D Firms/Technical Institutes as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of In-House R&D Laboratories as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Trade Fairs, Conferences, and Exhibitions as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Scientific Journals and Other Trade or Technical Publications as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Participation in Nationally Funded Research Programs as a Source of Knowledge for Exploring New Firm Opportunities Mean Importance of Participation in EU Framework Programs as a Source of Knowledge for Exploring New Firm Opportunities Mean Level of Participation in Strategic Alliances as a General Source of Knowledge

78 90 91 92 94 95 96 97

98

99

100

101

102

103 104

Figures and Tables

5.15 Mean Level of Participation in R&D Agreements as a General Source of Knowledge 5.16 Mean Level of Participation in Technical Cooperation Agreements as a General Source of Knowledge 5.17 Correlation Coefficients among Sources of Knowledge 5.18 Correlation Coefficients among Experience Metrics and Knowledge Source Metrics 5.19 Estimated Positive Marginal Effects from Equation (6.1) 5.20 Estimated Positive Marginal Effects from Equation (6.1) for Firms in the High-tech Sector 5.21 Estimated Positive Marginal Effects from Equation (6.1) for Firms in the Low-tech Sector 5.22 Estimated Positive Marginal Effects from Equation (6.1) for Firms in the KIBS Sector 5.23 Positive and Statistically Significant Relationship between Education and Experience of First-listed Founder and Knowledge Sources, Based on Equation (6.1) 6.1 The Roles and Responsibilities of an Entrepreneur 6.2 Mean Percentage Change in Firm Sales, 2007–09 6.3 6.4

6.5 6.6 6.7 6.8 6.9

Estimated Direct Effect of Experience → Entrepreneurial Behavior Based on a Linear Version of Equation (6.1b) Estimated Direct Effect of Experience → Knowledge on Entrepreneurial Behavior Based on a Linear Version of Equation (6.2b)

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106 107 108 110 112 113 113 114

115 120 121 124

126

Comparison of the Human Capital and KSTE Perspectives on Entrepreneurial Behavior 127 Mean Percentage of Firms with a Female First-Listed Founder 130 Mean Percentage Change in Firm Sales, by Gender, Country, and Industrial Sector, 2007–09 131 Estimated Direct Effect of Experience → Entrepreneurial Behavior, by Gender and Industrial Sector 132 Estimated Effect of Experience → Knowledge → Entrepreneurial Behavior, by Gender and Industrial Sector 133

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Figures and Tables

6.10 Summary of the Human Capital and the KSTE Perspectives on Entrepreneurial Behavior, by Gender

135

Unless otherwise noted, all tables and figures were prepared by the authors, based on data from the AEGIS survey.

Foreword

This is an important book for three reasons. First, it provides an outstanding example of the recombinant processes that lead to the generation of new knowledge. Second, it combines two strands of the economic literature, the economics of knowledge and the economics of entrepreneurship, which have been growing quite apart. And third, in so doing, it gives economics a new tool that contributes to both the economics of knowledge and the economics of entrepreneurship, with important implications for innovation and knowledge policy. The knowledge spillover theory of entrepreneurship (KSTE) stems directly from the advances of the economics of knowledge. The Arrovian analysis of knowledge as an economic good had identified its limited appropriability as a key problem. The early economics of knowledge had stressed the negative implications of market failure, which led to an underinvestment in the generation of knowledge. The path-breaking analysis of Zvi Griliches then enabled researchers to appreciate the other side of the coin: the opportunities for third parties to benefit from knowledge spillovers – that is, the uncontrolled leakage of knowledge. The limited appropriability of knowledge has both negative and positive effects. According the “new growth theory,” the positive effects are actually larger than the negative ones, and thus account for the growth of output and total factor productivity. More recently the economics of knowledge has been used to clarify how knowledge spills over. Knowledge does not spill from the air; instead, dedicated mechanisms are needed for the uncontrolled leakage of knowledge to be absorbed and used as an input, both directly in the technology production function and in the production of innovations and indirectly in the knowledge production function, to contribute to the eventual production of new knowledge. The search for

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Foreword

such dedicated mechanisms can be regarded as in the forefront of the advanced economics of knowledge. Starting from the Schumpeterian appreciation of the imitation between competitors in product markets, the literature has been able to identify other complementary mechanisms: i) user-producer interactions along vertical value chains; ii) the mobility of skilled personnel among firms, between firms and research centers, and within regional clusters that make knowledge interactions closer and easier; iii) and institutional mechanisms that favor personal interactions and, consequently, the dissemination of knowledge. The KSTE can be seen as a major contribution to the economics of knowledge, as it identifies a new channel through which knowledge spillovers can be useful. So far, the literature has focused primarily on the knowledge interactions that take place among incumbents, paying little attention to the role of newcomers as users of knowledge spillovers both in introducing innovations and in generating new technological knowledge. This book explores systematically how and why knowledge spillovers provide entrepreneurs the opportunity to create new firms. The authors’ rich and systematic evidence collected and analyzed shows clearly how rates of creation of new firms and flows of entrepreneurship are associated with the opportunities provided by the regional concentration of innovative activities in sciencebased fields where the advances of knowledge have been most rapid. Entrepreneurship is an indispensable mechanism for the effective exploitation of the opportunities provided by the limited appropriability of knowledge. This book explores in detail the limited ability of incumbents, especially corporations, to take advantage of all the opportunities provided by knowledge spillovers. At the same time, the analysis makes clear that the spillovers that entrepreneurs are able to use are not second-best opportunities. Departing from the Penrosian notion of interstices, the authors show how spillover entrepreneurship guides the creative destruction that has characterized the shakeup of traditional sectors such as electronics and big pharma with the introduction of digital and bio-technologies. These same achievements offer a radical contribution to the economics of entrepreneurship. Since its Schumpeterian beginnings, the economics of entrepreneurship has been little able to provide an economic analysis of its economic determinants. The original intuition that the supply of entrepreneurship could be regarded as a specific form of endowment, much like oil or the weather, has opened the door to anthropological and sociological explanations in search of the cultural, institutional, and religious determinants of the willingness and ability of entrepreneurs to create new firms. This “economic” vacuum was partly

Foreword xv

filled by William Baumol, who elaborated a theory about the role of the mechanisms that support the identification, selection, and development of the dedicated skills and personal characteristics that account for the creation of new firms. This approach provided a clue to elaborate an endogenous – and hence economic – explanation of entrepreneurship. So far, economics has failed to explain why some nations in some historic periods have been better able than others to generate more wealth by means of entrepreneurship. This book takes two remarkable steps forward. It provides an economic theory of entrepreneurship: entrepreneurship thrives when and where knowledge spillovers are abundant and provide major opportunities to generate new knowledge, to introduce new technologies, and to create new firms. At the same time, the book highlights the role of entrepreneurship in the generation of new knowledge. Economics now has a better theory to explain the role of entrepreneurship in contributing to the wealth of nations. Entrepreneurship is a powerful mechanism of the exploitation of knowledge that cannot be fully appropriated by its producers. Nations, regions, and industries that are characterized by a larger stock of knowledge and by knowledge-governance mechanisms that favor its access and secondary use are likely to experience more entrepreneurship and, consequently, faster rates of growth of output and efficiency than those with a limited stock of knowledge and institutional setups that constrain its recombinant use to generate additional knowledge and introduce innovations. As the authors show, the geographic and industrial maps of entrepreneurship and of the regional and scientific distributions of the stock of knowledge coincide in space and time. This book makes an important contribution to the economics of knowledge by stressing the variety of knowledge that contributes to the generation of new knowledge. From this viewpoint, the authors contribute to a major area of investigation by moving away from the standard assumption that knowledge is homogeneous. According to the rich evidence the authors analyze, in fact, knowledge is a highly differentiated bundle of varieties of knowledge drawn from a variety of sources, and the generation of new knowledge is possible only through the selection and integration of these sources. In this context, entrepreneurship plays a specific role that previously had not been properly stressed. The implications of these results are quite important, and range across different items. In the selection and training of entrepreneurs, greater attention should be paid to the ability to interact with the variety of knowledge holders and to identify the industrial and regional loci where

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Foreword

it is easier to access and absorb knowledge spillovers. Interactive abilities and skills are necessary components of the background of potential entrepreneurs. The feedback between spillover entrepreneurship and knowledge generation can become a major source of dynamic increasing returns. It is not only true, in fact, that the larger are knowledge spillovers, the larger is spillover entrepreneurship; the reverse causation is at work as well: the larger the spillover entrepreneurship, the larger the generation of additional knowledge and variety of knowledge, which, in turn, yield more knowledge spillovers. Finally, the implications for knowledge and innovation policy are most important. Support of entrepreneurship should take into account not only the distribution of the stock of knowledge and its advances, but also the composition of the knowledge stock in terms of its variety. It seems far more effective to support entrepreneurship in geographic and industrial spaces where the stock of technological knowledge is larger and more heterogeneous than in spaces that provide access to low levels of homogenous knowledge stocks. This book provides key arguments for the implementation of a selective approach to the support of entrepreneurship and for the dissemination of knowledge, including a reduction in the exclusivity of intellectual property rights. Since the better the conditions of access to the knowledge stock, the larger the flow of entrepreneurship, the selective reduction of the exclusivity of intellectual property rights for the creation of new firms could yield a positive-sum game at the system level. The limited size of new firms is likely to reduce only marginally the expected stream of profits for “inventors,” and hence their incentive to generate new knowledge. The economic benefits of the marginal flows of entrepreneurship sensitive to reduced levels of exclusivity likely would be much larger in terms of an increased rate both of the introduction of innovations and of the generation of technological knowledge. In sum this book makes an important contribution to the knowledge spillover theory of entrepreneurship and, consequently, to the economics of knowledge, the economics of entrepreneurship and innovation, and knowledge policy. As such it deserves wide dissemination among scholars and practitioners and widespread didactic use in economics departments, business schools, and schools of government. Cristiano Antonelli, Dipartimento di Economia e Statistica, Università di Torino and BRICK (Bureau of Research in Innovation Complexity and Knowledge), Collegio Carlo Alberto.

SOURCES OF KNOWLEDGE AND ENTREPRENEURIAL BEHAVIOR

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chapter one

Introduction

The only source of knowledge is experience. – Albert Einstein It is beyond a doubt that all our knowledge begins with experience. – Immanuel Kant

Antecedents of Entrepreneurial Behavior Our purpose in writing this book is to search for an understanding of the nature and importance of the relationship between sources of knowledge and entrepreneurial behavior. Before searching, we begin with an understanding of how one’s experiences – which color the palate on which ideas are formed – and temperament lead to how one perceives both events and opportunities. One’s perception of opportunities and the ability to act on them is, in our mind, a definition of entrepreneurship. Thus, it follows from our perspective that there is both a tractable and a conceptual relationship between experiences, the sources of knowledge on which one relies, and entrepreneurial actions. To arrive at this conclusion, we offer a bit of a historical trace. It begins in the hamlet of Wrighton, in the county of Somerset in southwest England. There, in 1632, John Locke was born. Educated in medicine at Oxford, Locke soon transcended his formal training to become one of the most influential philosophers of his time, earning, posthumously, the titles of Founder of British Empiricism and Father of Classical

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Sources of Knowledge & Entrepreneurial Behavior

Liberalism.1 Locke’s most famous treatise is An Essay Concerning Human Understanding ([1690] 1996). In it, Locke elaborated on many themes, several of which are fundamental to setting the stage for our discussion about sources of knowledge as antecedents of entrepreneurial behavior. He starts, as we do in this book, with an exploration of the genesis of ideas.2 Locke emphasized that all ideas emanate from sensation or reflection: “Every man being conscious to himself, that he thinks, and that which his mind is employed about whilst thinking being the ideas, that are there, ’tis past doubt, that men have in their minds several ideas … All ideas come from sensation or reflection” ([1690] 1996, 33, emphasis in original). Regarding sensation, Locke emphasized one’s perception of things, which is fundamental to understanding entrepreneurial behavior: [O]ur senses, conversant about particular sensible objects, do convey into the mind several distinct perceptions of things, according to those various ways, wherein those objects do affect them: and thus we come by those ideas we have of yellow, white, heat, cold, soft, hard, bitter, sweet, and all those which we call sensible qualities, which when I say the senses convey into the mind, I mean, they from external objects convey into the mind what produces there those perceptions. This great source, of most of the ideas we have, depending wholly upon our senses, and derived by them to the understanding, I call SENSATION. (33–4, emphasis in original)

And regarding reflection, Locke, perhaps influenced by his Puritanical upbringing, acknowledged that one’s soul, or internal senses, tempers one’s perceptions: [T]he other fountain, from which experience furnishes the understanding with ideas is the perception of the operations of our own minds within us, as it is employed about the ideas it has got; which operations, when the soul comes to reflect on, and consider, do furnish the understanding with another set of ideas, which could not be had from things without: and such are, perception, thinking, doubting, believing, reasoning, knowing, willing, and all the different actings of our own minds; which we being conscious of, and observing in ourselves, do from these receive into our understandings, as distinct ideas, as we do from bodies affecting our senses. This source of ideas, every man has wholly in himself: and though it be not sense, as having nothing to do with external objects; yet it is very like it, and might properly enough be called internal sense. But as I call the other sensation, so I call

Introduction 5 this REFLECTION, the ideas it affords being such only, as the mind gets by reflecting on its own operations within itself. (34, emphasis in original)

But, as clear as Locke was about sensation and reflection being the “fountains of knowledge” from which ideas spring, he was also clear that one’s mind is not a blank slate: there is a precursor to this knowledge, and that precursor is one’s experiences. More eloquently: Let us then suppose the mind to be, as we say, white paper, void of all characters, without any ideas; how comes it to be furnished? Whence comes it by that vast store, which the busy and boundless fancy of man has painted on it, with an almost endless variety? Whence has it all the materials of reason and knowledge? To this I answer, in one word, from experience; in that, all our knowledge is founded; and from that it ultimately derives itself. Our observation employed either, about external sensible objects [i.e., sensations], or about the internal operations of our minds, perceived and reflected on by ourselves [i.e., reflection], is that, which supplies our understandings with all the materials of thinking. These two are the fountains of knowledge, from whence all the ideas we have, or can naturally have, do spring. (33, emphasis in original)

One philosopher whom Locke influenced was David Hume. In An Enquiry Concerning Human Understanding, published in 1748, Hume referred to experiences in terms of impressions, feelings, and sensations. Under the subheading, “Of the Origins of Ideas,” Hume wrote: Every one will readily allow, that there is a considerable difference between the perceptions of the mind … and when he afterwards recalls to his memory this sensation, or anticipates it by his imagination. These faculties may mimic or copy the perceptions of the senses; but they never can entirely reach the force and vivacity of the original sentiment … Here therefore we may divide all the perceptions of the mind into two classes or species, which are distinguished by their different degrees of force and vivacity. The less forcible and lively are commonly denominated Thoughts or Ideas. The other species want a name … Let us use a little freedom, and call them Impressions … By the term impression, then, I mean all our more lively perceptions … And impressions are distinguished from ideas, which are the less lively perceptions, of which we are conscious … In short, all the materials of thinking are derived either from our outward or inward sentiment: the mixture and composition of these belongs alone to the mind and will. Or, to express myself in philosophical language, all our ideas or

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Sources of Knowledge & Entrepreneurial Behavior more feeble perceptions are copies of our impressions or more lively ones. (Hume [1748] 2007, 7–8)

To justify or to prove his argument, he wrote: “When we analyze our thoughts or ideas, however, compounded or sublime, we always find that they resolve themselves into such simple ideas as were copied from a precedent feeling or sentiment [that is, experience]. Even those ideas, which, at first view, seem the most wide of this origin, are found, upon a nearer scrutiny, to be derived from it … we shall always find that every idea which we examine is copied from a similar impression” (8). Recalling that the title of this book is Sources of Knowledge and Entrepreneurial Behavior, and having set the stage – albeit a historical and philosophical one – that one’s experiences influence the sources of knowledge one draws upon, we now turn to how those sources of knowledge influence entrepreneurial behavior. Moving to, in our opinion, some of the bold thinkers in the fields of economics and entrepreneurship, we capsulize a few salient points from the writings of Theodore Schultz and Fritz Machlup. These points suggest a framework, or possibly even a direction, through which or by which the relationship between experience, knowledge, and entrepreneurial behavior – in that order – might be understood. Schultz, the recipient of the 1979 Nobel Prize in Economic Sciences, bridged the connection between experience and entrepreneurial behavior in terms of the connection between knowledge and education: “The main purpose of this study is to explore how education and experience influence the efficiency of human beings to perceive, to interpret correctly, and to undertake action that will appropriately reallocate their resources. The central questions to keep in mind are: To what extent are these allocative abilities acquired? Are education and experience measurable sources of these abilities? What factors determine the economic value of the stocks of such abilities that various individuals possess?” (Schultz 1975, 827). In fact, Schultz answered his own questions from the previous passage: “Our knowledge of a person’s abilities consists of inferences drawn from his performance. An ability is thus perceived as the competence and efficiency with which particular acts are performed” (828). And a primary factor that determines “the economic value of the stock of such abilities” (to use Schultz’s terminology) is education. Tying education directly to entrepreneurial behavior – specifically, entrepreneurial behavior in a dynamic sense, meaning one’s ability to deal with disequilibria – reflects an entrepreneurial response to an opportunity, perhaps even an opportunity created by one’s own idea. As Schultz made clear, “[t]here

Introduction 7

is enough evidence to give validity to the hypothesis that the ability to deal successfully with economic disequilibria is enhanced by education and that this ability is one of the major benefits of education accruing to people privately in a modernizing economy” (843). But Schultz was well aware that these connections are neither linear nor smooth; addressing them is merely “the first step on what appears to be a long new road” (843). This new road is sure to contain many potholes, dead ends, and detours that, for some, bring about purposeful redirections. Machlup, however, among others, filled in some of the potholes, and indeed artfully turned several of the dead ends and detours into purposeful redirections. He argued that formal education is only one source of knowledge; knowledge is also gained experientially and at different rates by different individuals. Individuals can accrue knowledge from their day-to-day experiences, which “will normally induce reflection, interpretations, discoveries, and generalizations” (Machlup 1980, 179). Moreover, the cost of acquiring knowledge is related to differential abilities: “Some alert and quick-minded persons, by keeping their eyes and ears open for new facts and theories, discoveries and opportunities, perceive what normal people of lesser alertness and perceptiveness, would fail to notice. Hence new knowledge is available at little or no cost to those who are on the lookout, full of curiosity, and bright enough not to miss their chances” (179). Machlup emphasized that there are five types of knowledge, and his taxonomy of knowledge types anticipates (and parallels to some extent) our measurement of sources of knowledge in Chapter 5.3 Specific to the “knower” of knowledge:4 I propose to distinguish five types of knowledge: (1) Practical knowledge: useful in his work, his decisions, and actions; can be subdivided, according to his activities, into a) Professional knowledge b) Business knowledge c) Workman’s knowledge d) Political knowledge e) Household knowledge f) Other practical knowledge (2) Intellectual knowledge: satisfying his intellectual curiosity, regarded as part of liberal education, humanistic and scientific learning, general culture; acquired, as a rule, in active concentration with an appreciation of the existence of open problems and cultural values. (3) Small-talk and pastime knowledge: satisfying the nonintellectual curiosity of his desire for light entertainment and emotional stimulation,

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Sources of Knowledge & Entrepreneurial Behavior including local gossip, news of crimes and accidents, light novels, stories, jokes, games, etc.: acquired, as a rule, in passive relaxation from “serious” pursuits; apt to dull his sensitiveness. (4) Spiritual knowledge: related to his religious knowledge of God and the ways to the salvation of the soul. (5) Unwanted knowledge: outside his interests, usually accidentally acquired, aimlessly retained. (Machlup 1962, 21–2)

Foray (2004) offers a different, yet important, taxonomy of types or categories of knowledge. Specifically, he suggests that “[m]uch knowledge is produced by invention, that is, it does not exist as such in nature and is ‘produced’ by man. Other types of knowledge stem from discoveries, that is, the accurate recognition of something which already exists but which was concealed. Invention is the result of production; discovery the result of revealing” (14). Motivated by Foray’s view, and by the scholarship that preceded it – for example, Cohen and Levinthal 1989; Nelson and Winter 1982a; Shane 2000 – we are of the opinion that many sources of knowledge are easily recognizable, but it takes an entrepreneurial eye to recognize in the first place that these sources even exist, and it then takes entrepreneurial knowledge – perhaps based on experience and/or education – to know how to use such sources.5 As we have written about many times (see, for example, Hébert and Link 2009), entrepreneurship can be described as perception and action, but some action is more effective because of the knowledge the entrepreneur possesses. A point made clear through our references to the scholars discussed above is that knowledge is heterogeneous in its nature. As Antonelli and Fassio (2014, 16) note, “knowledge is not homogeneous. Instead it should be regarded as a highly differentiated bundle of knowledge items.” We will emphasize this point about the heterogeneity of knowledge again in Chapter 2, and we will refer to that characteristic of knowledge many times thought the rest of this book. We do so not to beat a dead horse, as some might say, but to segue from the epistemological to the practical. In other words, we are in one sense setting the stage for an empirical conclusion that not all sources of knowledge are equally influential on entrepreneurial behavior. Human Capital versus Epistemological Perspectives In this book we argue, and descriptively demonstrate, that the influence of experience and education on the sources of knowledge on which one draws represents, from our perspective, a statement about

Introduction 9 Figure 1.1. Antecedents of Entrepreneurial Behavior from an Epistemological Perspective Sensations and Reflections

Experience

Knowledge

Entrepreneurial Behavior

Education

the antecedents of entrepreneurial behavior6 – namely, that sources of knowledge influence one’s perceptions and ideas (one’s ability to perceptive an opportunity) and that one’s actions based on that perception are what defines one’s entrepreneurial behavior. We illustrate this notion simply in Figure 1.1. Figure 1.1 clearly has the appearance of a linear model originating with one’s experiences and ending with the observation of one’s entrepreneurial behavior.7 Of course, building on our discussion of Machlup’s views of entrepreneurship, there are likely detours and purposeful redirections from start to finish in the process illustrated in the figure. Although Figure 1.1 is in a sense a roadmap for the remaining chapters in this book, it might also be viewed as an extension of the human capital origins of entrepreneurial behavior – that is, of entrepreneurship. Edward Lazear, arguably one of the leading scholars at the frontier of the theoretical relationship between human capital and entrepreneurial behavior, conceptualized as follows: [Entrepreneurship is] the process of assembling necessary factors of production consisting of human, physical, and information resources and doing so in an efficient manner. Entrepreneurs put people together in particular ways and combine them with physical capital and ideas to create a new product or to produce an existing one at a lower or competitive cost. Because the entrepreneur must bring together many different resources, he or she must have knowledge, at least at a basic level, of a large number of business areas. An entrepreneur must possess the ability to combine talents and manage those of others … [It then follows that] individuals with a broader range of skills, acquired either through investment or through endowments, are more likely to be entrepreneurs. (2005, 649–50, 662)

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Sources of Knowledge & Entrepreneurial Behavior

One might interpret Lazear’s description of an entrepreneur and of entrepreneurship (what an entrepreneur does) as a reflection of an individual’s human capital – that is, one’s endowment of relevant resources. The bottom line is that, for Lazear and many other scholars, human capital is simply the starting point for a discussion, and an understanding, of entrepreneurship. One might also think of Lazear’s conceptualization as providing some of the seed to grow into what we call dynamic entrepreneurship.8 As Hébert and Link (2009, 101) observe, “[i]n a static world the entrepreneur is a passive element because his actions merely constitute repetitions of past procedures and techniques already learned and implemented. Only in a dynamic world does the entrepreneur become a robust figure.” A dynamic, rather than a static, perspective of entrepreneurship is in concert with the Schumpeterian view that the entrepreneur is the driving force for innovation. According to Schumpeter, an entrepreneur can be distinguished from other agents in the economy in terms of willingness to pursue innovative activity: “The function of entrepreneurs is to reform or revolutionize the pattern of production by exploiting an invention, or more generally, an untried technological possibility for producing a new commodity or producing an old one in a new way … To undertake such new things is difficult and constitutes a distinct economic function, first because they lie outside of the routine tasks which everybody understands, and secondly, because the environment resists in many ways” (Schumpeter 1942, 13). The antecedents of dynamic entrepreneurship, as we are guided by Schumpeter, are generally one’s experiences (that is, human capital). But one might also view Schumpeter’s ideas as going beyond Lazear’s model and volumes of related research by implying that there is an intervening step between human capital and entrepreneurial behavior – namely, the relationship between experience and knowledge. Schumpeter recognized that the knowledge that kindles an innovation can be new or already existing: “It is not the knowledge that matters, but the successful solution of the task sui generis of putting an untried method into practice – there may be, and often is, no scientific novelty involved at all, and even if it be involved, this does not make any difference to the nature of the process” (Schumpeter 1928, 378). Successful innovation in the Schumpeterian sense requires an act of will, not of intellect. It depends, therefore, on leadership, not on intelligence. An aptitude for leadership stems in part from the use of knowledge: people of action who perceive and react to knowledge do so in various ways,

Introduction 11

and those ways are engendered from one’s past experiences – that is, Experience → Knowledge in terms of Figure 1.1. Differential leadership means that “some are able to undertake uncertainties incident to what has not been done before; [indeed] … to overcome these difficulties incident to change of practice is the function of the entrepreneur” (Schumpeter 1928, 380). Lazear does not draw the relationship between human capital, which we are treating broadly as one’s experiences, and entrepreneurial behavior as one that follows a path through knowledge; rather, he goes directly from human capital to entrepreneurial behavior.9 Lazear is not the only scholar to do so: Nyberg and Wright (2015) summarize in their masterful trace of fifty years of human capital research that, when human capital leads to knowledge, the resulting path to entrepreneurial behavior falls under the rubric of human capital strategy. Thus, at the risk of overgeneralizing, Figure 1.2 might be a representation of a so-called human capital perspective for understanding conceptually and for illustrating empirically (as we do in this book) entrepreneurial behavior. Might it be the case that the empirical contributions to the field of entrepreneurship have previously resulted from identifying covariates with entrepreneurial behavior? And might it be the case that those contributions have explicitly treated, perhaps myopically, as we suggest in the following chapters, human capital as having a direct link to entrepreneurial behavior when, in fact, the influence of human capital on such behavior instead works indirectly through the sources of knowledge that one’s human capital identifies? Or might human capital have both a direct link to the sources of knowledge on which an entrepreneur relies and an indirect link to entrepreneurial behavior? If the response to the latter question is

Figure 1.2. Antecedents of Entrepreneurial Behavior from a Human Capital Perspective Sensations and Reflections

Experience

Education

Entrepreneurial Behavior

12  Sources of Knowledge & Entrepreneurial Behavior Figure 1.3. Antecedents of Entrepreneurial Behavior from Synthesized Human Capital and Epistemological Perspectives Sensations and Reflections Experience

Knowledge

Entrepreneurial Behavior

Education

“yes,” then Figure 1.3 might be a more accurate representation of the antecedents of entrepreneurial behavior. More formally, with reference to Figure 1.3, let the strength of the indirect relationship between experience (that is, human capital), working through knowledge, and entrepreneurial behavior be represented as γ1. Also, let γ2 represent the strength of the direct relationship between experience and entrepreneurial behavior via the curved dashed line in Figure 1.3. In the extant literature, the human capital perspective of entrepreneurial behavior has been estimated exclusively in terms of γ2. We suggest, however, that this view is limited, in the sense that it ignores the relationship between experience and entrepreneurial behavior working through knowledge – namely, γ1. So, if a quantitative approximation of γ1 is positive, then the approximated value γ1 represents what we call in this chapter an epistemological perspective of entrepreneurial behavior. However, to anticipate the arguments in Chapter 2, where we discuss the knowledge spillover theory of entrepreneurship (KSTE), the KSTE is precisely an epistemological perspective of entrepreneurial behavior. Therefore, a comparison of KSTE to the extant human capital approach to entrepreneurial behavior becomes simply a comparison of γ1 to γ2, where γ1 approximates the KSTE perspective and γ2 approximates the extant human capital perspective (via the curved dashed line in Figure 1.3): γ1 = the strength of the Experience → Knowledge → Entrepreneurial Behavior relationship,

and γ2 = the strength of the Experience → Entrepreneurial Behavior relationship.

Introduction 13

Overview of the Book The remainder of the book focuses on a comparison of γ1 to γ2, and is structured as follows. In Chapter 2, we introduce the knowledge spillover theory of entrepreneurship as the foundation and framework for the book. We explain the KSTE heuristically, and summarize, both in the chapter’s text and in Appendix 2.A, the literature the theory has spawned. With the theory and attendant literature as backdrops, we also emphasize in Chapter 2 the limitations of the KSTE, as well as the void of its emphasis in the literature as a foundation for understanding entrepreneurial behavior. The KSTE looks not to differences across individual traits and characteristics – that is, the theory does not look directly at human capital inputs; rather, it emphasizes contexts – in particular, knowledge contexts. Alternatively stated, the KSTE could be interpreted as a theory that emphasizes that the strength of the relationship between human capital and entrepreneurial behavior depends on how an entrepreneur uses human capital to perceive and react to alternative sources of knowledge, and then to use those sources that most effectively bring about the desired entrepreneurial behavior. In this sense, the KSTE is, or so we argue, tantamount to an epistemological perspective of entrepreneurial behavior. The empirical analyses that illustrate the predictive power of the KSTE are based on quantifiable information from the AEGIS database, which we describe in detail in Chapter 3. Briefly, the AEGIS project was funded by the European Commission under Theme 8, “Socio-Economic Sciences and Humanities,” of the 7th Framework Programme for Research and Technological Development. The focus of that project was on knowledgeintensive entrepreneurship (KIE), under the assumption that KIE is one potential means through which to obtain economic growth and societal well-being. (Note that we take liberty herein in using the term KIE also as an adjective to describe knowledge-intensive entrepreneurial firms.) The database contains information on 4,004 small entrepreneurial firms established between 2002 and 2007 in ten European Union countries: Croatia, the Czech Republic, Denmark, France, Germany, Greece, Italy, Portugal, Sweden, and the United Kingdom. The database also contains information on a number of dimensions about each KIE firm and its environment – that is, about the context that influences the behavior of the firm. For our purposes, the database specifically contains information related to alternative sources of knowledge that entrepreneurial firms use, as well as dimensions of the economic performance of these firms over time.

14

Sources of Knowledge & Entrepreneurial Behavior

In Chapter 4 we focus on the far-left element in Figures 1.1 through 1.3 – namely, Experience. We discuss the heterogeneity or myriad dimensions of experience that can be measured using information from the AEGIS database. From a pragmatic perspective, then, our ability to demonstrate the comparative elements of our argument that either Experience → Entrepreneurial Behavior (directly or indirectly) or that Experience → Knowledge → Entrepreneurial Behavior is constrained by the availability of meaningful measures of experience (and knowledge, as we discuss in Chapter 5). But that is the case with any empirical experiment for which the availability of data is one of the relevant constraints. In Chapter 5 we use information from the AEGIS database to develop constructs to measure a set of alternative sources of knowledge available to a firm, and we identify the types of entrepreneurs and firms that use these various sources and the extent to which they do so. More important, we examine descriptively the relationships among the human capital backgrounds of firms and their relative use and perception of the importance of particular sources of knowledge. We then use this descriptive information to reflect on the expanded KSTE from Chapter 2 to formulate our interpretation of identified antecedents of entrepreneurial behavior in Chapter 6, where we relate empirically sources of knowledge to entrepreneurial behavior (γ1), and compare that relationship to the direct relationship between experience and entrepreneurial behavior (γ2). That is, in Chapter 6, we compare the KSTE to the extant human capital perspective on entrepreneurial behavior. It is important to emphasize here, early on in the book, that this comparison is one of approximations. The empirical analyses throughout the book are descriptive: they are intended to illustrate relationships and to suggest, albeit subject to caveats, the relative strengths of these relationships. Because the AEGIS database has limited economic information about firms’ performance over time, the measure we focus on is the growth in firms’ sales over a predefined period. In Chapter 6 we empirically explore the implication of the expanded KSTE from Chapter 2 using this measure of entrepreneurial behavior. More specifically, we examine in an exploratory manner the constructs developed with reference to Figure 1.3 – namely, conceptual estimates of γ1 and γ2. Our culminating analysis in Chapter 6 allows us not only to suggest that the extant human capital literature on the direct effect of experience on entrepreneurial behavior underestimates the importance of experience and thereby the empirical evidence in support of the KSTE; it also gives us a pulpit from

Introduction 15

which to make the case for the importance of understanding epistemological perspectives that are related to entrepreneurial behavior. We also explore in Chapter 6 the implications of the KSTE both in a general sense and in a comparative sense across industrial sectors and the gender of firms’ founders. As an introduction to the issue of gender, we discuss its importance related to economic growth in the European Community, the member countries of the Organisation for Economic Co-operation and Development (OECD), and thus to European policies that affect entrepreneurial behavior. Finally, in Chapter 7, we offer a summary and concluding remarks, with an eye to the policy implications of our empirics. In particular, we find that no single source of knowledge drives entrepreneurship. Rather, there is a plethora of knowledge sources, sprouting a vigorous bouquet of entrepreneurial activities. Just as the Inuit are reported to have names for different types of snow, so too it might be for the sources of knowledge, ideas, and creativity that inspire entrepreneurs to choose a road less traveled. But that road, paved with the promise of fulfillment and reward, is one that defines entrepreneurship.

chapter two

The Knowledge Spillover Theory of Entrepreneurship

Knowledge which is acquired under compulsion obtains no hold on the mind. – Plato Having knowledge but lacking the power to express it clearly is no better than never having any ideas at all. – Pericles

Knowledge Spillovers In 1991 the Nobel Memorial Prize in Economic Sciences was awarded to Ronald Coase for his penetrating analysis of why a firm exists. His response to the question of why a firm exists had to do with the cost of transactions. According to Coase, a firm exists because of its organizational superiority achieved through authority or administration, as opposed to exchange relationships in markets. His focus on the cost of transactions suggested that the boundaries of the firm were dictated by the internal cost of transactions compared with the cost of market exchange: “The main reason why it is profitable to establish a firm would seem to be that there is a cost of using the price mechanism. The most obvious cost of ‘organizing’ production through the price mechanism is that of discovering what the relevant prices are” (Coase 1937, 390). According to Coase, a firm exists because it is more profitable to create an enterprise if the costs incurred from the coordination of economic

The Knowledge Spillover Theory of Entrepreneurship

17

activities via the market are greater than the administrative costs associated with coordinating those same economic activities internally – that is, within the boundaries of the firm. As Coase pointed out, “[t]he question always is, will it pay to bring an extra exchange transaction under the organizing authority? At the margin, the costs of organizing within the firm will be equal either to the costs of organizing in another firm or to the costs involved in leaving the transaction to be ‘organized’ by the price mechanism” (1937, 404). Oliver Williamson, the 2009 Nobel laureate in Economic Sciences, provided a more nuanced view of the role of transactions costs within the theory of the firm: “A transaction occurs when a good or service is transferred across a technologically separable interface. One stage of activity terminates and another begins” (1985, 1). Perhaps even more important, Williamson viewed transactions costs as reflecting not just technology but also human nature: “Transaction cost economics characterizes human nature as we know it by reference to bounded rationality and opportunism. The first acknowledges limits on cognitive competence. The second substitutes subtle for simple selfinterest seeking” (44). Both Coase (1937) and Williamson (1985) emphasized that costminimization behavior results in governance structures to minimize transactions costs. As Williamson (1985, 46) pointed out, “[e]conomizing on bounded rationality takes two forms. One concerns decision processes, and the other involves governance structures. The use of heuristic problem-solving is a decision process response. Transaction cost economics is principally concerned, however, with the economizing consequence of assigning transactions to governance structures in a discriminating way.” Confronted with the realities of bounded rationality, the costs of planning, adapting, and monitoring transactions expressly need to be considered. Which governance structures are more efficacious for which types of transactions? Ceteris paribus, modes that make large demands against cognitive competence are relatively disfavored. However, while Coase explained the existence of a firm, he never addressed the central issue of why an individual would start a new firm or, for our purposes in this book, what influences an individual’s decision to become an entrepreneur. In fact, he pondered: “A pertinent question to ask would appear to be (quite apart from … monopoly considerations … ), why, if by organizing one can eliminate certain costs and in fact reduce the cost of production, are there any market transactions at all? Why is not all production carried on by one big firm?” Coase (1937, 23). The discussion we offer in the following sections provides an answer to the question Coase left unaddressed,

18

Sources of Knowledge & Entrepreneurial Behavior

and, to relate to our discussion in Chapter 1, our answer emphasizes the consequences of knowledge – that is, the effect of the use of knowledge sources on entrepreneurial behavior (see Figures 1.1 through 1.3). The knowledge spillover theory of entrepreneurship (KSTE) provides an explanation of why an individual chooses to become an entrepreneur.1 And, to anticipate the conclusion of the theory, the choice to become an entrepreneur is not independent of context. The starting point of the KSTE rests on a factor of production that Coase did not explicitly consider – namely, knowledge. But the theory ultimately draws upon the Coasian axiom that it is the higher costs of transacting that knowledge within the organization, relative to taking that same knowledge and launching a new organization, that ultimately generates the incentive for an individual to choose to commercialize that knowledge within the context of a newly created entity, an entrepreneurial firm. Thus, to motivate the conclusion we draw from the KSTE, we begin with a discussion of factors of production within a knowledge production function. The Knowledge Production Function Should the neoclassical production function be characterized only in terms of the traditional factors of physical capital and labor, while ignoring the factor of knowledge? We say no. To illustrate, Solow (1956) based his Nobel Prize–winning growth model of production explicitly on physical capital and (unskilled) labor; subsequent research has generally confirmed the efficacy of the neoclassical production function, based on these two factors of production, in estimating economic output. But, as Nelson (1981, 1032) pointed out, “[s]ince the mid-1950s, considerable research has proceeded closely guided by the neoclassical formulation. Some of this work has been theoretical. Various forms of the production function have been invented. Models have been developed which assume that technological advance must be embodied in new capital. Most of the work has been empirical and guided by the growth accounting framework implicit in the neoclassical model.” However – and this is an important qualification – knowledge, or what Solow (1957) suggestively referred to as the manifestation of technical change, also plays a key role, albeit it a residual one, in his neoclassical model of economic growth. Solow interpreted the unexplained variance in economic output as reflecting technical change, which has been suggested to fall like manna from heaven. Nelson (1981, 1030) explained: “Robert Solow’s 1956 theoretical article was largely addressed to the pessimism about

The Knowledge Spillover Theory of Entrepreneurship

19

full employment growth built into the Harrod-Domar model … In that model he admitted the possibility of technological advance.” Today, what Solow (1957) referred to as technical change is more broadly called innovation, which might not be simply the result of manna from heaven, and therefore might not be exogenous to any strategy or effort. Rather, innovation might be endogenous to specific and targeted investments and strategies, which became clear through the knowledge production function, originally posited by Griliches (1979). According to Griliches (1979, 1990, 1992), the output of innovation emulates the output of goods and services more generally in that it requires inputs. Those inputs, however, are considerably different from that of a traditional neoclassical production function.2 Rather than focus exclusively on the inputs of physical capital and unskilled labor, Griliches instead shifted the lens of his analysis for innovation (I) to a very different factor of production: knowledge and ideas (Kn), or, I = f(Kn)

(2.1)

The following question is not addressed, much less answered, through equation (2.1): From where does knowledge originate? Does, in fact, knowledge – specifically, technical knowledge – simply fall like manna from heaven, as implicit in Solow’s model? We offered an approach to addressing this question from an epistemological perspective in Chapter 1, but within the context of a production function and the KSTE we defer to Griliches (1979), who had an answer. Knowledge could be endogenously created through both investments in research and development (R&D, RD) and creative people working with ideas, or what might broadly be termed human capital (HC):3 Kn = F(RD, HC)

(2.2)

To anticipate subsequent chapters, we build on the relationship Kn = F(RD, HC) in several ways. The variable RD is one source of knowledge, albeit it is exogenous to the firm, and the variable HC reflects the experience base of a firm as embodied in its entrepreneurial founders and workers. Thus equation (2.2) has as determinants both investments in R&D (RD) and human capital (HC), which emanates from experiences. In a sense, equation (2.2) suggests higher- and lower-ordered sources of knowledge. R&D is a source that determines Kn, but Kn in turn is a source of knowledge that will affect innovative (that is, entrepreneurial) behavior.

20

Sources of Knowledge & Entrepreneurial Behavior

As an aside, perhaps Griliches (1979) did not go far enough because, as Antonelli (2007, 451) argues, “[k]nowledge is not only an output, but also an input into the generation of further knowledge.” Subsequent to the introduction of the knowledge production function by Griliches, a new literature rapidly emerged testing its validity (Griliches 1984, 1990). Research across a broad spectrum of national, industry, and temporal contexts generally confirmed the validity of the knowledge production function or, more specifically for our context, it confirmed the importance of the relationship between knowledge and both entrepreneurial behavior and economic performance. For example, at the country level, empirical results reported in the literature are generally consistent with the knowledge production function. Those countries with high levels of investments in R&D, university research, and human capital – such as Switzerland, Japan, and Sweden – also tend to exhibit high levels of innovative activity as typically measured in a single dimension such as patent counts. By contrast, countries with low levels of investments in R&D, university research, and human capital, as is prevalent throughout many parts of Africa, for example, tend to exhibit low levels of innovative activity (Griliches 1984, 1990). Similarly, in the context of industry, empirical findings also generally confirm the validity of the knowledge production function (Griliches 1984). Those industries with high levels of investments in R&D, university research, and human capital – such as computers, pharmaceuticals, and scientific instruments – also tend to exhibit high levels of innovative activity. By contrast, industries with low levels of such investments – such as shoes, textiles, and apparel – tend to exhibit low levels of innovative activity. Thus, the early wave of studies that empirically tested the model of the knowledge production function generally confirmed its validity in the sense that they found a positive relationship between knowledge inputs and innovative activity at both the country and industry level of analysis – that is, in two very distinct contexts. To anticipate Chapter 3, the data we rely on in this book are firm-specific, but we aggregate them to both the country and the industrial sector level for descriptive purposes. The Schumpeterian Paradox Our purpose in the previous section was to show that the model of the knowledge production function has been empirically validated across two important contexts: at the country level and at the industry level. However, when the knowledge production function has been considered

The Knowledge Spillover Theory of Entrepreneurship

21

and then subjected to empirical scrutiny in the context of the firm (that is, across firms), the results have been ambiguous, and have not provided a general validation. Again, to anticipate latter chapters, our analysis in Chapter 6, in particular, does advance the knowledge production literature through at least a partial validation at the firm level – that is, a validation that relies on the firm as the relevant unit of observation. The knowledge production function implies that those enterprises with higher levels of R&D and more extensive human capital, which we call knowledge-deepening enterprises, should also exhibit a higher level of innovative behavior and performance. It has been well documented that the bulk of industry or private sector R&D is undertaken by large firms (Acs and Audretsch 1990), and large firms tend to employ a work force characterized by higher levels of human capital (Acs and Audretsch 1990; Brown, Hamilton, and Medoff, 1990). In fact, Schumpeter (1942), in his treatise Capitalism, Socialism and Democracy, which appeared long before the empirical studies on firm size and, for example, R&D investments were published, identified large corporations as being inherently more innovative than their smaller counterparts due to their superior ability to harness knowledge inputs.4 According to Schumpeter, not only was the large corporation thought to have superior productive efficiency; it also exhibited an engine of technological change and a superior level innovative activity: “What we have got to accept is that (the large-scale establishment or unit of control) has come to be the most powerful engine of ... progress and in particular of the long-run expansion of output not only in spite of, but to a considerable extent through, this strategy which looks so restrictive” (Schumpeter 1942, 106). Galbraith (1958, 87) similarly viewed the large corporation as having an inherent innovative advantage: “Because development is costly, it follows that it can be carried on only by a firm that has the resources which are associated with considerable size.” In unequivocally endorsing the Schumpeterian hypothesis, Galbraith concluded: “There is no more pleasant fiction than that technical change is the product of the matchless ingenuity of the small man forced by competition to employ his wits to better his neighbor. Unhappily, it is a fiction. Technical development has long since become the preserve of the scientist and engineer. Most of the cheap and simple inventions have, to put in bluntly and unpersuasively, been made” (1958, 86–7). Schumpeter ultimately, and seemingly logically, predicted that the inherent innovative disadvantage confronting small companies would trigger their demise: “Since capitalist enterprise, by its very achievements, tends to automize progress, we conclude that it tends to make itself superfluous – to

22

Sources of Knowledge & Entrepreneurial Behavior

break to pieces under the pressure of its own success. The perfectly bureaucratic giant industrial unit not only ousts the small- or medium-sized firm and ‘expropriates’ its owners, but in the end it also ousts the entrepreneur and expropriates the bourgeoisie as a class which in the process stands to lose not only in its income but also, what is infinitely more important, its function” (Schumpeter 1942, 134). The empirical evidence, however, advanced to some degree by Link’s (1980) empirical analysis of the returns to R&D across firms of varying sizes, is mixed regarding its support for the Schumpeterian hypothesis that large firms demonstrate an innovative advantage. Rather, due in part to advanced econometric techniques and access to more disaggregated data sources, compelling and unequivocal evidence quickly mounted to suggest that smaller firms are actually more innovative than are their larger counterparts. Instead of having an innovative advantage, large firms were found to be burdened with inferior performance in a number of dimensions vis-à-vis small companies (Acs and Audretsch, 1987, 1988, 1990). Thus, compelling empirical evidence generally has been found to support the model of the knowledge production function, as generally represented by equation (2.2), in the context of both country and industry, but not always in that of the firm. The anomaly posed by these contradictory empirical findings raised the question that became known as the Schumpeterian Paradox: if knowledge inputs, such as R&D, university research, and human capital, are crucial for generating innovative behavior and, hence, performance, as characterized by the model of the knowledge production function in equation (2.2), then how and why are smaller firms able to exhibit such stronger levels of innovative outputs vis-à-vis their larger counterparts? Resolving the Schumpeterian Paradox A first clue to resolving the Schumpeterian Paradox was offered by a growing body of studies that focused on the geography of knowledge spillovers (Acs, Audretsch, and Feldman 1992; Audretsch and Feldman 1996; Audretsch and Stephan 1996; Feldman 1994; Feldman and Audretsch 1999; Jaffe 1989; Link and Scott 2006). In particular, a sophisticated body of scholarly literature provided compelling empirical evidence that, although knowledge and ideas do spill over from the firm or organization in which they are generated – such as an R&D laboratory within a firm or research laboratory at a university – that knowledge and the associated ideas tend to decay as they transmit across geographic space. Notably, studies have found that innovative activities

The Knowledge Spillover Theory of Entrepreneurship

23

tend to cluster spatially within close geographic proximity to the knowledge source (Audretsch and Feldman 1996; Link and Scott 2006). Romer (1986) and Lucas (1988, 1993), among others, developed theories to explain why knowledge tends to spill over, especially locally. Those efforts were the genesis of important work to come. Theories of localization were also critical to supplementing theories of knowledge spillovers in order to explain not only that knowledge does spill over from the firm or organizational source where it was created to the application and commercialization by other organizations and individuals; but also that such knowledge spillovers tend to be geographically bounded. Theories of knowledge spillovers and localization theories were combined to highlight, as well as to explain, the emergence of important spatially localized clusters of innovative activity, such as parks located in Silicon Valley, California; Austin, Texas; and Research Triangle, North Carolina. Thus, due to the geographic localization inherent in knowledge spillovers, compelling systematic empirical evidence generally confirmed the validity of the knowledge production function in the context of the spatial level, depicted by the city, region, or state (Acs, Audretsch, and Feldman 1992, 1994; Jaffe 1989): regions with higher levels of investments in R&D and university research also tend to exhibit higher levels of innovative activity, while regions with low levels of investment in new knowledge tend to exhibit lower levels of innovative activity. Localized knowledge spillovers were used to interpret, if not to reconcile, the Schumpeterian Paradox. Small firms were able to access knowledge that they did not generate themselves through the spillovers created in other organizations located within close geographic proximity. Thus, the resolution of the Schumpeterian Paradox suggests that the model of the knowledge production function does indeed have both construct validity and empirical support, but for a slightly more nuanced context that is not necessarily firm-centric, but region-centric. The Knowledge Filter The mode of the spillover mechanism, or the transmission mode, is often unobservable and thus by its nature mysterious: as Marshall (1890) intimated more than a century ago, knowledge and ideas were simply in the air. If Solow (1956, 1957) had suggested that knowledge falls like manna from heaven in a stochastic manner, Romer (1986) instead can be thought to have suggested that knowledge spills over from the geographic neighbor that had created the knowledge in the first place,

24

Sources of Knowledge & Entrepreneurial Behavior

through its purposeful investments in R&D and human capital.5 There was a reason that knowledge was in the air: it was due to geographic location and exactly which firms and which knowledge investments were in close geographic proximity. A different view, however, has challenged the implicit assumption in the models of endogenous growth by Romer (1986) and others that spillovers from investments in new knowledge are automatic and inevitably result in commercialization to generate innovative activity. Audretsch, Keilbach, and Lehmann (2006) and Acs, Audretsch, and Lehmann (2013) instead suggest that what is termed the knowledge filter effectively impedes the spillover of knowledge and thus the generation of ideas for commercialization and innovation. The concept of a knowledge filter poses a barrier that impedes or preempts the commercialization of investments in both research and knowledge. Although he did not use the phrase “knowledge filter,” US senator Birch Bayh’s concern about the paucity of commercialization and innovation emanating from costly public investments in university research clearly recognized that knowledge spillovers are anything but automatic. In his introductory remarks on 13 September 1978 when first presenting the Dole-Bayh bill to Congress – which eventually led to the Bayh-Dole Act of 1980 (see Stevens 2004, 95), Bayh said “[a] wealth of scientific talent at American colleges and universities – talent responsible for the development of numerous innovative scientific breakthroughs each year – is going to waste as a result of bureaucratic red tape and illogical government regulation.” One might argue that the existence of a knowledge filter led Bayh to challenge the efficacy of investments in both research and knowledge. As he remarked when the Bayh-Dole bill was approved on 23 April 1980, “[w]hat sense does it make to spend billions of dollars each year on government-supported research and then prevent new developments from benefiting the American people because of dumb bureaucratic red tape?” (Stevens 2004, 97). The Bayh-Dole Act – more formally, the University and Small Business Patent Procedure Act of 1980 – might be interpreted as an example of a policy designed to penetrate the knowledge filter by addressing legal impediments.6 There are even more fundamental reasons, however, to suggest that knowledge does not result automatically in commercialization, ultimately spurring innovative activity. Kenneth Arrow, the 1972 Nobel laureate in Economic Sciences, provided path-breaking analysis of what exactly distinguishes ideas – or, following from Figure 1.1 in Chapter 1, what might be referred to as

The Knowledge Spillover Theory of Entrepreneurship

25

the output from knowledge – from more traditional economic goods and services. Three characteristics make knowledge and ideas inherently different. The first involves the degree of uncertainty associated with the viability of the idea. An economic good such as an automobile or a service such as a haircut involves familiarity and high levels of certainty on both the supply and the demand side. There is considerable public information about how to produce such goods and services, as well as about what the market demand might be. By contrast, Arrow (1962) emphasized that considerable uncertainty characterizes both the demand and the supply side of knowledge or ideas that are new. It might not be known what the good or service ultimately will look like and how it can be produced; similarly, the demand for the good or service also might be relatively unknown. The second characteristic involves high degrees of asymmetries across agents or individuals: what one individual decision maker places a high potential value on, another might weigh as being considerably less valuable. And the third characteristic involves a high cost of transacting the conditions that generate such asymmetries in the valuation of knowledge and ideas, so that their elimination is nontrivial and, in all likelihood, highly costly. For example, like many companies, Xerox has long invested millions of dollars each year in R&D and human capital in an effort to generate knowledge and ideas that lead to inventions and, subsequently, to new commercial products and, ultimately, to successful innovations. One of those new ideas in the 1970s that resulted from costly investment in R&D, university research, and human capital was the personal computer, yet “decision-makers at the company did not recognize the potential value of this idea or invention, and concluded that while it was an interesting device for sophisticated engineers to play around with, it had minimal potential commercial value. After all, their decision making context was an era when people had become used to turning to the mainframe computer as the solution to their computational needs” (Audretsch and Lehmann 2016, 36). Why would a company invest scarce financial and human resources in generating new ideas and knowledge only to turn its back on those created in its own research laboratory? The answer is the knowledge filter. Arrow’s (1962) three conditions inherent in knowledge and new ideas – uncertainty, asymmetries, and high cost of transactions – resulted in Xerox’s decision makers placing a low value on the new idea of the personal computer. Who would have known with certainty that the device would prove to be among the most lucrative of innovations by the end of the twentieth century?

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Sources of Knowledge & Entrepreneurial Behavior

Endogenous Entrepreneurship A different example of the knowledge filter is illustrated through the actions of IBM. IBM invested considerable financial and human resources in its R&D activities when it targeted the development of Scientific Data Systems (SDS)/SAPE software. A group of young engineers consisting of Dietmar Hopp, Klaus Tschira, Hans-Werner Hector, Hasso Plattner, and Claus Wellenreuther and employed in one of the company’s European establishments near Mannheim, Germany, came up with a new idea for the software. IBM, however, decided to abandon its strategy and chose not to develop the new software (Audretsch 2007). Due to the same conditions inherent in new knowledge that Arrow (1962) identified, the idea got stuck in the knowledge filter. Had nothing else happened, the idea, which was the fruit of IBM’s own costly R&D and human capital, would have remained discarded and never commercialized by the company. Instead the team of young software engineers was so convinced that the new idea was valuable that they decided to leave IBM and strike out on their own. The company they founded is Systemanalyse und Programmentwicklung (System Analysis and Program Development), today known around the world as SAP. The assumptions implicit in both the Solow (1956, 1957) neoclassical and Romer (1986) endogenous growth models hold up not only for this example, but also for a broad spectrum of similar examples and case studies where the knowledge neither falls like manna from heaven nor is automatically accessed by existing companies located within close geographic proximity to the organization creating that knowledge in the first place (Audretsch 1995, 2015). It is the entrepreneur who provides the conduit for the spillover of knowledge by commercializing ideas through a new entity that otherwise might not have been commercialized by the organization that created them. The economics literature characterizes the decision an individual makes to become an entrepreneur in the model of entrepreneurial choice, which is a special case of the more general model of occupational choice, as7 Pr (E) = f (Πie – Wj),

(2.3)

where Pr (E) represents the probability than an individual chooses to become an entrepreneur, Πe stands for the expected (e) profits accruing from entrepreneurship, and W is the wage earned from being an

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employee. The subscript i represents the firm in which entrepreneurial employee j is employed. However, the model of entrepreneurial choice leaves perhaps the most compelling question unaddressed: why do some people become entrepreneurs while others in similar firms and earning similar wages abstain from doing so? The development of a rich and robust literature provides a compelling answer: opportunity. McClelland’s (1961) pioneering research found that the answer lies in differences in individuals’ characteristics, proclivities, propensities, and personalities. As Eckhardt and Shane (2003, 337) conclude, “some actors are more likely to discover a given opportunity than others.” Such differences revolve around the aversion toward risk; preferences for independence and autonomy; access to key resources, such as finance, talent, and networks; and experience. This strand of literature leads to the view that DNA plays a non-trivial role in shaping the decision to become an entrepreneur (Verheul et al. 2015). An alternative view, posited by Audretsch, Keilbach, and Lehmann (2006), rather than probing the potential entrepreneur’s intrinsic propensities and inclinations, looks instead to the context in which the individual works and lives. A context rich in knowledge and in fact is realized will create more entrepreneurial opportunities, because some new ideas will not make it through the knowledge filter. (And, to anticipate our discussion in Chapter 6, realizing the importance of context, in fact, demonstrates the entrepreneurial characteristic of perception of opportunity.) Expected profits accruing from entrepreneurship, Πe, will be greater in a high-knowledge context than in an impoverishedknowledge context, which, in turn, will induce more entrepreneurship. In the above examples, it was the knowledge created from the R&D laboratories at Xerox and IBM, combined with those firms’ decisions not to pursue and commercialize the new ideas, that ultimately created the opportunities that Steve Jobs leveraged to found Apple Computer and that the team of young researchers at IBM used to found SAP. What we infer from these examples is that a high-knowledge context generates more entrepreneurial opportunities than does a low-knowledge context, which is characterized by a paucity of new ideas. In these examples, the decision to become an entrepreneur might have less to do with DNA and more to do with the opportunities perceived and opportunities generated in a knowledge-rich context. In the model of the knowledge production function, the firm is assumed to exist exogenously (Griliches 1979). The firm then undertakes strategies characterized through, for example, investments in R&D, university

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research, and human capital in order to generate ideas and knowledge. By contrast, linking the role of knowledge to the model of entrepreneurial choice results in a model of endogenous entrepreneurship, where, given an exogenous knowledge context, individuals will be induced into entrepreneurship to appropriate the expected value of their own ideas. The knowledge spillover theory of entrepreneurship was introduced by one of the authors of this book (Audretsch 1995), who appropriately made a distinction between the organizational context in which knowledge and opportunities are generated, the new organizational context in which that knowledge is commercialized, and the opportunities that are actualized through entrepreneurship. Although the firm is exogenous in the Griliches (1979) model of the knowledge production function, it becomes endogenous through entrepreneurship in the KSTE. In positing the KSTE, Audretsch provided a reconciliation of various findings in the empirical literature challenging the model of the knowledge production function at the level of the firm, giving rise to the Schumpeterian Paradox (Audretsch 1995, 179–80): “The findings challenge an assumption implicit to the knowledge production function – that firms exist exogenously and then endogenously seek out and apply knowledge inputs to generate innovative output … It is the knowledge in the possession of economic agents that is exogenous, and in an effort to appropriate the returns from that knowledge, the spillover of knowledge from its producing entity involves endogenously creating a new firm.” How are these small and often new firms able to generate innovative output when undertaking a generally negligible amount of investment into knowledge-generating inputs, such as R&D? The KSTE provides an answer: they do so by commercializing knowledge and ideas created by expenditures on research in universities and incumbent companies. Appendix 2.A shows emphatically that there is a growing body of compelling evidence to support the KSTE. In general, the studies referenced in the appendix find that high-knowledge contexts generate more entrepreneurial activity than do low-knowledge contexts. Those contexts range in scope from the levels of the firm, industry, city, and region to the nation. What became known first as the Swedish Paradox and later the European Paradox raises the important question: why are economies with relatively high levels of investments in knowledge still burdened by dismal economic performance, stagnant growth, and high rates of unemployment? The answer rests with the presence and role of entrepreneurship (Audretsch 2015).

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In the context of developed countries, it might be that a necessary condition for economic growth is to have high levels of investment in knowledge, such as through R&D, university research, and human capital, as posited by the models of endogenous growth, but the presence of entrepreneurship to realize these levels is needed for sufficiency. To ensure that costly investments in knowledge actually spill over to commercialization and innovative activity that drive economic growth and generate high levels of sustainable employment, entrepreneurship is needed as the conduit (Audretsch, Keilbach, and Lehmann 2006). The Role of Geography The KSTE is not neutral with respect to geography, as we have already suggested. Rather, what has been referred to as the Localization Hypothesis posits that “[k]nowledge spillover entrepreneurship will tend to be spatially located within close geographic proximity to the source of knowledge actually producing that knowledge” (Audretsch, Keilbach, and Lehmann 2006, 50). Even if knowledge spills over, it tends to decay as it is transmitted across geographic space. The tacit nature of that knowledge, which we discuss below, requires spatial proximity for the spillover. Thus, agents might respond to knowledge that is not commercialized by the organization that generates it by forming a new firm, but that entrepreneurial activity tends to be within close geographic proximity to the source of the knowledge.8 A rich body of empirical evidence, some of which is summarized in Appendix 2.A, has confirmed the Localization Hypothesis. Studies generally find that entrepreneurship tends to remain geographically clustered in close proximity to the firm or organization that generates knowledge and entrepreneurial opportunity. Klepper (2009), for example, suggests that the localization of spinoffs from Fairchild served to seed not just what he terms the “Fairchildren,” but ultimately the emergence of Silicon Valley as an innovative region. The Missing Link The KSTE explains not only why some individuals become entrepreneurs while others refrain from doing so, but also why this distinction matters. A generation ago, entrepreneurship was largely viewed as extraneous to economic performance, jobs, or standard of living (Audretsch 2007). In the capital-driven economy characterized by the framework described by Solow (1956, 1957), there did not seem to be much place for small or

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even new businesses. With the advent of knowledge as a driving force of economic performance, however, although the focus shifted from physical capital to human capital and R&D, there still seemed to be little space left for small or new businesses. After all, R&D was considered a largescale organizational phenomenon, and the endogenous growth models, centered on the driving forces of R&D, university research, and human capital, seemingly left little room for entrepreneurship. The KSTE changed all that. In this theory, entrepreneurs and entrepreneurship, rather than remaining at the margins of the economy, emerged as the key conduit taking knowledge and ideas generated in one organizational context and commercializing them in the organizational context of a new firm or organization. Confronted with an imposing knowledge filter, it was realized that a paucity of entrepreneurship retards economic growth, competitiveness, and employment creation. A plethora of empirical studies, including various measures of entrepreneurship in models of economic growth across a broad range of contexts, provides compelling evidence of this process. Much of this evidence finds that economic performance is positively and significantly related to entrepreneurship, as summarized in the literature in the appendix to this chapter. That said, we are aware of scholarship that finds no significant effect (for example, Naudé 2011) and even a negative effect (for example, Blanchflower 2000; van Stel and Storey 2004; see also Boschma 2017 and Foray 2014). Knowledge Heterogeneity As knowledge becomes increasingly important as the driving force of economic performance and, in particular, growth and employment, endogenous entrepreneurship driven by opportunities arising from investments in knowledge also becomes increasingly prevalent. Thus, although the KSTE remains limited to a context that is rich in knowledge, the relevance and poignancy of that context has rapidly diffused on a global level. The KSTE resonates increasingly as a dominant view explaining not just why the phenomenon exists, but also why it is important in our knowledge-driven twentyfirst century. But, although the KSTE certainly has global implications, for the purposes at hand it does provide a framework and perhaps a justification for this book and our documentation and analysis of sources of knowledge and the role and influence those sources have on entrepreneurial behavior. To build on the notion that knowledge is the driving force of both economic performance and entrepreneurial behavior – the topic of Chapter 6 – it is important to emphasize that in this chapter we have

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portrayed knowledge as it has generally been portrayed in the economics and management literatures, where knowledge is considered to be homogeneous and unidimensional. That singular dimension recognizes that knowledge is not ubiquitously a constant, in that it is the same anywhere and everywhere. In our discussion so far, we have implied that knowledge has economic value, as we illustrated in Figure 1.1, Knowledge → Entrepreneurial Behavior. Thus, we conclude this chapter by reemphasizing the heterogeneity of knowledge. The following discussion in this section has a place in epistemology, but, more important for this book, it sets the stage for our subsequent measurement in Chapter 5 of different sources of knowledge and their varying effects on entrepreneurial experience.

Economic versus non-economic knowledge One might think about a maxim that is often credited to Alexander Pope: a little bit of knowledge is a dangerous thing. More precisely, what Pope wrote in An Essay on Criticism (1709, lines 217–20) was: A little Learning is a dang’rous thing; Drink deep, or taste not the Piërian spring: There shallow draughts intoxicate the brain, And drinking largely sobers us again.

Misunderstanding the dichotomy between economic and non-economic knowledge can be a “dang’rous thing” when it comes to understanding not only the KSTE, but also its implications for being a driving force of economic performance. When the adjective economic precedes the word knowledge, it is implied that knowledge has economic value (Arrow 1962). Even the novice scholar with only an introductory economics course under his or her belt will know that economic value is not the same as personal value; economic value is determined by markets under Western-style capitalism. The distinction between economic value and personal value is not trivial when it comes to understanding knowledge or, for the purposes at hand, for understanding alternative sources of knowledge. For most goods and services – say, trousers or haircuts – there are well-established markets, and those markets clear to establish a market price. Such markets generally do not exist for knowledge and ideas, however, and so there is no market-clearing price with which to establish their value. An idea can constitute non-economic knowledge at one point in time, but economic knowledge at a later point. For example, many important

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innovations – such as the smart phone, the personal computer, and social media – began as a simple idea or bit of knowledge. At some point, individuals began to realize that these innovations or ideas for innovations might have economic value. Clearly, over time, that realization became a reality. The point at which knowledge shifts from non-economic to economic is, however, anything but clear. The border between economic and non-economic knowledge is a distinction that we echo throughout this book, primarily though the existence (or lack) of relationships between experience and knowledge, and between knowledge and entrepreneurial behavior. In other words, simply because we define with the data at hand to offer a measure of a particular source of knowledge, the relationship between that source and entrepreneurial behavior is an empirical issue and one possibly fraught with construct validity.

Tacit versus codified knowledge Although Arrow (1962) is widely acknowledged as pioneering the economics of knowledge, after an introductory statement his seminal article rarely uses the word knowledge: “INVENTION is here interpreted broadly as the production of knowledge. From the viewpoint of welfare economics, the determination of optimal resource allocation for invention will depend on the technological characteristics of the invention process and the nature of the market for knowledge” (609). Instead Arrow was concerned with the economics of information (a word he uses repeatedly). It was not until several decades after the publication of Arrow’s article that a sharp distinction was made between knowledge and information. The key to distinguishing knowledge from information lies in another key distinction: that between tacitness and codification. Ideas that are codified generally are agreed upon and can be articulated and written down, which lend themselves to communication via the Internet, rendering physical proximity not particularly relevant. By contrast, ideas on which there is no general agreement and that cannot be articulated in a coherent written form are generally characterized as being tacit in nature (Dosi 1988; Feldman 1994). As Antonelli (2014, 235) explains, “[k]nowledge tacitness implies that dedicated interactions among agents are necessary to make its use possible.” Making a key distinction between tacit content and codified content is the most efficient mode of transmission. Thus, ideas, or content, that can be codified are better characterized as information, while ideas, or content, that lend themselves to codification are better characterized as knowledge.

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Information, because it is codified, is better characterized by risk, where the outcomes can be identified and the likelihood of those outcomes estimated. By contrast, tacit knowledge is better characterized by uncertainty. The main distinction between uncertainty and risk was identified nearly a century ago by Knight (1921) as having unidentified outcomes and, therefore, an inability to estimate or assign any likelihood of those outcomes: “With the introduction of uncertainty – the fact of ignorance and the necessity of acting upon opinion rather than knowledge – into this Eden-like situation [that is, a world of perfect information], its character is entirely changed … With uncertainty present doing things, the actual execution of activity becomes a real sense a secondary part of life; the primary problem or function is deciding what to do and how to do it” (268). A physical presence involving face-to-face communication is typically requisite for the transmission of tacit knowledge. Feldman (1994), for example, identifies the tacit nature inherent in knowledge as distinct from codified information in shaping the importance of geography in developing her theory of localization in the transmission of knowledge spillovers.

Incremental versus radical knowledge A somewhat different dimension involving knowledge is whether the innovative activity it propagates is incremental or radical in nature (Dosi 1988; Grant 1996; Kogut and Zander 1992). An incremental innovation is one that builds upon or extends an existing product or service; a radical innovation is one that creates a completely new product or service. The invention of a new fender for automobiles is an incremental innovation; the Internet is a radical innovation. Kogut and Zander (1992) provide a perspective for distinguishing between radical and incremental innovations. An incremental innovation reinforces the existing competencies of the firm or organization that makes the innovation, while a radical innovation is competence destroying, in that it makes the existing competencies of the firm or organization less valuable. Knowledge that generates incremental innovations is also characterized by a relatively lower degree of uncertainty, while knowledge that generates radical innovations is characterized by a high degree of uncertainty. Although the anticipated or expected outcome of incremental knowledge is fairly predictable, this is not the case with radical knowledge. Acs and Audretsch (1988, 1990) were able to analyze innovations generated in one year in the United States, classifying them according five levels of significance varying from incremental innovation to radical

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innovation. Their empirical evidence clearly identified a skewed distribution of innovative activity in terms of innovation significance, or the extent to which an innovation was incremental or radical. Far and away the largest number of innovations fell into the least significant category, suggesting they were incremental. As the level of innovation significance increased, the number of innovations fell, suggesting that only a small share of innovative activity could be characterized as radical. Thus, although both innovative and radical types of knowledge generate innovations, the resulting innovative activity is likely to be different.

Specialized versus diversified knowledge A considerably different aspect or dimension of knowledge involves its applicability. In particular, the applicability of knowledge can be highly specialized with a focus on a particular application or context, or it can be diverse in nature with a broad span of applications across products and contexts. Glaeser et al. (1992) found that diverse knowledge tends to have a greater impact on the economic performance of cities than does specialized knowledge. Similarly, Feldman and Audretsch (1999) found that knowledge that is diverse, in that it comes from different but complementary disciplines and fields of basic research, tends to generate a greater amount of innovative activity, while knowledge that is specialized, in that it comes from a singular research discipline, tends to generate less innovative activity.

Basic versus applied knowledge Yet another dimension of knowledge is the degree to which it is basic or applied. Basic research is generally undertaken to generate knowledge for its own sake, and is the gold standard of the traditional academic disciplines (Gallaher, Link, and Petrusa 2006; Link 1996). This standard, so dominant at universities throughout the world, emanated from Wilhelm von Humboldt (Turner 1972). Prior to his time, almost all universities were vassals of the state and church, and their activities had to be approved by and in accordance with the values and wishes of these two dominant institutions. Certainly the fate of Copernicus when he challenged the papal authority that the sun revolved around the world reflected that the arbiter of the value of knowledge was no less an authority than the Catholic Church. As Audretsch and Lehmann (2016, 64) point out, “[t]he historical and institutional linkages between the church

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and the university were challenged and disrupted by Humboldt in Berlin during the early 1800s. Von Humboldt was a giant of a scholar in philosophy and linguistics, who among other things, served as the Prussian Minister of Education and later founded the University of Berlin. In particular, von Humboldt triggered a new tradition for universities centering on freedom of thought, learning, intellectual exchange, and research and scholarship as the salient features of the university.” As the model for the university Humboldt championed spread across Europe, the United States, and Canada, universities were liberated, so to speak, to pursue research and teaching that resulted in knowledge for the sake of knowledge. Nowhere was basic knowledge more valued than in Germany. As G. Stanley Hall, who founded the American Psychological Association, and later became president of Clark University, exclaimed, “[t]he German University is today the freest spot on earth … Never was such burning and curiosity … Shallow, bad ideas have died and truth has always attained power … Nowhere has the passion to push on to the frontier of human knowledge been so general. Never have so many men stood so close to nature and history or striven with such reverence to think God’s thoughts after Him exactly” (Hall 1891, 6–8). By contrast, applied knowledge is not driven by the goal of knowledge for the sake of knowledge; rather, it aims to solve a particular problem or challenge confronting a particular element of society. Applied knowledge has value not because of what it contributes to a traditional scholarly discipline, but because of the solutions it provides to actual problems.9 Along with the shift in competitiveness in the post–Second World War economy from physical capital to knowledge came the recognition that basic research that generated basic knowledge did not readily translate into applied knowledge to fuel innovation and economic growth. In order for basic knowledge generated from basic research to be applicable to commercialization and innovation, it had to be translated into applied knowledge through applied research. There was, however, an important precedent: to help win the war, American universities had been given a mandate to develop new technologies for deadly weapons and equipment. One of the engineers that helped the universities contribute to developing the atomic bomb was Vannevar Bush. Subsequent to the end of the war with Japan, Bush made it his mission to continue what we today would characterize as the knowledge spillovers from the university to solve societal problems and challenges. In particular, he championed the reorientation and reprioritization of the mission and role of knowledge created by university research. As articulated in Science, the Endless Frontier,

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commissioned by President Franklin D. Roosevelt, this goal was to set the scene for the subsequent three-quarters of a century (Bush 1945).10 There was an even earlier precedent, dating back nearly a century before that valued not just basic knowledge, but also applied knowledge: the Morrill Act, championed by President Abraham Lincoln during Civil War, and enacted by Congress in 1862. Known formally as the Land-Grant College Act of 1862, the act provided each state with land with the stipulation that it be dedicated in perpetuity to funding agricultural and mechanical colleges. The purpose of the act was to establish infrastructure for the generation of applied research and applied knowledge with the goal of supporting the agricultural sector of each state (Audretsch 1995; Link 2006). Thus, in abandoning the Humboldt model, with its inherent value for knowledge for the sake of the scholarly discipline, in favor of providing benefits and solutions to societal problems and challenges through applied research and knowledge, the role of the university has undergone a major transformation.

Routinized versus entrepreneurial contexts Another dimension of the inherent heterogeneity of knowledge is the organizational context in which knowledge is used to generate innovative activity. Not all firms or organizations will place the same value on particular knowledge. Nelson and Winter (1974, 1978, 1982b), for example, suggested that systematic differences in the content of knowledge were what they termed “technological regimes” (Malerba 1992; Malerba and Orsenigo 2000). Winter (1984) differentiated what he termed the routinized regime from the entrepreneurial regime. As he explained, “[a]n entrepreneurial regime is one that is favorable to innovative entry and unfavorable to innovative activity by established firms; a routinized regime is one in which the conditions are the other way around” (297). Distinct knowledge regimes arise from different competitive advantages in identifying new economic opportunities and in assessing and deciding upon commercializing or pursuing opportunities that are inherently uncertain. Certain types of knowledge might be more conducive to innovation in large, incumbent firms, while other types of knowledge might be more conducive to innovation in new startups and small firms. Kreps (1991), for example, argues that the cost of monitoring leads firms to implement rules and routines in order to avoid, or at least to mitigate, agency problems that might arise from the pursuit by individuals within a bureaucracy of their own agenda rather than that of the organization. Although the firm would accrue benefits from the reduced cost of monitoring its employees,

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Holmstrom (1989, 323) concludes that it would also be more likely to forgo new ideas and knowledge that might generate innovations: “Monitoring limitations suggest that the firm seeks out activities which are more easily and objectively evaluated. Assignments will be chosen in a fashion that are conducive to more effective control. Authority and command systems work better in environments which are more predictable and can be directed with less investment information. Routine tasks are the comparative advantage of a bureaucracy and its activities can be expected to reflect that.” Similarly, Williamson (1985, 201) explains why a hierarchical bureaucracy impedes the competitive advantage of large incumbent enterprises from commercializing knowledge into innovative activity: “Were it that large firms could compensate internal entrepreneurial activity in ways approximating that of the market, the large firm need experience no disadvantage in entrepreneurial respects. Violating the congruency between hierarchical position and compensation appears to generate bureaucratic strains, however, and is greatly complicated by the problem of accurately imputing causality.” This leads Williamson also to conclude that large and small firms will have a different competitive advantage, depending upon the nature of any particular knowledge content: I am inclined to regard the early stage innovative disabilities of large size as serious and propose the following hypothesis: An efficient procedure by which to introduce new products is for the initial development and market testing to be performed by independent investors and small firms (perhaps new entrants) in an industry, the successful developments then to be acquired, possibly through licensing or merger, for subsequent marketing by a large multidivision enterprise … Put differently, a division of effort between the new product innovation process on the one hand, and the management of proven resources on the other may well be efficient. (1985, 205–6)

Gort and Klepper (1982) show that the innovative advantage of incumbent established companies over new startups is shaped by the underlying knowledge conditions. In industries where knowledge cannot be easily transferred across firms, incumbent establishment companies tend to have a competitive advantage in innovative activity. By contrast, as Mueller (1972) and Williamson (1985) explain, if knowledge is easily transferred across enterprise boundaries, new startups tend to have a competitive advantage in innovative activity. These two distinct situations conform to the concept of technological regimes posited by Nelson and Winter (1982a) and Winter (1984).

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Acs and Audretsch (1988, 1990) provide empirical evidence supporting this dimension of knowledge heterogeneity. They find that the different innovative activity of large and small firms reflects different knowledge regimes, which provides support for Winter’s (1984) hypothesis that innovations by large and small firms are promoted under differing knowledge conditions. Industries where the innovation rate is particularly high for large companies reflect the routinized regime; industries where the innovation rate is particularly high for small firms reflect the entrepreneurial regime (Audretsch 1995). Thus, an important dimension of the heterogeneous nature of knowledge is the systematic competitive advantage of different organizational types in applying knowledge to generate innovative activity. One key dichotomy reflecting these different knowledge contexts and how they work in actual firms in actual industries involves the technological regime. Under a routinized technological regime, large incumbent firms tend to have a competitive advantage in generating innovative activity from the knowledge content, while under an entrepreneurial technological regime, new and small firms tend to have a competitive advantage in commercializing knowledge content into innovations. National Systems of Innovation The role that entrepreneurship plays as a conduit for the spillover of knowledge and as a driver of innovative activity is embedded in what has been termed national systems of innovation (Edquist 1997; Lundvall 1992; Nelson 1993). According to the framework provided by the leading thinkers who developed the concept, which dates backs to List (1841), the national system of innovation posits that knowledge is the result of both interactive and cumulative interaction among actors in a particular and specific institutional context. Ultimately it is this interaction between actors and institutions within the institutional context that shapes innovative activity. The prominent role played by institutions is why they are referred to as constituting a “system.” As Nelson and Rosenberg (1993, 4–5) explain, the system “is that of a set of institutional actors that, together, plays the major role in influencing innovative performance.” Thus, although the Griliches (1979) model of the knowledge production function is institutional free in that the amount of knowledge is taken as exogenous and the institutional context plays no role, the national systems of innovation model explicitly posits that the institutional context will shape both the type and the amount of knowledge and how that knowledge ultimately generates innovative activity.

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Filippetti and Archibugi (2011) highlight three key assumptions underlying the model of national systems of innovation. The first is the systematic heterogeneity of economic performance across countries. The second is that, in contrast to the Griliches model, economic performance is influenced not solely by differences in technological and innovational capabilities, but also by heterogeneity with respect to institutions. The third assumption involves the key role public policy plays in shaping the key institutions that, in turn, influence innovation and, ultimately, a country’s economic performance. The literature on national systems of innovation has made great strides in identifying particular types of institutions that constitute the system that shapes innovative activity. Fagerberg, Mowery, and Verspagen (2009); and Hall and Soskice (2001) have identified a broad range of institutions devoted to research, education, technology, science, and finance, in addition to those mandated to influence labor markets, taxes, competition within and across industries, and intellectual property Thus, the role of geography is important for two reasons. First, it provides a spatial platform for the transmission of ideas and knowledge. As discussed earlier in this chapter, knowledge spillovers tend to be spatially bounded within close geographic proximity of the knowledge source. The second reason is that geography provides the spatial context for institutions. According to Asheim and Gertler (2004, 292), “geography is fundamental, not incidental, to the innovation process itself: that one simply cannot understand innovation properly if one does not appreciate the central role of spatial proximity and concentration in this process.” In extending the view of national systems of innovation to national systems of entrepreneurship, Acs et al. (2016, 2017) embed the knowledge generated and entrepreneurial opportunities in the national institutional context. Thus, rather than being exogenously determined, the salient parameters of the knowledge spillover theory of entrepreneurship, such as the knowledge filter, instead reflect a country’s particular institutional context. Conclusions A fundamental question concerning entrepreneurship is: why do some people choose to become entrepreneurs, while others do not, and why does that choice matter? The theories and models in the traditional entrepreneurship literature provide an answer based on one approach: the proclivities, propensities, inclinations, and tastes of individuals. If it mattered to society, it was because it mattered to the individual within

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her or his freedom to make that choice. No wonder the initial wave of academic entrepreneurship chairs went under the label Chairs of Free Enterprise (Hébert and Link 2009). By contrast, the knowledge spillover theory of entrepreneurship looks not to differences across individual traits and characteristics, but to contexts – in particular, knowledge contexts. Due to the conditions inherent in knowledge and ideas that were first identified and analyzed by Arrow (1962), it is virtually impossible to completely and exhaustively commercialize knowledge created in any organizational context that gives rise to the knowledge filter. Investments in R&D, university research, and human capital generate new ideas and knowledge, but the knowledge filter can impede their commercialization. Entrepreneurship acts as the missing link to (localized) economic performance by providing the conduit for the spillover of knowledge from the organization that generated it to the new firm or organization that actually commercializes it through innovation, ultimately spurring growth jobs and competitiveness. Understanding the heterogeneity of knowledge not only adds a level of completeness to the KSTE; it also sets the stage for how we quantify sources of knowledge in later chapters. Regarding the first motive for our discussion of the heterogeneity of knowledge, we emphasize an observation by Warsh (2006) that it took a revolution in thinking in economics and management to incorporate the key role of knowledge and ideas into economic analyses. Thus, to recognize and acknowledge the key role that knowledge and ideas play might be viewed as a purposeful step forward. Regarding the second motive, the remainder of this book is empirical in its nature, and our analysis might be among the first to attempt to quantify, albeit descriptively, the model in Figure 1.1. Toward that end, we rely on alternative measures of knowledge and knowledge sources. We have attempted in this chapter to offer small steps to emphasize the obvious – namely, that knowledge is anything but homogenous, and certainly not unidimensional. We have also suggested that recognizing the multidimensional nature of knowledge and ideas is another small step toward understanding that various sources of knowledge might have differing effects on entrepreneurial behavior as well as on innovation-based economic growth. We have not identified all of the various dimensions of the heterogeneous nature of knowledge, but our examples illustrate the intrinsically heterogeneous nature of knowledge and ideas. We anticipate an explosion in the near future of new thinking, new characterization, and new typologies of the various dimensions and manifestations of this key aspect of economic and social life: knowledge and ideas.

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Appendix 2.A Summary of Research Based on the Knowledge Spillover Theory of Entrepreneurship Author(s) (by date of publication)

Key Findings

Anselin, Varga, and Acs (1997)

Analyzes the degree of geographic spillovers between universities and innovative activity. Estimates a knowledge production function at both the state and the metropolitan statistical area (MSA) levels. Estimates the extent of geographic spillovers. Finds strong empirical evidence of local spillovers at the state level. At the MSA level, a distinction is made between R&D activities and university research in the MSA and in the surrounding counties. Evidence is found of local spatial externalities between university research and high-technology innovative activity, both directly and indirectly via private research and development.

Audretsch and Stephan (1999)

Finds that biotechnology company linkages with scientists serving on their scientific advisory boards are located within close geographic proximity when the scientist serves in a role involving the transmission of tacit knowledge. Otherwise, geographic location does not play a role. Geographic proximity is important for the knowledge spillovers.

Agarwal et al. (2004)

Analyzes 1977–97 data from the disk drive industry to identify that industry incumbents with both strong technological and market pioneering know-how generate fewer spinouts than firms that do not have such capabilities. Finds that the capabilities of an established, incumbent firm at the time a spinout is created has a positive effect on the subsequent generation, development, and performance of spinouts.

Audretsch, Lehmann, and Warning (2004)

Examines the effect of university spillovers on the locational choice of firm formation. Using a unique and hand-collected dataset of high-technology startups publicly listed in Germany, tests proposition that proximity to a university is influenced by the kind of science and type of knowledge spillover the university generates. Finds that younger high-tech startups (less than eight years old) settle near universities with a high academic output and a high number of students in the natural and social sciences; older firms, however, locate closer to technical universities only to satisfy demand for traditional industries (in the German case, engineering and machinery). (Continued)

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Author(s) (by date of publication)

Key Findings

Audretsch and Lehmann (2005)

Examines whether the KSTE holds for regions. Links investments in knowledge by universities and regions to the amount of entrepreneurial activity associated with each university. Using binomial regressions, the paper estimates how the number of young and high-tech firms located around universities depends on regional factors and the output of universities. Finds that the number of firms located close to a university is positively influenced by the knowledge capacity of the region and the knowledge output of the university.

Audretsch, Lehmann, and Warning (2005)

Examines the effect of locational choice as a firm strategy to access knowledge spillovers from universities. Based on a large dataset of publicly listed, high-tech startup firms in Germany, paper tests the proposition that proximity to the university is shaped by different spillover mechanisms – research and human capital – and by different types of knowledge spillovers – the natural sciences and the social sciences. The results suggest that spillover mechanisms and types are heterogeneous – in particular, that new-knowledge- and technologically based firms have a high propensity to locate close to universities, presumably to access knowledge spillovers. But the exact role geographic proximity plays is shaped by the particular knowledge context and type of spillover mechanism.

Acs, Plummer, and Sutter (2007)

Analyzes the knowledge filter between new knowledge and economically useful knowledge. Distinguishes between new ventures and incumbent firms as conduits permeating the knowledge filter. Uses spatial panel estimation techniques to provide a more robust set of findings, suggesting that startups are better able to penetrate the knowledge filter in regions that are growing than those that are declining.

Agarwal, Audretsch, and Sarkar (2007)

Challenges the underlying assumptions of the process of creative destruction, conceptualizing an alternative process that might characterize the dynamics between entrants and incumbents. Analyzes the underlying mechanism of knowledge spillover strategic entrepreneurship, whereby knowledge investments by existing organizations, when coupled with entrepreneurial action by individuals embedded in their context, result in new venture creation, heterogeneity in performance, and subsequent growth in industries, regions, and economies. Framework has implications for future research in entrepreneurship, strategy, and economic growth.

Audretsch and Keilbach (2007)

Prevailing theories of entrepreneurship typically revolve around the ability of individuals to recognize opportunities and then to act on them by starting a new venture. This has

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Key Findings

Audretsch and Keilbach (2007), continued

generated a literature asking why entrepreneurial behavior varies among individuals with different characteristics, while implicitly holding constant the external context in which individuals find themselves. Thus, the source of entrepreneurial opportunities is also implicitly taken as given. This paper, in contrast, identifies an important source of entrepreneurial opportunities: knowledge and ideas created in an incumbent organization. By commercializing knowledge that otherwise would remain uncommercialized through the startup of a new venture, entrepreneurship serves as a conduit of knowledge spillovers. According to the KSTE, a context with more knowledge will generate more entrepreneurial opportunities, while a context with less knowledge will generate fewer entrepreneurial opportunities. Based on a dataset linking entrepreneurship to the knowledge context, the paper provides empirical evidence that entrepreneurial opportunities are not exogenous, but instead are created systematically by investments in knowledge by incumbent organizations.

Audretsch and Keilbach (2008)

The knowledge paradox suggests that high levels of investment in new knowledge do not necessarily and automatically generate anticipated levels of competitiveness of growth – in particular, they do not automatically translate into balanced growth and competitiveness. The paper explains why knowledge investments are inherently unbalanced, so that the competitiveness and growth ensuing from knowledge are not equally spread across individuals, firms, and spatial units of observation, such as regions or countries. Based on a dataset linking entrepreneurial activity to growth in German regions, the paper shows that entrepreneurship serves a conduit of knowledge spillover.

Carlsson et al. (2009)

Provides a theoretical model with the microeconomic foundations of endogenous growth theory. Develops the knowledge spillover theory of entrepreneurship. At the heart of this model is investments by incumbent firms in new knowledge, which create a source of knowledge spillovers, enabling entrepreneurs to identify and exploit opportunities through the startup of a new firm.

Braunerhjelm et al. (2010)

Provides a model analyzing how growth depends on knowledge accumulation and its diffusion through both incumbents and entrepreneurial activities. Model shows that entrepreneurs represent the missing link in converting knowledge into economically relevant knowledge. (Continued)

44

Sources of Knowledge & Entrepreneurial Behavior

Author(s) (by date of publication)

Key Findings

Braunerhjelm et al. (2010), continued

Implementing different regression techniques for OECD countries over the period 1981–2002, the paper suggests that entrepreneurship drives economic growth. A Granger test confirms that causality goes in the direction from entrepreneurs to growth. Empirical evidence suggests that policies to facilitate entrepreneurship can be an important mechanism for knowledge diffusion and economic growth.

Acosta, Coronado, and Flores (2011)

Examines the relationship between knowledge spillovers from universities and new business location in high-tech sectors. Analyzes new business formation and knowledge spillovers originating from three main university outputs: knowledge-based graduates, research activities, and technological knowledge. Introduces a dataset of companies and universities in Spain and groups the data by region from 2001 to 2004. Provides compelling evidence that university spillovers are important in explaining the location of new businesses in high-tech sectors in Spain. Concludes that university graduates are a key conduit for knowledge spillovers, while research and university technology created at the university are less so, at least in the Spanish context.

Acs et al. (2012)

Suggests that knowledge spillovers might not occur automatically, as typically assumed in models of endogenous growth. The model includes a mechanism serving as a conduit for the spillover and commercialization of knowledge from the source creating it to the firms that actually commercialize the new ideas. Entrepreneurship is identified as one such mechanism facilitating the spillover of knowledge. Using a panel of entrepreneurship data from 18 countries, the paper shows that, in addition to measures of R&D and human capital, entrepreneurial activity also serves to promote economic growth.

Acs and Sanders (2012)

Introduces a model of endogenous growth that distinguishes between inventors and innovators, implying that stronger protection of intellectual property rights has an inverted U-shaped effect on economic growth. Intellectual property rights protection attributes to the inventor part of the rents of commercial exploitation that would otherwise accrue to the entrepreneur. Stronger patent protection thus would increase the incentive to do R&D and generate new knowledge, which would have a positive effect on entrepreneurship, innovation, and growth. At some point, however, further strengthening of patent protection would reduce the returns to entrepreneurship sufficiently to reduce the overall growth rate.

The Knowledge Spillover Theory of Entrepreneurship

Author(s) (by date of publication)

45

Key Findings

Guerrero and Urbano (2012)

Analyzes what constitutes an entrepreneurial university using theory and a framework from institutional economics and the resource-based view. Uses the Spanish Entrepreneurial University Scoreboard to identify the phenomenon and structural equation modeling to analyze the university. Identifies strategies to further benefit society – in terms of new business and employment – and, in particular, educational institutions.

Fritsch and Aamoucke (2013)

Finds that regional public research and education have a strong positive effect on new business formation in innovative industries, but not in industries classified as non-innovative. The presence and size of public academic institutions have a greater effect on the formation of innovative new businesses than has the quality of these institutions. The evidence for the interregional spillover of these effects is relatively weak. The results clearly demonstrate the importance of localized knowledge and, especially, of public research for the emergence of innovative new businesses.

Acs and Sanders (2013)

Provides a model that separates entrepreneurship from profit-motivated corporate R&D aimed at improving existing production processes. The model embeds the core idea of the KSTE in established, knowledge-based growth models by enriching their knowledge spillover structure. Introducing knowledge spillovers drives a wedge between the optimal and market allocation of resources to either new knowledge creation or commercialization. Suggests that the first-best allocation depends exclusively on the relative strength of knowledge spillovers between them, and offers ways to guide policy that could bring the market equilibrium closer to this optimum.

Audretsch and Belitski (2013)

Both the KSTE and the prevailing theory of economic growth treat opportunities as endogenous and generally focus on opportunity recognition by entrepreneurs. New knowledge created endogenously results in knowledge spillovers that inventors and entrepreneurs then commercialize. Paper argues that knowledge spillover entrepreneurship depends not only on ordinary human capital, but, more important, also on the creativity embodied in individuals and on the diverse urban environments that attract creative people, perhaps resulting in the self-selection of creative individuals into entrepreneurship or in the enabling of entrepreneurs to recognize creativity and commercialize it. Tests this creativity theory of knowledge spillover entrepreneurship using data on European cities. (Continued)

46

Sources of Knowledge & Entrepreneurial Behavior

Author(s) (by date of publication)

Key Findings

Block, Thurik, and Zhou (2013)

Applies the KSTE in analyzing differences in innovation outcomes. Hypothesizes that a high rate of entrepreneurship facilitates the process of turning knowledge into innovation, but has no effect on the relationship between knowledge and firm innovation. Using European country-level data, finds that a high rate of entrepreneurship increases the likelihood that knowledge will generate innovative activity.

Bonaccorsi et al. (2013)

Analyzes how the scientific specialization of universities influences the creation of startups across industries at the local level. Using the Pavitt-Miozzo-Soete taxonomy for eight industry categories – which reflect the characteristics of firms’ innovation patterns and, ultimately, the knowledge inputs that firms require – and data on new firm creation in Italian provinces, develops regression models separately for each industry category to link new firm creation to neighboring universities’ specialization in basic sciences, applied sciences and engineering, and social sciences and humanities. Results suggest that universities that specialize in applied sciences and engineering have a broad positive effect on new firm creation in a given province, and particularly in the services sector. In contrast, the positive effect of universities that specialize in basic sciences is limited to startup activity in science-based manufacturing, while universities that specialize in the social sciences and humanities have no significant effect on entrepreneurial startups at the local level.

Casper (2013)

Argues that the quality of a university’s regional environment can significantly affect its success in commercializing science. Uses social network analysis to examine the quality of social ties linking industry and university scientists in the San Francisco and Los Angeles biotechnology industries over the 1980–2005 period. Provides evidence of the validity of the theory that strong social networks linking inventors heightens university commercialization output. Despite similar research endowments, universities in San Francisco, with their cohesive inventor networks, have dramatically higher commercialization outputs than universities in Los Angeles. As well, the commercialization output of San Francisco universities increased substantially starting in the early 1990s, when such networks emerged in the region.

Leyden and Link (2013)

Extends the KSTE to develop a formal model of university/ business collaborative research partnerships. Introduces a model where the outcome is feasible and positive. Analysis shows that a university that wishes to partner in private sector

The Knowledge Spillover Theory of Entrepreneurship

Author(s) (by date of publication)

47

Key Findings

Leyden and Link (2013), continued

collaborative R&D – thus enhancing its value as a partner to both incumbent firms and startup entrepreneurs – needs to structure its program so that business enterprise revenues increase, and so do R&D costs but by a smaller proportion, a structure that would be consistent with the interests of both the firm and the university. However, this feasibility might be limited to situations where the university is subsidized to cover the cost of a public-private research partnership. Absent such subsidies, the university’s costs would have to be covered by charging a fee to participating business enterprises, which would result in the university’s serving as a substitute for, rather than as a complement to, private sector collaborative R&D, leaving private companies to view universities as unattractive partners.

Qian and Acs (2013)

Based on empirical analyses using data from US metropolitan areas, suggests that knowledge spillover entrepreneurship depends not only on new knowledge, but, more important, on the capacity for entrepreneurial firms to absorb external knowledge, recognize the economic value of that knowledge, and ultimately commercialize it.

Plummer and Acs (2014)

Extends the KSTE and posits that competition at the local level impedes entrepreneurial activity by reducing the incentive to exploit new knowledge. Provides an empirical test of the hypothesis based on spatial panel estimation. Finds a positive relationship between new knowledge and entrepreneurial activity, which is negatively moderated by localized competition, but that greater agglomeration counteracts that moderating effect.

Ghio et al. (2015)

Provides a bibliometric analysis of the KSTE based on all articles on the theory published in refereed journals between 1999 and 2013. Identifies the key academic journals, the main issues and subjects addressed, and backward and forward citations, as well as the authors and their connections in terms of co-authorships, to reconstruct the scientific community’s contribution to the theory.

Leyden (2016)

Introduces a theoretical model of the national system of entrepreneurship where the entrepreneurial environment is integrated into a functional economy. Analyzes the role policy can play in improving the entrepreneurial environment for both private and public sector entrepreneurs.

Source: Authors’ compilation.

chapter three

The AEGIS Database

Numbers are the universal language given to us by the deity as confirmation of the truth. – Saint Augustine There are lies, damned lies, and statistics. – Mark Twain

The AEGIS Project The AEGIS (Advancing knowledge-intensive entrepreneurship and innovation for growth and social well-being in Europe) project was funded by the European Community under Theme 8, “Socio-Economic Sciences and Humanities,” of the 7th Framework Programme (FP7) for Research and Technological Development.1 FP7 lasted from 2007 until 2013.2 The program, funded at more than €50 billion, was designed to be a key tool in responding to Europe’s needs in terms of jobs and competitiveness, and to maintain leadership in the global knowledge economy. FP7 had five major building blocks – cooperation, ideas, people, capacities, and nuclear research. The core of FP7 was the Cooperation Programme. Its objective was to foster collaborative research across Europe and other partner countries through projects by transnational consortia of industry and academia. Research was performed in ten key thematic areas: health; food, agriculture, fisheries, and biotechnology; information and communication

The AEGIS Database

49

technologies; nanosciences, nanotechnologies, materials, and new production technologies; energy; environment (including climate change); transport (including aeronautics); socio-economic sciences and the humanities; space; and security. The Ideas Programme supported frontier research as determined on the basis of scientific excellence. Research was carried out in science and technology, including engineering, as well as in the socio-economic sciences and the humanities. The People Programme provided support for the mobility and career development of researchers both inside the EU and elsewhere. The Capacities Programme strengthened the research capabilities that the European Community believed were needed to become a thriving knowledgebased economy, such as research infrastructure and research for the benefit of small and medium-sized enterprises (SMEs). Finally, the Nuclear Research Programme comprised research, technological development, international cooperation, dissemination of technical information, and exploitation activities, as well as training in aspects of nuclear activity. The focus of the AEGIS project was on knowledge-intensive entrepreneurship under the Ideas Programme. The implicit assumption was that KIE is a potential means through which to obtain economic growth and societal well-being.3 More specifically: The AEGIS project has three main objectives relative to and understanding of KIE in European Union (EU) countries: • At the micro level, it examines the act of knowledge-intensive entrepreneurship … , its defining characteristics, boundaries, scope and incentives in various sectors (high and low tech and services). Apart from the supply side, it focuses on the demand side and to the social and cultural dimensions related to KIE. • At the macro level it examines the link between KIE, economic growth and social wellbeing. Emphasis is placed on the way the socio-economic environment stokes “animal spirits” and benefits from them in the context of various shades of capitalism in Europe and elsewhere. • At the policy level it will try to translate its findings into diagnostics tools for country or sector specific assessment of KIE and provide operational policy recommendations, by taking into account different national/ regional and sectoral systems of innovation within EU and some key large fast growing countries (India, China and Russia). (PLANET 2011, 5)

According to AEGIS (2012, 4), “[k]nowledge-intensive entrepreneurship is [the] core interface between two interdependent systems: the

50

Sources of Knowledge & Entrepreneurial Behavior

knowledge generation and diffusion system, on the one hand, and the productive system, on the other. Both systems shape and are shaped by the broader social context – including customs, culture and institutions – thus also pointing at the linkage of entrepreneurship to that context.” Our choice to use the AEGIS database in this study was predicated on its KIE focus, since the database arguably is the most complete and detailed cross-sectional compilation of information on KIE firms. The survey instrument used for the collection of this information specifically addressed entrepreneurial (individual and firm) experience, sources of knowledge, and entrepreneurial behavior. We begin with a background discussion of a definition of KIE, because a precise (or close to precise) definition is critical in aligning ourselves with the extant literature on which the descriptive empirics in this book are based, because the analytics in this book follow from the KSTE from Chapter 2, and because we rely on the AEGIS database to explore sources of knowledge within the KSTE framework that might represent a knowledge filter (as also discussed in Chapter 2). It is surprising, however, just how few precise definitions there are of KIE.4 In fact, a number of examples of excellent scholarship in the literature use the term knowledge-intensive entrepreneurship, but from our vantage point most of the researchers associated with that literature fail to define what KIE actually means or is intended to mean, and, perhaps more important, how KIE specifically relates to their topics at hand (see, for example, Breschi et al. 2014; Madsen, Neergaard, and Ulhøi 2003; Neergaard and Madsen 2004; Wyrwich 2013). One definition of KIE is in the quoted excerpt from AEGIS (2012) just above. Other definitions appear, however, in the extant literature. For example, Malerba (2010, 4), arguably more precisely than do other scholars, defines KIE as follows: “Knowledge-intensive entrepreneurship concerns new ventures that introduce innovations in the economic systems and that intensively use knowledge. From this broad definition, it follows that knowledge-intensive entrepreneurship may take place in various ways: through the foundation of new firms or through the display of entrepreneurial spirit with existing firms or through the action of single individuals within non-profit organizations such as universities or public laboratories.” Table 3.1 provides a summary of definitions or clarifying statements that, although brief, in our view is nonetheless a fairly complete review of the competing definitions in the literature.

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51

Table 3.1. Definitions of Knowledge-Intensive Entrepreneurship Author(s)

Definition

Groen (2005, 70)

“Entrepreneurial processes can be defined as processes, in which an entrepreneur sees a business opportunity (ies), develops it to a business concept and [then] brings it into exploitation. When these processes are to a great extent based on relatively new (mostly academically derived) knowledge or technology, we speak of knowledge intensive entrepreneurial processes.”

Malerba (2010, 4)

“Knowledge-intensive entrepreneurship concerns new ventures that introduce innovations in the economic systems and that intensively use knowledge. From this broad definition, it follows that knowledge-intensive entrepreneurship may take place in various ways: through the foundation of new firms or through the display of entrepreneurial spirit with existing firms or through the action of single individuals within nonprofit organizations such as universities or public laboratories.”

PLANET (2011, 4)

“Knowledge-intensive entrepreneurship [refers to] a core interface between two interdependent systems: the knowledge generation and diffusion system, on the one hand, and the productive system, on the other. Both systems shape and are shaped by the broader social context – including customs, culture and institutions – thus also pointing at the linkage of entrepreneurship to that context.”

AEGIS (2012, 4)

“Knowledge-intensive entrepreneurship is [the] core interface between two interdependent systems: the knowledge generation and diffusion system, on the one hand, and the productive system, on the other. Both systems shape and are shaped by the broader social context – including customs, culture and institutions – thus also pointing at the linkage of entrepreneurship to that context.”

Caloghirou, Protogerou, and Tsakanikas (2014, 17–18)

“KIE represents a core interface between two independent systems: the knowledge generation and knowledge diffusion system on the one hand, and the productive system on the other.”

Hirsch-Kreinsen and Schwinge (2014, 2)

“KIE is considered an activity dealing with the uncertainties of discovering and exploiting new opportunities, often driven by individuals but also by established organizations.”

Source: Authors’ compilation.

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Sources of Knowledge & Entrepreneurial Behavior

These definitions share common themes. For consensus purposes, it seems to us that KIE might correctly be characterized as: • a dynamic activity, rather than a static one (for example, a process); • a process of perception and action (for example, one sees an opportunity, develops it to a concept, and exploits it as a technology or innovation); and • an innovative effort characterized by risk and uncertainty (for example, through actions, one deals with the uncertainties of discovering and exploiting new opportunities). In the definitions in Table 3.1, and especially in the quoted passage above from Malerba (2010, 4), entrepreneurial activity is associated with innovativeness and uncertainty. There is certainly precedent for this characterization of an entrepreneur. Hébert and Link (2009) trace historical thinking about who an entrepreneur is and what an entrepreneur does, and a brief summary of their rich history suggests that, over time, philosophers and scholars have viewed the entrepreneur as: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

the person who assumes the risk associated with uncertainty; the person who supplies financial capital; an innovator; a decision maker; an industrial leader; a manager or superintendent; an organizer and coordinator of economic resources; the owner of an enterprise; an employer of factors of production; a contractor; an arbitrageur; and an allocator of resources among alternative uses. (Hébert and Link 2009, xviii)

The genesis of associating innovativeness with entrepreneurial activity traces to Cantillon ([1775] 1931), Baudeau (1910), and Schumpeter ([1912] 1934) in his Theory of Economic Development. Schumpeter described innovation by emphasizing the kinds of new combinations that underlie economic development, which encompass the following: creation of a new good or new quality of good; creation of a new method of

The AEGIS Database

53

production; the opening of a new market; the capture of a new source of supply; and a new organization of industry. Over time, of course, the force of these new combinations dissipates as the new becomes part of the old. But this process does not change the essence of the entrepreneurial function. According to Schumpeter ([1912] 1934, 78), “everyone is an entrepreneur only 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.” Technically, Schumpeter defined innovation with reference to the production function, about which he wrote (1939, 62): “[The production function] describes the way in which quantity of product varies if quantities of factors vary. If, instead of quantities of factors, we vary the form of the function, we have an innovation.” Elements of the AEGIS Database According to Caloghirou, Protogerou, and Tsakanikas (2011, 3), the survey from which the AEGIS database was created is itself rather unique:5 “It is different from any other relevant survey … It is not a cross-sectoral survey for the production solely of R&D and innovation indicators [like the Community Innovation Survey], it is not a general population survey [like the Global Entrepreneurship Monitor], it does not cover only one country or focus mainly on the firm’s competitive environment and financing/capital investment [like the KfW-ZEW or Kauffman surveys].” Reflected in the development of the AEGIS survey are important elements that support the unique aim for the construction of the database: “to examine the multi-dimensional concept of KIE using many different dimensions (demand, institutional factors, innovation strategies, dynamic capabilities etc.) in order to identify motives, characteristics and patterns in the creation and growth of new firms” (Caloghirou, Protogerou, and Tsakanikas 2011, 3). The firms included in the AEGIS database are not a random sample of European enterprises. Instead, to have a large enough sample to study firms in all countries, the architects of the database realized, correctly in our opinion, that firms in smaller countries such as Croatia and the Czech Republic needed to be sampled at a higher rate than firms in larger countries such as France and Germany. To account for the non-random sampling, unless otherwise noted we use sample weights in the quantitative statistical analyses in subsequent chapters.6 The use of weighted data, however, produced empirical findings

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Sources of Knowledge & Entrepreneurial Behavior

similar to those from the use of unweighted data. In the tables in the following chapters, we report unweighted descriptive statistics for ease of replicability by other researchers who might wish to expand on our analyses. The AEGIS database contains information on 4,004 firms established between 2002 and 2007 in ten European countries. The AEGIS survey was conducted from late 2010 into 2011, so, at a minimum, a firm in the AEGIS sample would have been active for three years. The countries represented in the database are (alphabetically): Croatia, Czech Republic, Denmark, France, Germany, Greece, Italy, Portugal, Sweden, and the United Kingdom. The database includes a number of firms in these countries from the high-tech and low-tech sectors, and from the knowledge-intensive business services sector – although sectoral representation did not drive the construction of the database). The high-tech sector includes aerospace; computers and office machinery; radio-television communication equipment; medical, precision and optional instruments; pharmaceuticals; electrical machinery and apparatus; and machinery and equipment, chemical industry. The low-tech sector includes paper and printing; textiles and clothing; food, beverage, and tobacco; wood and furniture; basic metals; and fabricated metal products. Knowledge-Intensive Business Services (KIBS) includes telecommunications; computer and related activities; research and experimental development; and selected business services activities. See Table 3.2 and Table 3.A.1 in Appendix 3.A for more complete definitions of the industries within each sector. From both an innovation and an entrepreneurial perspective, activities in these ten countries are not homogeneous, due to differences in current technology and innovation policies, as well as differences in firm responses to policies previously in effect. We illustrate this point about country heterogeneity using information from the European Commission’s Innovation Union Scoreboard (European Commission 2016) (see Tables 3.3 through 3.5).7 Our purpose in describing these EU policies is to emphasize that innovation ecosystems differ across countries. As such, the relationships that we described in Chapter 1 in Figure 1.1 and reproduce here as Figure 3.1 are also likely to vary in several dimensions across countries. That is, the empirical validity of the Experience → Knowledge → Entrepreneurial Behavior relationship is not independent of the environment, or of contexts as defined by the KSTE, in which the shown linkages occur.

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55

Table 3.2. Distribution of AEGIS Firms, by Country and Sector Sector High-tech Country

Low-tech

KIBS

Total

(number of firms)

Croatia

35

115

50

200

Czech Republic

25

92

83

200

Denmark

34

69

227

330

France

68

196

306

570

Germany

67

160

330

557

Greece

22

184

125

331

Italy

57

316

207

580

Portugal

31

170

130

331

Sweden

34

108

192

334

United Kingdom

47

192

332

571

420

1,602

1,982

4,004

Total

Sources: Caloghirou, Protogerou, and Tsakanikas (2011); AEGIS database.

Table 3.3. Innovation Enablers, by Country

Sweden

United Kingdom

Portugal

Italy

Greece

√

Germany

√

France

Czech Republic

New doctorate graduated

Denmark

Human Resources

Croatia

“Enablers capture the main drivers of innovation performance external to the firm” (European Commission 2016, 7)

√

√

√ √

Population completed tertiary education √

Youth with upper secondary level education

√

Open, excellent, attractive research systems International scientific co-publications

√

Scientific publications among top 10% most cited Doctorate students from outside the European Union Finance and support R&D expenditures in the public sector Venture capital investments Note: Innovation leaders shown by √. Source: European Commission (2016).

√

Table 3.4. Innovation-based Firm Activities, by Country

United Kingdom

Sweden

√

Portugal

Greece

√

Italy

Germany

France

Denmark

Czech Republic

Firm Investments

Croatia

“Firm activities capture the innovation efforts at the level of the firm” (European Commission 2016, 7)

R&D expenditures in the business sector Non-R&D innovation expenditures

√

√

Linkages and entrepreneurship √

SMEs innovating in-house √

Innovative SMEs collaborating with others

√

Public-private co-publications Intellectual assets PCT patent applications PCT patent applications in societal challenges Community trademarks

√

√

Community designs Notes: The Patent Cooperation Treaty (PCT) assists applicants in seeking patent protection internationally for their inventions; innovation leaders shown by √. Source: European Commission (2016).

Table 3.5. Innovation Outputs, by Country

Innovators

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom

“Outputs capture the effects of firms’ innovation activities” (European Commission 2016, 8)

√

SMEs with product or process innovations

√ √

SMEs with marketing/organizational innovations Employment in fast-growing firms and innovative sectors

√

√

√ √

Economic effects Employment in knowledge-intensive activities Medium- and high-tech product exports Knowledge-intensive services exports Sales of new-to-market and new-to-firm innovations Licenses and patent revenues from abroad Note: Innovation leaders shown by √. Source: European Commission (2015).

√ √

√ √

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57

Figure 3.1. Antecedents of Entrepreneurial Behavior Sensations and Reflections

Experience

Knowledge

Entrepreneurial Behavior

Education

Table 3.3 suggests that the main drivers of innovation performance – or enablers of innovation – are supported by different policies across the ten countries in the AEGIS database. The designation as innovation leader in the tables by the symbol √ means that the innovation performance in the firms in the labeled country changed the most in the positive direction over the previous year (2014). Germany and Italy did not appear to be innovation leaders in any of the listed dimensions, whereas Portugal and Sweden were leaders in two dimensions. To the extent that any of the relationships in Figure 3.1 are tied to such enablers, they should be considered on a country-by-country basis.8 The descriptive information in Table 3.4 illustrates clearly the noticeable cross-country differences in innovation leadership when it comes to the efforts of firms to appropriate their innovation-based activities. France, Portugal, and the United Kingdom are the relatively weaker countries in this regard. Finally, as shown in Table 3.5, there are also cross-country differences in innovation leadership when it comes to how firms appropriate the outputs from their innovative activities. The identified outputs in the table are most relevant to France, Germany, Italy, and the United Kingdom. Again, this table emphasizes our point of cross-country differences in the validity, as well as the possible strength, of the Knowledge → Entrepreneurial Behavior relationship. More emphatically, consider the relationship between what we refer to, in a Schumpeterian-like manner, as innovation inputs and innovation outputs in Figure 3.2. To construct this figure, we counted, by country, the number of innovation-based firm activities (inputs) in Table 3.4 and compared them with the number of innovation outputs (outputs) in Table 3.5. As the figure shows, for some countries (France, Portugal, the United Kingdom), there were no inputs; for other countries (Croatia, Greece, Sweden), there were no outputs. From our perspective, there is no visible pattern in Figure 3.2 except that France,

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Sources of Knowledge & Entrepreneurial Behavior

Figure 3.2. Cross-Country Relationships between Innovation-based Firm Activities and Innovation Outputs 2.5

2

1.5

1

0.5

0 Croatia

Czech Denmark France Germany Greece Republic Inputs

Italy

Portugal Sweden

United Kingdom

Outputs

Source: Prepared by the authors. Note: Inputs are the number of innovation-based firm activities in each country from Table 3.4; outputs are the number of innovation outputs in each country from Table 3.5.

Portugal, and the United Kingdom have outputs but no inputs. At first blush this might be puzzling, especially from a policy perspective, since Tables 3.4 and 3.5 were constructed for policy purposes. But setting aside any policy implications, we interpret the pattern in Figure 3.2 to mean only that there are cross-country differences in innovation ecosystems, and not all elements of the innovation ecosystem have been identified in the two tables. The innovation policy instruments reported in Table 3.6, collected from Veugelers (2015), complement the data we presented earlier in this chapter, and emphasize our point that, when analyzing information in the AEGIS database, cross-country differences in innovation ecosystems – and thus in the global relationship between experience, knowledge, and entrepreneurial behavior – should be taken into account. As one illustration, note that, in Table 3.6, some policy instruments, such as incubators and public support of spinoffs, are rarely used, while others, such as financial instruments and technology transfers, are quite common.

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59

√

Collaborative R&D programs

√

√

Competitive funding of research

√

√

Direct business R&D support

√

Direct business innovation support

√

√

√

√

√

√

√

√

√

√

Awareness raising

United Kingdom

Cluster initiatives

Sweden

√

Portugal

√

Italy

France

√

Greece

Denmark

Centers of excellence

Policy Instrument

Germany

Czech Republic

Table 3.6. Innovation Policy Instruments, Selected EU Countries

√ √

√

√

√

√

√

√

√

√

√

√

√

√

√ √

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

Competence centers

E-society Financial instruments Intellectual property rights measures

√

Incubators

√

√

Mobility schemes Public procurement

√

√

√ √ √

√

√

√ √

√

√ √

√

√

√ √ √

√

√ √

Public sector innovation R&D infrastructure

√ √

Innovation vouchers

√

√ √

Innovation skills development Innovation support services

√

√

Innovation networks and platforms

√

√ √

Regional programs

√ √

√

√

Science and technology parks √

Spinoff support Support to human resources for R&D

√

Support to start-ups

√

√

Support to venture capital √

√

√ √

√

√

√

√

√

Tax incentives Technology transfer

√

√

√

√

Note: Policy instrument data were not available for Croatia. Source: Veugelers (2015).

√

√

√

√

√

√

√ √

√

√ √

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Sources of Knowledge & Entrepreneurial Behavior

Summary Throughout the rest of this book, we treat separately the experiences embodied in firms and the sources of knowledge they use. And based on our interpretation of the tables above, we describe and analyze experiences and sources of knowledge generally, but, more important, we also examine them on a country-by-country basis. We also treat these dimensions that lead to entrepreneurial behavior differently by industrial sector to account for differences in the competitive and technical infrastructures that affects entrepreneurs. Referring back to Figure 3.1, we argued in Chapter 1 from an epistemological perspective that experiences are the genesis for the sources of knowledge on which entrepreneurs rely. It follows from the tables in this chapter that an entrepreneur’s industrial and policy environment, the innovation ecosystem in which the entrepreneur’s firm operates, or the firm’s contexts from a KSTE perspective might well influence the firm’s experience base and thus its knowledge base. In the following chapter, we offer alternative measures to characterize the experience base of firms, and we describe how these measures differ across countries and industrial sectors. We do the same for sources of knowledge in Chapter 5 and for entrepreneurial behavior in Chapter 6.

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61

Appendix 3.A Industries by Sector Table 3.A.1. Segmentation of Industries by Sector Sector

Industry Code

High-tech manufacturing Aerospace

35.3

Computers and office machinery

39

Radio-television and communication equipment

32

Medical, precision and optical instruments

33

Pharmaceuticals

24.2

Medium- to high-tech manufacturing Electrical machinery and apparatus Machinery and equipment Chemical industry (excluding pharmaceuticals)

31 29 24 (less 24.4)

Medium- to low-tech manufacturing Basic metals

27

Fabricated metal products

28

Low-tech manufacturing Paper and printing

21, 22

Textile and clothing

17, 18, 19

Food, beverages, and tobacco

15, 16

Knowledge-based business services Telecommunications

64.2

Computer and related activities

72

Note: We follow Caloghirou, Protogerou, and Tsakanikas (2011) and combine high-tech and medium- to high-tech manufacturing industries in the broader high-tech sector; similarly, medium- to low-tech and low-tech industries are combined in the broader low-tech sector. The KIBS sector remains unchanged. Source: Caloghirou, Protogerou, and Tsakanikas (2011, 16).

In the remainder of this appendix we rely on the AEGIS data to offer a more indepth understanding of the characteristics of the high-tech, low-tech, and KIBS sectors. In particular, we examine the entrepreneurial- and innovation-based

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Sources of Knowledge & Entrepreneurial Behavior

Table 3.A.2. Impact of a Firm’s Capacity to Adapt Products/Services to Different Customers or Market Niches on Its Ability to Create and Sustain a Competitive Advantage, by Sector Sector High-Tech Mean impact: innov Percentage impact: Pr(innov)

4.32 85.9

Low-Tech 4.15 78.4

KIBS 4.26 83.1

n = 3,947; 5 = huge impact, 1 = no impact Source: Authors’ compilation.

characteristics of firms in each sector. These characteristics will be relevant for our understanding of the statistical relationships discussed in later chapters. One question on the AEGIS survey not directly related to experiences or sources of knowledge is: “Please indicate the contribution of the following factors in creating and sustaining the competitive advantage of this company. On a 5-point scale, where 1 is no impact and 5 is huge impact: Capacity to adapt the products/services to the specific needs of different customers or market niches.” We interpret this question as one that approximates the innovativeness of the KIE firm, where innovativeness is defined in terms of carrying out new combinations of inputs in the Schumpeterian sense – that is, in terms of changing the production function. Table 3.A.2 shows the mean values of this survey question, which we refer to as the variable innov, for each of the three industrial sectors. The mean value of innov is smallest for the firms in the low-tech sector, slightly higher for firms in the KIBS sector, and highest for firms in the high-tech sector. To our intuition, firms in the high-tech sector are reasonably the more innovative, other factors not held constant.

chapter four

The Experience Base of Firms

Avoid the precepts of those thinkers whose reasoning is not confirmed by experience. – Leonardo da Vinci From the experience of the past we derive instructive lessons for the future. – John Quincy Adams

Experience versus Experience The Merriam-Webster Dictionary offers five primary meanings for the word experience.1 The first meaning is “direct observation of or participation in events as a basis of knowledge”; the second is “practical knowledge, skill, or practice derived from direct observation of or participation in events or in a particular activity”; the third is “the conscious events that make up an individual life”; the fourth is “something personally encountered, undergone, or lived through”; and the fifth meaning listed in the dictionary is “the act or process of directly perceiving events or reality.” There is at least one thread common to these five definitions: experience is special. Experience has an idiomatic characteristic: note from the five definitions quoted above that experience involves “direct observation,” is “derived from direct observation,” and is a “conscious event,” “personally encountered” and “directly perceived.” It is no wonder that several academic disciplines, economics in particular, have placed experience under

64

Sources of Knowledge & Entrepreneurial Behavior

the rubric of human capital. It was Becker (1964, 3), recipient of the Nobel Memorial Prize in Economics in 1992 and arguably the pioneer scholar in the field of human capital, who defined human capital as “the knowledge, information, ideas, skills, and health of individuals.” Becker’s definition suggests too that experiences are personal and individual. In fact, as we discussed in Chapter 1 with our references to Locke, Hume, and others, experience is in many ways unique to the beholder. To illustrate the point that experience is not a homogeneous dimension, either in concept or in the characterization of a founder of a firm or of the firm’s resource base, consider two professors, each of whom taught a class in Principles of Economics in each of five years at the same university. The first professor used the same lecture notes over and over each year, changing nothing except the date on the course syllabus. The second professor revised and updated his or her lecture notes every year. The first professor had one year of experience teaching the course and used that one year of experience five different times; the second professor had five years of experience teaching the course over the same five-year period. In this chapter we discuss all of the experience metrics available in the AEGIS database. Because experience is unique to the beholder – that is, because experience is a heterogeneous dimension – one should not be surprised that these metrics have a uniqueness unto themselves. From a statistical perspective this means that the experience metrics across firms might not be correlated with one another; from an entrepreneurial perspective this means that experience metrics reflect and quantify different dimensions of the resource base of the entrepreneur and his or her firm. With reference to Figures 1.1 and 1.3 in Chapter 1, the uniqueness of both an individual’s and a firm’s experiences implies that the strength of the Experience → Knowledge relationship or the Experience → Entrepreneurial Behavior relationship – in a sense a template to describe the arguments we set forth in this book – will vary across experience metrics. We have no a priori preference about the explanatory power of one experience metric over another. Experience Metrics We consider seven alternative metrics to quantify or measure the experience base of a firm. The uniqueness of each metric does not imply anything about the correlation of the measures, and the fact that there are seven does not imply a relative ranking among them in terms of either preciseness or predictive power. That some measures are correlated and others are not does not diminish the importance of any one of the

The Experience Base of Firms

65

dimensions of the experience base of a firm. Rather, when there is a lack of correlation among certain measures, it simply suggests that the concept of an entrepreneur’s or firm’s experience base is multidimensional, in that it spans a number of different aspects, and also that those aspects are heterogeneous and in some cases orthogonal, reflecting distinct components that make up the overall concept of the firm’s experience. The following experience measures are in no specific order of importance: age of the firm, number of employees in the firm, number of founders of the firm, education level of the founders, years of experience of the founders in the current sector, the experience-based nascent nature of the firm, and the occupational-based nascent nature of the firm. We calculated the mean value of each measure by country and industrial sector. Although we revisit these measures in later chapters, we present descriptive information on each here to emphasize the heterogeneity of the data in the AEGIS database across countries and industrial sectors. Our repeated emphasis on country and sectoral differences in our experience base measures anticipates our later disaggregated country and disaggregated industrial sector analysis. Reported below, by subsection, are the mean values of each of the seven experience measure in both tabular form and graphic form,2 the former for completeness of information and the later for ease of visualization. The specific AEGIS survey questions relevant to these measures are in Appendix 4.A to this chapter. In some instances experience refers to the firm level – that is, for example, the age of the firm. In other instances experience refers to individuals – that is, the educational level of the first-listed founder. The level of focus is obviously different, but the conceptual intent is the same: as a firm ages, for example, so do all or at least many of its employees.

Age of the firm As shown in Table 4.1 and in Figure 4.1, the mean age of all of the firms in the AEGIS database is 7.11 years (see the bottom right cell in Table 4.1). There is some variation in the mean age of firms across countries (see the last column in the table). In Croatia, for example, the overall mean age is 8.12 years, but in Germany it is only 6.52 years. Perhaps the most noticeable difference is in the marginally greater mean age of firms in the lowtech sector compared with those in the other two sectors, both overall and generally by country (see the bottom row of the table). These differences, especially in the cases of Greece and Sweden, and the fact that they are slight can been seen more clearly in Figure 4.1.

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Table 4.1. Mean Firm Age, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(mean age, in years)

Croatia

7.57

8.17

8.38

8.12

Czech Republic

7.76

7.57

7.30

7.48

Denmark

6.35

6.42

6.75

6.64

France

7.31

6.87

6.90

6.94

Germany

6.75

7.01

6.24

6.52

Greece

6.50

7.77

7.16

7.45

Italy

7.21

7.07

7.51

7.24

Portugal

7.06

6.76

7.02

6.89

Sweden

6.91

8.26

6.47

7.10

United Kingdom

7.66

7.66

7.37

7.49

Overall

7.12

7.32

6.95

7.11

n = 4,004

Figure 4.1. Mean Firm Age, by Country and Industrial Sector 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Croatia

Czech Denmark France Germany Greece Republic High-Tech

n = 4,004

Low-Tech

Italy KIBS

Portugal Sweden

United Kingdom

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67

Number of employees The mean number of employees, which we view as a measure of firm size, is reported in Table 4.2 and Figure 4.2. Overall the mean number of employees is 12.01 (bottom right cell in Table 4.2), but that number varies across the three sectors (bottom row of the table) and, as shown in the associated figure, across countries. The largest mean number of employees is in the high-tech sector in the Czech Republic, and the smallest is in the low-tech sector in Denmark. Seen clearly in Figure 4.2, firm size, as measured in terms of employees, is on average the lowest across sectors in France and Sweden. The greatest dispersion in the number of employees across the three sectors is in the Czech Republic, followed by Portugal and the United Kingdom. The mean number of employees in the high-tech sector in the Czech Republic dominates Figure 4.2.

Founders of the firm The firms in the AEGIS database have, on average, 1.41 founders, a number that appears to be fairly constant across countries. Firms in some countries, such as Germany, Greece, and Italy, have a slightly greater number of founders, as shown in Table 4.3 and Figure 4.3, but the variability is visibly less than with the number of employees (compare Figure 4.2 and Figure 4.3), and the mean number of founders is never greater than 2.0.

Educational level of founders The mean educational level of the first-listed founder who responded to the AEGIS survey questions is presented in Table 4.4 and Figure 4.4.3 As shown in Appendix 4.A, the survey question asked about education for up to four founders, but because on average there were fewer than 2.0 founders per firm, and because there was little variation in the educational background of founders in firms with multiple founders, we considered only the education level of the first-listed (and presumably the most important) founder.4 Throughout the rest of this book, we maintain the adjective first-listed to avoid any incorrect generalizations from our empirical findings.

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Table 4.2. Mean Number of Firm Employees, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(mean number of employees)

Croatia

12.13

22.08

15.77

18.76

Czech Republic

37.32

12.41

11.98

15.35

Denmark

11.36

4.46

10.24

9.15

France

8.32

4.83

6.67

6.24

Germany

9.56

10.39

11.75

11.10

Greece

23.23

17.45

21.26

19.27

Italy

16.87

14.57

11.21

13.60

8.10

15.32

25.18

18.52

Portugal

6.56

6.93

4.77

5.65

United Kingdom

Sweden

20.04

8.52

11.05

10.94

Overall

13.90

12.11

11.54

12.01

n = 4,004 Note: To construct this table, we assumed that one part-time employee equals one-half of a full-time employee. We also assumed that respondents to the AEGIS survey who reported 0 employees interpreted the question to mean additional employees; in those instances, when 0 employees was reported, we changed that value to 1.

Figure 4.2. Mean Number of Firm Employees, by Country and Industrial Sector 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 Croatia

Czech Denmark France Germany Greece Republic High-Tech

n = 4,004

Low-Tech

Italy KIBS

Portugal Sweden

United Kingdom

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69

Table 4.3. Mean Number of Firm Founders, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(mean number of founders)

Croatia

1.29

1.14

1.30

1.20

Czech Republic

1.60

1.28

1.34

1.35

Denmark

1.47

1.20

1.25

1.26

France

1.40

1.18

1.34

1.29

Germany

1.42

1.33

1.61

1.50

Greece

1.82

1.43

1.63

1.53

Italy

1.77

1.56

1.70

1.63

Portugal

1.29

1.37

1.55

1.43

Sweden

1.53

1.18

1.22

1.24

United Kingdom

1.66

1.30

1.42

1.40

Overall

1.51

1.34

1.44

1.41

n = 4,004 Note: If a respondent to the AEGIS survey reported 0 founders, we changed the value to 1.

Figure 4.3. Mean Number of Firm Founders, by Country and Industrial Sector 2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 Croatia

Czech Denmark France Germany Greece Republic High-Tech

n = 4,004

Low-Tech

Italy KIBS

Portugal Sweden

United Kingdom

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Table 4.4. Mean Educational Level of Firm’s First-listed Founder, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(mean years of education)

Croatia

15.26

14.62

16.11

15.09

Czech Republic

14.73

14.17

16.81

15.34

Denmark

12.39

12.79

15.60

14.70

France

14.51

13.39

16.68

15.31

Germany

14.51

14.19

16.15

15.39

Greece

15.43

14.18

16.99

15.33

Italy

13.98

13.01

15.06

13.84

Portugal

11.61

11.98

15.07

13.16

Sweden

14.69

14.91

16.22

15.59

United Kingdom

14.91

13.86

15.78

15.08

Overall

14.23

13.59

16.01

14.86

n = 3,809 Notes: We assumed that an elementary education corresponds to six years of education, a secondary education to twelve years, a bachelor’s degree to sixteen years, a postgraduate degree to eighteen years, and a PhD to twenty-one years of education. The data in this table refer only to the values reported on the AEGIS survey for the firstlisted founder. Justifications for this assumption are: (1) on average, there were fewer than 2.0 founders per firm among the firms in the AEGIS database; and (2) there was little or no variation in the mean education of other founders when present.

Across both countries and sectors the overall mean education level for the first-listed founder was slightly less than a bachelor’s degree: 14.86 years, where 16.00 years equals a bachelor’s degree (see the notes to Table 4.4). First-listed founders of firms in the KIBS sector had relatively more education than those in the high-tech or low-tech sectors. Overall, the mean educational level was 16.01 years for firms in the KIBS sector, 14.23 years in the high-tech sector, and 13.59 years in the low-tech sector. In fact the mean education level for first-listed founders in the KIBS sector was higher than a college education, culminating in a bachelor’s degree in six of the ten countries, with the highest for Greece (16.99 years) and the lowest in Italy (15.06 years). As well, first-listed founders in the KIBS sector had, on average, more than two years of additional education than first-listed founders in the low-tech sector.

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71

Figure 4.4. Mean Educational Level of Firm’s First-listed Founder, by Country and Industrial Sector 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Croatia

Czech Denmark Republic

France

Germany Greece

High-Tech

Low-Tech

Italy

Portugal Sweden

United Kingdom

KIBS

n = 3,809

Current-sector experience of founders As Tables 4.5 and Figure 4.5 show for every country except Denmark and Sweden, the mean years of experience of the first-listed founder in the current industrial sector was greatest among firms in the high-tech sector. First-listed founders had on average 13.33 years of prior experience in the current sector, but in the high-tech sector they had 15.15 years of prior experience. The least experienced founders, in terms of prior experience in the current sector, were those in the KIBS sector in the Czech Republic, at 9.73 years; the most experienced were those in the high-tech sector, also in the Czech Republic, at 19.56 years.5 There is noticeable variation across countries in terms of founder experience, especially among the high-tech sector firms. One possible explanation is that experience was not the measure used to create a representative sample of KIE firms.

Occupation of founders Although not included in the five quantifiable experience measures we describe and discuss above, the information in Table 4.6 illustrates conclusively that the last occupation of first-listed founders was as an

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Sources of Knowledge & Entrepreneurial Behavior

Table 4.5. Mean Years of Experience in Current Sector of Firm’s First-listed Founder, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

11.04 9.73 13.34 12.96 12.72 13.57 12.17 9.79 16.94 14.54 13.20

11.29 11.70 12.34 13.49 13.25 14.99 13.62 10.93 14.75 14.40 13.33

(mean years of experience)

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

13.56 19.56 9.97 17.33 14.79 17.48 16.49 12.45 13.48 15.41 15.15

10.68 11.18 10.21 13.01 13.63 15.65 14.05 11.51 11.30 13.91 13.01

n = 3,869 The data in this table refer only to the values reported on the AEGIS survey for the firstlisted founder. Justifications for this assumption are: (1) on average, there were fewer than 2.0 founders per firm among the firms in the AEGIS database; and (2) there was little or no variation in the mean education of other founders when present.

Figure 4.5. Mean Years of Experience in Current Sector of Firms’ First-listed Founder, by Country and Industrial Sector 25.00

20.00

15.00

10.00

5.00

0.00 Croatia

Czech Denmark Republic

France

Germany Greece

High-Tech

n = 3,869

Low-Tech

Italy KIBS

Portugal Sweden

United Kingdom

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73

employee of a firm in the same industry as the current surveyed firm (see the response key in Table 4.6). To illustrate, 38 per cent of the firms in the high-tech sector of Croatia were previously employed in a firm in the same industry (response 3); 24 per cent were previously employed in a different industry (response 4); and 18 per cent were previously the owner of a firm that was still in existence (response 1). Only two instances in Table 4.6 depart from the generalization that first-listed founders in the AEGIS firms were, on average, previously employed in the same industry in which they were currently employed. Among firms in the Greek high-tech sector, most first-listed founders were still owners of an existing firm. (The AEGIS survey did not ask respondents about the industry of the existing firm that was still owned.) Also, in the KIBS sector in Italy, most first-listed founders were previously self-employed. (The survey did not ask respondents about the industry of their selfemployment.) We interpreted a founder’s last occupation as one indicator of his or her prior experience base.

Experience-based nascent firms We used the information in Table 4.5, related to the years of experience in the current sector by first-listed founders, and the information in Table 4.6, related to the last occupation of first-listed founders, to construct additional measures of the experience base of AEGIS firms. These two additional measures are intended to dichotomize firms as nascent or established, and we use them only for descriptive purposes, but nascency is an established concept in the entrepreneurship literature (Davidsson 2006). Although the literature is replete with definitions of how to define or characterize a nascent firm or entrepreneur, as represented in the surveyed literature in Appendix 4.B, suffice it to say for our purpose of exploring the linkages between experience and knowledge, we define a nascent firm in terms of a founder who has started a new firm. We view all other firms in the AEGIS database as established firms.6 For analytical purposes, we define an experienced-based nascent firm as one whose first-listed founder has no experience in the current sector. In other words, experienced-based nascent firms are those in Table 4.5 with years of experience equal to 0. The percentages of firms so-defined in each country are shown in Table 4.7. Overall, 13.67 per cent of the AEGIS firms were experienced-based nascent firms. The variability among countries and industrial sectors is clearly evident in Figure 4.6. For example, more than 30 per cent of the AEGIS firms in Denmark’s high-tech sector were nascent, but less than 5 per cent of

Table 4.6. Distribution of Last Occupation of Firm’s First-listed Founder, by Country and Industrial Sector Sector Country

High-tech

Low-tech

KIBS

Croatia

38% from 3 24% from 4 18% from 1 29% from 3 21% from 5 17% from 1 17% from 4 50% from 3 29% from 4 50% from 3 29% from 4 45% from 3 15% from 1 15% from 5 13% from 4 29% from 1 24% from 3 14% from 2 14% from 4 37% from 3 18% from 1 14% from 2 14% from 4

40% from 3 23% from 4 11% from 1 30% from 3 28% from 5 15% from 4 13% from 1 45% from 3 29% from 4 42% from 3 32% from 4 44% from 3 18% from 4 15% from 5

57% from 3 15% from 1 11% from 4 50% from 3 18% from 5

Czech Republic

Denmark France Germany

Greece

Italy

Portugal

Sweden

United Kingdom

39% from 3 16% from 4 16% from 5 13% from 1 13% from 2 59% from 3 18% from 4 12% from 1 49% from 3 19% from 4

52% from 3 17% from 4

50% from 3 22% from 4 40% from 3 29% from 4 43% from 3 15% from 4 15% from 5 12% from 1 53% from 3 14% from 4 11% from 1

31% from 3 19% from 1 17% from 2 13% from 4 10% from 5 42% from 3 18% from 4 12% from 1

26% from 5 24% from 3 13% from 1 13% from 4 11% from 2 36% from 3 28% from 4 10% from 1 10% from 5

40% from 3 26% from 4 14% from 5 51% from 3 19% from 4

53% from 3 29% from 4 53% from 3 18% from 4

n = 3,947 Notes: Percentages are rounded. The data in this table refer only to the values reported on the AEGIS survey for the first-listed founder. Justifications for this assumption are: (1) on average, there were fewer than 2.0 founders per firm among the firms in the AEGIS database; and (2) there was little or no variation in the mean education of other founders when present. Only occupations with 10 per cent or more of first-founder’s previous occupation are shown. Key: 1 = owner of a firm still in existence; 2 = owner of a firm that has ceased to exist; 3 = employee of a firm in the same industry; 4 = employee of a firm in a different industry; 5 = self-employed; 6 = university or research institute employee; 7 = government employee; 8 = unemployed; 9 = none of the above – this is his/her first job; 10 = do not know.

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75

Table 4.7. Mean Percentage of Experienced-based Nascent Firms, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(mean percentage)

Croatia Czech Republic Denmark France Germany

17.65

20.91

11.11

17.99

0.00

13.79

8.97

10.05

32.35

20.59

9.25

13.98

6.06

24.62

14.00

16.76 11.15

9.09

11.04

11.65

14.29

10.53

6.03

9.09

7.27

19.61

13.64

16.28

Portugal

12.90

23.17

12.90

18.18

Sweden

3.03

21.50

4.81

10.09

United Kingdom

21.50

18.62

7.39

11.96

Overall

11.46

18.58

10.16

13.67

Greece Italy

n = 3,869 Source: Authors’ compilation, based on the data used to compile Table 4.5.

Figure 4.6. Mean Percentage of Experienced-based Nascent Firms, by Country and Industrial Sector 35 30 25 20 15 10 5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

n = 3,869

Greece

Low-Tech

Italy KIBS

Portugal

Sweden

United Kingdom

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Sources of Knowledge & Entrepreneurial Behavior

those in Sweden, and in the Czech Republic’s high-tech sector there were no nascent firms.

Occupational-based Nascent Firms We also defined occupational-based nascent firms as those whose firstlisted founder’s last occupation was at a university or in government or had no prior occupational experience – where, for example, the current firm was the founder’s first job. The occupational-based nascent firms in Table 4.8, are those whose first-listed founder responded to the survey question about occupation with a “6,” a “7,” or a “9” (the key in Table 4.6 is reproduced in Table 4.8) – that is, firms with a first-listed founder who had no occupational experience in the private sector. As shown in Table 4.8. Mean Percentage of Occupational-based Nascent Firms, by Country and Industrial Sector Sector High-tech Country Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

Low-tech

KIBS

Overall

(mean percentage) 14.71 8.33 5.88 5.88 5.97 4.76 7.01 0.00 5.88 10.64

13.39 9.09 7.25 6.15 7.59 5.56 8.65 8.28 9.26 6.28

8.70 14.10 11.45 10.16 9.81 5.74 12.31 7.69 4.71 7.58

12.50 11.10 10.00 8.27 8.69 5.57 9.79 7.27 6.31 7.39

6.95

7.90

9.19

8.44

n = 3,947 Key: 1 = owner of a firm still in existence; 2 = owner of a firm that has ceased to exist; 3 = employee of a firm in the same industry; 4 = employee of a firm in a different industry; 5 = self-employed; 6 = university or research institute employee; 7 = government employee; 8 = unemployed; 9 = none of the above – this is his/her first job; 10 = do not know. Source: Authors’ compilation, based on the data used to compile Table 4.6.

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77

Figure 4.7. Mean Percentage of Occupational-based Nascent Firms, by Country and Industrial Sector 16 14 12 10 8 6 4 2 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

Italy

Portugal

Sweden

United Kingdom

KIBS

n = 3,947

Table 4.8, the greatest percentage of occupational-based nascent firms was in the Croatian high-tech sector and the smallest was in the high-tech sector in Portugal. As in Figure 4.6, the variability across countries and industrial sectors in Figure 4.7 is clearly evident, regardless of how one measures nascency.

Similarities and differences across experience measures In an effort to approximate the extent to which the seven experience measures are related to one another – that is, the extent to which they measure similar dimensions or characteristics of a firm’s experience base – we calculated the correlation among the seven measures (Table 4.9). It should not be surprising, given our discussion about the term experience at the beginning of this chapter, that some of these metrics are closely related to one another, while others are not. Each experience measure

78

Sources of Knowledge & Entrepreneurial Behavior

age emp found

nasocc

nasexp

exp

educ

found

emp

age

Table 4.9. Correlation Coefficients among Measures of Experience

1.00 .0419*** .0710***

1.00 .0759***

1.00

educ

–.0244

.0193

.0765***

1.00

exp

–.0229

–.0185

.0181

–.0844***

nasexp

.0246

–.0037

–.0051

.0026

–.0387**

1.00

nasocc

.0401**

–.0077

.1168***

–.1467***

–.0016

.0346**

1.00 1.00

Note: *** significant at the .01 level; ** significant at the .05 level. Key: age = age of the firm in years emp = number of firm employees found = number of firm founders educ = years of education of a firm’s first-listed founder exp = years of experience in current sector of a firm’s first-listed founder nasexp = 1 if the firm is an experienced-based nascent firm; 0 otherwise nasocc = 1 if the firm is an occupational-based nascent firm; 0 otherwise

quantifies a unique characteristic of the experience base of firms. In addition – and this nuance is not captured in the correlation matrix in Table 4.9 because data for all countries and all industrial sectors are pooled together – there are undoubtedly cross-country and cross-sector differences in the experience base of firms. Measures of number of employees (emp) and number of founders (found) are positively correlated with firm age (age), and the estimated correlation coefficient among those variables is statistically significant at the .01 level. Simply, older firms have had time to grow (that is, to survive) and thus to hire more employees (that is, to expand); older firms are perhaps older because of the cumulative human capital embodied in the greater number of founders. Mean education (educ) is positively correlated with number of founders (found), also at the .01 level, but not with firm age (age) or number of employees (emp). A comparison of the low-tech sector’s relatively smaller number of founders (in Table 4.3) and relatively lower level of education (in Table 4.4) is striking, and it is perhaps responsible for this overall positive correlation.

The Experience Base of Firms

79

Also correlated is years of experience (exp) and educational level (educ) of first-listed founders. The estimated correlation coefficient is negative and statistically significant at the .01 level, meaning that firstlisted founders with more years of experience in the current sector also had relatively lower levels of education. Perhaps with education comes an ability to succeed in one sector and then move to another sector, holding constant the fact that more education by definition means fewer work years for individuals of the same age. Nascent firms, measured either in terms of experience (nasexp) or occupation (nasocc), have by definition less experience. The correlation coefficients for both nascent measures and years of experience of the first-listed founder (exp) are negative and significant at the .05 level or better. Also, occupational-based nascent firms had a larger number of founders, and their first-listed founders had more education. The correlation coefficient between nasocc and both found and educ is positive and significant at the .05 level or better. Summary Observations If a picture is worth a thousand words, then a visual comparison of Figures 5.1 through 5.7 are worth seven thousand words. That comparison suggests there are important cross-country and cross-sectoral differences in the experience measures discussed in this chapter. For the metrics of age and education, it appears that cross-sectoral differences are greater than cross-country differences. There appear to be key differences both cross-country and cross-sector with respect to number of founders and years of experience. The greatest variability across countries is evident with respect to number of employees. The most important finding we present here, in our opinion, is that a singular metric for experience is unlikely. Rather, multiple types and metrics of experience exist, and are important in significantly differently ways for very different contexts, reflecting differences across firms, industries, and countries. In the following chapter, we discuss the various measures of sources of knowledge available to firms in the AEGIS database, and again present these measures by country and industrial sector. To substantiate the directional influence of experience on knowledge as described in Figure 1.1 in Chapter 1, which is as an initial step toward understanding and perhaps validating the KSTE, we also present relevant and supportive statistical findings.

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Appendix 4.A AEGIS Survey Questions Related to Experience Measures Note: Within the quoted questions, we have changed the words company and business to firm. Firm age (Table 4.1 and Figure 4.1): The AEGIS survey began to be administered in late 2010 and concluded in 2011. Thus firm age was calculated as 2011 less the firm’s response to the survey question: “In which year was your firm established?” Number of employees (Table 4.2 and Figure 4.3): The survey asked: “What is the total number of full time employees in your firm and what is the total number of part time employees in your firm?” Number of founders (Table 4.3 and Figure 4.3): The survey asked each of four founders: “Who founded your firm.” The number of firm founders was simply a count of responses to this question. Education of first-listed founder (Table 4.4 and Figure 4.4): The survey asked each of four founders: “What is/are the highest educational attainment of the founder(s)?” Responses could be: elementary education, secondary education, bachelor’s degree, postgraduate degree, PhD. We assumed that an elementary education corresponds to six years of education, a secondary education to twelve years, a bachelor’s degree to sixteen years, a postgraduate degree to eighteen years, and a PhD to twenty-one years of education. Experience of the first-listed founder (Table 4.5 and Figure 4.5): The survey asked each of four founders: “Approximately how many years of profession experience did the founder(s) have in the current sector of your firm before the establishment of this firm?” Previous occupation (Table 4.6): The survey asked each of four founders: “What was the last occupation of the founder(s) before the establishment of this firm?” Responses could be: owner of a firm still in existence, owner of a firm that had ceased operations, employee of a firm in the same industry, employee of a firm in a different industry, self-employed, university or research institute employee, government employee, unemployed, none of the above – this was his/her first job.

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Appendix 4.B Definitions of Nascent Entrepreneurship Author(s) (listed alphabetically)

Key Term(s)

Definition

Alsos and Kolvereid (1998)

nascent entrepreneur

Individual affirmed that he or she, alone or with others, was currently trying to start up a new business or had started a business during the previous year

Alsos and Ljunggren (1998)

nascent entrepreneurs

Individual affirmed that he or she, alone or with others, was currently trying to start up a new business or had started a business during the previous year

Arenius and Minniti (2005)

nascent entrepreneur

Individual affirmed that he or she, alone or with others, was currently trying to start a new business, and had done something to help start a business in the previous 12 months and expected personally to own all or part of the business

Bönte and Piegeler (2013)

entrepreneurship

Taking steps to start a new business

Brush, Manolova, and Edelman (2008)

nascent entrepreneur

Individual affirmed that he or she, alone or with others, was currently trying to start a new business or had started a business during the previous year (for him-, her-, themselves or for his, her, their employer), but only if the respondent expected to be owner/part owner of the new firm, having been active in trying to start it in the previous 12 months, and if the effort was still in the startup or gestation phase and was not an infant firm

Carter et al. (2003)

nascent entrepreneurs

Individual who, by him- or herself or with others, was trying to start a business (either for him- or herself or for an employer), who also anticipated becoming an owner and affirmed that there had been any organizing activity during the previous 12 months

Carter, Gartner, and Reynolds (1996)

nascent entrepreneurs

Individual affirmed that he/she was trying to start a business and had initiated or completed behaviors associated with doing so (for example, sought a bank loan, filed for incorporation, leased equipment, hired employees) (Continued)

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Author(s) (listed alphabetically)

Key Term(s)

Definition

Cassar and Craig (2009)

nascent entrepreneur

Respondent expected to have some ownership of the new firm, and was actively trying to start the new firm in the previous 12 months

Chandler, Honig, and Wiklund (2005)

nascent entrepreneur

Individual was currently starting an independent business

Davidsson, Gordon, and Bergmann (2011)

nascent entrepreneur

Individual engaged in an ongoing but not yet operational startup

Davidsson and Honig (2003)

nascent entrepreneur

Respondent affirmed that he/she, alone or with others, was trying to start a new firm and to do so independently (as opposed to doing so as part of an assignment, in which he/she would be a nascent intrapreneur), and had carried out at least one gestation activity by the time of the interview

Delmar and Davidsson (1999)

nascent entrepreneur

Individual responded that he/she was trying to start a business, met the lower bond of having “initiated” or “completed” at least one of 24 gestation activities, an d fell below the upper bound of having completed the startup process

Delmar and Davidsson (2000)

nascent entrepreneur

Individual had completed at least one business gestation activity when interviewed

Delmar and Gunnarsson (2000)

nascent entrepreneur

See Delmar and Davidsson (1999)

Delmar and Shane (2003)

founder of a new venture

Respondent was in the process of starting a new business alone or jointly with others, had taken the first activity to start the venture during the first nine months of the survey year, the new venture was not part of an effort by an existing organization, and the respondent was a member of the founding team (rather than acting as a consultant or passive investor)

Dimov (2010)

nascent entrepreneur

“Individual responded affirmatively to whether or not he/she, alone or with others, at the time were trying to start up a new business or had started a business during the last year (for him/her/themselves or his/her/their employer). Also needed to respond affirmatively to expecting to be owners/part owners of the new firm, having been active in trying to start the new firm in the last 12 months, and if the effort was still in the startup or gestation phase and was NOT an infant firm”

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Author(s) (listed alphabetically)

Key Term(s)

Definition

Diochon, Menzies, and Gasse (2007)

nascent entrepreneur

Affirmative answer to questions “Among the adults living in your household, is there anyone who, alone or with others, is now trying to start a new venture?” “Will you be an owner, in part or in whole of this company or venture that you are trying to launch, alone or with others for your own business or that of your employer?” “During the last 12 months, have you done anything to help start this new business…or any other activity that would help launch a business?”

Eckhardt, Shane, and Delmar (2006)

new venture

Effort by person or persons to create a new organization that engages in commercial activity

Edelman, Manolova, and Brush (2008)

nascent entrepreneur

Individual, alone or with others, was trying to start a new business for him- or herself or his or her employer; subsequently affirmed active involvement in startup process and ownership of the new venture

Gatewood, Shaver, and Gartner (1995)

potential entrepreneur

Pre-venture clients of a small business development center

Grilo and Thurik (2005a)

nascent entrepreneurship

The process of thinking about starting a business and taking steps toward setting one up

Grilo and Thurik (2005b)

latent entrepreneurship (as distinct from nascent)

Preference for self-employment

Grilo and Thurik (2008)

nascent entrepreneur

Individual thinking about starting a business and taking steps toward setting one up

Hessels et al. (2011)

nascent entrepreneur

Individual actively involved in setting up a business

Hessels, van Gelderen, and Thurik (2008)

nascent entrepreneur

Individual actively involved in starting a new firm

Honig, Davidsson, and Karlsson (2005)

nascent entrepreneur

Individual who, by him- or herself or with others, is trying to start a new independent firm, has completed at least one gestation activity, and is not developing a new venture for an established firm.

Kim, Aldrich, and Keister (2006)

nascent entrepreneur

Individual in the process of organizing and assembling the resources needed for a new business (Continued)

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Author(s) (listed alphabetically)

Key Term(s)

Definition

Liao and Welsch (2003a)

nascent entrepreneur

“Individual affirmed that he/she was, alone or with others, trying to start a business for himself/herself or his/her employer, and affirmed that this effort was for his/her employer. Additionally, the individual expected to be an owner or part owner of the new firm, had been active in the past 12 months in trying to start the new firm, and was still in the start-up/gestation phase (as opposed to infancy)”

Liao and Welsch (2003b)

nascent entrepreneur

Identified using the typical Personal, Social and Emotional Development (PSED) process

Liao and Welsch (2008)

nascent entrepreneur

Identified using the typical PSED process

Liao, Welsch, and Tan (2005)

nascent entrepreneur

Identified using the typical PSED process

Lichtenstein et al. (2007)

nascent entrepreneur

Individual in the process of starting a business and planned on becoming an owner in the business, but had not yet generated positive cash flow

Matthews, Ford, and Human (2001)

nascent entrepreneur

Individual who, alone or with others, was trying to start a new business

Matthews and Human (2000)

nascent entrepreneur

Individual who, alone or with others, was trying to start a new business (for him- or herself or for an employer), expected to be an owner or part owner of the new firm, had been active in trying to start the firm in the past 12 months, and the firm was not an infant one

Parker and Belghitar (2006)

nascent entrepreneur

Individual actively involved in starting a new business

Reynolds (1997)

nascent entrepreneur

Individual who had initiated or completed behaviors associated with starting a new firm (for example, sought a bank loan, filed for incorporation, leased equipment, hired employees)

Reynolds (2009)

nascent entrepreneur

Individual who intended to start a new business, alone or with others (including any form of self-employment or the sale of goods or services to others), was attempting to start a new business or venture as part of his or her normal employment, or that he or she was the owner of a business that he or she helped to manage

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Author(s) (listed alphabetically)

Key Term(s)

Definition

Reynolds et al. (2004)

nascent entrepreneur

Individual who chooses to start a new business (conception); a nascent independent entrepreneur if the startup is independent, or a nascent corporate entrepreneur if supported by existing businesses

Reynolds and Miller (1992)

new firm

A new participant in a system of social and economic relationships

Rotefoss and Kolvereid (2005)

nascent entrepreneur

Individual who responded affirmatively to the questions: “Are you today, alone or together with someone else, trying to start or acquire a business?” and “Have you, alone or together with someone else, started or acquired a business during the last year or the survey year?” In addition, the individual must have undertaken at least two gestation activities

Ruef, Aldrich, and Carter (2003)

nascent entrepreneur

Individual in the process of starting a business

Samuelsson and Davidsson (2009)

new venture startup

Individual involved in the venture-creation process (by him-or herself or with others) either independently or as part of a job assignment, and who had completed at least two gestation activities, but not if the individual had invested money, received revenues, and registered the venture as a legal entity

Townsend, Busenitz, and Arthurs (2010)

new venture creation; nascent entrepreneur

Individual currently trying to start a new business

van der Zwan et al. (2013)

nascent entrepreneurship

The process of thinking about starting a business and taking steps toward starting one

van der Zwan, Thurik, and Grilo (2010)

nascent entrepreneur

Individual taking steps to becoming selfemployed, but not yet officially established

van Gelderen, Thurik, and Bosma (2005)

nascent entrepreneur, entrepreneurship

Individual undertaking activities to create a business; the founding effort

van Gelderen, Thurik, and Pankaj (2011)

nascent entrepreneurship

The process of starting up a business

van Stel, Storey, and Thurik (2007)

nascent entrepreneur

Individual taking active steps to start a business (Continued)

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Author(s) (listed alphabetically)

Key Term(s)

Definition

Verheul et al. (2012)

latent and nascent entrepreneurship

The decision and preparations to create a new venture

Wagner (2004)

nascent entrepreneur

Individual who, alone or with others, is actively involved in starting a new business that will (in whole or in part) belong to him or her that has not paid anyone full-time wages or salaries for more than three months

Wagner (2006)

nascent entrepreneur

Individual in the process of starting his or her own business

Wennekers et al. (2005)

nascent entrepreneur

Individual who has created an autonomous, independent business or new venture sponsored by an existing business that might be a new branch or subsidiary.

Note: We are pleased to acknowledge the able assistance of Chi Wong in the preparation of this table. Source: Authors’ compilation.

chapter five

Sources of Knowledge

Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information on it. – Samuel Johnson An investment in knowledge always pays the best interest. – Benjamin Franklin

Knowledge versus Knowledge Along with its five meanings for the word experience, the Merriam-Webster Dictionary also offers four principal meanings for knowledge. The first is “the fact or condition of knowing something with familiarity gained through experience or association, or acquaintance with or understanding of a science, art, or technique”; the second meaning is “the fact or condition of being aware of something, or the range of one’s information or understanding”; the third is “the circumstance or condition of apprehending truth or fact through reasoning, or cognition”; and the fourth meaning of knowledge is “the fact or condition of having information or of being learned.” As we discussed in Chapter 2, the inherent heterogeneity of knowledge involves the organizational contexts in which knowledge can be used to generate innovative activity. The observed heterogeneity of knowledge also involves entrepreneurial differences in the perception of a given source of knowledge and in the ability to use that knowledge

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effectively. Just because a certain knowledge content can or will be used to influence entrepreneurial behavior – and thus to generate innovative activity – does not mean that it holds the same value across firms and other organizations. Although constrained by the available information in the AEGIS database on sources of knowledge, we were able to identify and examine sixteen such sources. And, to anticipate our use of these sources in Chapter 6, we concluded that it is an empirical issue, rather than a theoretical or conceptual one, as to which of these sources aligns most closely with the model of Knowledge → Entrepreneurial Behavior. Knowledge Source Metrics The AEGIS database is rich in alternative sources of knowledge. The sixteen sources of knowledge in the survey or, to be more accurate, the sixteen survey questions that appear to us to deal with sources of knowledge can be divided into three broad categories: knowledge relied on in the formation of the firm, knowledge used to explore new firm opportunities, and general knowledge to enhance the firm’s overall economic performance.1 The specific AEGIS survey questions relevant to these measures are in Appendix 5.A to this chapter. To anticipate the reader’s response to the tables and figures associated with these sixteen sources of knowledge, we acknowledge that there is considerable cross-country variation in the relative importance of sources. Why is this the case? We suspect it is a consequence of differences in national systems of innovation (see Chapter 2); however, although validation of our suspicion is clearly important, it is beyond the scope of this book. In Chapter 1, we referred to five types of knowledge originally identified by Machlup (1962, 21–2): practical knowledge, intellectual knowledge, small-talk and pastime knowledge, spiritual knowledge, and unwanted knowledge. The sources of knowledge defined in the AEGIS database partially map into these types – for example, sources that fall into our category of knowledge relied on in the formation of the firm might align well with Machlup’s practical knowledge since, in our view, knowledge about products, markets, customers, and suppliers is indeed practical knowledge. As well, knowledge for exploring new firm opportunities might align well with Machlup’s category of intellectual knowledge, particularity knowledge from research institutes, universities, external commercial labs, in-house R&D, scientific publications or conferences, and participation in nationally funded research programs. Finally, the three

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89

sources that fall under our category of general knowledge sources can, in theory, capture all of Machlup’s types of knowledge, but more than likely they related to intellectual knowledge.

Knowledge sources relevant to the formation of the firm Two factors or types of knowledge are potentially important in the formation of the firm: design knowledge about potential products, and knowledge about potential markets. Tables 5.1 and 5.2 and the companion Figures 5.1 and 5.2 describe numerically and graphically differences in the relative importance of these two sources of knowledge across countries and industrial sectors according to the responses to the AEGIS survey. Based on the responses to the AEGIS survey about the relative importance of each source (bottom row of Tables 5.1 and 5.2), it appears that, on average, knowledge about the market was relatively more important that design knowledge for the formation of the firm, with the overall importance of the former one response level greater (4.05 compared with 3.04) than for the latter. In fact, in the spectrum of response categories from 5 to 1, if we assume that a score greater than 3 means important and a score of 3 or less means not important, then knowledge of the market clearly was an important source of knowledge for the formation of the firm, but design knowledge was at best marginally important in all of the selected countries and across all sectors. Design knowledge seems to have been most important in Greece, and was marginally important only in the high-tech and low-tech sectors.

Knowledge sources for exploring new firm opportunities The AEGIS survey identified eleven knowledge sources as potentially important for exploring new firm opportunities: customers; suppliers; competitors; public research institutes; universities; external commercial labs, R&D firms, and/or technical institutes; in-house R&D laboratories; trade fairs, conferences, and exhibitions; scientific journals or other trade or technical publications;2 participation in nationally funded research programs; and participation in EU Framework Programs.3 Retaining our important-versus-not-important dichotomy for discussion purposes – that is, a survey response greater than 3 means important – customers as a source of knowledge was clearly important, and more so than any of the other ten sources (Table 5.3 and Figure 5.3), across all

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Table 5.1. Mean Importance of Design Knowledge as a Factor in the Formation of the Firm, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

3.51

3.48

3.24

3.43

Czech Republic

2.64

2.41

2.09

2.30

Denmark

3.03

3.27

2.46

2.68

France

3.12

2.85

2.96

2.94

Germany

3.42

2.57

2.74

2.77

Greece

4.05

4.02

3.81

3.94

Italy

3.35

3.16

3.37

3.25

Portugal

3.16

3.14

3.40

3.24

Sweden

2.88

2.76

2.91

2.86

United Kingdom

3.40

1.57

2.77

2.96

Overall

3.26

3.12

2.92

3.04

n = 3,982

Figure 5.1. Mean Importance of Design Knowledge as a Factor in the Formation of the Firm, by Country and Industrial Sector 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 3,962; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

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91

Table 5.2. Mean Importance of Knowledge of the Market as a Factor in the Formation of the Firm, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

4.23

4.17

4.16

4.18

Czech Republic

3.44

3.91

3.75

3.79

Denmark

3.82

3.93

3.95

3.93

France

3.78

3.86

3.83

3.83

Germany

4.03

4.21

3.99

4.06

Greece

4.45

4.25

4.17

4.23

Italy

4.28

4.24

4.04

4.17

Portugal

4.26

4.25

4.12

4.20

Sweden

4.15

3.85

4.09

4.02

United Kingdom

4.15

4.12

4.04

4.08

Overall

4.05

4.11

4.00

4.05

n = 3,996

Figure 5.2. Mean Importance of Knowledge of the Market as a Factor in the Formation of the Firm, by Country and Industrial Sector 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 3,962; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

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Table 5.3. Mean Importance of Customers as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Country Croatia

4.31

4.34

4.18

4.30

Czech Republic

4.40

4.12

4.11

4.15

Denmark

4.59

4.28

4.38

4.38

France

4.19

4.36

4.28

4.30

Germany

4.27

4.46

4.29

4.34

Greece

4.68

4.27

4.24

4.29

Italy

4.04

4.40

4.48

4.26

Portugal

4.77

4.74

4.78

4.76

Sweden

4.50

4.30

4.51

4.40

United Kingdom

4.62

4.58

4.51

4.54

Overall

4.29

4.10

4.40

4.41

n = 4,004

Figure 5.3. Mean Importance of Customers as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

93

countries and sectors: every cell in Table 5.3 has a response greater than 4.0. Cross-sector differences in the importance of customers as a knowledge source is clearly seen in Figure 5.3 As Tables 5.4 and 5.5 and Figures 5.4 and 5.5 show, suppliers and competitors were also important sources of knowledge for exploring new firm opportunities, both across countries and across sectors, with 3.36 the overall response for suppliers and 3.27 for competitors. In the KIBS sector, however, knowledge from suppliers was regarded as only marginally important, at 3.01, while the importance score for suppliers was less than 3 in five of the ten countries. In general, firms in the high-tech and low-tech sectors gave more importance to suppliers as a source of knowledge than did firms in the KIBS sector. By contrast, competitors as a source of knowledge for exploring new firm opportunities scored higher than suppliers among firms in the KIBS sector, while firms in the high-tech and low-tech sectors, on average, did not rate competitors so highly. The one anomaly in the two tables and figures is Portugal, possibly due to intra-country differences in the distribution of industries. For the most part, firms in all sectors responded strongly about the importance of both suppliers and competitors as sources of knowledge for exploring new firm opportunities. There is no evidence that public research institutes, universities, or external commercial labor/R&D firms/technical institutes were regarded as important sources of knowledge for exploring new firm opportunities (see Tables 5.6, 5.7, and 5.8, and Figures 5.6, 5.7, or 5.8). As Table 5.7 and Figure 5.7 show, only among high-tech firms in Portugal was the constructed response barrier of 3 passed. In-house R&D laboratories were seen as an important source of knowledge for exploring new firm opportunities, but not as important as customers (see Table 5.3 and Figure 5.3) (Table 5.9 and Figure 5.9). This source appears to have been most important among Croatian and Italian firms. Trade fairs, scientific journals, participation in national funded research programs, and participation in EU Framework programs were not generally seen as important sources of knowledge for exploring new firm opportunities (see Tables 5.10, 5.11 , 5.12 , and 5.13 , and Figures 5.10, 5.11 , 5.12 , and 5.13 ), although trade fairs and scientific journals were relatively more important than the other two sources.

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Table 5.4. Mean Importance of Suppliers as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

3.80

3.96

3.22

3.75

Czech Republic

3.28

3.32

2.89

3.14

Denmark

3.53

3.41

2.81

3.01

France

3.68

3.64

2.92

3.26

Germany

3.25

3.43

2.82

3.04

Greece

4.05

3.86

3.22

3.63

Italy

3.82

3.99

3.29

3.69

Portugal

4.42

4.31

3.84

4.13

Sweden

3.29

3.16

2.91

3.03

United Kingdom

3.49

3.63

2.88

3.18

Overall

3.63

3.73

3.01

3.36

n = 4,004

Figure 5.4. Mean Importance of Suppliers as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

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Table 5.5. Mean Importance of Competitors as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

3.60

3.87

3.80

3.81

Czech Republic

3.52

3.36

3.01

3.24

Denmark

3.15

3.17

2.91

2.99

France

3.18

3.28

3.09

3.17

Germany

3.01

3.24

2.99

3.06

Greece

3.86

3.63

3.41

3.56

Italy

3.54

3.47

3.37

3.44

Portugal

3.68

3.61

3.50

3.57

Sweden

3.26

3.06

3.09

3.10

United Kingdom

3.21

3.26

3.09

3.15

Overall

3.34

3.41

3.14

3.27

n = 4,004

Figure 5.5. Mean Importance of Competitors as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

96

Sources of Knowledge & Entrepreneurial Behavior

Table 5.6. Mean Importance of Public Research Institutes as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

2.60

2.77

2.54

2.68

Czech Republic

1.84

1.75

1.65

1.72

Denmark

1.79

1.72

2.00

1.92

France

1.87

1.73

1.92

1.85

Germany

2.07

1.88

1.82

1.87

Greece

2.55

2.34

2.40

2.37

Italy

2.33

2.29

2.51

2.37

Portugal

2.55

2.53

2.70

2.60

Sweden

1.74

1.76

2.00

1.90

United Kingdom

2.45

1.87

2.05

2.02

Overall

2.16

2.10

2.09

2.10

n = 4,004

Figure 5.6. Mean Importance of Public Research Institutes as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

97

Table 5.7. Mean Importance of Universities as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

2.80

2.69

2.60

Czech Republic

1.84

1.70

1.75

2.69 1.74

Denmark

1.65

1.64

1.96

1.86

France

1.74

1.54

1.93

1.77

Germany

2.19

2.01

2.08

2.07

Greece

2.55

2.30

2.42

2.36

Italy

2.32

2.18

2.59

2.34

Portugal

3.06

2.78

2.94

2.87

Sweden

1.82

1.82

2.14

2.00

United Kingdom

2.53

1.73

1.82

1.85

Overall

2.21

2.07

2.13

2.12

n = 4,004

Figure 5.7. Mean Importance of Universities as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

98

Sources of Knowledge & Entrepreneurial Behavior

Table 5.8. Mean Importance of External Commercial Labs/R&D Firms/Technical Institutes as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

2.77

2.88

2.58

Czech Republic

1.76

1.76

1.63

2.79 1.71

Denmark

1.68

1.74

1.97

1.89 1.73

France

1.85

1.63

1.77

Germany

2.04

1.84

1.74

1.81

Greece

2.50

2.33

2.38

2.37

Italy

2.35

2.46

2.25

2.38

Portugal

3.00

2.56

2.55

2.60

Sweden

1.76

1.58

1.70

1.67

United Kingdom

2.36

1.81

1.86

1.89

Overall

2.18

2.11

1.95

2.04

n = 4,004

Figure 5.8. Mean Importance of External Commercial Labs/R&D Firms/Technical Institutes as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

99

Table 5.9. Mean Importance of In-House R&D Laboratories as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

4.20

4.04

3.72

Czech Republic

3.88

3.23

3.46

3.99 3.41

Denmark

2.59

2.25

2.33

2.34

France

2.68

2.81

2.89

2.84

Germany

3.42

2.90

3.17

3.12

Greece

4.00

3.18

3.75

3.45

Italy

4.02

3.99

4.09

4.03

Portugal

3.39

2.95

3.51

3.21 3.25

Sweden

3.18

3.03

3.38

United Kingdom

3.64

3.17

3.31

3.29

Overall

3.44

3.25

3.25

3.27

n = 4,004

Figure 5.9. Mean Importance of In-House R&D Laboratories as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

100

Sources of Knowledge & Entrepreneurial Behavior

Table 5.10. Mean Importance of Trade Fairs, Conferences, and Exhibitions as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

3.86

3.83

3.32

3.71

Czech Republic

3.28

2.72

2.87

2.85

Denmark

2.85

3.07

2.46

2.63

France

2.35

2.58

2.38

2.45

Germany

3.34

3.18

3.01

3.10

Greece

3.68

3.09

3.15

3.15

Italy

3.21

3.16

2.87

3.06

Portugal

3.74

3.42

3.29

3.40 2.75

Sweden

2.91

2.90

2.64

United Kingdom

2.98

2.99

2.82

2.89

Overall

3.14

3.09

2.80

2.95

n = 4,004

Figure 5.10. Mean Importance of Trade Fairs, Conferences, and Exhibitions as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

101

Table 5.11. Mean Importance of Scientific Journals and Other Trade or Technical Publications as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

3.74

3.57

3.72

Czech Republic

3.08

2.59

3.04

3.64 2.84

Denmark

2.15

2.65

2.60

2.56

France

2.21

2.22

2.75

2.50

Germany

3.24

2.96

3.10

3.08

Greece

3.23

2.74

3.19

2.95

Italy

2.75

2.83

3.15

2.94

Portugal

3.13

2.94

3.44

3.15 2.86

Sweden

2.38

2.94

2.89

United Kingdom

3.00

2.46

2.81

2.71

Overall

2.85

2.77

2.97

2.87

n = 4,004

Figure 5.11. Mean Importance of Scientific Journals and Other Trade or Technical Publications as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 4 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

102

Sources of Knowledge & Entrepreneurial Behavior

Table 5.12. Mean Importance of Participation in Nationally Funded Research Programs as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

2.26 2.00 1.71 1.74 2.00 2.45 2.51 2.35 1.59 2.38 2.08

2.23 1.45 1.64 1.43 1.48 2.33 2.25 2.31 1.58 1.60 1.89

2.12 1.53 1.86 1.69 1.57 2.42 2.53 2.41 1.62 1.65 1.86

2.21 1.55 1.80 1.61 1.60 2.37 2.42 2.38 1.60 1.70 1.90

n = 4,004

Figure 5.12. Mean Importance of Participation in Nationally Funded Research Programs as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

103

Table 5.13. Mean Importance of Participation in EU Framework Programs as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = extremely important; 1 = not important)

Croatia

2.40

2.32

2.34

2.34

Czech Republic

2.04

1.46

1.60

1.59

Denmark

1.56

1.52

1.61

1.58

France

1.66

1.42

1.58

1.53

Germany

1.84

1.49

1.53

1.56

Greece

2.73

2.41

2.48

2.46

Italy

2.58

2.31

2.53

2.42

Portugal

2.32

2.40

2.36

2.38 1.56

Sweden

1.59

1.47

1.61

United Kingdom

2.12

1.53

1.57

1.61

Overall

2.05

1.91

1.80

1.87

n = 4,004

Figure 5.13. Mean Importance of Participation in EU Framework Programs as a Source of Knowledge for Exploring New Firm Opportunities, by Country and Industrial Sector 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

Greece

Low-Tech

n = 4,004; 5 = extremely important; 1 = not important

Italy KIBS

Portugal

Sweden

United Kingdom

104

Sources of Knowledge & Entrepreneurial Behavior

General knowledge sources The AEGIS survey identified three general sources of knowledge: participation in strategic alliances, participation in R&D agreements, and participation in technical cooperative agreements. Although the survey did not specifically indicate to respondents that these are potential sources of knowledge, we interpreted the lack of such specificity to imply that these are general sources of knowledge applicable to the economic, strategic, and competitive health of the firm. However, the mean level of participation in these relational alliances and agreements as a mechanism for gleaning general knowledge was low. Again, using a response of 3 or less as a participation divider, it is evident from the bottom right cells of Tables 5.14 through 5.16 that these knowledge sources simply were not relied upon to much extent, although participation in strategic alliances was higher, on average, by firms in the KIBS sector than by firms in the other sectors. Moreover, as we revisit in Chapter 6,

Table 5.14. Mean Level of Participation in Strategic Alliances as a General Source of Knowledge, by Country and Industrial Sector Sector High-tech Country

Low-tech

KIBS

Overall

(5 = very often; 1 = not at all)

Croatia

2.14

1.82

2.54

2.06

Czech Republic

2.36

1.97

2.14

2.09

Denmark

1.97

1.88

2.64

2.42

France

1.85

1.59

2.12

1.91

Germany

2.01

2.08

2.53

2.34

Greece

2.68

1.76

2.91

2.26

Italy

2.44

1.90

2.63

2.21

Portugal

2.52

1.89

2.55

2.21

Sweden

2.18

1.78

2.54

2.25

United Kingdom

2.49

1.82

2.63

2.35

Overall

2.21

1.84

2.52

2.22

n = 4,004

Sources of Knowledge

105

these sources did not necessarily enhance entrepreneurial behavior even when they were used.

Relationships among knowledge sources To explore the relationships among the sixteen broadly defined and conceptually different sources of knowledge available to the AEGIS firms, we turned to simple correlations, as reported in Table 5.17. Two relationships are worth noting. First, all of the correlation coefficients are positive, perhaps suggesting complementarities among them. It is important to emphasize, too, that we did not control for country and industrial sector in the table. Second, all of the correlation coefficients except for three are statistically significant at the .01 level, implying relatively strong complementary relationships. To facilitate discussion, we shaded several of the numerically larger correlation coefficients.

Figure 5.14. Mean Level of Participation in Strategic Alliances as a General Source of Knowledge, by Country and Industrial Sector 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

n = 4,004; 5 = very often; 1 = not at all

Greece

Low-Tech

Italy KIBS

Portugal

Sweden

United Kingdom

106

Sources of Knowledge & Entrepreneurial Behavior

Table 5.15. Mean Level of Participation in R&D Agreements as a General Source of Knowledge, by Country and Industrial Sector Sector High-tech

Low-tech

Country

KIBS

Overall

(5 = very often; 1 = not at all)

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

1.66 1.96 1.65 1.81 1.99 2.00 2.25 2.19 1.76 2.23 1.96

1.77 1.36 1.55 1.44 1.41 1.45 1.74 1.67 1.36 1.42 1.54

2.30 1.41 1.62 1.69 1.65 2.29 2.04 1.91 1.69 1.58 1.75

1.89 1.46 1.61 1.62 1.62 1.80 1.90 1.81 1.59 1.58 1.69

n = 4,004

Figure 5.15. Mean Level of Participation in R&D Agreements as a General Source of Knowledge, by Country and Industrial Sector 2.5

2

1.5

1

0.5

0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

n = 4,004; 5 = very often; 1 = not at all

Greece

Low-Tech

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge

107

Table 5.16. Mean Level of Participation in Technical Cooperation Agreements as a General Source of Knowledge, by Country and Industrial Sector Sector High-tech

Low-tech

Country

KIBS

Overall

(5 = very often; 1 = not at all)

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

2.63 2.60 2.29 1.93 2.33 2.55 2.56 2.61 2.35 2.21 2.35

2.46 1.89 2.39 1.66 1.94 1.78 2.17 1.99 1.58 1.58 1.93

3.14 2.23 2.34 2.05 2.08 2.67 2.65 2.52 2.09 1.91 2.24

2.66 2.12 2.35 1.90 2.07 2.17 2.38 2.26 1.95 1.83 2.12

n = 4,004

Figure 5.16. Mean Level of Participation in Technical Cooperation Agreements as a General Source of Knowledge, by Country and Industrial Sector 3.5 3 2.5 2 1.5 1 0.5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

n = 4,004; 5 = very often; 1 = not at all

Greece

Low-Tech

Italy KIBS

Portugal

Sweden

United Kingdom

108

Sources of Knowledge & Entrepreneurial Behavior

coop

rdagg

strat

EU

nat

jour

fairs

rd

labs

univ

inst

compet

sup

cust

market

design

Table 5.17. Correlation Coefficients among Sources of Knowledge

design 1.00 market

.139 1.00

cust

.070 .162 1.00

sup

.157 .149

.285 1.00

compet .096 .168

.216

.287 1.00

inst

.148 .113

.080

.202 .251 1.00

univ

.147 .083

.081

.174 .231 .747 1.00

labs

.178 .118

.076

.236 .210 .622 .608 1.00

rd

.023 .117

.083

.107 .128 .259 .255 .290 1.00

fairs

.162 .156

.118

.229 .212 .290 .271 .296 .231 1.00

jour

.125 .078

.061

.119 .172 .358 .356 .324 .209 .441 1.00

nat

.177 .095

.067

.172 .170 .542 .550 .504 .267 .320 .329 1.00

EU

.160 .096

.084

.182 .178 .496 .499 .459 .251 .310 .289 .801 1.00

strat

.060 .104

.083

.019† .054 .152 .165 .126 .131 .088 .133 .148 .129 1.00

rdagg

.158 .063

.037† .068 .091 .355 .375 .356 .257 .188 .189 .424 .356 .410 1.00

coop

.142 .048† .062

.129 .101 .249 .261 .282 .174 .164 .191 .269 .242 .419 .512 1.00

n = 4,004 Note: All correlations are significant at the .01 level except as noted by †. Key: design = design knowledge as a factor for the formation of the company market = market knowledge as a factor for the formation of the company cust = customer knowledge as a factor for the formation of the company sup = supplier knowledge as a factor for the formation of the company compet = competitors as a source of knowledge for exploring new firm opportunities inst = public research institutes as a source of knowledge for exploring new firm opportunities univ = universities as a source of knowledge for exploring new firm opportunities labs = external commercial labs, R&D firms, and/or technical institutes as a source of knowledge for exploring new firm opportunities rd = in-house R&D as a source of knowledge for exploring new firm opportunities fairs = trade fairs, conferences, and exhibitions as a source of knowledge for exploring new firm opportunities jour = scientific journal or other trade or technical publications as a source of knowledge for exploring new firm opportunities nat = participation in nationally funded research programs as a source of knowledge for exploring new firm opportunities EU = participation in EU Framework Programs as a source of knowledge for exploring new firm opportunities strat = participation in strategic alliances as a general source of knowledge rdagg = participation in R&D agreements as a general source of knowledge coop = participation in technical cooperative agreements as a general source of knowledge

Sources of Knowledge

109

Regarding the shaded correlation coefficients, the correlation relationships among public research institutes (inst), universities (univ), external commercial laboratories (labs), national research programs (nat), and EU Framework Programs (EU) suggest that AEGIS firms view these four external sources of knowledge as relatively stronger complements. Although the importance of these external sources of technical knowledge is positively correlated with the importance of inhouse R&D activity (rd), that relationship, somewhat to our surprise, is not as numerically large as the other four. One explanation might be that the firms in the AEGIS database are relatively small and young, and as they mature they might develop an in-house R&D program to complement their use of external sources of technical knowledge. A second explanation is consistent with the knowledge spillover theory of entrepreneurship: external knowledge is a key source for small and young enterprises, driving their efforts to innovate. In this sense, external knowledge serves to complement the creation of internal knowledge. Also, the three general sources of knowledge – strategic alliances (strat), use of R&D agreements (rdagg), and use of technical cooperative agreements (coop) – are all strongly complementary among themselves. The Experience-to-Knowledge Relationship As we suggested in Chapter 1 and in Figure 1.1, experience is the foundation on which knowledge is built and thus, as we have emphasized in this chapter, experience is, at least in part, the foundation for knowing which sources of knowledge the firm should rely on for its economic, strategic, and competitive health. We begin to explore the Experience → Knowledge relationship among AEGIS firms by calculating the correlation coefficient among the experience metrics in Chapter 5 and the sources of knowledge metrics discussed in this chapter: see Table 5.18, where experience metrics are in the columns and knowledge source metrics are in the rows. Several distinct relationships are suggested from the statistical correlation patterns in the table. We discuss these patterns in terms of the three broad categories of knowledge sources discussed in the text: knowledge sources relevant to the formation of the firm, knowledge sources for exploring new firm opportunities, and general knowledge sources. Again it is important to keep in mind that we calculated these effects without controlling for country or industrial sector.

110

Sources of Knowledge & Entrepreneurial Behavior

Table 5.18. Correlation Coefficients among Experience Metrics and Knowledge Source Metrics

design market cust sup compet inst univ labs rd fairs jour nat EU strat rdagg coop

age

emp

found

educ

exp

nasexp

nasocc

.018 –.013 –.010 .004 .007 .023 .025 –.001 .020 .0004 .020 .021 .030* –.014 –.008 –.001

.015 .040** .019 .021 .057*** .041*** .045*** .052*** .046*** .019 .002 .060*** .053*** .046*** .051*** .041***

.055*** .014 –.021 –.034** –.013 .058*** .061*** .047*** .078*** .010 .016 .056*** .064*** .078*** .085*** .069***

–.007 –.056*** –.034** –.239*** –.061*** .016 .082*** –.012 .074*** –.034** .117*** .015 –.019 .167*** .148*** .064***

.049*** .089*** .005 –.025 –.025 .056*** .031* .028* .007 –.014 .013 .019 .003 –.032** .012 –.003

–.040** –.090** –.003 .064*** .005 –.016 –.007 –.005 –.013 .018 –.029** –.012 .008 –.054*** –.026 –.035**

.002 –.057*** .017 –.055*** .006 .070*** .081*** .040** .047*** .023 .033** .060*** .029* .020 .100*** .033**

n = 4,004 Note: *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level. Key: See the key to Table 5.17 age = age of the firm in years emp = number of firm employees found = number of firm founders educ = years of education of a firm’s first-listed founder exp = years of experience in current sector of a firm’s first-listed founder nasexp = 1 if the firm is an experienced-based nascent firm; 0 otherwise nasocc = 1 if the firm is an occupational-based nascent firm; 0 otherwise

Regarding the two knowledge sources relevant for the formation of the firm (design and market), there does not appear to be a general pattern, suggesting that firms with a greater experience base consistently relied on one source more or less often than another. Focusing on knowledge relying on experience about customers (cust) or about the market (market), it appears that firms with a first-listed founder with more education

Sources of Knowledge

111

(educ) relied less on design knowledge (design) for the formation of the firm. It also appears that larger firms (emp) and those with more experienced first-listed founders (exp) relied on knowledge of the market (market) more than on design knowledge for the formation of the firm. Finally, it appears that nascent firms, either experience-based (nasexp) or occupational-based (nasocc), also relied less on knowledge about the market (market). As discussed above, the two most frequently relied-upon knowledge sources for new firm opportunities – sources with importance scores greater than 3 – were competitors and in-house R&D. This finding is especially strong among firms in the high-tech sector. The pattern in Table 5.18 suggest that competitors (compet) were more often used by larger firms (emp) and less often by firms with more educated first-listed founders (educ). In-house R&D (rd) wasrelied on more often in larger firms (emp), firms with more founders (found), firms with more educated first-listed founders (educ), and occupational-based nascent firms (nasocc). Finally, larger firms (emp), firms with more founders (found), and firms with more educated first-listed founders (educ) relied more heavily on general knowledge sources associated with alliances or agreements (strat, rdagg, coop). To explore the Experience → Knowledge relationship in greater detail, we estimated the following probability (Pr) of importance model: Pr (knowledge source) = f (experience, country, sector),

(5.1)

where knowledge source is a dichotomous variable equaling 1 if a particular knowledge source has an importance score greater than 3, and 0 otherwise. The dependent variable, experience, equals the specific values of the alternative experience measures discussed in Chapter 5. Subsumed in the intercept term for a probit specification of equation (5.1) are German firms and firms in the low-tech industrial sector. The probit regression results for alternative specifications of equation (5.1) are reported in Tables 5.19 through 5.22. Only statistically significant marginal effects are reported; if an effect is not statistically significant, it is noted by the term nse (no statistical effect). In Table 5.19 we

112

Sources of Knowledge & Entrepreneurial Behavior

Table 5.19. Estimated Positive Marginal Effects from Equation (6.1), with Country and Sector Held Constant age

emp

found

educ

exp

nasexp

nasocc

design

nse

nse

.0257**

nse

nse

nse

nse

market

nse

nse

nse

nse

.0030***

nse

nse

cust

nse

nse

nse

nse

nse

nse

nse

sup

nse

nse

nse

nse

nse

nse

nse

compet

nse

nse

nse

nse

nse

nse

.0768***

inst

nse

nse

nse

.0041**

.0021***

nse

nse

univ

nse

nse

nse

.0083***

.0013***

nse

.1151***

labs

nse

nse

nse

nse

.0010*

nse

.0601***

rd

nse

nse

nse

.0128***

nse

nse

.0575**

fairs

nse

nse

nse

nse

nse

nse

nse

jour

nse

nse

nse

.0110***

.0013*

nse

.0525**

nat

nse

nse

nse

.0043***

.0009*

nse

.0580***

EU

nse

nse

nse

nse

nse

nse

.0343*

strat

nse

nse

.0203**

.0097***

nse

nse

nse

rdagg

nse

nse

.0186***

.0111***

nse

nse

.1122***

coop

nse

nse

.0207**

.0047***

nse

nse

.0398*

n = 4,004 Note: *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level; nse = no statistical effect. Please see the key to table 5.18.

report the results using information on all firms and then controlling for country and industrial sector; the results in Tables 5.20 through 5.22 use information on firms by industrial sector, and are summarized in Table 5.23. There is one obvious generalization based on the statistically significant estimated marginal effects of experience on knowledge source: more often than not, education (educ) is positively and significantly related to the probability of the use of more knowledge sources than any of the other experience measures. This is especially true overall (Table 5.19) and in both the high-tech and low-tech sectors (Tables 5.20 and 5.21) as summarized in Table 5.23.

Table 5.20. Estimated Positive Marginal Effects from Equation (6.1) for Firms in the High-tech Sector, Country Held Constant

design market cust sup compet inst univ labs rd fairs jour nat EU strat rdagg coop

age

emp

found

educ

exp

nasexp

nasocc

nse nse nse nse .0197* .0133* .0237*** nse nse nse nse nse nse .0160* nse nse

nse nse nse nse nse nse nse nse nse nse nse nse nse nse nse nse

nse nse nse nse nse .0507** .0514** nse .0676** nse nse nse nse nse nse nse

.0217*** nse nse nse nse .0116*** .0098** nse .0313*** nse nse nse nse nse .0252*** .0085*

.0063*** .0046** nse nse nse nse nse nse nse nse nse nse nse nse nse nse

nse nse nse nse nse nse nse nse nse nse nse nse nse nse nse nse

nse nse .1091* nse nse .1885*** .2003*** nse nse nse nse nse nse .1668** .2133*** .1795**

n = 420 Note: *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level; nse = no statistical effect. Please see the key to table 5.18.

Table 5.21. Estimated Positive Marginal Effects from Equation (6.1) for Firms in the Low-tech Sector, Country Held Constant

design market cust sup compet inst univ labs rd fairs jour nat EU strat rdagg coop

age

emp

found

educ

exp

nasexp

nasocc

nse nse nse nse nse nse nse nse nse nse .0091* nse nse nse nse nse

nse nse nse nse nse nse nse nse nse nse nse nse nse .0007** nse nse

nse nse nse nse nse nse nse nse nse nse nse nse nse nse nse .0231*

nse nse nse nse nse nse .0048* nse .0085** nse .0119*** nse nse .0079*** .0065*** nse

nse .0035*** .0016** nse nse .0017** nse nse nse nse nse nse nse nse nse nse

nse –.0754***

nse –.063* .0608* nse nse nse nse nse nse nse nse nse nse nse .0562** nse

.0848*** nse nse nse nse nse nse nse nse nse nse .0294* nse

n = 1,602 Note: *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level; nse = no statistical effect. Please see the key to table 5.18.

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Sources of Knowledge & Entrepreneurial Behavior

Table 5.22. Estimated Positive Marginal Effects from Equation (6.1) for Firms in the KIBS Sector, Country Held Constant age

emp

found

educ

exp

nasexp

nasocc

design

nse

nse

.0456***

nse

nse

nse

market

nse

nse

nse

nse

.0020*

nse

nse

cust

nse

nse

nse

nse

nse

nse

nse

sup

nse

nse

nse

nse

nse

nse

nse

compet

nse

.0004*

nse

nse

nse

.0633*

nse

inst

nse

nse

nse

.0066***

.0031***

nse

.0948***

univ

nse

nse

nse

.0112***

.0023***

nse

.1489***

labs

nse

nse

nse

nse

.0014*

nse

.0999***

rd

nse

nse

nse

.0108***

nse

nse

.1061***

fairs

nse

nse

nse

nse

nse

nse

nse

jour

nse

nse

nse

.0098***

.0034***

nse

.1053***

nat

nse

nse

nse

.0075***

.0013*

nse

.0779***

EU

nse

nse

nse

nse

nse

nse

strat

nse

nse

.0339**

.0124***

nse

rdagg

nse

nse

.0206**

.0113***

.0012*

nse

.1381***

coop

nse

nse

nse

nse

nse

nse

nse

–.0732**

.0724*** nse

n = 1,982 Note: *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level; nse = no statistical effect. Please see the key to table 5.18.

Our intent is to focus not on the estimated value of any particular marginal effect, but on the pattern of behavior revealed in these tables, which motivates our segue into Chapter 6. In a sense, the estimated marginal effects approximate the partial correlations between experience and knowledge source, other factors (such as country and industry sector) held constant.

Sources of Knowledge

115

Table 5.23. Positive and Statistically Significant Relationship between Education and Experience of First-listed Founder and Knowledge Sources, Based on Equation (6.1) Education (educ)

Experience in Current Sector (exp)

Sector

Sector

High-tech design

Low-tech

KIBS

√

High-tech

Low-tech

KIBS

√

√

√ √

market

√

cust sup compet inst

√

univ

√

√ √

√

√ √ √

labs rd

√

√

√

√

√

√

√

√

√

fairs jour nat EU strat rdagg

√

coop

√

√

√

√

√

√

Please see the key to table 5.18.

Conclusions The takeaway from the tables we have presented above is that the Experience → Knowledge relationship from Figure 1.1, reproduced here as Figure 5.17, is strongest when experience is measured in terms of the educational level of the first-listed founder (educ). This conclusion was not unexpected. We highlighted in Table 5.18 the positive and statistically

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Sources of Knowledge & Entrepreneurial Behavior

Figure 5.17. Antecedents of Entrepreneurial Behavior from an Epistemological Perspective Sensations and Reflections

Experience

Knowledge

Entrepreneurial Behavior

Education Source: Prepared by the authors.

significant correlation coefficients between education of the first-listed founder (educ) and all of the sources of knowledge. No other experience variable is correlated as many times.4

Sources of Knowledge

117

Appendix 5.A AEGIS Survey Questions Related to Knowledge Sources

Knowledge sources for the formation of the firm Design knowledge (Table 5.1 and Figure 5.1): The survey question was: “Please indicate the importance of design knowledge for the formation of the firm on a 5-point scale, where 1 is not important and 5 is extremely important.” Knowledge of the market (Table 5.2 and Figure 5.2): The survey question was: “Please indicate the importance of knowledge of the market for the formation of the firm on a 5-point scale, where 1 is not important and 5 is extremely important.”

Knowledge sources for exploring new firm opportunities Customers (Table 5.3 and Figure 5.3): The survey question was: “Please evaluate the importance of customers as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Suppliers (Table 5.4 and Figure 5.4): The survey question was: “Please evaluate the importance of suppliers as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Competitors (Table 5.5 and Figure 5.5): The survey question was: “Please evaluate the importance of competitors as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Public research institutes (Table 5.6 and Figure 5.6): The survey question was: “Please evaluate the importance of public research institutes as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Universities (Table 5.7 and Figure 5.7): The survey question was: “Please evaluate the importance of universities as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” External commercial labs (Table 5.8 and Figure 5.8): The survey question was: “Please evaluate the importance of external commercial labs / R&D firms / technical institutes as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.”

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In-house R&D (Table 5.9 and Figure 5.9): The survey question was: “Please evaluate the importance of in-house know how (R&D laboratories in your firm) as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Trade fairs, conferences, and exhibitions (Table 5.10 and Figure 5.10): The survey question was: “Please evaluate the importance of trade fairs, conferences, and exhibitions as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Scientific journals and other trade or technical publications (Table 5.11 and Figure 5.10): The survey question was: “Please evaluate the importance of scientific journals and other trade or technical publications as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Participation in nationally funded research programs (Table 5.12 and Figure 5.12): The survey question was: “Please evaluate the importance of participation in nationally funded research programs as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.” Participation in EU Framework Programs (Table 5.13 and Figure 5.13): The survey question was: “Please evaluate the importance of participation in EU funded research programs (Framework Programs) as a source of knowledge for exploring new firm opportunities on a 5-point scale, where 1 is not important and 5 is extremely important.”

General sources of knowledge Strategic alliances (Table 5.14 and Figure 5.14): The survey question was: “Please indicate to what extent your firm has participated in strategic alliances on a 5-point scale, where 1 is not at all and 5 is very often.” R&D agreements (Table 5.15 and Figure 5.15): The survey question was: “Please indicate to what extent your firm has participated in R&D agreements on a 5-point scale, where 1 is not at all and 5 is very often.” Technical cooperative agreements (Table 5.16 and Figure 5.16): The survey question was: “Please indicate to what extent your firm has participated in technical cooperative agreements on a 5-point scale, where 1 is not at all and 5 is very often.”

chapter six

Sources of Knowledge and Entrepreneurial Behavior

The greatest enemy of knowledge is not ignorance; it is the illusion of knowledge. – Stephen Hawking Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information on it. – Samuel Johnson

The Behavior of Entrepreneurial Firms It is important to keep in mind that the many roles of an entrepreneur that Hébert and Link (2009) define – previously discussed in Chapter 3 and listed in Table 6.1 – are based on their extensive review of the extant literature spanning centuries of scholarly thought about who an entrepreneur is and what he or she does. To illustrate, Savary’s Dictionnaire Universel de Commerce (1723) defined an entrepreneur simply as one who undertakes a project; a manufacturer; a master builder (cited in Hébert and Link 2009). But, as documented by Hoselitz (1960), the French used a form of the word, entreprendeur, as early as the fourteenth century. Taking the Hébert and Link listing in Table 6.1 as historically complete – even though some more contemporary definitions of an entrepreneur do not associate his or her actions with being innovative and risk taking – it follows logically, or so we contend, that an entrepreneurial firm is one that exhibits one or more of the behavioral or performance characteristics

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Sources of Knowledge & Entrepreneurial Behavior

Table 6.1. The Roles and Responsibilities of an Entrepreneur An entrepreneur is the person who assumes the risk associated with uncertainty the person who supplies financial capital an innovator a decision maker an industrial leader a manager or superintendent an organizer and coordinator of economic resources the owner of an enterprise an employer of factors of production a contractor an arbitrageur an allocator of resources among alternative uses Source: Hébert and Link (2009).

so noted in the table. As we were constrained by the availability of data constructs in the AEGIS database, however, we were unable to map either experience or sources of knowledge into the myriad dimensions that might represent the behavior of an entrepreneurial firm. Alternatively, we selected one dimension of firm behavior through which to examine the antecedents of entrepreneurial behavior from both a human capital perspective – Experience → Entrepreneurial Behavior – and from the KSTE or epistemological perspective – Experience → Knowledge → Entrepreneurial Behavior. The manifestation of entrepreneurial behavior that we consider in this chapter is firm performance as measured by the change in firm sales over time. This metric is not only a meaningful measure of performance that most firms reported in the AEGIS survey; it is also of topical relevance. Recall from Chapter 3 that information in the AEGIS database was collected over the period of late 2010 to early 2011. EU countries, much like the experience of the United States, were just beginning to recover from a devastating economic recession during that time. Clearly, one metric that measures relevant entrepreneurial behavior is how KIE firms responded to the economic slowdown. The relevant AEGIS survey question was: “Please estimate the average increase/decrease in firm sales over the period of 2007–2009,” during which, as the European Commission (2009) documents, the economic recession in EU countries was most severe. As Table 6.2 reports, the increase in the growth in sales, as shown in the bottom-right cell of the table, was 13.80 per cent over that period, although mean growth in the low-tech sector was about one-half of that in the high-tech sector and appreciably less than in the KIBS sector (see also Figure 6.1. Although there were

Sources of Knowledge and Entrepreneurial Behavior 121 Table 6.2. Mean Percentage Change in Firm Sales, by Country and Industrial Sector, 2007–09 Sector High-tech

Low-tech

Country

KIBS

Overall

(percentage change)

Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

−4.83 11.48 37.65 13.22 13.66 2.23 17.09 10.13 60.21 29.36 18.99

−6.81 8.23 11.01 9.20 25.82 −0.20 8.50 4.98 23.08 10.28 9.13

5.06 7.12 14.78 20.18 19.61 −0.79 18.06 10.31 24.88 18.17 16.46

−3.50 8.18 16.35 15.57 20.67 −0.26 12.75 7.55 27.89 16.44 13.80

n = 4,004

Figure 6.1. Mean Percentage Change in Firm Sales, by Country and Industrial Sector, 2007–09 70 60 50 40 30 20 10 0 -10

Croatia

Czech Denmark Republic

France

Germany

Greece

Italy

-20 High-Tech

n = 4,004

Low-Tech

KIBS

Portugal

Sweden

United Kingdom

122

Sources of Knowledge & Entrepreneurial Behavior

differences across sectors in each country, those in Denmark and Sweden are perhaps the most noticeable, with the average growth of sales in high-tech firms between three and four times that in low-tech firms in these countries.

The Experience → Entrepreneurial Behavior relationship We referred to Figure 1.3, reproduced here as Figure 6.2, in our discussion of the antecedents of entrepreneurial behavior from a human capital perspective. We also referred to this figure to introduce a new notation, the strength of the direct relationship (the dashed line in the figure) between experience and entrepreneurial behavior, represented by γ2. In Chapter 4 we measured the experience base of each firm in a number of different ways, including the age of the firm, its number of employees, the number of firm founders, the years of education of the firm’s first-listed founder, and the years of experience in the current sector of the firm’s first-listed founder. However, after examining the statistical relationship between these metrics associated with the experience base of a firm and its reliance on alternative sources of knowledge as defined in Chapter 5, it appears to us that there is only one appropriate measure of the experience base of a firm, and that is years of education of the firm’s first-listed founder; see Tables 5.19 through 5.23 and the related discussion. Looking ahead, we approximate a measure of the statistical relationship between experience and entrepreneurial behavior (γ2) and that between sources of knowledge and entrepreneurial behavior (γ1). To make meaningful comparisons, measures of both experience and sources of knowledge should be quantified in similar units. In Chapter 4 education (educ) was measured in years. As we explained in Chapter 5, each knowledge source is measured in terms of its relative importance or use on the basis of a five-point Likert scale, but we interpreted those measures dichotomously: the response was either greater than 3 (important) or 3 or less (not important). To ensure a statistically meaningful comparison of the Experience → Entrepreneurial Behavior model and the Experience → Knowledge → Entrepreneurial Behavior model, given the caveats that are fundamentally associated with measuring multifaceted concepts such as experience and knowledge in the first place, we defined a new variable: Deduc, which equals 1 if the educational level of the first-listed founder of the firm is greater than the overall mean educational level of 14.86 years, and 0 otherwise (see Table 4.4). The D in the name denotes that the variable is dichotomous (0 or 1).

Sources of Knowledge and Entrepreneurial Behavior  123 Figure 6.2. Antecedents of Entrepreneurial Behavior from a Human Capital Perspective Sensations and Reflections Experience

Knowledge

Entrepreneurial Behavior

Education

To estimate the strength of the direct relationship between experience (Deduc) and entrepreneurial behavior, as measured in the growth in firm sales (salesgr), Experience → Entrepreneurial Behavior, γ2, we considered a model of the general form: Entrepreneurial Behavior = f (Experience, Country, Sector);  salesgr = f (Deduc, Country, Sector).

(6.1a) (6.1b)

The vectors Country and Sector are self-explanatory. As we discussed in earlier chapters, the constructs we study in this book – experience and sources of knowledge – are heterogeneous in nature and thus our analyses control for cross-country and cross-sector effects. The estimated coefficient on Deduc can be interpreted to approximate the strength of the direct relationship between experience and entrepreneurial behaviour, as represented in Figure 6.2 as Experience → Entrepreneurial Behavior, γ2. We present the regression results from a linear specification of equation (6.1) in Table 6.3. Clearly, the estimated coefficient on Deduc is positive and statistically significant. The statistical significance of Deduc across all industrial sectors is driven by the influence of Deduc on the growth in sales (salesgr) in the high-tech sector. The estimated coefficients on Deduc in the low-tech and KIBS sectors are positive, but not statistically significant. As in the previous chapter, we report only those coefficients or effects that are statistically significant; we interpret non-statistically significant coefficients or effects to be equal to zero. We conclude from Table 6.3 that an initial approximation of the overall strength of the direct relationship between experience and entrepreneurial behavior, γ2, is 4.88, a value we use as our empirical approximation of the extant human capital perspective of entrepreneurial behavior. Below,

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Sources of Knowledge & Entrepreneurial Behavior

Table 6.3. Estimated Direct Effect of Experience → Entrepreneurial Behavior Based on a Linear Version of Equation (6.1b) Estimated Direct Effect Sector Variable

High-tech

Low-tech

KIBS

Overall

Deduc

17.43***

nse

nse

4.88**

Controls

Country

Country

Country

Country, Sector

n = 4,004 Note: salesgr is conditional on the commercialization of the firm. *** significant at the .01 level; ** significant at the .05 level; nse = no statistical effect. Key: The variable Deduc = 1 if the educational level of the first-listed founder of the firm is greater than the overall mean educational level of 14.86 years, and 0 otherwise. See Table 5.4.

we compare this value to alternative values that approximate the KSTE perspective of entrepreneurial behavior, γ1. The alterative values correspond to the alternative ways we have measured sources of knowledge. The strength of the direct relationship between experience and entrepreneurial behavior in high-tech firms is 17.43. Among firms in the lowtech and KIBS sectors, experience has no measurable direct effect on entrepreneurial behavior.

The Experience → Knowledge → Entrepreneurial Behavior relationship Based on the empirical findings in Chapter 5, we reduced our emphasis on experience to a single variable that measures whether a firm’s firstlisted founder has an educational level above or below the mean level of all first-listed founders in the AEGIS database, Deduc. With respect to sources of knowledge, however, there are sixteen alterative measures to consider (see Chapter 5, especially Appendix 5.A. As in the previous section, to estimate the strength of the relationship Experience → Knowledge → Entrepreneurial Behavior, we considered a model of the general form Entrepreneurial Behavior = f (Knowledge, Country, Sector), salesgr = f (knowledgei, Country, Sector)

(6.2a) (6.2b)

Sources of Knowledge and Entrepreneurial Behavior 125

for i = 1 – 16, reflecting the sixteen alternative sources of knowledge. Only the variable knowledgei enters equations (6.2a) and (6.2b). Deduc is not in either specification because, according to the KSTE, experience affects the choice of sources of knowledge on which to rely, and thus how the variable knowledgei is measured (for example, 1 – 5 reduced to 0/1) already reflects the influence of experience. Recall that the sixteen knowledge variables were measured in terms of their relative importance or use. As with education, we dichotomized each knowledge variable to equal 1 if its relative importance or use was greater than 3, and 0 otherwise. Thus, as with the newly defined education variable (Deduc), we note each knowledge variable with a D to denote that it is dichotomously measured. The estimated coefficient on each of the sixteen knowledge variables can be interpreted to approximate the strength of the direct effect of the Experience → Knowledge → Entrepreneurial Behavior relationship, which we previously referred to as reflecting a KSTE perspective or an epistemological perspective of entrepreneurial behavior. Table 6.4 shows the results from the linear specifications of equation (6.2b) using each of the sixteen sources of knowledge to represent the variable knowledgei. The specifications of equation (6.2b) were estimated to obtain overall approximations of the direct effects of each knowledge variable on entrepreneur behavior, γ1, as well as approximations of the direct effects by industrial sector, as was done in Table 6.3. Overall, the Experience → Knowledge → Entrepreneurial Behavior relationship has a positive direct effect on nine of the sixteen knowledge measures. In seven of the nine cases, the approximation of the direct effect of the Experience → Knowledge → Entrepreneurial Behavior relationship is greater than the approximation of the direct effect of the Experience → Entrepreneurial Behavior relationship (that is, 4.88 from Table 6.3), the two exceptions being competitors as a source of knowledge for exploring new firm opportunities (Dcompet) and in-house R&D as a source of knowledge for exploring new firm opportunities, (Drd). Selecting among high-tech firms, we can come to the same conclusion, although at the sectoral level more often than not the approximation of the direct effect of the Experience → Knowledge → Entrepreneurial Behavior relationship is zero. Those seven instances in which the direct effect of knowledge on entrepreneurial behavior is greater than the direct effect of experience on entrepreneurial behavior correspond to the following sources: universities (Duniv), fairs (Dfairs), nationally funded research programs (Dnat), EU Framework Programs (DEU), strategic alliances (Dstrat), R&D

Table 6.4. Estimated Direct Effect of Experience → Knowledge on Entrepreneurial Behavior Based on a Linear Version of Equation (6.2b) Estimated Direct Effect Sector Variable

High-tech

Ddesign Dmarket Dcust Dsup Dcompet Dinst Duniv Dlabs Drd Dfairs Djour Dnat DEU Dstrat Drdagg Dcoop Controls

nse nse nse nse nse nse nse nse nse nse nse nse nse 27.89*** nse nse Country

Low-tech nse nse nse 3.15* nse nse nse nse nse 4.49** 3.27* 5.94** nse 7.69*** nse 7.35*** Country

KIBS

Overall

nse nse nse nse 5.41** nse 10.55* nse 7.87*** 8.89*** 4.49* 9.64** 8.22** nse 16.87*** 8.49** Country

nse nse nse nse 4.02*** nse 5.22** nse 4.01** 4.99*** nse 6.60*** 5.47** 7.00*** 11.69*** 8.36*** Country, Sector

n = 4,004. *** significant at the .01 level; ** significant at the .05 level; nse = no statistical effect. Key: Ddesign = design knowledge as a factor for the formation of the firm Dmarket = market knowledge as a factor for the formation of the firm Dcust = customer knowledge as a factor for the formation of the firm Dsup = supplier knowledge as a factor for the formation of the firm Dcompet = competitors as a source of knowledge for exploring new business opportunities Dinst = public research institutes as a source of knowledge for exploring new business opportunities Duniv = universities as a source of knowledge for exploring new business opportunities Dlabs = external commercial labs, R&D firms, and/or technical institutes as a source of knowledge for exploring new business opportunities Drd = in-house R&D as a source of knowledge for exploring new business opportunities Dfairs = trade fairs, conferences, and exhibitions as a source of knowledge for exploring new business opportunities Djour = scientific journal or other trade or technical publications as a source of knowledge for exploring new business opportunities Dnat = participation in nationally funded research programs as a source of knowledge for exploring new business opportunities DEU = participation in EU Framework Programs as a source of knowledge for exploring new business opportunities Dstrat = participation in strategic alliances as a general source of knowledge Drdagg = participation in R&D agreements as a general source of knowledge Dcoop = participation in technical cooperative agreements as a general source of knowledge

Sources of Knowledge and Entrepreneurial Behavior 127 Table 6.5. Comparison of the Human Capital and KSTE Perspectives on Entrepreneurial Behavior Perspectives/Effects

Approximation

Human capital perspective γ2: Direct effect Experience → Entrepreneurial Behavior

4.86

KSTE perspective γ1: Effect Experience → Knowledge → Entrepreneurial Behavior

6.37

Note: The effect of the Experience → Knowledge → Entrepreneurial Behavior is based on the mean of the nine significant direct effect across 16 sources of knowledge in Table 6.4.

agreements (Drdagg), and technical cooperative agreements (Dcoop) as sources of knowledge for exploring new firm opportunities. If we were to generalize based on a comparison of the findings from the estimations of equations (6.1b) and (6.2b), the evidence we have presented could be interpreted to mean that the KSTE perspective on entrepreneurial behavior is a more likely explanation of entrepreneurial behavior than is the human capital perspective. Stated alternatively, there appears to be relatively more information available, at least based on information from the AEGIS database, that experience that works through the choice of sources of knowledge dominates experience that has a direct effect of entrepreneurial behavior. See Table 6.5 for numerical comparisons of these two perspectives. The approximate strength of the human capital perspective comes from Table 6.3; the approximate strength of the KSTE perspective comes from averaging the statistically significant effects in Table 6.4 In Chapter 2 we referred to the Localization Hypothesis (Audretsch, Keilbach, and Lehmann 2006, 50): “Knowledge spillover entrepreneurship will tend to be spatially located within close geographic proximity to the source of knowledge actually producing that knowledge.” In our view, participation in strategic alliances, in R&D agreements, and in technical cooperative agreements is “closer” to the firm than is participation in nationally funded research programs or in EU Framework Programs, where “closer” refers to proximity because a firm’s resources are directly involved. It should therefore be no surprise that these three so-called participation relationships have a considerably greater effect on entrepreneurial behavior, as measured in terms of growth in sales, than have the latter two relationships. Similarly, it should be no surprise, based on

128

Sources of Knowledge & Entrepreneurial Behavior

the Localization Hypothesis, that these so-defined local sources represent the greater numerical gap between the approximated strength of the knowledge / entrepreneurial behavior relationship and the strength of the experience / entrepreneurial behavior relationship (4.88). In other words, (γ1 – 4.88) is numerically greater when γ1 is represented by the approximate strength of Dstrat, Drdagg, and Dcoop on the salesgr relationship. Gender and Entrepreneurial Behavior There is a vast literature related to gender and the economic performance of entrepreneurial firms. To generalize, the empirical evidence is mixed as to how female-owned entrepreneurial firms perform relative to their male-owned counterparts. Much of the extant literature on this topic is fragmented; it is based on specific samples of data relative to specific countries, and this fact makes generalizations about the performance of female- versus male-owned entrepreneurial firms difficult. We summarize a portion of this literature in Appendix 6.A. Suffice it to say, as motivation for this section of the chapter, a gender-based comparison of the antecedents of entrepreneurial behavior has been conspicuously neglected and warrant further investigation. Our analysis not only expands the body of thought summarized in Appendix 6.A; it also introduces a new dimension into the debate, which we hope to stimulate through this book, between the human capital perspective and the KSTE or epistemological perspective of entrepreneurial behavior. As well, our analysis sets the stage for a related policy discussion about gender and entrepreneurship in EU countries, where the topic is one of policy importance. More than a decade ago, and certainly well before the economic recession that plagued EU countries in the latter 2000s, the OECD noted: “[It is critically important to] improve the factual and analytical underpinnings of the role of women entrepreneurs in the [EU] economy … women entrepreneurs play an important role in the entrepreneurial economy, both in their ability to create jobs for themselves and to create jobs for others” (Organisation for Economic Co-operation and Development 2004, 6). More recently a World Bank study echoed the OECD’s sentiments: “Entrepreneurship … is important from the perspective of job creation, private sector development, and wealth creation [in Central and Eastern Europe and Central Asia]. Women’s participation in entrepreneurship can enhance the expansion of these economic goods and simultaneously lead to less inequality in the two largest sub-

Sources of Knowledge and Entrepreneurial Behavior 129

groups in the population: men and women” (Sattar 2012, 63). And with respect to issues related not only to a better understanding of gender entrepreneurship, but also to attendant policies to enhance it, the OECD took offered the following perspective (Adema et al. 2014, 21): Policy makers wishing to strengthen the economic impact of women entrepreneurs need a better understanding of the factors contributing to the growth and success of female-owned firms. … Policies that foster female entrepreneurship often come under the umbrella of programmes for small enterprises. However, they are likely to impact relatively strongly on women entrepreneurs, since most run small businesses. A mix of general policies for SMEs and instruments explicitly targeting women can be effective in prompting interest and entry into entrepreneurship.

Overall, as shown in the bottom-right cell of Table 6.6, 15.21 per cent of the KIE firms in the AEGIS database had a first-listed founder who was female. Because the overall mean number of founders is 1.41, and because across countries and industrial sectors the mean number of founders is never greater than 2.0 (see Table 4.3), using the number of first-listed founders to quantify the percentage of female-founded firms in a country or sector is not an unreasonable first-order approximation. Table 6.6 and Figure 6.3 show that this share varied across industrial sectors, with only 8.57 per cent of high-tech firms having a female firstlisted founder versus 18.60 per cent of low-tech firms, and also across countries, ranging from a low of 8.16 per cent in Greece to a high of 23.26 per cent in Portugal, which might reflect cross-country variations in the distribution of particular industries – a topic that merits additional study. Is gender related to entrepreneurial performance? More precisely, is gender related to the human capital perspective of entrepreneurial behavior or to the KSTE perspective? As Table 6.7 shows (bottom-right cells), mean growth in sales over the 2007–09 period was greater in firms with a first-listed founder who was male (14.21 per cent growth) than in firms with a first-listed founder who was female (11.47 per cent growth). This comparative relationship holds for firms in both the high-tech and low-tech sectors (Figure 6.4), but firms in the KIBS sector with a female first-founder enjoyed slightly higher growth in sales over the period than those with a male first-founder (16.63 per cent versus 16.44 per cent). Across countries, firms with a female first-listed founder enjoyed greater sales growth than those with a male first-founder in

130

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Table 6.6. Mean Percentage of Firms with a Female First-Listed Founder, by Country and Industrial Sector Sector High-tech

Low-tech

Country

KIBS

Overall

(mean percentage of firms)

Croatia

11.43

26.09

10.00

19.50

Czech Republic

4.00

8.69

14.46

10.50

Denmark

2.94

17.39

12.78

12.73

10.29

22.45

13.73

16.32

Germany

7.46

15.00

9.09

10.59

Greece

9.09

4.61

8.80

8.16

Italy

12.28

21.20

18.36

19.31

Portugal

12.90

28.82

18.46

23.26

Sweden

5.88

20.37

17.71

17.37

United Kingdom

6.38

14.83

15.06

14.19

Overall

8.57

18.60

13.87

15.21

France

n = 4,004

Figure 6.3. Mean Percentage of Firms with a Female First-Listed Founder, by Country and Industrial Sector 35 30 25 20 15 10 5 0 Croatia

Czech Denmark Republic

France

Germany

High-Tech

n = 4,004

Greece

Low-Tech

Italy KIBS

Portugal

Sweden

United Kingdom

Sources of Knowledge and Entrepreneurial Behavior 131 Table 6.7. Mean Percentage Change in Firm Sales, by Gender, Country, and Industrial Sector, 2007–09 Sector High-tech

Low-tech

KIBS

Overall

Gender of First-listed Firm Founder Male

Female Male

Female Male

Female Male

Female

(mean percentage change)

Country Croatia Czech Republic Denmark France Germany Greece Italy Portugal Sweden United Kingdom Overall

−7.06 11.96 38.79 13.03 13.55 4.95 12.88 11.63 62.56 30.30 19.21

12.50 – – 14.86 15.00 −25.00 47.14 0.00 22.50 15.67 16.69

−4.94 7.38 10.79 10.21 21.82 0.21 10.45 7.42 24.51 11.21 9.83

−12.10 17.13 12.08 5.70 48.46 −5.14 1.24 −1.06 17.50 4.82 6.08

6.56 8.65 14.91 21.68 18.36 0.18 13.27 10.64 24.84 20.04 16.44

−8.40 −1.92 13.90 10.76 32.03 −10.91 39.37 8.88 25.06 7.64 16.63

−2.14 8.50 16.83 16.92 18.71 0.51 11.73 9.21 29.11 18.00 14.21

−9.10 5.43 13.05 8.68 37.27 −8.96 17.04 2.09 22.10 6.97 11.47

n = 4,004 Note: In both the Czech Republic and Demark, there was only one firm with a female first-founder in the high-tech sector.

Figure 6.4. Mean Percentage Change in Firm Sales, by Gender and Industrial Sector, 2007–09 20

15

10 High-tech 5

0 Male Female

n = 4,004

Low-tech

KIBS

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Sources of Knowledge & Entrepreneurial Behavior

Table 6.8. Estimated Direct Effect of Experience → Entrepreneurial Behavior, by Gender and Industrial Sector Estimated Direct Effect Sector High-tech

Low-tech

KIBS

Overall

Gender of First-listed Firm Founder Male

Female

Deduc

21.66***

nse

Controls

Country

Variable

Male

Female

nse

nse

Country

Male

Female

7.87***

nse

Country

Male

Female

7.59***

nse

Country, Sector

n = 4,004 *** significant at the .01 level; nse = no statistical effect.

both Germany (37.27 per cent versus 18.71 per cent) and Italy (17.04 per cent versus 11.73 per cent). Recall from Table 6.3 that, overall, as well as in high-tech firms, Experience, measured in terms of education, Deduc, is positively related to Entrepreneurial Behavior, measured in terms of growth in sales (salesgr), through equation (6.1b). We suggest that this model is appropriate for approximating the strength of the extant human capital perspective on entrepreneurial behavior as represented by γ2. However, when the sample of firms is divided on the basis of the gender of the firstlisted founder, in no instance is the education of female-founded firms statistically related to growth in sales (see Table 6.8). Overall, among firms in the high-tech and KIBS sectors with a male first-listed founder, education is positively and statistically related to sales growth. We interpret these findings to mean that the extant human capital perspective on entrepreneurial behavior is relevant among firms with a first-listed male founder in all industrial sectors, but that this perspective simply does not hold for firms in the low-tech sector whose first-listed founder is female. Recall that we identified in Table 6.4 those sources of knowledge that are significantly related to sales growth, and presented the estimated effects of knowledge on sales by industrial sector, based on equation (6.2b). To re-estimate the direct effects of the sixteen sources of knowledge on sales, we divided the sample of KIE firms on the basis of the

Sources of Knowledge and Entrepreneurial Behavior 133 Table 6.9. Estimated Effect of Experience → Knowledge → Entrepreneurial Behavior, by Gender and Industrial Sector Estimated Direct Effect Sector High-tech

Low-tech

KIBS

Overall

Gender of First-listed Firm Founder Variable

Male

Female

Male

Female

Male

Female

Male

Female

Ddesign

nse

nse

nse

nse

nse

nse

3.40**

nse

Dmarket

nse

nse

nse

nse

nse

nse

nse

nse

Dcust

nse

nse

nse

nse

nse

nse

nse

nse

Dsup

nse

39.77**

nse

8.43*

nse

nse

nse

8.77*

Dcompet

nse

nse

nse

nse

6.66**

nse

5.17***

Dinst

nse

nse

nse

nse

nse

nse

nse

nse

Duniv

nse

nse

nse

nse

7.78**

27.38**

nse

12.81**

Dlabs

nse

nse

nse

nse

nse

nse

nse

nse

Drd

nse

nse

nse

nse

7.83***

nse

4.81***

nse

Dfairs

nse

nse

5.95***

nse

6.56**

19.96**

4.56**

nse

Djour

nse

nse

nse

Dnat

nse

39.19*

DEU

nse

nse

Dstrat

26.89***

93.32***

Drdagg

nse

48.83*

Dcoop Controls

nse Country

nse

nse

10.03*

nse

nse

nse

nse

5.81**

nse

nse

32.01**

4.38*

19.43***

nse

14.47**

nse

43.66***

nse

27.60***

6.40**

12.61*

nse

nse

7.23***

nse 23.41***

nse 11.07*** Country

nse

14.25***

39.59**

9.95***

nse

7.27** Country

nse

8.02*** nse Country, Sector

n = 3,809 *** significant at the .01 level; ** significant at the .05 level; * significant at the .10 level; nse = no statistical effect. Key: See key to Table 6.4

gender of the first-listed founder and re-estimated equation (6.2b). The results are in Table 6.9. From Table 6.9, and again treating an estimated coefficient that is not statistically significant as meaning that source of knowledge has zero effect on entrepreneurial behavior, sales growth was higher in firms with

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Sources of Knowledge & Entrepreneurial Behavior

a female first-listed founder that relied on supplier knowledge (Dsup), universities (Duniv), nationally funded research programs (Dnat), EU Framework Programs (DEU), and R&D agreements (Drdagg). Firms with a male first-listed founder, in contrast, relied on design knowledge (Ddesign), knowledge from competitors (Dcompet), in-house R&D (Drd), trade fairs, conferences, and exhibitions (Dfairs), strategic alliances (Dstrat), R&D agreements (Drdagg), and technical cooperative agreements (Dcoop). It appears from Table 6.9 that some sources of knowledge, based on equation (6.2b), did not influence sales growth regardless of the gender of the firm’s founder: market knowledge (Dmarket), customer knowledge (Dcust), public research institutes (Dinst), external commercial labs (Dlabs), and scientific journals or other trade or technical publications (Djour). Still with reference to Table 6.9, firms with a female first-listed founder appear, in general, to have had an advantage over their malefounded counterparts in terms of a selected set of sources of knowledge, but the importance of those sources varies by industrial sector. For example, among high-tech firms, the relevant sources were supplier knowledge (Dsup), nationally funded research programs (Dnat), strategic alliances (Dstrat), and R&D agreements (Drdagg); in each instance, relying on these sources had a greater effect on sales growth among firms with a female first-listed founder than among firms with a male founder. Among low-tech firms, reliance on alternative sources varies by the gender of the first-listed founder. Firms with a male first-listed founder relied, on average, relatively more on knowledge from trade fairs (Dfairs), nationally funded research programs (Dnat), and technical cooperative agreements (Dcoop) for leveraging their sales growth. In comparison, firms witrh a first-listed female founder had, on average, greater sales growth from relying on knowledge from suppliers (Dsup), scientific journals (Djour), EU Framework Programs (DEU), and strategic alliances (Dstrat). In the KIBS sector, when both female- and male-founded firms benefited from the same knowledge sources – universities (Duniv), trade fairs (Dfairs), and R&D agreements (Drdagg) – female-founded firms appear to have leveraged that knowledge more effectively than malefounded firms, as evident from the larger and statistically significant marginal effect of knowledge on sales growth. There was an advantage – and to reiterate, a statistical advantage – in terms of sales growth among

Sources of Knowledge and Entrepreneurial Behavior 135 Table 6.10. Summary of the Human Capital and the KSTE Perspectives on Entrepreneurial Behavior, by Gender Approximation Perspectives/Effects

Male

Female

7.50

0.00

5.94

18.40

Human capital perspective Direct effect Experience → Entrepreneurial Behavior KSTE perspective Effect Experience → Knowledge → Entrepreneurial Behavior

Note: The effect of Experience → Knowledge → Entrepreneurial Behavior is based on the mean of the significant direct effect across sixteen sources of knowledge.

firms with a first-listed male founder in the use of knowledge from competitors (Dcompet), in-house R&D (Drd), and technical cooperative agreements (Dcoop). And, in this regard, the advantage went to firms with a female founder in terms of knowledge gained from participation in nationally funded research programs (Dnat) and EU Framework Programs (DEU). To distill the gender effect, in Table 6.10 we compare the human capital and the KSTE, or epistemological, perspectives on entrepreneurial behavior. The extant human capital perspective clearly dominated the KSTE perspective among firms with a male first-listed founder, but the KSTE perspective clearly dominated among firms with a female founder. This finding suggests that male entrepreneurs tend to have the competitive advantage in terms of human capital entrepreneurship; by contrast, female entrepreneurs tend to have the advantage in terms of knowledge spillover entrepreneurship. Concluding Observations The analyses presented in this chapter can be summarized as follows. Knowledge-intensive entrepreneurial firms rely on alternative sources of knowledge, but, in general (see Table 6.4), not all sources have the same effect on entrepreneurial behavior. When entrepreneurial behavior manifests itself as growth in sales, the types of knowledge sources that are relatively more important are: knowledge for exploring new business opportunities, and general knowledge.

136

Sources of Knowledge & Entrepreneurial Behavior

Regarding knowledge relied on for exploring new business opportunities, the relatively more important sources are: • • • • •

knowledge from competitors (Dcompet); knowledge from universities (Duniv); knowledge from in-house R&D (Drd); knowledge from trade fairs, conferences, and exhibitions (Dfairs); knowledge from participation in nationally funded research programs (Dnat); and • knowledge from participation in EU Framework Programs (DEU). Regarding general knowledge, the relatively more important sources are: • participation in strategic alliances (Dstrat); • participation in R&D agreements (Drdagg); and • participation in technical cooperative agreements (Dcoop). The overall effect of knowledge sources on growth in sales is enhanced through experience, as predicted by the KSTE. The antecedents of sales growth appear strongest among firms where education leverages the firm’s ability to choose those knowledge sources that are most effective in bringing about sales growth. And the importance of these various sources of knowledge varies not only across industrial sector, but also across firms on the basis of the gender of their first-listed founder. Overall, the KSTE perspective on entrepreneurial behavior is the dominant explanation; however, when firms are distinguished by the gender of their founder, there are mixed results about which is the dominant paradigm.

Appendix 6.A Literature on Gender and Entrepreneurial Behavior

Author(s)

Research Question

Finding(s)

Data Description

Aldrich, Elam, and Reese (1997)

Do gender-based differences in networking behavior affect access to entrepreneurial resources?

No, female entrepreneurs are as active and successful as male entrepreneurs.

Survey data from Research Triangle area of North Carolina

Alsos and Ljunggren (1998)

Does the business startup process differ by gender?

There are some gender differences, but they do not lead to lower startup probabilities for women.

Interview data from Norway

Aterido and HallwardDriemeier (2011)

Does gender account for productivity gaps across firms?

Results are sensitive to classification of enterprises. Men and women do differ in entrepreneurial characteristics, but this is not enough to explain the gender gap.

Enterprise Surveys

Bardasi, Sabarwal, and Terrell (2011)

Are there gender differences in firm performance?

Yes, women tend to have smaller firms due to concentrating in sectors with smaller, less efficient firms. There is no evidence of gender discrimination in access to credit.

World Bank Enterprise Survey

Barnir (2014)

Are there gender differences in the effects of entrepreneurial impetus and human capital on habitual entrepreneurship?

Human capital has a greater effect for women than for men. Men and women have different impetus factors related to new repeat ventures.

Panel Study of Entrepreneurial Dynamics II (US)

Bellu (1993)

What are the predictors of female entrepreneurial performance?

Task motivation and attributional style, such as attitudes toward failure, are predictors of success. Individual variables are just as important as environmental variables.

Interview data from greater New York area.

(Continued )

(Continued) Author(s)

Research Question

Finding(s)

Data Description

Birley (1989)

Are female entrepreneurs different from male entrepreneurs?

The motivations for becoming an entrepreneur are different for men and women.

Literature review

Boden and Nucci (2000)

What is the relationship between gender, business characteristics, and business survival?

Survival is greater for owners with ten or more years of work experience and four or more years of higher education, which women are less likely than men to have.

US Census Bureau’s Characteristics of Business Owners

Brush (1992)

What are the trends in research on women-owned businesses?

Mostly empirical research, which can be categorized into various themes. Future research should improve theoretical foundations.

Systematic literature review

Chaganti (1986)

What are the management styles of women entrepreneurs?

Women entrepreneurs have management styles consistent with a successful entrepreneur model, albeit more feminine.

Interview data from Pennsylvania

Chaganti and Parasuraman (1997)

How does gender affect business performance and management style?

Women-owned businesses have significantly smaller annual sales, and non-financial goals are relatively more important.

Survey data from northeastern United States

Chell and Baines (1998)

Does gender affect business performance?

No significant differences. Spouse-owned businesses underperform.

Interview and survey data from United Kingdom

Cohoon (2011)

Which gender differences matter for high-tech entrepreneurship?

Both male and female entrepreneurs are motivated by pecuniary rewards. Barriers to women’s entrepreneurship likely stems from the unequal distribution of successful traits, not discrimination.

Survey data from United States

Coleman (2005)

How does human capital affect the performance of women-owned small firms?

Women-owned firms are smaller than men-owned firms, controlling for human capital, but they are more profitable and have a higher sales growth rate.

Survey of Small Business Finances (US)

Coleman (2007a)

What is the relationship between human and financial capital and firm performance for small firms in the services and retail sectors? Are there gender differences?

Human capital positively affects the profitability of female-owned firms, whereas financial capital positively affects the profitability of male-owned firms.

Survey of Small Business Finances (US)

Coleman (2007b)

How do growing women-owned firms compare with non-growing women-owned firms?

Variables associated with growth, such as limited liability entities, are the same for both female- and male-owned firms.

Survey of Small Business Finances (US)

Coleman and Kariv (2013)

How do gender and financial strategy interact to affect firm performance?

Female and male entrepreneurs use different financial strategies, which affects firm performance.

Panel Study of Entrepreneurial Dynamics

Dautzenberg (2012)

Are there gender differences in the business ownership of technologybased firms?

Women are strongly underrepresented among high-tech firms. Female single entrepreneurs and entrepreneurial teams play only a minor role in technologybased industries.

Data from Germany

de los Dolores González and Husted (2011)

How does gender affect the number and innovativeness of business opportunities identified by future entrepreneurs?

Gender differences are not significant. People with greater prior knowledge of customer needs tend to identify more opportunities.

Survey data from northeastern Mexico

Dolinsky et al. (1993)

What is the effect of education on female business ownership?

Greater levels of education lead to the increased likelihood of females becoming and remaining entrepreneurs.

National Longitudinal Survey of Labor Market Experience (Continued )

(Continued) Author(s)

Research Question

Finding(s)

Data Description

Du Rietz and Henrekson (2000)

Do female entrepreneurs underperform compared with male entrepreneurs?

No, with appropriate control variables there is no gender difference, except in sales.

Survey data from Sweden

Fairlie and Robb (2009)

Are there gender differences in business performance? Why?

Yes, female-owned businesses have less startup capital and less human capital than do male-owned businesses.

Characteristics of Business Owners Survey (US)

Huarng, Mas-Tur, and Yu (2012)

What is the relationship between the skills of women entrepreneurs and their motivations, barriers, and performance?

Lack of education and managerial skills are the two most important variables determining success.

Survey data from Valencia, Spain

Jiang, Zimmerman, and Guo (2012)

What is the relationship between intangible resources and the growth of women-owned firms?

Social, human, and reputational capital are related to business growth, all of which are moderated by social competence.

Case studies from midAtlantic US states

Johnson and Storey (1993)

Do female-owned firms differ from male-owned firms?

Differences are small; women tend to be older, less well qualified, and less likely to access credit, and their businesses are smaller on average in terms of employees.

Survey data from United Kingdom

Kalleberg and Leicht (1991)

Are there gender differences in the survival of small businesses?

No, female-owned firms are no more likely to fail and are no less successful than male-owned firms.

Survey data from southcentral Indiana

Kalnins and Williams (2014)

Do gender differences in the survival rates of businesses depend on setting?

Yes, survival rates depend on industry and geographic area more than on gender.

Sales tax data from Texas

Koellinger, Minniti, and Schade (2013)

Are there gender differences in entrepreneurial propensity?

The lower rate of female business ownership is primarily due to a lower propensity to start businesses as opposed to differences in failure rates.

Global Entrepreneurship Monitor

Lee et al. (2009)

Do the critical success factors of women-owned business differ across countries?

Some differences exist, but might depend on government support. Business performance is positively related to the success of women-owned business.

Survey data from South Korea and United States

Lerner and Malach-Pines (2011)

What role do culture and gender play in family business?

There are significant cross-cultural differences in the owners of family businesses, but little difference between male and female owners of family businesses.

Global Entrepreneurship Monitor

Lewellyn and Muller-Kahle (2015)

Why are there gender differences in entrepreneurial activity?

Micro and macro factors interact to influence entrepreneurial activity among men and women.

Global Entrepreneurship Monitor

Loscocco and Robinson (1991)

What are the barriers to the success of female-owned small businesses?

Lack of access to capital and government contracts. Similar institutional barriers to women employees.

Literature review, US Treasury data

Manolova et al. (2012)

Why are there gender differences in the growth rates of nascent entrepreneurs?

Men grow their businesses for financial success, whereas women have other non-economic goals.

Panel Study of Entrepreneurial Dynamics (US)

McClelland et al. (2005)

Do traits of female entrepreneurs differ across countries?

Yes, traits of female entrepreneurs, such as motivation or growth strategies, differ across countries, although there are some commonalities. They can also differ within countries.

Cross-national case study data from Singapore, South Africa, Australia, New Zealand, Canada, and Ireland.

Nelson (1987)

What are the information needs of female entrepreneurs?

Female entrepreneurs do not have unique startup information needs. All kinds of information are equally important. Advice of a significant other is important.

Survey data from Dallas, Texas

(Continued )

(Continued) Author(s)

Research Question

Finding(s)

Data Description

Pablo-Martí, García-Tabuenca, and Crespo-Espert (2014)

Are there gender differences in entrepreneurial activities in Spain?

Male and female reasons for success and survival are the same, but significant differences exist in personal characteristics and motivations.

Survey data from Spain

ReichbornKjennerud and Svare (2014)

Do men and women entrepreneurs have different growth strategies?

Men and women have similar traits as entrepreneurs, but tend to differ in their values and growth strategies. Women are more interested in “staying small,” while men are more interested in growing their business.

Case studies from Norway

Riding and Swift (1990)

Are there gender differences in accessing credit?

Financing conditions are less favorable to women, but this stems from the nature of the businesses women pursue, not necessarily from gender bias.

Survey data from Canada

Robb and Watson (2010)

Are there gender differences in firm performance?

No, after controlling for demographic variables there are no gender differences in firm performance in terms of survival rates or return on assets, or in risk-adjusted terms.

Databases from United States and Australia

Robb and Watson (2012)

Are there gender differences in firm performance?

No, there is no difference if performance is measured appropriately.

Kauffman Firm Survey

Roomi, Harrison, and BeaumontKerridge (2009)

What factors influence the growth of women-owned small and mediumsized enterprises?

Most women opt not to develop growthoriented businesses. Women who do are blocked by access to resources such as capital, production inputs, and childcare.

Survey data from England

Rosa, Carter, and Hamilton (1996)

How is gender related to small business performance?

The relationship between gender and performance is complex, but gender is a significant determinant of performance. Female owners are less likely to own multiple businesses and or to plan expansion.

Survey data from United Kingdom

Shaw et al. (2009)

How do gender and entrepreneurial capital interact to affect firm performance?

Gender is socially constructed, which poses challenges for women entrepreneurs that are reflected in levels of entrepreneurial capital and firm performance.

Survey and interview data from United Kingdom

Singh, Reynolds, and Muhammad (2001)

Are there gender differences in firm performance?

Female entrepreneurs are concentrated in low-growth, informal sectors. Policies might need to be differentiated by gender.

Interview data from Java, Indonesia

Sonfield et al. (2001)

Are there gender differences in strategic decision making?

No, there are no significant gender differences in venture innovation/risk situation and strategy.

Survey data from United States

Verheul, Risseeuw, and Bartelse (2002)

Are there gender differences in entrepreneurial strategy and human resource management?

Yes, gender differences exist with respect to the path to entrepreneurship, growth levels, degree of diversification, and type of leadership.

In-depth interview data from the Netherlands

Watson (2002)

Are there gender differences in business performance?

No, there is no difference if controlling for input measurements, such as total income to total assets.

Business Growth and Performance Surveys (Australia)

Watson (2003)

Are there gender differences in failure rates?

No, there are no significant differences if controlling for industry.

Survey data from Australia

Yordanova and Davidkov (2009)

Do female and male entrepreneurs differ in certain characteristics?

Yes, female entrepreneurs are more likely to be younger, have a participative management style, and have smaller businesses.

Survey data from Bulgaria

Zolin, Stuetzer, and Watson (2013)

Do female-owned firms underperform compared with maleowned firms?

No, there is no evidence of significant underperformance.

Comprehensive Australian Study of Entrepreneurial Emergence

Source: Prepared by the authors, drawing in part from Link and Strong (2016).

chapter seven

Lessons Learned

As our circle of knowledge expands, so does the circumference of darkness surrounding it. – Albert Einstein “Begin at the beginning,” the King said, very gravely, “and go on till you come to the end: then stop.” – Lewis Carroll, Alice in Wonderland

Our Main Findings A generation ago, no one looked to knowledge as a source of competitive advantage and enhanced economic performance – entrepreneurial or otherwise. The leading scholarly journals in economics were replete with analyses of how to generate, manage, and harness the key ingredient delivering economic performance: physical capital. Whether in the form of factories, equipment, or plants, physical capital seemingly had little to do with what has been the focus of this book: knowledge. At the macroeconomic level, a great debate raged among the profession’s leading scholars about how best to induce investments in physical capital. Did investments in capital respond more robustly to lower interest rates (Jorgenson 1963) or to growth opportunities through the accelerator principle (Eisner 1960, 1963)? Whichever side of this debate one came down on, one thing was clear: the goal was investment – the more the better.

Lessons Learned

145

At the microeconomic level, or the level of the firm and industry, there was no doubt that the key to strong economic performance was physical capital. Chandler’s (1977) widely influential treatise on organizational strategy, The Visible Hand: The Managerial Revolution in American Business, revolved around the ability to harness and efficiently govern large-scale, capital-intensive production. The strategic advantage so bestowed was reinforced in Chandler’s (1990) equally influential Scale and Scope: The Dynamics of Industrial Capitalism. From our perspective, the contribution of our book is not to suggest that the previous generation of scholars and policy thought leaders was wrong in the singularity of its focus on physical capital as the driver of performance at all levels of the economy. They were no doubt right for their time. But times change. Just as Romer (1986) startled his colleagues in macroeconomics by suggesting that knowledge had replaced physical capital as the driving force underlying economic performance, earlier Griliches (1979) made a parallel discovery at the microeconomic level of the firm. What the leading scholars, at both the broader macroeconomic and disaggregated microeconomic levels, had discovered is that something had changed. It was not that goods based on capital-intensive production methods were no longer in demand: consumers throughout the developed world and increasingly in the less developed economies still had an almost insatiable taste for automobiles, household goods and other highly processed manufactured goods. Rather, what these scholars discovered were the implications, or consequences, of what today is broadly recognized as globalization. Although demand has not changed, globalization has changed the comparative advantage of places and, as a consequence, the geography of production. Changes in technology and political regimes have triggered a restructuring in the location of production, rendering a shift in comparative advantage in many capital-intensive industries to low-cost developing countries and, as a consequence, to knowledge-intensive industries in high-cost developed countries. What scholars such as Romer and Griliches stumbled upon was the advent of the knowledge economy. If the emergence of knowledge as the driving force of economic performance proved surprising to scholars and thought leaders in policy and management, the new role for entrepreneurship as a conduit for transforming those ideas into innovation must have surprised them even more. In the capital-driven economy, virtually no one, whether in academic research or in public policy, looked to new and small firms to deliver innovation and, ultimately, jobs, growth, and competitiveness. It

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became clear, however, that, as competitive advantage shifted away from the traditional factor of physical capital toward knowledge, organizational competitive advantage also shifted toward entrepreneurial new and young companies. It was often entrepreneurs who were able not just to recognize the economic opportunities created along with the creation of new ideas and knowledge, but also to act on those opportunities by creating a new firm. Thus, the surprise was that it seemed to take a different organizational structure inherent in the entrepreneurial process to take advantage of a different factor of production – namely, knowledge. Thought leaders in management and policy were quick to respond. Following a decade of economic stagnation and rising unemployment, the European Council in Lisbon at the turn of the century committed Europe to becoming not just the knowledge leader, but also the entrepreneurship leader in the world (Audretsch 2015). Shortly after, German chancellor Gerhard Schroeder declared 2004 to be “The Year of Innovation,” in an effort to restore prosperity to what the Economist had termed “The Sick Man of Europe” (Audretsch and Lehmann 2016). Although no consensus might have existed concerning the efficacy of particular policy instruments, there was virtual consensus about the policy goal: to generate a knowledge-based entrepreneurial economy conducive to innovation, which ultimately would drive economic performance. Thus the intellectual breakthrough, for both scholars (including economists) and thought leaders in management and policy, was a new focus on knowledge-based entrepreneurship as shaping performance at virtually every level of the economy, ranging from individuals to firms and industries to cities, states, and entire regions and nations. That there could be more than one type or just several types of knowledge, reflecting disparate knowledge sources, however, has escaped most of the studies in the literature – until, we believe, this book. The intellectual breakthrough in this book is not that knowledge matters, and especially for entrepreneurship – that had been established convincingly and validated empirically by the scholarly giants upon whose shoulders we stand. Rather, what this book raises is that knowledge – especially entrepreneurial knowledge – is not a homogeneous phenomenon: there are multiple sources of knowledge, and they all might not play the same role or have the same effect on performance, particularly entrepreneurial performance. That knowledge is, in fact, heterogeneous and based on a broad range of sources would not surprise most readers, let alone expert scholars and thought leaders in management and policy. What prevented previous

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studies from analyzing the effect of multiple heterogeneous knowledge sources on entrepreneurial behavior was a paucity of data and measurement. For society to allocate scarce and costly resources to systematic measurement and the creation of a database, a phenomenon has to be considered – or even more strongly, proven – valuable to society. Within the span of just a handful of decades, this was not the case for knowledge. Why would society, in the form most typically of public policy, devote precious resources to measuring a phenomenon that seemingly had little effect on economic performance? Only since the advent of the knowledge economy and entrepreneurial society have governments concluded that it makes sense to invest in the large-scale measurement and collection of data about knowledge. Thus, recent years have seen multiple new sources of such data, ranging from patents to research and development to new product introductions. Although most databases have been satisfied to contribute a solitary measure, or at best a handful of measures, of knowledge and their sources; the AEGIS database provides a plethora of knowledge sources, ranging from the more traditional measures such as R&D to the novel such as trade fairs and exhibitions, all of which are ultimately crucial for understanding entrepreneurial behavior. Based on our analysis of the AEGIS database, we find that there is no single source of knowledge that matters for entrepreneurial performance for all industry and national contexts. Rather, different sources of knowledge have disparate effects on entrepreneurial behavior, depending upon both the industrial and national context. What does stand out and hold across industrial and national contexts, however, is the key role played by human capital – in particular, experience – in shaping entrepreneurial performance. That human capital is important for entrepreneurship has been long established; a strikingly new finding from this study is the way in which human capital influences entrepreneurial performance. The extant literature, almost exclusively, has examined the effect of human capital on entrepreneurial behavior directly, or perhaps including it with a handful of other measures of knowledge, such as R&D, so that the effect was measured after controlling for other knowledge sources. By contrast, one key finding in this book is that controlling for other knowledge sources essentially ignores the indirect influence of human capital on an entrepreneur’s strategy of which and how much among various knowledge sources to access. By ignoring the effect of human capital on the accessing of other knowledge inputs, virtually every study in the extant literature has ignored one of the most important ways human capital influences entrepreneurship. And, as we approximate

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in this book, the traditional view of human capital as having a direct effect on entrepreneurial behavior is overshadowed by the perspective of human capital as having an effect on entrepreneurial behavior by influencing the use of alternative sources of knowledge. Broader Implications Our findings suggest that the sources and types of knowledge that drive entrepreneurship are considerably broader and more varied than is reflected in the vast extant literature. The heterogeneous nature of entrepreneurial knowledge and its sources has a number of important implications for both scholars and thought leaders in management and policy. In terms of scholarly research, knowledge-driven entrepreneurship generally has been linked to one source or, at most, a handful of sources. Certainly the vast literature on knowledge spillover entrepreneurship generally links entrepreneurial behavior to opportunities created from just a handful of knowledge sources, principally R&D and human capital. The thinking suggested that opportunities created from investments in R&D and human capital that are completely exhausted and appropriated by incumbent firms generate the opportunity for entrepreneurship. A key implication of this is that, although this thinking is not incorrect, it is incomplete. In fact, myriad and diverse sources of knowledge create opportunities for entrepreneurship. Although we have identified a considerably wider spectrum of knowledge sources for entrepreneurship, ranging from the years of experience acquired by a founder to strategic alliances, customer knowledge, public research institutes, external commercial labs, and scientific journals or other trade or technical publications, to name just a few, the AGEIS database is more likely to be a beginning than an end. Moving forward, there are no doubt bountiful research opportunities for scholars to expand and build upon those knowledge sources we have identified and analyzed in this book. As for the human genome, it might take a plethora of diverse and wide-ranging studies spanning multiple contexts and dimensions until we can be sure that the search for entrepreneurial knowledge sources is complete. Perhaps the most obvious starting point is to ask entrepreneurs themselves. After all, who would know better? There are also sweeping and significant implications for policy and management. First, when weighing potential fertile sources of knowledge to

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promote in an effort to enhance entrepreneurial activity, the spectrum of such sources should be broadened and expanded, and the lens through which we view and understand the spectrum should be gender oriented. Policy makers generally have mirrored scholarly research in their narrow focus on entrepreneurial knowledge. In particular, a science-based orientation reflected a technical bias. For example, as universities responded to the passage of the Bayh-Dole Act of 1980 by establishing offices of technology transfer, the focus and orientation was almost exclusively on technical fields, such as engineering and other applied sciences (Link, Siegel, and Wright 2014); there was little sense or understanding that knowledge for entrepreneurship might be generated in other academic disciplines and fields, such as music, media studies, or fine arts. As for scholars, it might be instructive to interview entrepreneurs about the various sources of their inspiration and ideas. It would be important, however, not to restrict such inquiries to local entrepreneurs who are taking advantage of existing opportunities, but to try to learn from entrepreneurs in other spatial, national, and industry contexts. For example, Audretsch and Lehmann (2016) report that the success of Germany’s vaunted Mittelstand – small- and medium-sized enterprises, especially the high-performing “hidden champions” among them – stems from a number of compelling policies and institutions that enable access to knowledge entrepreneurial firms in most other national contexts simply do not have. In particular, they highlight the role that the German apprentice system plays in providing highly skilled, motivated, creative technical workers; a system of institutions, such as the Fraunhofer Institutes, to facilitate translational research from basic to applied with a commercial orientation; and a stout infrastructure that provides easy and inexpensive mobility that, in turn, facilitates networks and the spillover of knowledge and ideas. Even more striking is the role institutions and policy have played in propelling the Mittelstand and all of Germany into becoming among the world’s export leaders. Foreign languages – in particular, English – as well as culture are emphasized to facilitate travel and communication with other cultures and nations. It is no coincidence that German entrepreneurs exceed their counterparts in most other countries in identifying opportunities in other countries and cultural contexts. If entrepreneurship is about identifying and creating opportunities and acting upon those opportunities, Germany has managed to do exactly that in nurturing and developing numerous capacities, institutions, and policies to look beyond its borders to identify and discover new economic opportunities. Simon (2009) identifies the bountiful

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entrepreneurial opportunities harvested from costly investments in a cultural orientation toward internationalization and knowledge of other national and cultural contexts; as he explains, “[t]he best language is the language of the customer.” Instruction in foreign languages and other cultures and overseas studies programs are rarely thought of as sources of knowledge for entrepreneurship; in this book we have not proved, established, or even hinted that they are. But with the case of the German Mittelstand in mind, they might be. What we hope we have done is establish that knowledge that generates entrepreneurial opportunities comes from a much richer and broader set of sources than most thought leaders in policy and management currently have in mind. A different implication involves the key role of human capital, which, if anything does, seems to hold the key to entrepreneurship. However, although the typical study measures human capital in terms of educational attainment, we suggest that human capital embodies a broader set of experiences, influences, and values than just educational attainment. Although a simple and narrow focus on educational attainment facilitates ease of measurement, it also ignores many of the key components, influences, and experiences that contribute mightily to human capital, at least when it comes to entrepreneurship. It might be that a broader, more experiential approach to human capital formation is more conducive to entrepreneurship. Conclusions A generation ago the prevalent view among scholars and thought leaders in policy and management was that entrepreneurs were born, not made (McClelland 1961). In its incipiency, the field of entrepreneurship reflected this view with a determined focus on the traits, propensities, inclinations, proclivities, and characteristics that make entrepreneurs different from other people. A major contribution of the knowledge spillover theory of entrepreneurship has been to shift the focus of the entrepreneurial decision away from genetic and personality disposition to context – in particular, the knowledge context. This is not to say that genetic disposition has no role to play in entrepreneurship (Verheul et al. 2015). Rather, what has changed is the importance of the knowledge context of the nascent entrepreneur. The KSTE effectively shifts the focus from the characteristics of the individual to the knowledge context of the individual.

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The knowledge context, however, which provides the platform for entrepreneurial opportunities, has been considered in a remarkably narrow manner in that it has revolved around very specific measures and indicators, such as R&D, patents, and educational attainment. The major contribution of this book, in contrast – while confirming the major pillar underlying the KSTE that the knowledge context certainly matters for entrepreneurship – is to show that context is considerably broader and more nuanced than most studies in the extant literature have considered. Most strikingly, knowledge is not restricted to emanating from a single source or handful of sources. Rather, knowledge that generates entrepreneurial activity is not just heterogeneous in that it spans a broad range of dimensions; it also stems from myriad disparate sources. You never know what is enough unless you know what is more than enough. – William Blake

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Notes

1. Introduction 1 The ideas expressed in this opening section are an extension of those in Link (2017). 2 “If, in the history of epistemology, any sources of knowledge deserve to be called the classical basic sources, the best candidates are perception, memory, consciousness (sometimes called introspection), and reason (sometimes called intuition). Some writers have shortened the list under the heading, ‘experience and reason’” (Audi 2002, 72). 3 In Chapter 5, we map our quantifiable measures of knowledge sources into Machlup’s five types of knowledge. 4 Machlup’s taxonomy segues to the observations below about the heterogeneity of knowledge. 5 Foray (2004) insightfully refers to knowledge as a matter of cognitive ability. 6 We are certainly not the first to investigate sources of knowledge. As Andersson and Beckmann (2009, 1) note, and with which we agree, economists have investigated knowledge in the past “in such disguised form such as human capital, technology or innovations.” 7 The term linear model frequently has been used to describe the innovation process wherein a firm’s investments in basic research leads to applied research, and applied research leads to development, and development leads to production. Although many point to Vannevar Bush’s 1945 report, Science – the Endless Frontier for the origin of this term, Godin (2006, 640–1) claims: “[T]he model owes little to Bush. It is rather, a theoretical construct of industrialists, consultants, and business schools, seconded by economists.” According to Godin, “the first and most complete description of a [linear model] came from [Raymond] Stevens, vice president at Authur D. Little, and was published in the United States National Resources Planning Board report titled Research: A National Resource in 1941” (646); see National Research Council (1941).

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8 Our exposition of the view of dynamic entrepreneurship draws directly from Audretsch, Kuratko, and Link (2016, 608): “Dynamic entrepreneurship, as we are using the term, refers to the action taken given a systematicallydetermined opportunity has been perceived.” 9 In fact, Lazear’s (2005) empirical demonstration of his theory of entrepreneurship is based on a partial correlation between the number of businesses an entrepreneur starts and his or her education and experience. 2. The Knowledge Spillover Theory of Entrepreneurship 1 Much of the discussion that follows in this chapter is based on Audretsch (2005) and Audretsch, Keilbach, and Lehmann (2006). 2 See Antonelli (2014) for an alternative specification of a technical knowledge function and how it affects output. 3 We acknowledge that the implied functional forms for equations (2.1) and (2.2) are likely different by our use of the f() and F() notation. 4 Link (1980) expanded on this Schumpeterian notion by demonstrating that larger firms also realize a greater return to their investments in R&D. 5 Antonelli (2014) refers to this as a “knowledge externality.” 6 For institutional discussions of the Bayh-Dole Act, see Leyden and Link (2015); and Link and Link (2009). 7 For a more formal model, see Lazear (2005). 8 We refer back to the Localization Hypothesis in Chapter 6, where we argue that some sources of knowledge are “closer” to a firm than others. 9 As Link (1996) points out, the distinction between basic research and applied research or – as we are describing it from a knowledge output perspective – between basic knowledge and applied knowledge, has a definable policy origin. 10 Perhaps in only a few other places is the influence of Bush more obvious than in the role of basic research on productivity growth; see Hall, Layson, and Link (2014). 3. The AEGIS Database 1 In Greek mythology, the word Aegis refers to the powerful shield carried by Athena and Zeus. Although the use of the acronym is not explained in European Community documents, it might imply that the database itself contains powerful information for an understanding of knowledgeintensive entrepreneurship; see, for example, Caloghirou, Protogerou, and Tsakanikas (2011). 2 The following description of FP7 draws directly from European Commission, Research and Innovation, “What Is FP7? The Basics,” available online at

Notes to pages 49–63

3

4

5

6

7

8

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https://ec.europa.eu/research/fp7/understanding/fp7inbrief/what-is _en.html, accessed 20 April 2016. We have written about the AEGIS database several times; see, for example, Cunningham and Link (2016); Link and Swann 2016). Duplication of text to describe this database is unavoidable. One might think that the definition of KIE is an outgrowth of how scholars have thought about knowledge-intensive firms. For example, Blackler (1995, 1022) writes: “Knowledge-intensive firms [are] organizations staffed by a high proportion of highly qualified staff who trade in knowledge itself.” In our view, however, using knowledge-intensive firms as a starting point for KIE is not that productive. In the AEGIS survey and in the explanatory text by Caloghirou, Protogerou, and Tsakanikas (2011), the terms firm, company, and business appear to be used interchangeably. For the purpose of standardization, we impose our preference for the term firm throughout. The sampling weights are, by country: Croatia (11.985), Czech Republic (15.230), Denmark (23.909), France (100.249), Germany (66.470), Greece (12.628), Italy (89.371), Portugal (16.492), Sweden (62.533), and United Kingdom (21.764). As described in Caloghirou, Protogerou, and Tsakanikas (2011), the sampling process was challenging due to the desire to have adequate representation of smaller countries and across industries. The desire to include new firms, rather than firms that had recently changed legal status, and to impose other restrictions to ensure that the data included firms in the desired age range further complicated the data-collection process. Caloghirou, Protogerou, and Tsakanikas (2011) provide detailed information on the sampling process. We thank an anonymous referee for pointing out that some of the findings in these tables are counterintuitive. For example, why are there no human resource enablers in Sweden? Sweden, widely acknowledged as one of the most innovative countries, logically should have an important endowment of human capital enabling. Explanations for these findings are, however, beyond the scope of our book. The more analytical reader will realize that this discussion also sets the stage for the inclusion of fixed effects in the regression models that we consider in subsequent chapters.

4. The Experience Base of Firms 1 Merriam-Webster, “Definition of Experience,” available online at http://www .merriam-webster.com/dictionary/experience, accessed on 16 April 2016.

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2 The reader will note that we report the number of firms relevant to the calculations in each table. Not all respondents answered every question on the AEGIS survey, so the number of responses for some experience metrics is less than 4,004. 3 The human capital literature generally considers an individual’s age and education together. We do not use as an experience measure the age of the founder(s) in our analysis because the AEGIS survey does not ask for the actual age but for the age in deciles (18–29, 30–39, and so on). 4 This same reasoning applies below to the experience of the first-listed founder. 5 One should anticipate an overall negative correlation coefficient between mean education level and mean years of experience given that there is both much greater education and much less experience among founders of firms in the KIBS sector, especially since 1,982 of the 4,004 firms are from the KIBS sector. 6 For a recent analysis of nascent versus established entrepreneurial firm behavior, see Gicheva and Link (2016). 5. Sources of Knowledge 1 Here, and in the subsections below, we list and disc knowledge sources in the same order they were presented in the AEGIS survey, not in any order of presumed or actual importance. 2 Cohen, Nelson, and Walsh (2002), in a study of the contribution of university and government research laboratories to industrial innovation, find that the most important source of information is publications and reports on research related to industrial R&D. 3 See Antonelli (2014) for a discussion of the absorption costs associated with acquiring knowledge from other firms and the exploitation costs of acquiring knowledge from universities or similar intuitions. 4 Looking across Tables 5.19 through 5.22, it is also the case, although these are hybrid experience measures, that occupational-based nascent firms rely on a number of knowledge sources overall (see Table 5.19) and relatively more so in the high-tech and KIBS sectors than in the low-tech sector.

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Index

Figures and tables indicated by page numbers in italics Aamoucke, R., 45 Acosta, M., 44 Acs, Z.J., 24, 33–4, 38, 39, 41–2, 44–5, 47 Adams, John Quincy, 63 AEGIS database: introduction and summary, 13, 20, 60; background, 48–9; counterintuitive findings, 155n7; distribution of firms in, 53–4, 55; focus on knowledgeintensive entrepreneurship (KIE), 49–50; industries by sector, 54, 61; on innovation capacity, 61, 62; on innovation enablers, 55, 57; on innovation outputs, 56, 57; innovation policy instruments and, 58, 59 ; on innovation-based firm activities, 56, 57; name of, 154n1; relationships between innovation-based firm activities and innovation outputs, 57, 58; sampling process and weights, 53–4, 155n6; survey questions on experience, 80; survey questions on knowledge sources, 117–18.

See also experience metrics; knowledge, sources of Agarwal, R., 41, 42 age: of firms, 65, 66, 78, 80; of founders, 156n3 Aldrich, H.E., 83, 85, 137 alliances, strategic, 104, 105, 109, 111, 118, 125, 127, 134 Alsos, G.A., 81, 137 Andersson, Å.E., 153n6 Anselin, L., 41 Antonelli, Cristiano, 8, 20, 32, 154n2, 154n5, 156n3 Apple, 27 applied knowledge, 35–6, 154n9 Archibugi, D., 39 Arenius, P., 81 Arrow, Kenneth, 24–6, 32, 40 Arthurs, J.D., 85 Asheim, B., 39 Aterido, R., 137 Audretsch, David B., 24, 27–8, 33–5, 38, 41–3, 45, 149, 154n8 Augustine, Saint, 48

178

Index

Baines, S., 138 Bardasi, E., 137 Barnir, A., 137 Bartelse, G., 143 basic knowledge, 34–6, 154n9 Baudeau, N., 52 Bayh, Birch, 24 Bayh-Dole Act (University and Small Business Patent Procedure Act, 1980), 24, 149 Beaumont-Kerridge, J., 142 Becker, G.S., 64 Beckmann, M.J., 153n6 Belghitar, Y., 84 Belitski, M., 45 Bellu, R.R., 137 Bergmann, H., 82 Birley, S., 138 Blackler, F., 155n4 Blake, William, 151 Block, J.H., 46 Boden, R.J., Jr., 138 Bonaccorsi, A., 46 Bönte, W., 81 Bosma, N., 85 Braunerhjelm, P.B., 43–4 Brush, C.G., 81, 83, 138 Busenitz, L.W., 85 Bush, Vannevar, 35–6, 153n7, 154n10 Caloghirou, Y., 51, 53, 155n6 Cantillon, R., 52 Carlsson, B., 43 Carroll, Lewis, 144 Carter, N.M., 81, 85 Carter, S., 142 Casper, S., 46 Cassar, G., 82 Chaganti, R., 138 Chandler, A., 145

Chandler, G.N., 82 Chell, E., 138 Coase, Ronald, 16–17 codified knowledge, 32–3 Cohen, W.M., 156n2 Cohoon, J.M., 138 Coleman, S., 139 commercial labs. See research and development (R&D) competitors, 93, 95, 111, 117, 125, 134–5 conferences. See trade fairs Copernicus, 34 Coronado, D., 44 Craig, J., 82 Crespo-Espert, J.L., 142 Croatia, 13 customers, 89, 92, 93, 117, 134 Czech Republic, 13 da Vinci, Leonardo, 63 Dautzenberg, K., 139 Davidkov, T., 143 Davidsson, P., 82–3, 85 de los Dolores González, M., 139 Delmar, F., 82–3 Denmark, 13 design knowledge, 89, 90, 110–11, 117, 134 Dimov, D., 82 Diochon, M., 83 disequilibria, 6–7 diversified knowledge, 34 Dolinsky, A.L., 139 Du Rietz, A., 140 dynamic entrepreneurship, 10, 154n8 Eckhardt, J.T., 27, 83 economic knowledge, 31–2 Edelman, L.F., 81, 83

Index 179 education, 6–7 educational level of founders: AEGIS data on, 67, 70–1; AEGIS survey question on, 80; gender and, 132; relationship to knowledge sources, 110–11, 112, 115–16, 122; relationship to other experience metrics, 78–9, 156n5 Einstein, Albert, 3, 144 Elam, A.B., 137 employees, number of, 67, 68, 78, 80, 111 endogenous entrepreneurship, 28, 30 entrepreneurial behavior: introduction, 3, 15; antecedents of, 8, 9, 11, 12, 57; characteristics, 52, 119–20; comparison of human capital and KSTE perspectives on, 127; comparison of human capital and KSTE perspectives on by gender, 135; competitive advantage of, 145–6; dynamic entrepreneurship, 10, 154n8; endogenous entrepreneurship, 28, 30; epistemological perspective, 12; human capital perspective, 9–10, 11, 12; individual perspective, 39–40; innovation and, 52–3; Lazear on, 9–11, 154n9; opportunity factor, 27; policy emphasis on, 146; role in economic performance, 28–30; Schumpeter on, 10–11. See also experience; gender; human capital; knowledge; knowledge spillover theory of entrepreneurship; nascent entrepreneurship entrepreneurial choice, 26–7 entrepreneurial contexts, 36–8

epistemological perspective, 12. See also knowledge spillover theory of entrepreneurship EU Framework Programs, 93, 103, 109, 118, 125, 127, 134–5 European Council, 146 European Paradox, 28–9 exhibitions. See trade fairs experience, 3–6, 63–4. See also human capital experience metrics: introduction and conclusions, 14, 64–5, 79, 115–16, 122; AEGIS survey questions on, 80; age of the firm, 65, 66, 78, 80; currentsector experience of founders, 71, 72, 79–80, 156n5; experience-based nascent firms, 73, 75, 76, 79, 111; gender and experience’s effect on entrepreneurial behavior, 132; number of employees, 67, 68, 78, 80, 111; number of founders, 67, 69, 78, 80, 111; occupation of founders, 71, 73, 74, 80; occupational-based nascent firms, 76, 77, 79, 111, 156n4; relationship with entrepreneurial behavior, 122–4; relationship with knowledge sources, 109–12, 110, 112–15, 114, 136; relationships between, 77–9, 78, 156n5. See also educational level, of founders experience-based nascent firms, 73, 75, 76, 79, 111 Fagerberg, J., 39 Fairlie, R., 140 Fassio, C., 8 Feldman, M.P., 33, 34 Filippetti, A., 39 firm: theory of, 16–17; use of term, 155n5

180

Index

firm formation, knowledge sources for, 89, 90–1, 110–11, 117 firm opportunities, knowledge sources for, 89, 92, 93, 94–103, 117–18, 136 firm sales: AEGIS data on, 120, 121, 122; education and, 136; by gender, 129, 131, 132–4 Flores, E., 44 Foray, D., 8, 153n5 Ford, M.H., 84 founders: age of, 156n3; currentsector experience, 71, 72, 79–80, 156n5; female first-listed founders, 129, 130; number of, 67, 69, 78, 80, 111; occupation, 71, 73, 74, 80. See also educational level, of founders France, 13 Franklin, Benjamin, 87 Fritsch, M., 45 Galbraith, J.K., 21 García-Tabuenca, A., 142 Gartner, W.B., 81, 83 Gasse, Y., 83 Gatewood, E.J., 83 gender: introduction and conclusions, 15, 128, 136; comparison of human capital and KSTE perspectives by, 135; experience’s effect on entrepreneurial behavior and, 132; female first-listed founders, 129, 130 ; firm sales and, 129, 131, 132–4; knowledge sources’ effect on entrepreneurial behavior and, 132–5, 133 ; literature on, 128, 137–43; policy importance, 128–9 general knowledge sources, 104–5, 111, 118, 136 genetics, 150

geographic localization. See Localization Hypothesis Germany, 13, 146, 149–50 Gertler, M.S., 39 Ghio, G., 47 Glaeser, E.L., 34 globalization, 145 Godin, B., 153n7 Gordon, S.R., 82 Gort, M., 37 Greece, 13 Griliches, Z., 19–20, 38, 145 Grilo, I., 83, 85 Groen, A.J., 51 Guerrero, M., 45 Gunnarsson, J., 82 Guo, G.C., 140 Hall, G. Stanley, 35 Hall, P.A., 39 Hallward-Driemeier, M., 137 Hamilton, D., 142 Harrison, P., 142 Hawking, Stephen, 119 Hébert, R.F., 10, 52, 119 Henrekson, M., 140 Hessels, J., 83 Hirsch-Kreinsen, H., 51 Holmstrom, B., 37 Honig, B.L., 82–3 Hoselitz, B.F., 119 Huarng, K.-H., 140 Human, S.E., 84 human capital: antecedents of entrepreneurial behavior from, 9–10, 11, 12; comparison to KSTE, 127; comparison to KSTE by gender, 135; definition, 64; in knowledge production function, 19–21; production function and, 18; role in

Index 181 entrepreneurship, 147–8, 150; role in entrepreneurship by gender, 132; traditional focus on, 144–5. See also experience Humboldt, Wilhelm von, 34–5 Hume, David, 5–6 Husted, B.W., 139 IBM, 26, 27 incremental knowledge, 33–4 information, vs. knowledge, 32 innovation: association with entrepreneurial activity, 52–3; capacity for, 61, 62; enablers of, 55, 57; firm activities based on, 56, 57; incremental vs. radical innovations, 33–4; in knowledge production function, 19–20; linear model and, 153n7; Localization Hypothesis, 22–3; national systems of innovation, 38–9; outputs from, 56, 57; relationships between innovation-based firm activities and innovation outputs, 57, 58 innovation policy instruments, 58, 59 Innovation Union Scoreboard, 54 intellectual knowledge, 7 invention, 8 Italy, 13 Jiang, C.X., 140 Jobs, Steve, 27 Johnson, Samuel, 87, 119 Johnson, Steve, 140 journals, scientific, 93, 101, 118, 134 Kalleberg, A.L., 140 Kalnins, A., 140 Kant, Immanuel, 3 Kariv, D., 139

Karlsson, T., 83 Keilbach, M., 24, 27, 43 Keister, L.A., 83 Kim, P.H., 83 Klepper, S., 29, 37 Knight, F.H., 33 knowledge: introduction and conclusions, 14, 30–1, 135–6, 150–1; acquisition costs, 156n3; definitions, 87; future research opportunities, 148; vs. information, 32; policy implications, 148–50; relationship with entrepreneurial behavior, 8, 124–5, 126, 127–8, 135–6, 147; relationship with entrepreneurial behavior by gender, 132–5, 133; relationship with experience, 109–12, 110, 112–15, 115–16; relationships between different sources, 105, 108, 109; scholarship on, 147, 153n6; shift to economy based on, 145; traditional understanding, 4–8, 153n2. See also knowledge, as heterogeneous; knowledge, sources of; knowledge spillover theory of entrepreneurship knowledge, as heterogeneous: introduction and conclusions, 8, 31, 40, 87–8; basic vs. applied knowledge, 34–6, 154n9; economic vs. non-economic knowledge, 31–2; incremental vs. radical knowledge, 33–4; measurement of, 146–7; routinized vs. entrepreneurial contexts, 36–8; specialized vs. diversified knowledge, 34; tacit vs. codified knowledge, 32–3 knowledge, sources of: introduction and conclusions, 88–9, 115–16;

182

Index

AEGIS survey questions on, 117–18; competitors, 93, 95, 111, 117, 125, 134, 135; customers, 89, 92, 93, 117, 134; design knowledge, 89, 90, 110–11, 117, 134; EU Framework Programs, 93, 103, 109, 118, 125, 127, 134–5; external commercial labs, R&D firms, and technical institutes, 93, 98, 109, 117, 134; for firm formation, 89, 90–1, 110–11, 117; for firm opportunities, 89, 92, 93, 94–103, 117–18, 136; for general knowledge, 104–5, 111, 118, 136; in-house R&D labs, 93, 99, 109, 111, 118, 125, 134–5; market knowledge, 89, 91, 110–11, 117, 134; national research programs, 93, 102, 109, 118, 125, 127, 134–5, 156n2; public research institutes, 93, 96, 109, 117, 134; research and development (R&D) agreements, 104, 106, 109, 118, 125, 127, 134; scientific journals, 93, 101, 118, 134; strategic alliances, 104, 105, 109, 111, 118, 125, 127, 134; suppliers, 93, 94, 117, 134; technical cooperation agreements, 104, 107, 109, 111, 118, 127, 134–5; trade fairs, conferences, and exhibitions, 93, 100, 118, 125, 134; universities, 93, 97, 109, 117, 125, 134, 156n2 knowledge filter, 24–6, 40 knowledge production function, 19–23, 27–8, 38 knowledge spillover theory of entrepreneurship (KSTE): introduction and conclusions, 13, 14–15, 18, 136, 150; comparison to human capital, 127; comparison to human capital by gender, 135;

endogenous entrepreneurship in, 28, 30; on entrepreneurial opportunity, 27; on entrepreneurial role in economic performance, 28–30; as epistemological perspective, 12; global implications, 30; knowledge heterogeneity and, 40; literature on, 28, 41–7; Localization Hypothesis, 22–4, 29, 39, 127–8; national institutional context, 39, 54; observation of spillover mechanism, 23. See also experience; knowledge knowledge-intensive entrepreneurship (KIE), 13, 49–50, 51, 52, 155n4. See also AEGIS database Koellinger, P., 140 Kogut, B., 33 Kolvereid, L., 81, 85 Kreps, D., 36 Kuratko, D.F., 154n8 Land-Grant College Act (Morrill Act, 1862), 36 languages, foreign, 150 Lazear, Edward, 9–11, 154n9 leadership, 10–11 Lee, S.S., 141 Lehmann, E.E., 24, 27, 34–5, 41–2, 149 Leicht, K.T., 140 Lerner, M., 141 Lewellyn, K.B., 141 Leyden, D.P., 46–7 Liao, J., 84 Lichtenstein, B.B., 84 linear model, 9, 153n7 Link, Albert N., 10, 22, 46–7, 52, 119, 154n4, 154n8, 154n9 List, F., 38

Index 183 Ljunggren, E., 81, 137 Localization Hypothesis, 22–4, 29, 39, 127–8 Locke, John, 3–5 Loscocco, K.A., 141 Lucas, R.E., 23 Machlup, Fritz, 7–8, 88, 153n4 Malach-Pines, A., 141 Malerba, F., 50, 51, 52 Manolova, T.S., 81, 83, 141 market knowledge, 89, 91, 110–11, 117, 134 Marshall, A., 23 Mas-Tur, A., 140 Matthews, C.H., 84 McClelland, D., 27 McClelland, E., 141 Menzies, T., 83 Miller, B., 85 Minniti, M., 81, 140 Mittelstand, 149–50 Morrill Act (Land-Grant College Act, 1862), 36 Mowery, D.C., 39 Mueller, D.C., 37 Muhammad, S., 143 Muller-Kahle, M.I., 141 nascent entrepreneurship: definitions, 73, 81–6; experiencebased nascent firms, 73, 75, 76, 79, 111; occupational-based nascent firms, 76, 77, 79, 111, 156n4 national systems of innovation, 38–9 Nelson, G.W., 141 Nelson, R.R., 18–19, 36–8, 156n2 non-economic knowledge, 31–2 Nucci, A.R., 138 Nyberg, A.J., 11

occupation, of founders, 71, 73, 74, 80 occupational-based nascent firms, 76–7, 79, 111, 156n4 opportunity, 27. See also firm opportunities Organisation for Economic Cooperation and Development (OECD), 128–9 overseas studies programs, 150 Pablo-Martí, F., 142 Pankaj, P., 85 Parasuraman, S., 138 Parker, S.C., 84 pastime knowledge, 7–8 Pericles, 16 physical capital. See human capital Piegeler, M., 81 PLANET, 51 Plato, 16 Plummer, L.A., 42, 47 policy: gender implications, 128–9; heterogeneous knowledge implications, 148–50; innovation policy instruments, 58, 59 Pope, Alexander, 31 Portugal, 13 practical knowledge, 7 production function, 18, 53. See also knowledge production function Protogerou, A., 51, 53, 155n6 public research institutes, 93, 96, 109, 117, 134 Qian, H., 47 radical knowledge, 33–4 Reese, P.R., 137 Reichborn-Kjennerud, K., 142

184

Index

research and development (R&D): agreements, 104, 106, 109, 118, 125, 127, 134; external labs, 93, 98, 109, 117, 134; in-house labs, 93, 99, 109, 111, 118, 125, 134–5; in knowledge production function, 19, 20, 21, 154n4; traditional view of, 30 research programs, national, 93, 102, 109, 118, 125, 127, 134–5, 156n2 Reynolds, P.D., 81, 84–5 Reynolds, R.G., 143 Riding, A.L., 142 Risseeuw, P., 143 Robb, A., 140, 142 Robinson, J., 141 Romer, P., 23, 26, 145 Roomi, M.A., 142 Rosa, P., 142 Rosenberg, N., 38 Rotefoss, B., 85 routinized contexts, 36–8 Ruef, M., 85 Sabarwal, S., 137 sales growth. See firm sales Samuelsson, M., 85 Sanders, M.W.J.L., 44, 45 SAP, 26, 27 Sarkar, M.B., 42 Schade, C., 140 Schroeder, Gerhard, 146 Schultz, Theodore, 6–7 Schumpeter, J.A., 10–11, 21–2, 52–3 Schumpeterian Paradox, 22–3 Schwinge, I., 51 scientific journals, 93, 101, 118, 134 7th Framework Programme (FP7), 48–9. See also AEGIS database Shane, S., 27, 82–3 Shaver, K.G., 83

Shaw, E., 143 Silicon Valley, 29 Simon, H., 149–50 Singh, S.P., 143 small-talk, 7–8 Solow, Robert F., 18–19, 23, 26, 29 Soskice, D., 39 specialized knowledge, 34 spiritual knowledge, 8 Stephan, P.E., 41 Storey, D., 85, 140 strategic alliances, 104, 105, 109, 111, 118, 125, 127, 134 Stuetzer, M., 143 suppliers, 93, 94, 117, 134 Sutter, R., 42 Svare, H., 142 Sweden, 13, 155n7 Swedish Paradox, 28–9 Swift, C.S., 142 tacit knowledge, 32–3 Tan, W.-L., 84 technical cooperation agreements, 104, 107, 109, 111, 118, 127, 134–5 technical institutes. See research and development (R&D) Terrell, K., 137 Thurik, R., 46, 83, 85 Townsend, D.M., 85 trade fairs, 93, 100, 118, 125, 134 transaction cost theory, 16–17 Tsakanikas, A., 51, 53, 155n6 Twain, Mark, 48 United Kingdom, 13 universities: basic vs. applied knowledge, 34–6; as knowledge source, 93, 97, 109, 117, 125, 134, 156n2; technology transfer initiatives, 149

Index 185 University and Small Business Patent Procedure Act (Bayh-Dole Act, 1980), 24 unwanted knowledge, 8 Urbano, D., 45 van der Zwan, P., 85 van Gelderen, M., 83, 85 van Stel, A., 85 Varga, A., 41 Verheul, I., 86, 143 Verspagen, B., 39 Veugelers, R., 58 Wagner, J., 86 Walsh, J.P., 156n2 Warning, S., 41, 42 Warsh, D., 40 Watson, J., 142, 143

Welsch, H., 84 Wennekers, S., 86 Wiklund, J., 82 Williams, M., 140 Williamson, Oliver, 17, 37 Winter, S.G., 36–8 women. See gender World Bank, 128–9 Wright, P.M., 11 Xerox, 25, 27 Yordanova, D., 143 Yu, T.H.-K., 140 Zander, U., 33 Zhou, H., 46 Zimmerman, M.A., 140 Zolin, R., 143