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1042-2587-9M62$1 50 Copyright 1991 by Baylor University Theorizing about Entrepreneurship William D. Bygrave Charles W. Hofer / \ t the start of the 1980s, entrepreneurship was, at best, a potentially promising field of scholarly inquiry. However, by the end of that decade, due primarily to im- pressive advances in its body of empirical knowledge, entrepreneurship could claim to be a legitimate field of academic inquiry in all respects except one: it lacks a substantial theoretical foundation. A major challenge facing entrepreneurship researchers in the 1990s is to develop models and theories built on solid foundations from the social sciences.' Theory building in entrepreneurship faces obstacles, some of which are enor- mous enough to faze even the foolhardy. In this brief article we discuss some of these obstacles and what they imply for theory building. We start with problems caused by researchers' inability to agree on a definition of entrepreneurship. Next we will char- acterize the entrepreneurial process. Then we will offer our suggestions of what an "ideal" model of entrepreneurship should comprise. We will then examine how pop- ulation ecology models measure up to our "ideal" standards. Finally, we will end by suggesting several new mathematical constructs that entrepreneurship research might explore. SOME DEFINITIONS Good science has to begin with good definitions. Perhaps some empiricists believe they can function without precise definitions, but we doubt it. How, for instance, can empiricists know what phenomena they are studying if they cannot define what they have observed? What is clear is that theorists cannot function without exact definitions. After all, models and theories predict the outcome of operations. And it is impossible to operationalize a concept that cannot be defined. As Bridgman (1927) wrote and Dewey (1929) reiterated: "the concept is synonymous with the corresponding set of opera- tions," All researchers recognize the importance of definitions, but we entrepreneurship scholars have been embroiled in a never-ending debate over the definition of an entre- preneur. Of course, we can infer a definition of a term by "observing what a man does with it, not by what he says about it" (Bridgman 1927). But this is not good science. In the absence of a universally accepted scientific definition of an entrepreneur, it is the responsibility of every researcher to state clearly what is meant when the term is used. 1. Here is an example from another of the management sciences: Perhaps the most used theory in the business strategy paradigm is Porter's five-forces model (Porter, 1980). Its roots are firmly planted in concepts from economics. Winter, 1991 13

Theorizing about Entrepreneurship

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1042-2587-9M62$1 50Copyright 1991 byBaylor University

Theorizing aboutEntrepreneurshipWilliam D. BygraveCharles W. Hofer

/ \ t the start of the 1980s, entrepreneurship was, at best, a potentially promisingfield of scholarly inquiry. However, by the end of that decade, due primarily to im-pressive advances in its body of empirical knowledge, entrepreneurship could claim tobe a legitimate field of academic inquiry in all respects except one: it lacks a substantialtheoretical foundation. A major challenge facing entrepreneurship researchers in the1990s is to develop models and theories built on solid foundations from the socialsciences.' Theory building in entrepreneurship faces obstacles, some of which are enor-mous enough to faze even the foolhardy. In this brief article we discuss some of theseobstacles and what they imply for theory building. We start with problems caused byresearchers' inability to agree on a definition of entrepreneurship. Next we will char-acterize the entrepreneurial process. Then we will offer our suggestions of what an"ideal" model of entrepreneurship should comprise. We will then examine how pop-ulation ecology models measure up to our "ideal" standards. Finally, we will end bysuggesting several new mathematical constructs that entrepreneurship research mightexplore.

SOME DEFINITIONS

Good science has to begin with good definitions. Perhaps some empiricists believethey can function without precise definitions, but we doubt it. How, for instance, canempiricists know what phenomena they are studying if they cannot define what theyhave observed? What is clear is that theorists cannot function without exact definitions.After all, models and theories predict the outcome of operations. And it is impossible tooperationalize a concept that cannot be defined. As Bridgman (1927) wrote and Dewey(1929) reiterated: "the concept is synonymous with the corresponding set of opera-tions,"

All researchers recognize the importance of definitions, but we entrepreneurshipscholars have been embroiled in a never-ending debate over the definition of an entre-preneur. Of course, we can infer a definition of a term by "observing what a man doeswith it, not by what he says about it" (Bridgman 1927). But this is not good science. Inthe absence of a universally accepted scientific definition of an entrepreneur, it is theresponsibility of every researcher to state clearly what is meant when the term is used.

1. Here is an example from another of the management sciences: Perhaps the most used theory in thebusiness strategy paradigm is Porter's five-forces model (Porter, 1980). Its roots are firmly planted inconcepts from economics.

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Articles describing empirical research, for example, should specify as precisely aspossible the population being studied.

However, since scholars have been unable to agree on a definition of an "entrepre-neur" in the 75 years or thereabouts since Schumpeter produced his seminal work onentrepreneurs, we would be unwise to attempt to produce an authoritative definition insuch a short work. What may be useful, however, is to shift the focus from which suchdefinitions are made. One example of the productivity of such shifts is the field ofStrategic Management/Business Policy. The original focus of that field was on the"roles and functions of the general manager," as indicated in Figure 1. Unlike thedefinition of "entrepreneur," there were no major disagreements about what a "generalmanager" was. Nonetheless, the research productivity of that field was quite low duringthe three decades during which the "general manager" served as the primary focus ofthe field. Starting in tbe mid-1960s, however, the focus of tbat field shifted from the"roles and functions of the general manager" to "the strategic processes of the orga-nization" with the publication of Chandler's (1962) and Andrews' (1971) seminal works(see Figure 1). And with this shift in focus has come a major "sea-change"^ in tbemagnitude of research done in that field.

In a similar fashion, it may be useful to shift our focus from "the characteristics andfunctions of the entrepreneur" and the myriad definitions of what constitutes an entre-preneur, and to focus, instead, on the nature and characteristics of the "entrepreneurialprocess," as depicted in Figure 2. Given this shift in focus, two working definitionsimmediately follow.

• An Entrepreneurial Event involves the creation of a new organization to pursue anopportunity (Bygrave, 1989a, b, c).

• The Entrepreneurial Process involves all the functions, activities, and actionsassociated with the perceiving of opportunities and the creation of organizations topursue them.

Furthermore, based on these two definitions, it is then possible to define an enterpreneuras follows:

• An Entrepreneur is someone who perceives an opportunity and creates an orga-nization to pursue it.

Before proceeding, we would note that we have deliberately crafted these definitions tohave as few restrictions as possible because authoritative definitions are not necessaryfor the purpose of this article.

THE ENTREPRENEURIAL PROCESS

Let us start with the entrepreneurial process because this is at the heart of the matter.If researchers could develop a model or theory to explain entrepreneurial processes, theywould have the key that unlocks the mystery of entrepreneurship. To be good, such amodel or theory must be deterministic in the sense that a given set of antecedents results

2, Peter Drucker coined the term "sea-change" in 1980 in his text. Managing in Turbulent Times. Ac-cording to Drucker. a "sea-change" is a change that is substantial in magnitude, comprehensive andpervasive in its impact on all aspects of the system affected, enduring in its influence over time, andgenerally rapid or even discontinuous in its occurrence.

14 ENTREPRENEURSHIP THEORY and PRACTICE

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Figure 1The Changing Definitions of the Field of BusinessPolicy/Strategic Management

The Traditional Boundary of Business Policy

The AFunctions and /

Responsibilities 1of General \

Management Y

The\ Strategic1 Processes/ of the

Organization

The Current Boundaries of Strategic Management

Some Major Contributors to the Redefinition of the Fieid

Barnard (1938)

Drucker(1954)

Seiznick (1957)

Ansoff, et ai.(1974)

Ansoff (1979)

Schendel/Hofer(1979)

Chandler (1962)

Source: Hofer, 1986. p. x.

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Figure 2Changing the Eocus of the Field of Entrepreneurship

The Traditional Definition of the Field

The CharacteristicsoftheEntrepreneurialProcess

The Characteristicsand Functions ofthe Entrepreneur

A Revised Definition of the Field

Some of the Key Questions in the Field

Focused on the EntrepreneurFocused on the Entrepreneurial

Process

1. Who becomes entrepreneurs?2. Why do people become entrepreneurs?3. What are the characteristics of successful

entrepreneurs?4. What are the characteristics of unsuccessful

entrepreneurs?

1, What's involved in perceiving opportunitieseffectively & efficiently?

2. What are the key tasks in successfullyestablishing new organizations?

3. How are these tasks different from thoseinvolved in successfully managing ongoingorganizations?

4, What are the entrepreneur's uniquecontributions to this process?

in a single, specific outcome. With that kind of predictive power, we would have the keyto economic growth! Need we say any more!! Entrepreneurship would be the giant of thebusiness sciences, perhaps all the social sciences!! It would be, to borrow Penrose'stheory-classifying schema, a "useful theory."^

3. For the physical sciences, Penrose (1989) proposed three broad categories of theories: (I) Superb, (2)Useful, and (3) Tentative, Superb theories, such as Euclidean geometry. Newtonian mechanics, and Ein-steinian special relativity, make predictions that are amazingly accurate. To date, however, all such theoriesfall exclusively in the province of the mathematical and physical sciences. According to Penrose, there areno basic theories in any other science that can be classified as "Superb." Darwinian natural selection comesclosest, but it is still a good way off. Useful theories are rather more untidy than superb ones. Thus, whilethey generally make good predictions, their predictive power falls far short of the amazing accuracy ofsuperb theories. Tentative theories, by contrast, are just that—tentative! Their predictions are. at best, vagueand of limited accuracy, and they generally lack significant empirical support. Consequently, it seems to usthat the very best we can hope for in the field of entrepreneurship is useful models and theories.

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Some of the important characteristics of the entrepreneurial process are the following

• It is initiated by an act of human volition.• It occurs at the level of the individual firm.• It involves a change of state.• It involves a discontinuity.• It is a holistic process.• It is a dynamic process.• It is unique.• It involves numerous antecedent variables.• Its outcomes are extremely sensitive to the initial conditions of these variables.

Taken together, these characteristics create a set of parameters and criteria that willhave to be met by any "ideal" model of entrepreneurship. It is, therefore, appropriateto examine each of them more carefully and completely,

AN "IDEAL" MODEL FOR ENTREPRENEURSfflP

The characteristics of the entrepreneurial process described above are extremelydemanding theoretically. What do they imply for an "ideal" model of entrepreneurship?

First and foremost, the essence of entrepreneurship is the entrepreneur (see, e.g.,Mitton, 1989). So a useful model of entrepreneurship must recognize the importance ofhuman volition. However, this requirement poses a major, and probably insurmount-able, challenge for those who favor mathematical modeling approaches, because "thereis an essential non-algorithmic aspect to the role of conscious action" (Penrose, 1989),

The act of becoming an entrepreneur involves changing the extemal environmentfrom one state (that without the venture) to another (that with the venture). It alsorepresents a basic discontinuity in the competitive structure of the industry involved.Sometimes it even involves the creation of the industry itself. In addition, it is holistic;i.e., the creation of the venture and its probabilities of success can be described andevaluated only as part of the total industry system. It is dynamic as well, since both theventure and the industry of which it is a part are evolving over time. It is also uniquesince no other industry or competitive situation will be exactly like it or evolve in exactlythe same way. Furthermore, the entire process is incredibly sensitive to a multitude ofantecedent variables: the number, strength, and positioning of competitors; the re-sources, positioning, and strategy ofthe venture; the size, growth, and needs of currentand future customers, etc. In total, these specifications would be more than enough todisconcert even the most gifted applied mathematician who tried to build a theory on thelaws of the physical sciences, let alone on the suppositions of the social sciences. Andyet any theory of entrepreneurship must be rooted in the social sciences, such as an-thropology, psychology, sociology, economics, and politics, because these are the sci-ences that describe the key variables that underlie the process of venture creation (see,for example, Stevenson, 1987).

Finally, a theory of entrepreneurial processes must have "classical" determinism asopposed to "probabilistic" determinism. This means it must have predictive powervis-a-vis specific individual firms, rather than just for a population of firms.

POPULATION ECOLOGY AS A MODEL EOR ENTREPRENEURSfflP

Population ecology is prominent as a theory of the birth, survival, and death oforganizations (e.g., Hannan & Freeman, 1977). It provides, therefore, one set of models

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on which the field of entrepreneurship might be built. Let's see how it measures up toour " idea l" model described above.

In their basic forms, population ecology models predict the probability of births anddeaths within a population of firms in a given industry niche. However, just as anactuary can forecast tbe number of births and deaths in a human population but cannotpredict the specific persons who will be bom or die, likewise, population ecologistscannot predict the fate of specific firms. Thus, population ecology models make statis-tical predictions at the population level rather than the individual firm level. Likepre-Fisherian statistics, these models deal with averages, whereas the most interestingentrepreneurial events are to be found in the variances, usually several standard devia-tions from the mean. The destiny of emerging industries is determined by Schumpeterianentrepreneurs who, above all else, are anything but average! They are tmly exceptional,but population ecology is unable to recognize them.

Furthermore, in its extreme form population ecology does not even allow for thevolition ofthe entrepreneur. It is Darwinian—the environment is all potent. Some ofthesubsequent population ecology models, such as the social ecology models, have grownmore Lamarckian—their environments are still dominant, but the entrepreneur/organization can at least leam from experience. Nonetheless, population ecology's/un-damental concepts come not from the social sciences, as one might expect for a theoryof human organizations, but from the field of biology. Thus, even though it is tme thatsome population ecology models do incorporate a few suppositions from the fields ofsociology (drawn largely from Stinchcombe's (1965) seminal article), administrativescience, and organizational politics (Young, 1988), it is also tme, as Young pointed out,that these few social science suppositions do not alter or modify in any substantial waythe biological theories on which these population ecology models are based, nor arethey, for the most part, modified by it. In short, population ecology models violate twoof the major characteristics of an " ideal" entrepreneurship model, namely that it makepredictions at the level of the individual firm, and that it allow a role for human volition.

The most critical test of any model or theory, however, is its ability to predict futureoutcomes with accuracy. And that, it appears to us, is where population ecology comesup most short. Thus, it has been almost fifteen years since Hannan and Freeman firstproposed their theory, yet we know of NO instance where population ecologists havemade accurate predictions about future births and deaths in any industry! All the tests ofthe theory to date have been based on historical data sets. To be sure, some of these datasets, such as that in Carrol! and Delacroix's (1983) study of the newspaper industries inArgentina in the nineteenth century, are too ancient to be used for making predictionsabout the 1980s and 1990s. But other population ecology researchers have looked atmodem industries in recent times. These include Brittain and Freeman's (1980) study ofthe U.S. semiconductor industry; Freeman and Hannan's (1983) study ofthe restaurantsin 18 Califomia cities; and Romanelli" s (1989) study ofthe U.S. minicomputer industry.If any of these researchers had made forecasts about future fluctuations of births anddeaths in the industries they studied, they could have provided a valuable test of pop-ulation ecology's predictive power. But they did not do so. Until such a test is made,however, population ecology will remain, at best, a tentative model with no provenpredictive power. This is especially true since one recent population ecology "predic-tion" has failed. Specifically, in a review of population/organizational ecology litera-ture, Singh and Lumsden (1990) concluded that the empirical evidence supporting thedensity dependence of organizational foundings was "overwhelmingly strong" in alarge variety of organizational populations. Subsequently, Singh and his colleagues usedorganizational ecology theory to hypothesize about the founding rate of biotechnologyfirms. But when they tested their hypothesis on 448 U.S. biotechnology foundings

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between 1973 and 1987, the empirical evidence contradicted their hypothesis (Shan,Singh, & Amhurgey, 1991).

Of course, population ecology does offer some prescriptions. For instance, it pre-scribes when it is better to be a specialist than a generalist and vice versa. But similarprescriptions can also be made by other theories, such as Porter's five-forces model. Itis no surprise, then, that practicing entrepreneurs and business executives have neverheard of population ecology or organizational ecology, whereas many are familiar withPorter's model (1980). Porter's theory is widely accepted because it fiows logically fromconcepts firmly rooted in economics. It also uses the language of economics and busi-ness. In contrast, population ecology uses the language of biology.

Consequently, even though population ecology models are used frequently in thefield of entrepreneurship, they are a long way from our "ideal" model. It is also true,of course, that our "idea!" model has some very exacting specifications. Nonetheless,it seems to us that population ecology models can never meet our "ideal" specificationsunless they abandon their biological foundations—which might not be such a bad idea,because many of their hypotheses can be deduced from existing social science models(Young, 1988) without the need for assistance from biology.

Put more directly, the biological algorithms that lie at the foundation of existingpopulation ecology models impose several basic limitations on their ability to meet thespecifications of an "ideal" entrepreneurship model. First, they are based on analyticfunctions, while the essence ofthe entrepreneurial process is a fundamental discontinuityin the industry involved. Second, they treat organizations as black boxes, closed to aninspection of their inner workings, whereas the entrepreneur inside that box is crucial toour "ideal" entrepreneurship model. Third, they are deterministic only in a "probabi-listic" sense, rather than in a "classical" sense, and, therefore, can make predictionsonly about populations, not about individual firms. Moreover, even their probabilisticpredictive power for populations has never been proven. In short, if we want an "ideal"algorithm for a theory of entrepreneurship, we must look elsewhere.

ENTREPRENEURSmP AND MODERN MATHEMATICS

Modem mathematics has developed a number of techniques that, at first glimpse,might help us as we try to develop "ideal" models for entrepreneurship. At the sametime, we would emphasize that we do not subscribe to the Laplacian fantasy that al!entrepreneurial processes can be described with mathematical models. Rather, we be-lieve that these developments in modem mathematics allow more precise descriptions ofvarious key aspects of the entrepreneurship field—so much so, in fact, that we shoulddo our best to ensure that we use the most appropriate modeling techniques availableinstead of relying almost exclusively on linear regression models, which often distort thebasic phenomena being modeled. Here, briefiy, is a list of some of the new techniquesthat are worth exploring:

1. Changes of state of physical systems have simplistic parallels with changes ofphase in organizations, including start-ups. Physicists have made great strides in theirability to model changes of state. Perhaps their techniques can be applied to entrepre-neurship. For example, they might be used to model the transition from the pre-start-upphase to the start-up phase of an entrepreneurial event.

2. Self organized criticality may explain the dynamics of some economic markets(Bak & Chen, 1988, 1991). Large interactive systems evolve toward a critical state inwhich a minor event can lead to a sudden, dramatic change, such as the October 1987stock market crash. It has the feel of what happens in entrepreneurial industries. For

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instance, venture capitalists continued to invest like lemmings in Winchester drivestart-ups long after it should have been clear that the potential retums did not justify therisk (Sahlman & Stevenson, 1986).

3. Catastrophe theory is a mathematics for systems that make sudden transitionsfrom one stable state to another, which is not unlike the entrepreneurial event (Thom,1983; Ekeland, 1988), Bygrave 1989b, for example, looked at whether catastrophetheory could explain Bill Foster's founding of Stratus.

4. Chaos theory produces models in which outcomes are incredibly sensitive tochanges in the initial conditions. As such it has the feel of entrepreneurship (see, e.g.,May, 1976; Bygrave, 1989b). As an example: If Fred Terman had not fallen ill withtuberculosis when vacationing in Palo Alto, there might not be a Silicon Valley today(Rogers & Larsen, 1984).

5. Positive-feedback economics is a new economic theory in which positive feed-back (as opposed to negative feedback of conventional economics) magnifies the effectsof small economic shifts. I t ' 'elucidates a mechanism whereby small chance events earlyin the history of an industry or a technology can tilt the competitive balance" (Arthur,1990). It could help us understand the entrepreneurial process in knowledge-basedindustries. For instance, in a study of 448 biotechnology firms created from 1973-1987,Shan, Singh, and Amburgey (1991) discovered that the relationship between foundingrates and density contradicted what negative-feedback (or diminishing retums) econom-ics predicted. On the other hand, their finding was consistent with positive-feedbackeconomics (Bygrave, 1991).

6. Quantum mechanics is able to deal with systems that make sudden transitions.Among other things, it is able to predict the appearance of particles, and explain howparticles tunnel through seemingly impenetrable barriers. Again, it has charming par-allels to entrepreneurship. Recently, Baumol (1991) suggested that rather as the Heisen-berg uncertainty principle imposes limits on observing quantum events, so too there arelimits on observing mega-entrepreneurial events of the kind that create new industries.Each one is unique. If you could describe it completely, you could replicate it, and itwould become management instead of entrepreneurship.

7. Topology is an exciting branch of mathematics that studies the properties ofgeometrical figures. It is at the cutting edge of science. Thanks to topology, sometheoretical physicists even believe that they may be at the brink of a "theory of every-thing" (Davies & Brown, 1988). Topology can give significant quantitative resultsabout continuous phenomena without requiring a knowledge of detail. Catastrophe the-ory, for example, uses topology to classify the ways in which a dynamical system passesthrough an instability. Topology has been applied to economics and sociology. It surelyhas something to offer entrepreneurship besides catastrophe theory.

CONCLUDING COMMENTS

There is little likelihood of an entrepreneurial model ever being developed that willmeet our " idea l" specifications. In fact, we hope that we have shown that it is extremelydifficult to develop even "useful" entrepreneurship models. On the other hand, ifentrepreneurship researchers feel that they must pursue mathematical models, we urgethem to look beyond regression analysis for several reasons. First, regression analysis isreductionist, while entrepreneurship is holistic. Also, regression analysis usually gen-erates smoothly changing analytic functions, while entrepreneurship deals with suddenchanges and discontinuities. In addition, regression analysis assumes stable models builtwith relatively few variables, rather than unstable models with many variables. In short,

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it is time to abandon the linear, incremental thinking that regression models inculcate.Altemative mathematical techniques, most of which are untapped by entrepreneurshipresearchers, offer far more promise for dealing with the critical characteristics of entre-preneurial processes such as discontinuities, changes of state, sensitivity to initial con-ditions, and multiplicity of antecedent variables.

We will end with this quote from Charles Darwin (1863) on the appropriate balancebetween theory and observation:

Let theory guide your observations, but till your reputation is well established, besparing in publishing theory. It makes persons doubt your observations.

REFERENCES

Andrews, K. R. (1971). The concept of corporate strategy. Homewood, IL: Dow Jones-lrwin.

Arthur, W. B. (1990). Positive feedbacks in the economy. Scientific American. 262(2), 92-99.

Bak, P., & Chen. K. (1988). Self-organized crilicality. Physical Review, 38(\), 364-373.

Baumol. W. J. (1991). Entrepreneurship theory: Existence and inherent bounds. Presented at the Confer-ence on Entrepreneurship Theory, University of Illinois at Urbana-Champaign, October 18-19.

Bridgman. P. W. (1927). The logic of modern physics. New York: Macmillan.

Brittain, J. W.. & Freeman, J. (1980). Organizational proliferation and density dependent selection. InJ. R. Kimberly and R. H, Miles (Eds.), The organizational life cycle, pp. 291-338. San Francisco: Jossey-Bass.

Bygrave, W. D. (1989a). The entrepreneurship paradigm (I): A philosophical look at its research method-ologies. Entrepreneurship Theory and Practice, 14(1), 7-26.

Bygrave, W. D. {1989b). The entrepreneurship paradigm(II): Chaos and catastrophes among quantumJumps. Entrepreneurship Theory and Practice, 14(2), 7-30.

Bygrave, W. D. (1989c). Micro, macro, and corporate entrepreneurs: Can they all fit in the same paradigm?Presented at the Academy of Management Annual Meeting, Washington, DC. August.

Bygrave, W. D. (1991). Theory building in the entrepreneurship paradigm. Presented at the Conference onEntrepreneurship Theory, University of Illinois at Urbana-Champaign. October 18-19.

Chandler, A. D., Jr. (1962). Strategy and .structure: Chapters in the history of American industrial enter-prise. Cambridge, MA: MIT Press.

Darwin, C. (1903). Letter to Scott, June 6, 1863. In F. Darwin & A. C. Seward (Eds.), More letters ofCharles Darwin, vol. II, p. 323. New York: Appleton.

Davies, P. C. W., & Brown. J, (1988). Superstrings: A theory of everything. Cambridge: CambridgeUniversity Press.

Dewey, J. (1929). The quest for certainty. New York: J.J. Little and Ives Company.

Ekeland, 1. (1988). Mathematics and the unexpected. Chicago: University of Chicago Press.

Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal ofSociology, 82(5), 929-964.

Hofer, C. W. (1986). Foreword. In I. C. MacMUlan & P. E. Jones, Strategy formulation: Power andpolitics (2nd ed.), pp. ix-xiii. St, Paul, MN: West Publishing Company.

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Hofer, C, W. (1987). Some guidelines for more effective strategic management research. Invited presen-tation, NOVA University, August,

May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459-467.

Mltton, D, G. (1989), The complete entrepreneur. Entrepreneurship Theory and Practice. / i(3), 9-19.

Penrose, R. (1989). The emperor's new mind: Concerning computers, minds, and the laws of physics. NewYork: Oxford University Press.

Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. NewYork: The Free Press.

Rogers, E. M., & Larsen, J. K, (1984). Silicon Valley fever. New York: Basic Books.

Romanelli, E. (1989), Environments and strategies of organization start-up: Effects on early survival.Administrative Science Quarterly, 34. 369-387.

Sahlman, W. A.. & Stevenson H, H. (\9^6). Capital market myopia. Journal of Business Venturing. / (I) .7-30,

Shan, W., Singh, J. V., & Amburgey, T. L. (1991). Modeling the creation of new biotechnology firms,1973-1987. Presented at the Academy of Management Annual Meeting, Miami, August.

Singh, J. V., & Lumsden, C, J. (1990). Theory and research in organizational ecology. Annual Review ofSociology. 16. 161-195,

Stevenson, H, H. (1987). Entrepreneurship education: Crisis or opportunity. Presented at the SeventhAnnual Meeting of the Strategic Management Society, October.

Stinchcombe, A. L. (1965). Social structure and organization. In J. G. March (Ed.), Handbook of orga-nizations, pp. 153-193. Chicago: Rand McNally.

Thom, R. (1983). Paraboles and catastrophes. Paris: Flammarion.

Young, R. C. (1988). Is population ecology a useful paradigm for the study of organizations? AmericanJournal of Sociology. 94(1), 1-23.

William D. Bygrave is the Frederic C. Hamilton Professor for Free Enterprise at Babson College,

Charles W. Hofer is Professor of Strategy and Entrepreneurship at the University of Georgia.

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