This study simultaneously considers the constructs of prior knowledge and potential financial reward to provide a deeper understanding of the discovery of opportunities to bring into existence future goods and services (Venkataraman, 1997). The results suggest that those individuals with greater prior knowledge of customer problems were able to discover more opportunities and opportunities of a greater scale (more innovative). Additionally, possible financial reward had a significant and positive relationship with the number of opportunities discovered. Finally, it was found that although prior knowledge of customer problems leads to increased discovery, it also moderates the relationship between financial reward and discovery. The possibility of future reward had less effect on the level of discovery for those people with considerable prior knowledge than for those with less prior knowledge.

In a rapidly changing world, organizations need to continually identify new opportunities beyond existing competencies if they are to survive and prosper (Hamel & Prahalad, 1989; Mintzberg, 1994; McGrath, Tsai, Venkataraman, & MacMillan, 1996). Not surprisingly there has been considerable interest in why, when and how some people are able to discover opportunities, while others cannot or do not (Shane and Venkataraman, 2000). Both prior knowledge and the promise of financial gain have been central to recent investigations. Prior knowledge of a particular field provides individuals the capacity to recognize certain opportunities (Venkataraman; 1997; Shane, 2000) and the promise of financial reward “switches on” an individual’s “motivated propensity” to discover that opportunity (Kirzner, 1979, 1985, 1997).

Despite the fact that investigating prior knowledge has the potential to add significant insights into entrepreneurial discovery, relatively little research has examined the connections between prior knowledge and financial reward. Consequently, in this study we investigated three questions that have not yet been sufficiently addressed in the literature: Does the level of an individual’s prior knowledge increase the level of entrepreneurial discovery (Shane [2000] represents an exception), does the level of possible financial reward increase the level of entrepreneurial discovery, and what is the relationship between level of prior knowledge and financial reward in the entrepreneurial discovery process? To address these questions, we conducted an experimental study examining whether the prior knowledge of customer problems is directly related to discovery, whether possible financial reward is related to discovery, and how prior knowledge of customer problems moderates the relationship between possible financial reward and entrepreneurial discovery.

Our results complement the work of previous scholars and shed new light on the discovery of opportunities. In particular, we show that (1) prior related knowledge of customer problems increases the number of opportunities discovered and the level of innovativeness of these opportunities, (2) financial reward was moderated by prior knowledge of customer problems in its relationship with the number of opportunities discovered and the degree of innovativeness of these opportunities. While we find that financial reward is an incentive for individuals with little prior knowledge of customer problems, it provides minimal incentive for those that have considerable prior knowledge of customer problems. The joint consideration of prior knowledge and incentive provides a more complete picture of entrepreneurial discovery than when these relationships are investigated independently. These findings support Austrian arguments that prior knowledge influences the discovery of opportunities and sheds new light on the relationship between prior knowledge, financial rewards and entrepreneurial discoveries.

The paper proceeds as follows.Therefore, we propose that as valuable as empirical research within the distinctive domain of entreprneurship is, we need a conceptual discussion that captures the existing literature on opportunity recognition and exploitation, allows for the interpretation of these studies as a collective work, and provides guidance where future research opportunities might be discovered. This paper makes a small but important step towards a discussion of a unifying framework for opportunity creation, discovery and exploitation, i.e., for entrepreneurship.  First, we introduce the relationship between prior knowledge and discovery, between possible financial reward and discovery, and then the joint effect of prior knowledge and possible financial reward on discovery. We hypothesize that both prior knowledge of customer problems and potential financial incentive lead to increased discovery. But we also hypothesize that the prior knowledge of customer problems moderates the relationship between possible financial reward and discovery. Second, we describe the research method to empirically test the hypotheses. Third, the results are presented and discussed in relation to the literature and interviews with six entrepreneurs.

Prior Knowledge and Discovery

The level of prior related knowledge has been found to increase the ability to evaluate and utilize outside knowledge (Tilton, 1971; Allen, 1977; Mowery, 1983; Cohen and Levinthal, 1990). This prior knowledge creates a “knowledge corridor” that allows individuals to discover certain opportunities (Kirzner, 1985; Nelson and Winter, 1982; Venkataraman; 1997). The entrepreneur must perceive the market environment correctly, identify the true driving forces and critical factors; and infer the real relational dynamics among these elements (Gaglio and Katz, in press).

Shane (2000) proposes three major dimensions of prior knowledge that are important to the process of entrepreneurial discovery: prior knowledge of markets, prior knowledge of ways to serve markets, and prior knowledge of customer problems. In this paper we focus on prior knowledge of customer problems. Von Hippel (1988) argued that entrepreneurs start new companies to solve customer problems that they had learned about through experience and Shane (2000) found that prior knowledge of customer problems influenced the discovery of solutions. We consider the extent of discovery to include both the number of opportunities discovered and the innovativeness (radicalness) of those opportunities.¹

Hypothesis 1: Increased prior knowledge of customer problems will increase (a) the number of opportunities discovered and (b) the innovativeness of those opportunities.

Financial Incentive and Discovery

Knowledge obtained in a particular “knowledge corridor” can lead to profit making opportunities (Kirzner, 1985; Nelson and Winter, 1982), but the incentive to profit from useful knowledge is important and likely varies among individuals (Shane & Venkataraman, 2000). Kirzner (1985, 1979) theorizes about the incentive necessary for entrepreneurship and the discovery of opportunities. An individual with entrepreneurial alertness has a “motivated propensity” to formulate an image of the future (Kirzner, 1985, p. 56). Entrepreneurial alertness will not be activated unless the individual has an incentive to do so. In order to “switch on” an individual’s alertness they must perceive potential financial reward (pure gain) (Kirzner, 1985).² Thus,

Hypothesis 2: Increased financial reward will increase (a) the number of opportunities discovered and (b) the innovativeness of those opportunities.

Prior Knowledge, Financial Reward and Discovery

However, does possible financial reward (or pure gain) always have the same impact of “switching on” an individual’s alertness to the discovery of opportunities? We expect that prior knowledge may moderate this relationship. Such an expectation is consistent with Kuhn’s theory of scientific advancement.

Kuhn (1970) proposes that scientific discovery arises from an individual’s dissatisfaction with the status quo. This dissatisfaction does not necessarily arise from one anomaly or signal but the accumulation of a number of signals where there is a deviation of the outcome from what is expected. Only those individuals that integrate perceived anomalies into a pattern believe that the status quo may not be the most accurate description of “reality” (Johnson, Jamal, and Berryman, 1991; Gaglio and Katz, in press). The accumulated anomalies build to a critical size that creates a crises—a pronounced dissatisfaction with the status quo (Kuhn, 1970). When people reach a threshold of sufficient dissatisfaction with existing conditions and their perceived reality, they initiate action to resolve their dissatisfaction (Van de Ven, et al., 1999) giving rise to discovery (Kuhn, 1970).

From the above discussion, it appears that prior knowledge generates an intrinsic motivation to discover opportunities. Intrinsic motivation refers to individuals driven by deep interest and involvement in the work, by curiosity, enjoyment, or a personal sense of challenge (Amabile, 1997). Creativity studies have postulated the importance of an intrinsic motivational orientation (Amabile, 1990; Barron and Harrington, 1981) over and above an extrinsic one (Amabile, 1997). From these findings we propose that in the presence of prior knowledge, a strong intrinsic motivation is aroused and is the primary incentive that “switches on” alertness with possible financial reward (extrinsic motivation) less motivating. But in the absence of prior knowledge of customer problems, a strong intrinsic motivation is not aroused and possible financial reward “switches on” entrepreneurial alertness to the discovery of opportunities. Thus,

Hypothesis 3: Increased prior knowledge of customer problems will diminish the positive effect of possible financial gain on the (a) number of opportunities discovered and (b) the innovativeness of those opportunities.

The conceptual model described above is summarized in Figure 1. The figure illustrates that those individuals who have more prior knowledge of customers’ problems (H1), and those that have higher financial incentive (H2) will discover a greater number of opportunities and opportunities on a more innovative (radical) scale (See Figure 1). We also hypothesize that the level of prior knowledge of customer problems moderates the relationship between financial reward and discovery (H3).

Experimental studies, that manipulate information about opportunities, have been recommended as a possible approach to studying the discovery of opportunities (Shane, 2000). This study uses such an approach. This experimental method allows the researcher to control for possibly confounding variables not contained in the model and does not rely on a respondent’s (generally inaccurate) introspection (Fischhoff, 1988) or retrospective reporting. The tradeoff is that experiments require that stimuli be reduced to a manageable level. While the information within the decision exercise does not perfectly mirror the more complex “real life” decision, abstract representations have been found to be a valid method (Brehmer and Brehmer, 1988).  We complement this level of abstraction with qualitative data from interviews with entrepreneurs in the discussion section that follows.

Independent and Dependent Variables

The experiment presented information from a focus group that detailed customer problems with footwear. The focus group discussions represented five underlying dimensions of customer problems with footwear, i.e., style, comfort, quality, performance, and price. The experiments differed in the amount of information provided. Half the experiments contained 5–7 pieces of information for each underlying dimension and received information on all five underlying dimensions of customer problems. The other half of the experiments presented less information, 3–5 pieces of information for each underlying dimension and information on only three of the five underlying problems.

The experiment manipulated the ability of the respondents to accumulate the above information of customer problems with footwear. For half the experiments (evenly split among high and low awareness of customer problems) respondents received only information about customer problems that were relevant (the footwear market), were required to create a list of the underlying customer problems after the presentation of each focus group discussion, and were encouraged to refer back to previous presentations of focus group discussions and their previous lists of underlying customer problems. For the other half of the experiments, accumulation of knowledge of customer problems with footwear was made more difficult. Respondents received not only the focus group comments on footwear but also on every alternating page focus group comments on transportation (a distraction). They were not asked to create a list of problems as they progressed through the experiment, and they were not allowed to refer back to any previous pages. At the completion of the experiment, respondents were asked to list the customer problems that underlie the information presented and then to list solution(s) to these customer problems.

Before the respondents began creating both the list of problems and the solution list, they were offered a possible financial reward. For half of the experiments (evenly distributed across the other manipulations) the financial reward was represented by the promise of $50 for the most innovative solution(s). The other half of the experiments offered only $1.  $50 represented high financial reward and $1 represented low financial reward (they were dummy coded 1 and 0 respectively).

To operationalize the independent variable of prior knowledge of customer problems, the respondents’ problem lists were assessed by two coders to determine the level of acknowledgement of the underlying problems with footwear. The coders used a seven point scale anchored by little acknowledgement and considerable acknowledgement for each of the underlying problems. The level of prior knowledge of customer problems for an individual was the sum of these ratings across all five dimensions.

We had two dependent variables to capture the discovery of opportunities. The number of opportunities discovered was operationalized by the sum of the number of opportunities counted by the raters and innovativeness was operationalized by the sum of the ratings on scale for each problem dimension recorded on a seven point scale anchored by not very innovative and very innovative. The inter-rater reliability was acceptable indicating consistency among coders.³

Sample

The sample for this experiment was 78 MBA and executive MBA students at a large university in Colorado, USA. Though there has been some criticism of the use of students as subjects in behavioral research (Alpert 1967; Copeland, Francia & Strawser 1973), there have also been studies that have shown that graduate students are adequate surrogates for business decision makers in some situations (Remus, 1986; Khera & Benson, 1970). Additionally, Weick (1967) found that if a decision-making task is chosen for which experience is not a moderator variable, then the use of students might not be a serious limitation. In this study, prior knowledge was manipulated by the experiment and therefore did not require respondents to have any specific type of background nor does it require any particular experience. Our approach is consistent with that used by behavioral economics scholars where the use of student samples in experiments is common (e.g., Kahneman, Knetsch & Thaler, 1990; Boles & Messick, 1995; Strahilevitz & Lowenstein, 1998).

We use hierarchical regression as the statistical technique to explain variance in individuals’ opportunity discovery. Descriptive statistics are reported in Table 1. The mean level of prior knowledge of customer problems across the sample is 6.45 (s.d. = 3.66). Fifty percent of the sample received the offer of a high reward ($50) and the rest a low one ($1). The mean total number of opportunities discovered is 2.81 (s.d. = 1.82) and the mean total scale of these opportunities across the five possible problem dimensions is 4.31 (s.d. = 4.31). The inter-correlation matrix indicates significant positive correlations between the two dependent variables and between each dependent variable and prior knowledge of customer problems. Money is only significantly correlated to the number of opportunities discovered. There is only a low (non-significant) correlation between the two independent variables.

Table 2 reports the results of the hierarchical regression analysis. The base model, which includes only the main effects for prior knowledge of customer problems and financial reward explained a significant portion of the variance for both the number of opportunities discovered and the scale of those opportunities (R² = .177, p<.01; R² = .228, p<.01; respectively). The base model for the number of opportunities suggests that both prior knowledge of customer problems and financial reward are significant—the higher both the prior knowledge of customer problems and the higher the financial reward the greater the number of opportunities discovered. This provides support for hypotheses 1a and 2a, respectively. The base model for the innovativeness of opportunities discovered suggests that only prior knowledge of customer problems has a significant relationship—the higher the prior knowledge of customer problems, the more innovative the opportunities discovered. This finding provides support for hypothesis 1b. No significant relationship was found between financial reward and the innovativeness of the opportunities discovered and therefore no support for hypothesis 2b.

The extended model, relative to the base model, significantly increased the amount of variance explained. For the number of opportunities discovered the DR² is .075 (p<.01) and for scale of opportunities the DR² is .164 (p<.01). The main effects and the prior knowledge of customer problems x financial reward interaction explain a significant amount of variance in both the number of opportunities discovered and the scale of these opportunities. These findings suggest that the prior knowledge of customer problems x financial reward interaction explains a significant amount of variance over and above that explained by the main effects. The value inflation factors were below 10 indicating that multicolinearity is unlikely to have confounded the results.

To interpret the nature of the interaction, the relationship was plotted on a y-axis of discovery (number and then scale) and an x-axis of prior knowledge of customer problems for high and low levels of financial reward (cf. Baker and Cullen [1993] and Cohen and Cohen [1983]). They reveal that the nature of the interaction to be similar for both dependent variables. Increased prior knowledge of customer problems will diminish the positive effect of possible financial reward on entrepreneurial discovery. These findings that prior knowledge of customer problems moderate the relationship between financial reward and both the number of opportunities discovered and the scale of these opportunities provides support for hypotheses 3a and 3b respectively.

This study attempts to integrate the constructs of prior knowledge and financial reward to provide a deeper understanding of the discovery of opportunities to bring into existence future goods and services. Using Austrian theories of entrepreneurship and Kuhn’s theory of scientific discovery, we examined the joint effects of prior knowledge of customer problems and financial reward on the number of opportunities discovered by an individual and the innovativeness of those opportunities. The study tested three hypotheses and each is now discussed.

Prior Knowledge of Customer Problems and Discovery

Our findings suggest that those individuals with greater prior knowledge of customer problems, were able to discover more opportunities and those opportunities were more innovative. These results are consistent with Kuhn’s theory of scientific advancement. The findings are also consistent with work on innovations by Van de Ven et al. (1999) and work on entrepreneurial discovery by Shane (2000). Van de Ven et al. (1999) conclude that “innovations are not initiated on the spur of the moment, by a single dramatic incident . . . but rather there is an extended gestation period . . . of seemingly random events that occurred before concentrated efforts were launched to develop an innovation (p. 196).” Shane (2000) found that individuals who have developed particular knowledge were more likely to discover entrepreneurial opportunities in response to a given technological change.

The above finding is also consistent with our interviews with entrepreneurs. For example, one entrepreneur created a software business even though they knew nothing about software. This entrepreneur was in the business of delivering ready mix concrete to jobs around a city.  To increase efficiency he needed software that could track and schedule trucks to jobs. He knew the business, knew the scheduling problems of the business, and knew that no solution currently existed. He attributed the discovery of the software opportunity to his extensive knowledge of the problems faced by customers and the inadequacy of technology to provide solutions to these problems.

Financial Reward and Discovery

We were interested in whether possible financial reward would provide an incentive and result in the discovery of more opportunities and more innovative opportunities. We found partial support for this hypothesis. Possible financial reward had a significant and positive relationship with the number of opportunities discovered but not the scale of opportunities. It may be that incentive from financial reward leads to more ideas, but the innovative/radical scale opportunities are tied more to Kirzner’s expanded definition of pure gain and includes such things as fame, power and prestige.This certainly was the case for one of the entrepreneurs that we spoke with. Over time he became more and more willing to take larger risks on more radical ideas. He was in his 70’s, financially secure and yet something motivated him to continue to pursue these radical opportunities. Although he couldn’t articulate what that “something” was, it was likely tied to Kirzner’s expanded definition of pure gain and may have included power or prestige.

One entrepreneur commented that money motivates you to go after more innovations because it increases your chances, like extra tickets in the lottery. Another entrepreneur commented that doing it for the money does not encourage you to spend the time and effort into coming up with something creative. The lack of empirical support for the relationship between financial reward and the innovativeness of opportunities discovered could also be due to the nature of the relationship between possible financial reward and discovery being more complex.

Prior Knowledge of Customer Problems, Financial Reward and Discovery

We proposed that the relationship between financial reward and discovery is more complex than a simple main effect relationship and our findings were consistent with this hypothesis. Financial reward does not always provide an incentive for entrepreneurial discovery. We find that while financial reward is an incentive for individuals with little prior knowledge of customer problems, it provides minimal incentive for those that have considerable prior knowledge of customer problems.

A possible explanation for this finding is that those with greater prior knowledge of customer problems are more likely to have reached a threshold of sufficient dissatisfaction and are more motivated to discover opportunities to resolve this dissatisfaction. Such an explanation is consistent Van de Ven et al.’s (1990) finding that when people reach a threshold of sufficient dissatisfaction with existing conditions they initiate action to resolve their dissatisfaction (Van de Ven, et al., 1999). Such a finding is consistent with the notion of creative tension (Fritz, 1989, 1990). Creative tension is the gap between our envisioned future and the current reality (Senge, 1990). Those that have more prior knowledge of customer problems are more aware of the inadequacies of the current reality and likely have a greater creative tension. Faced with creative tension, individuals will work to move the current reality toward the envisioned future (Senge, 1990). Therefore dissatisfaction with the status quo, which arises from knowledge of market anomalies, appears to motivate entrepreneurial discovery.

Another explanation is that proposed by Cohen & Levinthal (1990: 130) in their work on absorptive capacity. They state “The prior possession of relevant knowledge and skill is what gives rise to creativity, permitting the sorts of associations and linkages that may have never been considered before.” Once individuals obtain prior knowledge, they become more creative. They find associations and linkages that before were not available to them, which causes them to become more driven to find a solution, with or without the promise of financial reward.

While all entrepreneurs interviewed believed that money was a motivator they indicated that it was not the only thing. One of the entrepreneurs interviewed commented that he was frustrated with the current range of products. He knew there was a problem and received a great deal of personal satisfaction in attempting to solve the problem. When people told him he could make a lot of money from his invention, he suggested to us that they were missing the point—it was not about the money but about solving a problem and making people’s lives easier. Another entrepreneur stated in reference to the motivation to innovate that: “finding a solution is the driving force.” Along similar lines another entrepreneur commented: “Personally, money was not the ultimate motivator. I have gotten satisfaction from it because I came up with it and it is a pretty neat idea. . . . It is the right way to do it, it makes it easier for everybody.”

This raises a number of interesting research questions. Does prior knowledge of customer problems lead directly to discovery or is dissatisfaction a mediating variable? More work needs to be done on, and around, the construct of dissatisfaction (or creative tension). An important part of such work will be to distinguish this “dissatisfaction” construct from the “entrepreneurial alertness” construct. While we have attempted to theoretically and empirically make this distinction, more needs to be done.

Finally, we are left with the question of whether this “dissatisfaction” is even part of the entrepreneurial process. “Dissatisfaction” maybe a motivator for a scientist, a religious philosopher, a person that experiments with drugs, as well as an entrepreneur. What makes the discovery of opportunities entrepreneurial is its commercial applicability—the creation of future goods and services. Commercial applicability is implicit in the concept of “pure gain” but not “dissatisfaction.” We would argue that where “dissatisfaction” interacts with “pure gain” in explaining the discovery of opportunities to bring into existence future goods and services, then it is part of the entrepreneurial process.

This paper makes several important contributions to the study of opportunities. Initially we have shown that the level of an individual’s prior knowledge increased the level of entrepreneurial discovery. This reinforces the work by Shane (2000) in which he found that differences in prior information influenced those who discovered entrepreneurial opportunities. Secondly, this study shows that the higher the financial reward the greater the number of opportunities discovered, although these opportunities are not necessarily more radical or innovative. Finally and most significant to the study of opportunities, we found that financial reward is an incentive for individuals with little prior knowledge of customer problems, but it provides minimal incentive for those that have considerable prior knowledge of customer problems. We include as possible explanations for this phenomena: 1) Individuals with greater prior knowledge are more likely to have reached a threshold of sufficient dissatisfaction and are more motivated to discover opportunities to resolve this dissatisfaction; and 2) Once individuals have prior knowledge they become more creative. They find associations and linkages that before were not available to them, which causes them to become more driven to find a solution, with or without the promise of financial reward.

1. It is well known that innovations come in varying degrees of complexity and levels of technological uncertainty (Droge, Vickery & Markland, 1994; Ettlie, Bridges & O’Keefe, 1984; Gaynor 1993). For example, incremental innovations are typically extensions to current product offerings or logical, and relatively minor, extensions to existing processes (Dewar & Dutton, 1986; Ettlie et al., 1984; Henderson & Clark, 1990; Sanderson & Uzumeri, 1997). On the other hand, radical innovations involve the development or application of significantly new technologies into previously non-existent markets. Therefore, it is important to consider not only the number of opportunities discovered but also each opportunity’s level of innovativeness (i.e., radicalness).

2. At first Kirzner (1973) defined “pure gain (profit) as the difference between two sets of prices, a purely monetary view” (pp 48). Later he suggested that pure gain does not necessarily include only pure profit, but also such things as fame, power, prestige, even the opportunity to serve a cause or to help other individuals (Kirzner, 1985 pp 28).

3. Inter rater reliability for accumulated anomalies is .82, number of opportunities generated .66, and scale of opportunities recognized is .82.

CONTACT: Dean A. Shepherd, University of Colorado at Boulder, Boulder, CO 80309-0419; (T) 3003-492-2062; Dean.Shepherd@colorado.edu

Allen, T. (1977) Managing the Flow of Technology. Cambridge, MA: MIT Press.

Alpert, B. (1967) “Non-businessmen as surrogates for businessmen in behaviorAllen, T. (1977) Managing the Flow of Technology. Cambridge, MA: MIT Press.

Alpert, B. (1967) “Non-businessmen as surrogates for businessmen in behavioral experiments.” Journal of Business 40: 203–207.

Amabile, T. M. (1993) “Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace.” Human Resource Management Review 3: 185–201.

Amabile, T.M. (1997) “Motivating creativity in organizations: On doing what you love and loving what you do.” California Management Review 40(1): 39–57.

Baker, D. and J. Cullen (1993) “Administrative reorganization and configurational context: The contingent effects of age, size and change in size.” Academy of Management Journal 36(6): 1251–1277.

Barron F. and D. Harrington (1981) “Creativity, intelligence and personality.” Annual Review of Psychology 32: 439–476.

Boles T. and D. Messick (1995) “A reverse outcome bias: The influence of multiple reference points on the evaluation of outcomes and decisions.” Organizational Behavior & Human Decision Processes 61(3): 262–275.

Brehmer, A. & Brehmer, B. (1988) “What have we learned about human judgment from thirty years of policy capturing?” In B. Brehmer & C. R. B. Joyce (Eds.) Advances in Psychology 54: Human Judgment, the SJT View, Amsterdam: Elseview Science Publishing.

Cohen, J., and P. Cohen, (1983) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. (2 ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Cohen, W. M. and D. A. Levinthal (1990)  “Absorptive Capacity: A New Perspective on Learning and Innovation.” Administrative Science Quarterly 35: 128–152.

Copeland, R. M., A. J. Francia and R. H. Strawser (1973) “Students as subjects in behavioral research.” Accounting Review 48: 365–372.

Fischhoff, B. (1988) “Judgment and decision making.” In R. Sternberg & E. Smith (Eds.) The Psychology of Human Thought, 155–187. Cambridge: Cambridge University Press.

Fritz, R. (1989) The Path of Least Resistance. New York: Ballantine, 1989.

Hamel, G. and C.K. Prahalad (1989) “Strategic Intent.” Harvard Business Review, 67(3): 63–76.

Johnson, P., K. Jamal and R. Berryman (1991) “Effects of framing on auditor decisions.” Organizational Behavior & Human Decision Processes 50(1): 75–105.

Kahneman, D., J. Knetsch and R. Thaler (1990) “Experimental tests of the endowment effect and the coase theorem.” Journal of Political Economy 98(6): 1325–1348.

Khera, I. P. and J. D. Benson (1970) “Are students really poor substitutes for businessmen in behavioral research?” Journal of Marketing Research 7: 529–532.

Kirzner, I. (1979) Perception, Opportunity and Profit. Chicago, IL: University of Chicago Press.

Kirzner, I. (1985) Discovery and the Capitalist Process. Chicago, IL: University of Chicago Press.

Kirzner, I. (1997) How Markets Work: Disequilibrium, Entrepreneurship and Discovery. London: Institute of Economic Affairs.

Kuen, Thomas S. (1970) The Structure of Scientific Revolutions. Chicago, IL: The University of Chicago Press, Second Edition.

McGrath, R., M. Tsai, S. Venkataraman and I. MacMillan (1996) “Innovation, competitive advantage and rent: A model and test.” Management Science 42(3): 389–403.

Mintzberg, H. (1994) “That’s not “turbulence,” Chicken Little, it’s really opportunity.” Planning Review 22(6): 7–9.

Mowrey, D. C. (1983) “The relationship between intrafirm and contractual forms of industrial research in American manufacturing 1900–1940.” Explorations in Economic History 20: 351–374.

Nelson, R. & S. Winter (1982) An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap.

Remus, W. (1986) “Graduate students as surrogates for managers in experiments on business decision making.” Journal of Business Research 14(1): 19–25.

Senge, P. (1990) “The leader’s new work: Building learning organizations.” Sloan Management Review 32(1): 7–23. Shane S. and S. Venkataraman (2000) “The promise of entrepreneurship as a field of research.” Academy of Management Review 25(1): 217–226.

Shane, S. (2000) “Prior knowledge and the discovery of entrepreneurial opportunities.” Organization Science 11(4): 448–469.

Strahilevitz, M. & G. Lowenstein (1998) “The effect of ownership history on the valuation of objects.” Journal of Consumer Research 25(3): 276–289

Tilton, J. (1971) International diffusion of technology: The case of semiconductors. Washington, DC: Brookings Institution.

Van de Ven, A., D. Polley, R. Garud and S. Venkataraman (1999) The Innovation Journey. New York: Oxford University Press.

Venkataraman, S. (1997) “The distinctive domain of entrepreneurship research: An editor’s prespective.” In Advances in Entrepreneurship, Firm Emergence, and Growth. J. Katz, R. Brockhaus, (Eds.) Greenwich, CT: JAI Press.

Von Hippel, E. (1988) The Sources of Innovation. New York: Oxford University Press.

Weick, K. (1967) “Organizations in the laboratory.” In Methods of Organizational Research, V. H. Vroom (Ed.), University of Pittsburgh Press.